Aircraft tire shoulder heat generation optimization method and system based on interlayer pixel-level modeling

Abstract

The invention provides an aircraft tire shoulder heat generation optimization method and system based on interlayer pixel-level modeling, and relates to the technical field of aircraft tires, and the method comprises the steps: outputting an interlayer pixel-level model; extracting basic parameters of a tire shoulder area; tire shoulder heat generation simulation is executed, and a tire shoulder area temperature distribution cloud picture is output; positioning the highest temperature extreme value and the coordinate position of the high-temperature accumulation area; taking minimization of the maximum temperature extreme value as a heat generation temperature optimization target, taking a condition that the area of a high-temperature accumulation area is smaller than 15% of the total area of a tire shoulder area as a heat generation range constraint condition, and setting a parameter adjustment vector array of basic parameters of the tire shoulder area; executing particle swarm iterative optimization, and outputting tire shoulder optimization correlation parameters; and outputting the thermodynamic optimization parameters of the tire shoulder. According to the method, the technical problem that the durability and the critical speed of the tire are finally affected due to the fact that a macroscopic model is mostly adopted for calculation in an aviation tire shoulder heat generation optimization method in the prior art, and a detailed physical process between different hierarchical structures is ignored is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aviation tires, in particular to an aviation tire shoulder heat generation optimization method and system based on interlayer pixel-level modeling. BACKGROUND

[0002] Aviation radial tires are an important part of modern aircraft, and their performance directly affects flight safety and economy. When the aircraft lands, the tire and the ground produce strong impact, and bear extremely high temperature, pressure and impact force. Especially under high-speed heavy load conditions, the heat generation phenomenon in the tire shoulder area is one of the key factors affecting tire performance and safety. The tire shoulder area is the part of the tire that contacts the ground. Under the action of high-speed running and high load, the tire shoulder part will quickly accumulate high temperature, which not only causes tire shoulder damage, tire wear and other problems, but also may cause early failure or tire burst of the tire. In this process, the critical speed of the tire becomes a key indicator of its performance. The critical speed is the highest speed that the tire can withstand, especially the maximum impact speed when the tire contacts the ground during landing. The higher the critical speed, the more flight scenarios the tire can adapt to, and the higher the load and impact it can withstand, thereby improving flight safety and overall adaptability of the aircraft.

[0003] The existing aviation tire shoulder heat generation optimization method mostly uses a macroscopic model for calculation, i.e., by simplifying the whole tire, the detailed physical processes between different hierarchical structures are ignored. Most simulation methods cannot accurately capture the details of interlayer heat conduction and mechanical transmission. This simplified modeling method causes the simulation of tire heat generation to be unable to truly reflect the coupling effects between different hierarchical structures, resulting in inaccurate prediction of temperature distribution and thermal stress distribution, and unable to effectively evaluate the thermal load and overheating risk of the tire shoulder area. Ultimately, the overall performance of the tire cannot be improved, especially in high-impact, high-temperature and high-speed scenarios, it is difficult to effectively solve the durability problem of the tire and improve the upper limit of the critical speed. SUMMARY

[0004] In view of the above defects or improvement needs of the prior art, the present application provides an aviation tire shoulder heat generation optimization method and system based on interlayer pixel-level modeling, which solves the technical problem that the existing aviation tire shoulder heat generation optimization method mostly uses a macroscopic model for calculation, ignoring the detailed physical processes between different hierarchical structures, ultimately affecting the durability and critical speed of the tire.

[0005] To achieve the above-mentioned purpose, the present application provides an aviation tire shoulder heat generation optimization method and system based on interlayer pixel-level modeling.

[0006] In a first aspect, the application provides a method for optimizing shoulder heat generation of an aviation tire based on interlayer pixel-level modeling, the method comprising: performing interlayer pixel-level modeling according to an initial hierarchical composite structure design of an aviation radial tire, and outputting an interlayer pixel-level model; extracting shoulder region basic parameters from the initial hierarchical composite structure design, wherein the shoulder region basic parameters include a shoulder curve initial curvature radius, a belt initial layering angle sequence, and a shoulder initial thickness ratio; loading the shoulder region basic parameters into the interlayer pixel-level model, and performing shoulder heat generation simulation under extreme working conditions with high-speed heavy-load working conditions as an application scenario constraint, and outputting a shoulder region temperature distribution cloud map; locating a highest temperature extreme value and a high-temperature aggregation area coordinate position by traversing the shoulder region temperature distribution cloud map, wherein the high-temperature aggregation area coordinate position identifies a high-temperature aggregation area; setting a parameter adjustment vector array of the shoulder region basic parameters with minimizing the highest temperature extreme value as a heat generation temperature optimization target and the high-temperature aggregation area being less than 15% of a total shoulder region area as a heat generation range constraint condition; performing particle swarm iterative optimization of the shoulder region basic parameters in the interlayer pixel-level model with the parameter adjustment vector array as an optimization variable set, and outputting shoulder optimization associated parameters; and performing random disturbance cross-parameter combination optimization on the shoulder optimization associated parameters, and outputting shoulder thermodynamic optimization parameters that satisfy a heat generation temperature extreme value threshold.

[0007] In an embodiment, interlayer pixel-level modeling is performed according to an initial hierarchical composite structure design of an aviation radial tire, and an interlayer pixel-level model is outputted, and the following processing is further performed: analyzing hierarchical characteristic parameters according to the initial hierarchical composite structure design, wherein the hierarchical characteristic parameters include a number of carcass ply layers, a number of belt layers, and hierarchical spatial distribution; constructing a multi-layer basic finite element model based on the hierarchical characteristic parameters, wherein the carcass ply layers, the carcass belt layers, and the carcass rubber layers in the multi-layer basic finite element model are connected according to a carcass spatial topology; performing layer-by-layer in-layer single-pixel-level grid division and interlayer multi-pixel-level interface coupling modeling in the multi-layer basic finite element model with a bead wire ring as a reference point, and outputting the interlayer pixel-level model.

[0008] In an embodiment, the particle swarm iteration optimization of the shoulder region basic parameters is performed in the interlayer pixel level model with the parameter adjustment vector array as the optimization variable set, and the shoulder optimization correlation parameters are output. Before that, the following processing is also performed: the model ID of the aviation radial tire and the index composition of the shoulder region basic parameters are taken as the double-dimensional retrieval conditions to network search the shoulder region historical parameter set, the historical temperature extreme value record set and the historical high-temperature region distribution set; after the particle swarm optimization space is constructed according to the index composition of the shoulder region basic parameters, Q historical particle points are located in the particle swarm optimization space according to the shoulder region historical parameter set; the Q temperature extreme values and the Q historical high-temperature region distributions are filled in the Q historical particle points according to the mapping relationship of the historical temperature extreme value record set and the historical high-temperature region distribution set, so as to complete the initialization of the particle swarm optimization space; after the Q historical particle points are traversed by using the highest temperature extreme value and the high-temperature aggregation area to perform particle point effectiveness screening, the feasible envelope space fitting is performed based on the screening result to obtain the parameter adjustment optimization space.

[0009] In an embodiment, the particle swarm iteration optimization of the shoulder region basic parameters is performed in the interlayer pixel level model with the parameter adjustment vector array as the optimization variable set, and the shoulder optimization correlation parameters are output. Before that, the following processing is also performed: the model ID of the aviation radial tire and the index composition of the shoulder region basic parameters are taken as the double-dimensional retrieval conditions to network search the shoulder region historical parameter set, the historical temperature extreme value record set and the historical high-temperature region distribution set; after the particle swarm optimization space is constructed according to the index composition of the shoulder region basic parameters, Q historical particle points are located in the particle swarm optimization space according to the shoulder region historical parameter set; the Q temperature extreme values and the Q historical high-temperature region distributions are filled in the Q historical particle points according to the mapping relationship of the historical temperature extreme value record set and the historical high-temperature region distribution set, so as to complete the initialization of the particle swarm optimization space; after the Q historical particle points are traversed by using the highest temperature extreme value and the high-temperature aggregation area to perform particle point effectiveness screening, the feasible envelope space fitting is performed based on the screening result to obtain the parameter adjustment optimization space.

[0010] In an embodiment, a random disturbance cross-parameter combination optimization is performed on the shoulder optimization correlation parameter, and an output shoulder thermodynamic optimization parameter that satisfies a heat generation temperature extreme value threshold is obtained. The following process is also performed: in the parameter optimization space, a cross-parameter combination orthogonal disturbance sampling is performed around the shoulder optimization correlation parameter with the belt layer parameter optimization scale group, the belt layer parameter optimization scale group, and the rubber layer parameter optimization scale group as disturbance scale constraints, and a plurality of disturbance sampling correlation parameters are obtained. After the plurality of disturbance sampling correlation parameters are loaded into the interlayer pixel level model, a shoulder heat generation simulation under extreme working conditions is performed with a high-speed heavy load working condition as an application scenario constraint, and a plurality of disturbance temperature distribution cloud maps are output. According to the temperature optimization characteristics of the plurality of disturbance temperature distribution cloud maps, a first disturbance optimization parameter is located. The cross-parameter combination orthogonal disturbance sampling and the shoulder heat generation simulation are iteratively performed with the first disturbance optimization parameter as an update starting point until the obtained updated temperature extreme value is ≤115℃ and the updated high-temperature area is less than 12% of the total area of the shoulder region, and the shoulder thermodynamic optimization parameter is output. The heat temperature extreme value threshold is a shoulder temperature extreme value ≤115℃ and a shoulder high-temperature area less than 12% of the total area of the shoulder region.

[0011] In an embodiment, the following process is also performed: the carcass belt is separated into an inner layer independent sublayer group, a middle layer independent sublayer group, and an outer layer independent sublayer group in a radial radiation order with the bead wire ring as a reference point. A single cord diameter is used as an initialization grid scale to grid the N inner layer independent sublayer grid into N initial sublayer grid cell sets in the inner layer independent sublayer group. A pre-defined stress gradient limit is used to perform an intra-layer grid iteration optimization on the N initial sublayer grid cell sets to obtain N inner layer pixel level models as an inner layer pixel level model group. A middle layer pixel level model group and an outer layer pixel level model are constructed by analogy. The inner layer pixel level model group, the middle layer pixel level model group, and the outer layer pixel level model are coupled and modeled between layers to output a belt pixel level model.

[0012] In an embodiment, the following processing is further performed: based on the initialization grid scale, the carcass ply grid processing is processed into an initial carcass grid cell set; the interlayer interface region of the initial carcass grid cell set and the interlayer pixel level model is traversed, and the output interlayer stress gradient distribution is calculated; the stress gradient limit value is compared with the interlayer stress gradient distribution, and W high-risk grid cells are located in the initial carcass grid cell set; grid scale iterative refinement is performed on the W high-risk grid cells until the updated stress gradient of the W high-risk grid cells meets the stress gradient limit value, and an updated carcass grid cell set is output; the W updated carcass grid cell set is used to replace the W high-risk grid cells in the initial carcass grid cell set, and a ply pixel level model is generated; and in this way, the interlayer pixel level model is taken as a reference to construct a rubber layer pixel level model.

[0013] In an embodiment, interlayer multi-pixel level interface coupling modeling is performed on the inner layer pixel level model group, the middle layer pixel level model group and the outer layer pixel level model, and a belt layer pixel level model is output, and the following processing is further performed: 2 layers of Cohesive units are implanted between adjacent models in the inner layer pixel level model group to perform belt inner layer coupling, and an inner layer pixel level coupling model is obtained; 1 layer of Cohesive units is implanted between adjacent models in the middle layer pixel level model group to perform belt middle layer coupling, and a middle layer pixel level coupling model is obtained; 3 layers of Cohesive units are implanted between the inner layer pixel level coupling model and the middle layer pixel level coupling model, and 2 layers of Cohesive units are implanted between the middle layer pixel level coupling model and the outer layer pixel level model to perform belt full layer level coupling, and the belt layer pixel level model is obtained.

[0014] In an embodiment, the following processing is further performed:

[0015] 3 layers of Cohesive units are implanted at the interlayer interface of the ply pixel level model and the belt layer pixel level model, and at the interlayer interface of the belt layer pixel level model and the rubber layer pixel level model, a bilinear constitutive relationship is defined, and an initialization interlayer model is obtained; after adjacent layer level interface grid nodes of the initialization interlayer model are forced to be aligned, a two-dimensional verification is performed on the initialization interlayer model, and the interlayer pixel level model is output, wherein the two-dimensional verification includes stress transfer verification and heat conduction verification.

[0016] In a second aspect, the application provides an air tire shoulder heat generation optimization system based on interlayer pixel-level modeling, comprising: an interlayer pixel-level modeling module for performing interlayer pixel-level modeling according to an initial hierarchical composite structure design of an air radial tire, and outputting an interlayer pixel-level model; a basic parameter extraction module for extracting shoulder area basic parameters from the initial hierarchical composite structure design, wherein the shoulder area basic parameters include a shoulder curve initial curvature radius, a belt initial layering angle sequence, and a shoulder initial thickness ratio; a shoulder heat generation simulation module for loading the shoulder area basic parameters to the interlayer pixel-level model, and performing shoulder heat generation simulation under extreme working conditions with high-speed heavy-load working conditions as application scenario constraints, and outputting a shoulder area temperature distribution cloud map; a coordinate position positioning module for traversing the shoulder area temperature distribution cloud map, and positioning a highest temperature extreme value and a high-temperature aggregation area coordinate position, wherein the high-temperature aggregation area coordinate position identifies a high-temperature aggregation area; a parameter adjustment vector array setting module for setting a parameter adjustment vector array of the shoulder area basic parameters with minimizing the highest temperature extreme value as a heat generation temperature optimization target, and with the high-temperature aggregation area being less than 15% of a total shoulder area as a heat generation range constraint condition; a particle swarm iterative optimization module for performing particle swarm iterative optimization of the shoulder area basic parameters in the interlayer pixel-level model with the parameter adjustment vector array as an optimization variable set, and outputting shoulder optimization associated parameters; and a cross-parameter combination optimization module for performing cross-parameter combination optimization of the shoulder optimization associated parameters with random disturbance, and outputting shoulder thermodynamic optimization parameters satisfying a heat generation temperature extreme value threshold.

[0017] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0018] By performing inter-layer pixel-level modeling, an inter-layer pixel-level model is output, so that the mechanical behavior and heat conduction characteristics between different layers of the aviation meridian tire can be accurately simulated. This modeling method breaks through the limitations of traditional macro-level models and can more accurately capture the micro-physical characteristics of different layers by refining to the single pixel level, which not only greatly improves the accuracy of tire heat generation analysis, but also makes the simulation results more realistic and reliable, providing a scientific basis for subsequent optimization; By extracting the basic parameters of the tire shoulder area from the initial layer composite structure design, detailed physical parameter basis is provided for tire shoulder heat generation simulation and optimization, which accurately describes the geometric characteristics of the tire shoulder area and ensures the accuracy of the heat generation simulation results, making the simulation results more realistic and effectively reflecting the performance of the tire in actual flight; High-speed heavy-load working conditions are used as application scenario constraints to perform tire shoulder heat generation simulation under extreme working conditions. In these extreme working conditions, the thermal stress and temperature change of the tire have a significant impact on performance, and traditional simulation often fails to reflect the true situation of the tire under extreme working conditions, especially in high temperature and high pressure, high speed (critical speed) and strong impact moments. Through optimized heat generation simulation, the temperature distribution cloud map of the tire shoulder area can be accurately obtained to locate the maximum temperature extreme value and high temperature aggregation area, which provides a clear direction for further thermodynamic optimization and ensures the performance of the tire near the critical speed; By using the particle swarm optimization algorithm to iteratively optimize the basic parameters of the tire shoulder area, the parameters can be adaptively adjusted during optimization to find the best solution in a complex multi-dimensional space. In the optimization process, a random disturbance cross-parameter combination optimization strategy is added to make the algorithm have better exploratory and convergent properties during optimization, breaking through the limitations of traditional optimization methods and quickly finding the most suitable parameter combination. Through such an optimization method, the optimal thermodynamic optimization parameters of the tire shoulder can be found in the shortest time to minimize the heat generation extreme value of the tire while avoiding the impact of high temperature aggregation on tire durability and performance; By setting the minimum highest temperature extreme value as the heat generation temperature optimization target and the high temperature aggregation area less than 15% of the total area of the tire shoulder area as the heat generation range constraint condition, the heat distribution of the tire shoulder area can be directly controlled during optimization. By optimizing the thermodynamic performance of the tire shoulder area, the endurance of the tire under high speed and heavy load conditions is improved. Through this optimization, the service life of the tire can be significantly prolonged, and reliable performance can be maintained in high stress and high temperature environments, thereby achieving the goal of improving the critical speed of the tire and making it more adaptable in more complex flight scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0020] Figure 1 A flowchart of the method for optimizing shoulder heat generation of an aviation tire based on interlayer pixel-level modeling provided by the present application is shown.

[0021] Figure 2 An example diagram of shoulder curve radius R45 in the method for optimizing shoulder heat generation of an aviation tire based on interlayer pixel-level modeling provided by the present application is shown.

[0022] Figure 3 An example diagram of shoulder curve radius R30 in the method for optimizing shoulder heat generation of an aviation tire based on interlayer pixel-level modeling provided by the present application is shown.

[0023] Figure 4 A flowchart of outputting an interlayer pixel-level model in the method for optimizing shoulder heat generation of an aviation tire based on interlayer pixel-level modeling provided by the present application is shown.

[0024] Figure 5 A flowchart of performing particle swarm iterative optimization in the method for optimizing shoulder heat generation of an aviation tire based on interlayer pixel-level modeling provided by the present application is shown.

[0025] Figure 6 A structural diagram of the system for optimizing shoulder heat generation of an aviation tire based on interlayer pixel-level modeling provided by the present application is shown.

[0026] Legend: interlayer pixel-level modeling module 10, basic parameter extraction module 20, tire shoulder heat generation simulation module 30, coordinate position positioning module 40, parameter adjustment vector array setting module 50, particle swarm iterative optimization module 60, cross-parameter combination optimization module 70. DETAILED DESCRIPTION

[0027] The present application provides a method and system for optimizing shoulder heat generation of an aviation tire based on interlayer pixel-level modeling, which is used to solve the technical problem that the prior art method for optimizing shoulder heat generation of an aviation tire mostly uses a macroscopic model for calculation, ignoring the detailed physical process between different hierarchical structures, which ultimately affects the durability and critical speed of the tire.

[0028] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0029] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship shown in the drawings, which are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0030] Unless otherwise explicitly indicated, throughout the specification and claims, the term "comprise" or its variants such as "contain" or "include" and the like will be understood to include the stated elements or components, without excluding other elements or components.

[0031] In one embodiment, the flowchart of the method for optimizing the shoulder heat generation of an aviation tire based on interlayer pixel-level modeling is provided, as shown in Figure 1 , the method comprises:

[0032] According to the initial hierarchical composite structure design of the aviation radial tire, interlayer pixel-level modeling is performed to output an interlayer pixel-level model.

[0033] The initial hierarchical composite structure design of the aviation radial tire is analyzed to obtain the hierarchical characteristic parameters of the multi-layer composite structure of the aviation radial tire, including the number of carcass ply layers, the number of belt layers and the hierarchical spatial distribution. Based on the extracted hierarchical characteristic parameters, a multi-layer basic finite element model is constructed. The multi-layer basic finite element model discretizes the mechanical properties and spatial distribution of each layer through a grid. Each layer is regarded as a separate sub-region, and each layer has different material properties, thickness, size and other characteristics. Through the finite element method, the interaction between different layers can be accurately simulated.

[0034] Based on the multi-layered finite element model, modeling is carried out through layer-by-layer meshing and interface coupling. Specifically, each layer is finely meshed so that each mesh element can accurately simulate the physical characteristics of that layer. Interface coupling between different layers is performed so that the mechanical and thermal transfer between different layers in the multi-layered model can be accurately simulated. Finally, a pixel-level model between layers is generated, which is a digital representation of each layer and the interface between layers, and is used for subsequent thermal simulation and optimization calculations.

[0035] Basic parameters of the shoulder region are extracted from the initial hierarchical composite structure design, including the initial radius of curvature of the shoulder curve, the initial layering angle sequence of the belt layer, and the initial thickness ratio of the shoulder.

[0036] Basic parameters for the tire shoulder region are extracted from the initial layered composite structure design. The tire shoulder region refers to the parts of the tire that contact the ground on both sides, and is typically a critical area for tire stress, heat generation, and wear. The initial radius of curvature of the tire shoulder curve reflects the degree of shoulder curvature. The size of the radius of curvature directly affects the stress distribution and heat distribution of the shoulder. The shoulder design of aircraft radial tires depends on various factors such as load-bearing capacity, wear resistance, and durability. Generally, a larger radius of curvature in the shoulder facilitates heat dissipation, resulting in better durability; conversely, a smaller radius of curvature hinders heat dissipation, leading to poorer durability but better wear resistance. See also... Figure 2 Here is an example diagram showing a shoulder curve radius of R45. See [link / reference]. Figure 3 This is an example diagram showing a shoulder curve radius of R30. The performance of the same tire can vary depending on the shoulder curve design. The belt layer consists of multiple layers of steel wires or other reinforcing materials. The angle of each layer represents the arrangement direction of the steel wires. Different angles of the steel wire layers have a significant impact on the stress distribution in the tire shoulder area. Therefore, it is necessary to extract and record the layer angle of each layer as the initial layer angle sequence of the belt layer. The initial shoulder thickness ratio describes the thickness distribution of different material layers (such as the ply, belt layer, and rubber layer) in the tire shoulder area. This initial shoulder thickness ratio affects the heat conduction performance and structural strength of the tire shoulder.

[0037] By extracting the basic parameters of the tire shoulder area, a preliminary analysis of its thermal and mechanical properties can be conducted. The tire shoulder area typically needs to cope with large temperature changes and forces in different directions, therefore, the basic parameters of the tire shoulder area are crucial for optimized design.

[0038] After loading the basic parameters of the tire shoulder region into the interlayer pixel-level model, the tire shoulder heat generation simulation under extreme conditions is performed with high-speed heavy-load working conditions as the application scenario constraint, and the temperature distribution cloud map of the tire shoulder region is output.

[0039] The basic parameters of the tire shoulder region are loaded into the constructed interlayer pixel-level model. This model reflects the physical characteristics of the tire shoulder region, particularly those related to thermal and mechanical properties. To simulate extreme environments in real-world applications, a high-speed, heavy-load condition is used as the application scenario constraint. This condition limits the tire usage conditions involved in the simulation. Specifically, under high-speed conditions, the friction and wind resistance experienced by the tire during driving cause a rapid increase in tire surface temperature; under heavy load conditions, tire deformation and material stress are also intensified, affecting the tire's heat distribution. Under high-speed, heavy-load conditions, the tire shoulder region of an aircraft tire generates a large amount of heat due to friction and deformation, thus affecting the heat distribution in the shoulder region. This simulation of extreme environments is particularly applicable to aircraft tires, especially during the moment of aircraft landing, when a strong impact occurs between the tire and the ground. The tire must withstand the instantaneous impact of high temperature, high pressure, high speed, and high impact. In this process, the critical speed of the tire is one of the key factors. The critical speed refers to the highest speed that the tire can withstand, especially the maximum impact speed at the moment of contact between the tire and the ground during aircraft landing. The higher the critical speed of the tire, the more complex the flight scenarios it can adapt to, such as landing and flight operations at higher speeds, thereby enhancing its safety and adaptability in more flight scenarios.

[0040] After inputting the high-speed, heavy-load operating condition as the application scenario constraint into the interlayer pixel-level model, a tire shoulder heat generation simulation under extreme conditions was performed. Through numerical calculation, the temperature distribution in the tire shoulder region under high-speed, heavy-load conditions was simulated. Specifically, under high-speed, heavy-load conditions, friction and energy conversion within the material form heat sources. These heat sources have different distributions in different parts of the tire (especially the tire shoulder region). On the one hand, heat diffuses between the tire carcass layers through thermal conduction; on the other hand, the heat generated by friction in the tire shoulder region gradually accumulates, forming a high-temperature zone. After the simulation, the resulting temperature distribution cloud map visually displays the temperature distribution in the tire shoulder region under high-speed, heavy-load conditions, showing the temperature changes in different areas.

[0041] Traverse the temperature distribution cloud map of the tire shoulder area to locate the highest temperature extreme value and the coordinate position of the high temperature accumulation area, wherein the coordinate position of the high temperature accumulation area is marked with the area of ​​the high temperature accumulation area.

[0042] By traversing the temperature distribution cloud map of the tire shoulder area, the area with the highest temperature is identified. Typically, the tire shoulder area is prone to forming the highest temperature extreme value under extreme conditions such as high speed and heavy load. These high temperature areas will directly affect the tire's service life and performance. The highest temperature extreme value refers to the highest temperature point reached in the tire shoulder area. This temperature may be close to or exceed the safe temperature threshold designed for the tire, so it needs special attention.

[0043] In addition to identifying the highest temperature extreme value, the coordinates of the high temperature accumulation area are also located. The coordinates of the high temperature accumulation area refer to the coordinates of the pixels in the temperature distribution cloud map of the tire shoulder area where the temperature exceeds a certain threshold. The coordinates of the high temperature accumulation area are marked with the area of ​​the high temperature accumulation area, which refers to the area of ​​abnormal temperature concentration. It usually appears as a large temperature gradient area on the temperature distribution map. The temperature values ​​of these areas are significantly higher than those of the surrounding areas, indicating that the heat load in these areas may have an adverse effect on the tire's durability.

[0044] By accurately locating the extreme temperatures and high-temperature accumulation zones, precise directions are provided for subsequent thermodynamic optimization, ensuring tire performance and durability under extreme conditions, especially at critical speeds. This enables tires to withstand higher speeds and more complex flight operations, enhancing tire adaptability and safety.

[0045] The optimization objective is to minimize the maximum temperature extreme value, and the constraint condition is that the area of ​​the high temperature accumulation zone is less than 15% of the total area of ​​the tire shoulder region. The parameter adjustment vector array of the basic parameters of the tire shoulder region is set.

[0046] The optimization objective is to minimize the maximum temperature extreme value, aiming to prevent excessively high maximum temperature extreme values ​​in the tire shoulder region under the same operating conditions. Reducing the maximum temperature extreme value helps avoid problems such as rubber softening, aging, and accelerated wear caused by overheating, thereby improving tire safety and lifespan. A constraint condition for the heat generation range is that the area of ​​the high-temperature accumulation zone should be less than 15% of the total area of ​​the tire shoulder region. This condition requires the design to ensure that the high-temperature area is not too large, thus avoiding heat accumulation due to localized overheating, which could lead to tire performance degradation or structural damage. Based on these objectives and constraints, a parameter tuning vector array is constructed. This array contains adjustment variables for all design parameters related to the heat generation performance of the tire shoulder region, including the radius of curvature of the tire shoulder region, the belt layer layering angle, and the thickness ratio. Each design parameter needs to be adjusted during the optimization process to achieve the goals of minimizing the maximum temperature extreme value and controlling the area of ​​the high-temperature accumulation zone.

[0047] Using the parameter tuning vector array as the set of optimization variables, particle swarm optimization of the basic parameters of the shoulder region is performed in the interlayer pixel-level model, and the shoulder optimization correlation parameters are output.

[0048] Particle swarm optimization (PSO) is used to iteratively optimize the basic parameters of the tire shoulder region. PSO is a heuristic optimization algorithm that simulates the foraging behavior of a flock of birds. It finds the optimal solution by continuously adjusting and searching the solution space through a swarm of particles. Specifically, based on a parameter tuning vector array, PSO generates an initial particle swarm. Each particle represents a parameter combination (i.e., a parameter tuning vector) and has its own position and velocity in the solution space. The particles search the solution space based on their position (i.e., the value of the design parameter) and velocity (i.e., the direction and magnitude of the design parameter change). During this process, each particle calculates its current objective function value, which corresponds to the highest temperature extreme and the maximum temperature. The evaluation value is the ratio of the area of ​​the high-temperature accumulation zone to the total area of ​​the tire shoulder region. The particles continuously adjust their positions in the solution space to minimize the objective function value. After multiple iterations, the particle swarm tends to an optimal solution, which represents the optimal design parameters of the tire shoulder region. Finally, the tire shoulder optimization correlation parameters are output. These parameters are the best design scheme found by the particle swarm during the optimization process, including the optimal values ​​of parameters such as the radius of curvature of the adjusted tire shoulder region, the layering angle of the belt layer, and the thickness ratio. Through these optimized parameters, the heat generation performance of the tire shoulder under extreme working conditions can meet the expected requirements, that is, the maximum temperature extreme value is minimized and the area of ​​the high-temperature accumulation zone is less than 15% of the total area of ​​the tire shoulder region.

[0049] The shoulder optimization parameters are subjected to random perturbation and cross-parameter combination optimization to output the shoulder thermodynamic optimization parameters that satisfy the extreme threshold of heat generation temperature.

[0050] Cross-parameter combinatorial optimization with random perturbation involves perturbing and sampling the shoulder optimization-related parameters obtained in the previous optimization step to explore more possible parameter combinations. Random perturbation refers to randomly adjusting the shoulder optimization-related parameters within a certain range, simulating uncertainty and diversity. Through perturbation, it is possible to escape local optima and explore a wider range of possible solutions in the solution space. Cross-parameter combinatorial optimization means combining and intervening among multiple parameters. That is, instead of adjusting a single parameter, it perturbs the combination of multiple parameters, analyzes their interaction effects, and thus finds a more optimized solution.

[0051] Iterative perturbation sampling is performed, continuously narrowing the search range and further adjusting parameters through step-by-step iterations. After each perturbation, a tire shoulder heat generation simulation under extreme conditions is executed, simulating the temperature distribution of the tire under high-speed, heavy-load conditions to verify the effectiveness of the perturbation. The tire shoulder heat generation simulation results are presented in the form of a temperature distribution cloud map. Based on the temperature distribution cloud map, it is checked whether the highest temperature extreme value meets the heat generation temperature extreme value threshold. For example, the heat generation temperature extreme value threshold is set as a tire shoulder temperature extreme value ≤ 115℃, and the high-temperature area of ​​the tire shoulder is less than 12% of the total area of ​​the tire shoulder region. If the conditions are met, it indicates that the perturbation optimization is effective, and the parameters at this time are output as the tire shoulder thermodynamic optimization parameters. These tire shoulder thermodynamic optimization parameters represent the optimal design after multiple perturbation optimizations and can be applied in actual production to ensure that the tire's heat generation performance reaches the best state under extreme conditions. Through this optimization, the tire's service life can be significantly extended, and reliable performance can be maintained in high-stress, high-temperature environments, thereby achieving the goal of improving the tire's critical speed and making it more adaptable to more complex flight scenarios.

[0052] In one implementation, see Figure 4 Based on the initial layered composite structure design of the aviation radial tire, interlayer pixel-level modeling is performed, and the interlayer pixel-level model is output, including:

[0053] The initial hierarchical composite structure design analyzes the hierarchical characteristic parameters, which include the number of carcass ply layers, the number of belt layers, and the hierarchical spatial distribution. Based on the hierarchical characteristic parameters, a multi-layer foundation finite element model is constructed, wherein the carcass ply layers, carcass belt layers, and carcass rubber layers in the multi-layer foundation finite element model are connected according to the carcass spatial topology. Using the bead wire ring as the reference point, single-pixel-level mesh generation within each layer and multi-pixel-level interface coupling modeling between layers are performed layer by layer in the multi-layer foundation finite element model, and the inter-layer pixel-level model is output.

[0054] Hierarchical feature parameters are extracted from the initial hierarchical composite structure design. These parameters describe the various layers of the tire and their internal structure. Specifically, the number of carcass plies refers to the number of plies used to enhance the tire's structural strength. These plies are composed of fiber fabrics (such as nylon, polyester, and steel wire) and are typically located in the middle of the carcass. The belt layer, usually made of steel wire, provides lateral strength and stability, serving as a crucial support layer that determines the tire's durability and load-bearing capacity. The hierarchical spatial distribution describes the spatial distribution of each layer, including plies, belt layers, and rubber layers, within the tire structure. For example, it identifies which layers are located on the outside, which on the inside, their relative positions, and their thickness distribution. These hierarchical feature parameters provide fundamental information about the overall tire structure, aiding in the accurate division and connection of the various layers during subsequent modeling.

[0055] Finite element analysis (FEM) is a commonly used engineering simulation technique for analyzing the performance of structures, materials, heat flow, and stress. Based on extracted hierarchical feature parameters, a multi-layered finite element model is constructed. In this model, the various layers of the tire carcass are connected according to their actual spatial topology, following the tire's actual geometry and structure. The tire carcass consists of carcass plies, belt layers, and rubber layers. Each layer has different stiffness and material properties, thus requiring appropriate connection methods for modeling to simulate the mechanical behavior between layers. The multi-layered finite element model forms a complete tire structure through the interconnections between these different layers, ensuring that the coupling relationships and force transmission paths between each layer are accurately represented in the model. This multi-layered finite element modeling allows for a more realistic simulation of the tire's performance under actual operating conditions, such as load-bearing capacity, deformation, and stress distribution.

[0056] The tire bead wire is the core component of the tire structure, providing connection and support between the tire and the rim. When performing interlayer coupling modeling, selecting the tire bead wire as the reference point means that in the subsequent modeling process, all other layers are positioned and analyzed relative to this reference point. The selection of the reference point ensures that the geometry and load transfer path of the entire finite element model are correct, thus enabling accurate simulation analysis under different working conditions.

[0057] For a multi-layered finite element model, the structure of each layer is subdivided into multiple small finite element elements. The connection relationship between these finite element elements is defined by mesh generation. Single-pixel level mesh generation refers to performing very fine mesh generation on each layer to achieve pixel-level precision, so as to calculate mechanical behavior more accurately. Through this fine division, the distribution characteristics of stress, temperature, etc. in each tiny region can be simulated, providing more accurate data for subsequent simulation analysis.

[0058] The different layers are not completely rigidly connected, but the interaction between these layers is simulated through interface coupling. Interface coupling refers to the physical phenomena such as contact, friction, adhesion, and slippage between different material layers (including tire carcass ply, tire carcass belt layer and tire carcass rubber layer). These interface behaviors have an important impact on the overall mechanical performance of the tire. Interlayer multi-pixel level interface coupling modeling refers to simulating these interface behaviors through refined mesh and coupling model, so that the interaction between each layer can be accurately calculated.

[0059] The final output is a pixel-level interlayer model, which can accurately reflect the interrelationships between the tire layers and their thermal and mechanical properties. This provides important basic data for subsequent simulation and optimization analysis, and can more accurately simulate the performance of the tire shoulder area under extreme conditions.

[0060] In one implementation, using the parameter tuning vector array as the optimization variable set, particle swarm optimization of the basic parameters of the shoulder region is performed in the inter-layer pixel-level model, and the shoulder optimization correlation parameters are output. Prior to this, the following steps are included:

[0061] Using the model ID of the radial aviation tire and the index composition of the basic parameters of the tire shoulder region as two-dimensional search conditions, the historical parameter set, historical temperature extreme value record set, and historical high temperature region distribution set of the tire shoulder region are retrieved online. After constructing the particle swarm optimization space based on the index composition of the basic parameters of the tire shoulder region, Q historical particle points are located in the particle swarm optimization space according to the historical parameter set of the tire shoulder region. According to the mapping relationship between the historical temperature extreme value record set and the historical high temperature region distribution set, Q temperature extreme value records and Q historical high temperature region distributions are mapped and filled in the Q historical particle points to complete the initialization of the particle swarm optimization space. After filtering the particle point effectiveness by traversing the Q historical particle points using the highest temperature extreme value and the area of ​​the high temperature cluster, a feasible envelope space is fitted based on the filtering results to obtain the parameter tuning optimization space.

[0062] The model ID is a unique identifier for radial aviation tires, used to distinguish different types, designs, and specifications of tires. The model ID allows for quick identification of the specific tire type and retrieval of related historical datasets. Basic parameters of the tire shoulder region include the initial radius of curvature of the shoulder curve, the initial layering angle sequence of the belt layers, and the initial thickness ratio of the shoulder. These parameters are key factors affecting tire performance (especially heat generation performance), and different combinations of parameters correspond to different historical datasets.

[0063] By utilizing the model ID and basic parameters of the tire shoulder region, relevant historical datasets are retrieved online. These datasets include: a historical parameter set for the tire shoulder region containing parameter configurations for different past designs; a historical temperature extreme value set recording historical temperature extreme values ​​of the tire shoulder region under test conditions for different designs, with each design point's temperature extreme value reflecting the tire's thermal performance under specific conditions; and a historical high-temperature region distribution set recording the historical high-temperature distribution of the tire shoulder region under test conditions for different designs, including the size and location of high-temperature regions, with each design point's historical high-temperature region distribution corresponding one-to-one with the historical temperature extreme value. These historical datasets provide preliminary reference values ​​for particle swarm optimization, helping to determine reasonable initial particle swarm positions and parameter spaces, avoiding optimization from scratch, and improving computational efficiency and result reliability.

[0064] The particle swarm optimization space refers to the parameter space in the optimization process. Specifically, it is the set of specific parameter configurations represented by each particle in this space. In this space, each particle represents a possible solution. Through optimization iteration, the particles gradually approach the global optimum. Here, the construction of the particle swarm optimization space is based on the index of the basic parameters of the tire shoulder region. That is to say, the dimension and range of the particle swarm optimization space are determined by the variation range of these parameters and the actual engineering requirements. For example, parameters such as radius of curvature, layer angle, and thickness ratio are mapped to the dimension of the particle swarm to ensure that each particle represents an effective design configuration in this space.

[0065] By searching the historical parameter set of the tire shoulder region, Q historical particle points are located in the particle swarm optimization space, where Q is a positive integer. These Q historical particle points are specific design points selected based on historical data. These points represent known effective design schemes. By using the Q historical particle points as the starting points for particle swarm optimization, the optimization process can be accelerated and the search efficiency can be improved.

[0066] Each historical particle point corresponds to a specific tire design and has corresponding historical temperature extreme value records and historical high temperature region distributions. By combining each historical particle point with its corresponding historical temperature extreme value records and historical high temperature region distributions, and mapping these data into the historical particle point, it means that each historical particle point not only contains the basic parameters of the tire design, but also the specific manifestations of temperature extreme values ​​and high temperature region distributions. Through this process, each historical particle point has more comprehensive information in the particle swarm optimization space, which can effectively guide the optimization algorithm to converge to a more reasonable solution space.

[0067] During the optimization process, the objective of particle swarm optimization is to minimize the highest temperature extreme value and reduce the area of ​​high-temperature clusters. Therefore, by screening the Q temperature extreme value records and Q historical high-temperature region distributions of Q historical particle points, it is possible to effectively identify which historical particle points provided good temperature control during the optimization process. During the screening process, records with temperature extreme values ​​below a preset threshold are selected, for example, a temperature extreme value not exceeding 120℃. If a historical particle point's temperature extreme value record exceeds this preset threshold, it is deemed unqualified and will not participate in subsequent optimization. Simultaneously, the distribution of the Q historical high-temperature regions is checked; if the historical high-temperature region exceeds a set threshold, such as 15%, the historical particle point is also excluded. By restricting historical particle points to regions with good temperature performance through screening, optimization efficiency and accuracy are improved.

[0068] Based on these selected historical particles, a feasible envelope space is fitted. The feasible envelope space refers to the effective design region that can be explored in particle swarm optimization. It not only restricts the optimization process to only be carried out in a suitable solution space, but also helps to avoid the generation of invalid designs. By fitting, the parameter tuning and optimization space is obtained, ensuring that the particle swarm can focus on searching these effective regions in subsequent iterations, thereby improving the quality of the results and the efficiency of the optimization process.

[0069] In one implementation, see Figure 5 Using the parameter tuning vector array as the optimization variable set, particle swarm optimization of the basic parameters of the shoulder region is performed in the inter-layer pixel-level model, and the shoulder optimization correlation parameters are output, including:

[0070] The high-temperature accumulation area is projected onto the interlayer pixel-level model to obtain the volume coverage ratio of the high-temperature accumulation area on the tire carcass ply, tire carcass belt layer, and tire carcass rubber layer. Influence weight reassemblies for the ply, belt, and rubber layers are set. Based on these weights, the standard parameter tuning scales are corrected to obtain parameter tuning scale groups for the ply, belt, and rubber layers. Using the parameter tuning vector array as the optimization variable set, the first round of updates to the basic parameters of the tire shoulder region is performed in the parameter tuning optimization space mapping according to the ply, belt, and rubber layer parameter tuning scale groups, thus locating the first update. Particle point; after parsing the first updated particle point and extracting the update parameters of the first shoulder region, the update parameters of the first shoulder region are loaded into the interlayer pixel-level model. Under the constraint of high-speed heavy-load working conditions, the shoulder heat generation simulation under extreme working conditions is performed, and the first updated temperature distribution cloud map is output. If the first updated temperature extreme value of the first updated temperature distribution cloud map is less than the highest temperature extreme value, and the first updated high temperature area is less than the area of ​​the high temperature accumulation area, then the first updated particle point is used as the update starting point, and the search range is iteratively shrunk in the parameter tuning optimization space until the updated temperature extreme value is ≤120℃ and the updated high temperature area is less than 15% of the total area of ​​the shoulder region, and the shoulder optimization correlation parameters are output.

[0071] Projection involves projecting the geometry and temperature distribution of the high-temperature accumulation zone onto a pixel-level model between layers to obtain the impact of the high-temperature accumulation zone on each layer, including the carcass ply, carcass belt layer, and carcass rubber layer. Specifically, it involves calculating the volume coverage ratio of the high-temperature accumulation zone on each layer. The volume coverage ratio refers to the relative volume proportion of the high-temperature accumulation zone on different layers, estimated using simulation software or numerical methods (such as finite element analysis). This volume coverage ratio indicates the degree of influence of each layer on the shoulder temperature distribution, and thus indicates which layer parameters should be optimized to effectively reduce the temperature.

[0072] Based on the volume coverage ratio of different layers, a corresponding influence weight is assigned to each layer, resulting in the influence weight recombinations for the ply layer, belt layer, and rubber layer. For example, if the volume coverage ratio of the tire carcass rubber layer in the high-temperature accumulation zone is large, then the corresponding influence weight of the rubber layer is high. This indicates that adjusting the parameters of the tire carcass rubber layer is important for controlling the high-temperature accumulation zone in the tire shoulder. By setting these influence weights, a reference can be provided for the subsequent optimization process, determining the priority of each layer in the optimization.

[0073] The standard parameter tuning scale refers to the range of parameter changes for each layer during the initial optimization process. The standard parameter tuning scale is adjusted based on the weighted reorganization of the influence of the fabric layer, the belt layer, and the rubber layer. If the influence weight of a certain layer is large, it means that the layer has a greater impact on the temperature distribution, so a larger parameter tuning scale is needed to allow for more flexible adjustment of the parameters of that layer during the optimization process. Conversely, if the influence weight of a certain layer is small, the parameter tuning scale of that layer can be appropriately reduced to avoid unnecessary optimization waste.

[0074] Through correction, parameter adjustment scale groups for the ply, belt, and rubber layers were obtained. These parameter adjustment scale groups serve as control variables for optimization, indicating how to adjust the parameters of the ply, belt, and rubber layers during the optimization process to achieve the goal of optimizing the tire shoulder thermal performance.

[0075] The parameter tuning vector array is an array containing different parameter adjustment ranges for each layer. Its function is to provide the initial parameter tuning space for the particle swarm optimization algorithm. Based on the parameter tuning scale groups for the plywood layer, belt layer, and rubber layer, all possible combinations of optimization variables are mapped in the parameter tuning optimization space. In this process, by combining the parameter tuning vector array with the parameter tuning scale groups of each layer, the boundary and variable range of the parameter space are defined. The first round of updates is performed in the parameter tuning optimization space, that is, based on the parameter adjustment range of each layer in the parameter tuning vector array, the new parameter configuration is determined. The updated particle point represents the new parameter combination. As the first updated particle point, the first updated particle point contains the adjusted basic parameters of the tire shoulder area, including the adjusted radius of curvature, belt layer layer angle, and thickness ratio, which will be further refined in subsequent steps.

[0076] The first updated particle point is analyzed to obtain the corresponding first shoulder region update parameters. These parameters represent a new optimized configuration, which should theoretically reduce shoulder heat generation. The first shoulder region update parameters are then loaded into the previously constructed inter-layer pixel-level model. This process can be understood as applying the update results from the optimization algorithm to the finite element model to simulate the optimized performance. When simulating shoulder heat generation, a high-speed, heavy-load condition is chosen as the application scenario constraint. This condition simulates tire performance under high load and high-speed driving conditions, representing an extreme test of the shoulder's heat resistance and thermodynamic properties. It is used to simulate the temperature distribution of the shoulder region under extreme conditions. Through simulation calculations, the first updated temperature distribution cloud map is obtained, representing the temperature distribution of the shoulder region under extreme conditions.

[0077] The first updated temperature extreme value refers to the maximum temperature in the temperature distribution, i.e., the highest temperature in the tire shoulder region. The first updated high-temperature area refers to the proportion of the area occupied by the high-temperature region, especially the high-temperature concentration in the tire shoulder region. If the first updated temperature extreme value is less than the set maximum temperature extreme value, and the first updated high-temperature area is less than the area of ​​the high-temperature concentration region, it indicates that the current optimization result has improved the thermal performance and is close to the expected goal. If the current optimized first updated particle point meets the above conditions, then this first updated particle point will be used as the new update starting point to enter the next round of particle swarm optimization iteration.

[0078] In this process, the search range is iteratively narrowed, that is, the search range of the parameter tuning optimization space is further restricted in order to find a better parameter configuration more accurately. Specifically, iteratively narrowing the search range refers to gradually reducing the search space of particles through the particle swarm optimization algorithm, and ensuring that the extreme temperature and high-temperature area are further reduced after each optimization. In each iteration, the progress of optimization is judged according to the set convergence conditions. The convergence conditions include updating the extreme temperature ≤ 120℃ and updating the high-temperature area less than 15% of the total area of ​​the tire shoulder region. When the convergence conditions are met, it means that the optimization process has achieved the ideal goal and meets the design requirements. When both the updated extreme temperature and the updated high-temperature area meet the set criteria, the current iteration stops, and the tire shoulder optimization related parameters are output, including the optimized basic parameters of the tire shoulder region.

[0079] In one implementation, cross-parameter combination optimization with random perturbation is performed on the shoulder optimization correlation parameters to output shoulder thermodynamic optimization parameters that satisfy the extreme threshold of heat generation temperature, including:

[0080] In the parameter tuning and optimization space, the parameter tuning scale groups of the ply layer, belt layer, and rubber layer are used as perturbation scale constraints. Orthogonal perturbation sampling with cross-parameter combinations is performed around the tire shoulder optimization correlation parameters to obtain multiple perturbation sampling correlation parameters. After loading the multiple perturbation sampling correlation parameters into the interlayer pixel-level model, tire shoulder heat generation simulation under extreme conditions is performed with high-speed heavy-load working conditions as the application scenario constraint, and multiple perturbation temperature distribution cloud maps are output. According to the temperature optimization characteristics of the multiple perturbation temperature distribution cloud maps, the first perturbation optimization parameter is located. With the first perturbation optimization parameter as the update starting point, the orthogonal perturbation sampling with cross-parameter combinations and tire shoulder heat generation simulation are iteratively performed until the obtained updated temperature extreme value is ≤115℃ and the updated high temperature area is less than 12% of the total area of ​​the tire shoulder region. The tire shoulder thermodynamic optimization parameters are then output, wherein the extreme value threshold of the heat generation temperature is a tire shoulder temperature extreme value ≤115℃ and a tire shoulder high temperature area less than 12% of the total area of ​​the tire shoulder region.

[0081] The perturbation scale constraint is set based on the parameter tuning scale groups of the above three layers. That is, the perturbation will be carried out within the allowable range of each parameter tuning scale group. To efficiently explore the parameter tuning optimization space, an orthogonal perturbation sampling method is adopted. Orthogonal perturbation sampling is an experimental design method that maximizes information content by selecting different combinations of parameters while avoiding redundant influences between parameters. In this step, the parameters of the fabric layer, belt layer, and adhesive layer are combined and perturbed. Specifically, a set of parameters is randomly selected from each parameter tuning scale group and cross-combined according to the orthogonal sampling rule to ensure that different configurations at each layer are fully examined. This allows for the detection of a wide range of configurations in the parameter tuning optimization space, rather than being limited to a specific path or local area, thereby improving the comprehensiveness and effectiveness of the optimization. Each orthogonal perturbation sampling generates a set of perturbation sampling associated parameters as input for subsequent simulation calculations. This method allows for the acquisition of more simulation data under different configurations.

[0082] Multiple perturbation sampling correlation parameters were loaded into the interlayer pixel-level model. These parameters represent different combinations of various optimization variables, meaning that each perturbation sampling combination will affect different layers in the model, especially the performance of the tire shoulder area. After loading multiple perturbation sampling correlation parameters, tire shoulder heat generation simulation was performed to simulate the heat load and heat conduction of the tire under high-speed heavy load conditions. The simulation results give multiple perturbation temperature distribution cloud maps under different perturbation sampling combinations. The cloud maps show the temperature distribution of the tire shoulder area under different parameter combinations.

[0083] Temperature optimization characteristics refer to the extreme temperature values ​​and high-temperature areas in the perturbation temperature distribution cloud map. Among multiple perturbation temperature distribution cloud maps, the parameter combination that best meets the optimization objective is found. This combination should not only make the extreme temperature values ​​as low as possible, but also control the high-temperature area to make it as small as possible. The first perturbation optimization parameters are the preliminary optimal parameter combination found according to this standard, which serves as the basis for further optimization.

[0084] The obtained first perturbation optimization parameters are used as the starting point for the next optimization. Based on the first perturbation optimization parameters, orthogonal perturbation sampling with cross-parameter combinations is used to further explore the perturbation of the optimization variables. This process is an iterative optimization process, which aims to find a more ideal configuration in a wider parameter space. Each iteration generates a new set of perturbation sampling correlation parameters and inputs these parameters into the interlayer pixel-level model to perform the same high-speed heavy-load tire shoulder heat generation simulation to simulate the thermal response of the tire shoulder region under different parameter combinations. The simulation results output a new temperature distribution cloud map, and the temperature extremes and high-temperature areas are analyzed based on the cloud map.

[0085] After each iteration, it is determined whether the new simulation results meet the predetermined optimization objectives, namely, an updated extreme temperature ≤ 115℃ and an updated high-temperature area less than 12% of the total area of ​​the tire shoulder region. If the current updated extreme temperature and updated high-temperature area meet the standards, then the current parameter configuration can be considered optimized and meets the design requirements. The final tire shoulder thermodynamic optimization parameters are then output. These parameters represent the optimal thermal performance configuration of the tire shoulder region under specific high-speed, heavy-load conditions. Through these parameters, the temperature distribution of the tire can be effectively controlled, overheating problems can be reduced, and tire durability and safety can be improved. If the requirements are not met, iterative adjustments continue until the optimization objectives are met.

[0086] One implementation also includes:

[0087] Using the bead wire ring as a reference point, the tire carcass belt layer is separated into inner independent sub-layer groups, middle independent sub-layer groups, and outer independent sub-layers according to radial radial positioning. Using the single cord diameter as the initial mesh scale, the N inner independent sub-layers in the inner independent sub-layer group are meshed into N initial sub-layer mesh unit sets. A predefined stress gradient limit is used to perform intra-layer mesh iterative optimization on the N initial sub-layer mesh unit sets, resulting in N inner pixel-level models, which serve as the inner pixel-level model group. Similarly, the middle pixel-level model group and the outer pixel-level model are constructed. Inter-layer multi-pixel-level interface coupling modeling is performed on the inner pixel-level model group, the middle pixel-level model group, and the outer pixel-level model to output the belt layer pixel-level model.

[0088] The tire carcass belt layers are responsible for maintaining tire shape and transferring load. Depending on the tire's structure, the carcass belt layers have different material and functional properties at different radial positions. Using the bead wire as a reference point, these sub-layers are positioned in a radial radial sequence, meaning that the carcass belt layers are progressively divided along the tire's radial direction, from the bead to the shoulder, expanding outwards from the tire's center. This ensures that the mechanical properties of each layer are accurately described and calculated. The carcass belt layers are divided into three main parts: the inner independent sub-layer group, which is closest to the bead wire and bears the greatest load; the middle independent sub-layer group, located between the inner and outer layers, has the function of adjusting stiffness; and the outermost independent sub-layer group, which typically bears friction and pressure from the external environment. Each sub-layer group has different material, structural, and mechanical properties. This layered approach helps to accurately simulate the physical characteristics of each sub-layer, especially in thermal simulation and structural analysis.

[0089] The diameter of a single cord is a geometric dimension of the tire ply. In mesh modeling, choosing the single cord diameter as the initial mesh scale helps maintain a balance between accuracy and computational efficiency in the simulation model. The initial mesh scale determines the size of each mesh cell in the model; an excessively large mesh will lead to distortion of physical behavior, while an excessively small mesh will increase the computational load. Therefore, using the single cord diameter as the initial mesh scale ensures that each mesh cell can accurately represent the behavioral characteristics of the tire ply. In the inner independent sub-layer group, there are N inner independent sub-layers, where N is a positive integer. Through meshing, these N inner independent sub-layers are discretized into N initial sub-layer mesh cell sets, allowing each mesh cell to be calculated independently. This meshing method provides the foundation for finite element analysis, enabling mechanical and thermal analysis of each mesh cell in subsequent simulations.

[0090] Stress gradient refers to the rate at which stress distribution changes with location. In finite element analysis, setting stress gradient limits can help identify and correct unreasonable mesh partitioning, ensuring that the stress change within each mesh element conforms to the actual physical conditions. Predefined stress gradient limits mean that a stress gradient range has been set before optimization begins, and meshes exceeding this range will be further refined or adjusted.

[0091] Within an initial set of N sub-layer mesh elements, the mesh elements are iteratively optimized under predefined stress gradient constraints. This optimization aims to refine the mesh, enabling more accurate capture of the stress distribution within each sub-layer. Iterative optimization eliminates irregular or excessively large mesh elements, particularly in regions with high stress gradients, thus improving computational accuracy, especially in handling details within high-stress areas. After optimization, each mesh element becomes a smaller pixel-level unit, serving as an inner pixel-level model. This inner pixel-level model provides finer-grained stress and strain distribution information, suitable for subsequent high-precision simulations.

[0092] Similar to the processing of the inner independent sublayer group, the same process is applied to the middle independent sublayer group. By using predefined stress gradient limits as constraints, the mesh of each layer is optimized, the mesh distribution is refined, and finally, the middle pixel-level model group and the outer pixel-level model are generated. The processing of the outer independent sublayer is detailed in subsequent steps.

[0093] Interlayer interface coupling refers to the coupling of physical properties between different layers to accurately simulate the interactions between them, such as force transmission and heat diffusion. These interactions affect the overall tire performance and must be accurately represented in the model. Multi-pixel-level interface coupling means it's not just a simple interlayer contact problem, but also includes the interaction of the physical properties of each mesh cell between layers. Each pixel represents a very small area, thus capturing very subtle interface behaviors. By completing multi-pixel-level interface coupling modeling, a highly accurate belt layer pixel-level model is obtained. This model includes the interactions between the inner, middle, and outer sub-layers, providing detailed data support for subsequent tire performance analysis.

[0094] One implementation also includes:

[0095] Based on the initial mesh scale, the carcass ply is meshed into an initial carcass mesh unit set; the interlayer interface regions of the initial carcass mesh unit set and the interlayer pixel-level model are traversed to calculate and output the interlayer stress gradient distribution; the interlayer stress gradient distribution is compared with the stress gradient limit value to locate W high-risk mesh units in the initial carcass mesh unit set; the mesh scale is iteratively refined for the W high-risk mesh units until the updated stress gradient of the W high-risk mesh units meets the stress gradient limit value, and W updated carcass mesh unit sets are output; the W updated carcass mesh unit sets are used to map and replace the W high-risk mesh units in the initial carcass mesh unit set to generate the ply ply pixel-level model; and so on, using the interlayer pixel-level model as a reference, the rubber layer pixel-level model is constructed.

[0096] The initial grid scale determines the resolution of the model, that is, the size of the physical region represented by each grid cell. The meshing process divides the entire carcass ply into a set of multiple small grid cells, i.e., the initial carcass grid cell set. Each grid cell represents a small region in the carcass ply, and the physical quantities (such as stress and strain) therein can be solved by finite element analysis.

[0097] Tires consist of multiple distinct sublayers that interact with each other, especially at the interface regions where mechanical properties and temperature gradients can change drastically. First, the initial tire carcass mesh set and the different layers in the interlayer pixel-level model are spatially aligned to ensure correct matching between the mesh elements and the interlayer interface regions, thus avoiding errors introduced by interface mismatch. The stress gradient describes the rate of stress change in space. In the complex multilayer structure of a tire, the interlayer stress gradient distribution reflects the strength of the mechanical interactions between the layers. By traversing and analyzing the interface regions of the spatially aligned initial tire carcass mesh set and the interlayer pixel-level model, the interlayer stress gradient distribution is calculated, indicating which regions experience significant stress changes.

[0098] To ensure tire durability and safety, a pre-set stress gradient limit is established. This limit is determined during the design process based on material properties and safety standards. If the stress change rate in a certain area exceeds this limit, it indicates that excessive deformation, crack propagation, or material fatigue may occur, potentially leading to tire failure. Based on the pre-set stress gradient limit, the stress gradient distribution across the entire interlayer is compared. This comparison identifies which mesh elements have stress gradients exceeding the set limit. The W identified high-risk mesh elements represent the areas where the stress gradient exceeds the limit, signifying the parts of the tire structure most prone to failure or abnormal deformation.

[0099] For W high-risk mesh elements, iterative mesh refinement is performed, which involves further reducing the size of these high-risk mesh elements. This is to improve the resolution of local areas, enabling more precise capture of stress changes within these high-risk regions, especially in areas of stress concentration or uneven distribution. After mesh refinement, the stress gradient of the W high-risk mesh elements is recalculated. A finer mesh provides a more accurate stress distribution and gradient. The updated stress gradient is then compared to the original stress gradient limit. If the stress gradient of the refined mesh element still exceeds the limit, the mesh refinement continues iteratively until the stress gradient of the mesh element meets the set limit. After multiple rounds of iterative refinement, the final output set of W updated tire carcass mesh elements has higher resolution, and the stress distribution in high-risk areas meets design requirements, providing more accurate and detailed model data for subsequent tire performance analysis and optimization.

[0100] The W high-risk mesh elements in the original mesh model are replaced by a set of W updated carcass mesh elements. This replacement process is accomplished through a mapping operation. That is, the corresponding high-risk mesh elements in the original mesh model are identified, and then these high-risk mesh elements are replaced with the updated, finer set of carcass mesh elements. In this way, the updated mesh elements are integrated into the entire model, forming a more accurate structure. After the replacement operation is completed, a pixel-level model of the tire cord layer is generated.

[0101] Using a similar approach, with the interlayer pixel-level model as a benchmark, the structure of the rubber layer is accurately represented through fine mesh division, thereby simulating the interaction between different layers, ensuring that the mechanical properties of the entire tire are accurately simulated, and finally obtaining the rubber layer pixel-level model.

[0102] In one implementation, inter-layer multi-pixel-level interface coupling modeling is performed on the inner pixel-level model group, the middle pixel-level model group, and the outer pixel-level model to output a bundled layer pixel-level model, including:

[0103] It should be understood that Cohesive units are a type of finite element used to simulate the bonding behavior of material interfaces (such as rubber-cord interfaces). In this embodiment, two layers of Cohesive units are implanted between adjacent models within the inner pixel-level model group to perform inner-layer coupling, resulting in an inner-layer pixel-level coupled model. One layer of Cohesive units is implanted between adjacent models within the middle pixel-level model group to perform middle-layer coupling, resulting in a middle-layer pixel-level coupled model. Three layers of Cohesive units are implanted between the inner and middle pixel-level coupled models, and two layers of Cohesive units are implanted between the middle and outer pixel-level coupled models to perform full-layer coupling, resulting in the pixel-level model of the belt layer.

[0104] The inner pixel-level model group represents the inner belt layer in the tire structure. Cohesive elements are used to simulate the interface behavior between different layers. Here, they are inserted between adjacent models within the inner pixel-level model group to establish coupling within the inner layer. Specifically, two layers of Cohesive elements are implanted between adjacent models within the inner pixel-level model group, and different mesh elements are connected through Cohesive elements. This coupling process is to simulate the bonding or adhesive forces between different layers, ensuring that the model can correctly transmit mechanical information, such as stress transmission and strain distribution. By introducing Cohesive elements for coupling between the inner pixel-level model groups, an inner pixel-level coupled model is obtained. This model can accurately describe how the various parts of the inner belt layer interact when subjected to external loads.

[0105] Similar to the inner layer, the middle pixel-level model group represents the middle belt layer of the tire. Within the middle pixel-level model group, one layer of cohesive units is inserted between adjacent models. This layer of cohesive units is used to couple different mesh units in the middle layer to form a more refined structure. Compared to the inner layer, only one layer of cohesive units is inserted in the middle layer. This is because the coupling requirements of the middle layer are slightly simpler than those of the inner layer, or the strength and contact requirements of the middle layer mesh units themselves are relatively low. By inserting cohesive units for coupling, a middle pixel-level coupling model is obtained. This model can simulate the mechanical behavior of the middle belt layer, especially the interaction between different sub-layers.

[0106] The inner and middle pixel-level coupling models describe the inner and middle layers of the tire belt layer, respectively. To ensure that mechanical information can be correctly transmitted between the two, three layers of Cohesive elements are embedded between them. These Cohesive elements are used to simulate the interface behavior between the inner and middle layers and transmit the mechanical action between the interfaces. The three layers of Cohesive elements are used here to enhance the adhesion or coupling strength between the inner and middle layers, ensuring that the two influence each other and share the stress distribution during loading.

[0107] Similarly, the middle-layer pixel-level coupling model and the outer-layer pixel-level model describe the middle and outer layers of the tire belt layer, respectively. In order to ensure the stress and deformation transfer between the outer and middle layers, two layers of Cohesive units are implanted between them. These Cohesive units will simulate the interface behavior between the middle and outer layers to ensure that the mechanical effects are correctly transferred. The two layers of Cohesive units are compared with the three layers of Cohesive units between the inner and middle layers because the coupling requirement between the outer and middle layers is lower, or the strength of the outer layer material is greater and no excessive interface reinforcement is required.

[0108] By embedding Cohesive units layer by layer between the inner, middle and outer layers, a pixel-level model of the belt layer is finally obtained. This model contains the complete mechanical structure from the inner to the outer layer and can comprehensively describe the mechanical behavior of the tire belt layer, especially how different layers work together and how they transmit stress and strain under different loads and working conditions.

[0109] One implementation also includes:

[0110] Three layers of Cohesive elements are implanted at the interlayer interfaces of the pixel-level models of the fabric layer and the belt layer, as well as the interlayer interfaces of the pixel-level models of the belt layer and the adhesive layer. A bilinear constitutive relation is defined to obtain an initial interlayer model. Using MPC multi-point constraint equations, the mesh nodes of adjacent layer interfaces are forced to align to common nodes. Then, a two-dimensional verification is performed on the initial interlayer model to output the interlayer pixel-level model. The two-dimensional verification includes stress transfer verification and heat conduction verification.

[0111] Three layers of Cohesive elements were embedded between the pixel-level models of the fabric layer and the belt layer, and between the pixel-level models of the belt layer and the adhesive layer. These Cohesive elements were used to simulate the interfacial behavior between the layers, especially contact forces, adhesive forces, and slippage. Bilinear constitutive relations refer to the different manifestations of the material's mechanical behavior in two states: a linear elastic stage and a plastic stage. In the linear elastic stage, when the stress is within the elastic limit, the material follows elastic theory and exhibits a linear relationship. In the plastic stage, when the stress exceeds the elastic limit, the material begins to undergo plastic deformation, and the stress-strain relationship becomes nonlinear. By defining bilinear constitutive relations, the behavior of the material under different loading conditions can be accurately simulated, especially under high stress or extreme conditions. By introducing Cohesive elements and bilinear constitutive relations into the interface between the fabric layer, belt layer, and adhesive layer, an initial interlayer model was obtained. This initial interlayer model provides the foundation for subsequent optimization and verification.

[0112] MPC (Multi-Point Constraint) equations are a method to ensure the consistency of relative motion or displacement between multiple nodes. In the initialization of interlayer models, the contact and coupling between different layers may lead to inconsistent relative displacement between mesh nodes. Using MPC equations can force the mesh nodes between adjacent layers to align, ensuring that the interface between the fabric layer, belt layer and adhesive layer can accurately transfer stress and deformation, avoiding numerical errors and unnatural interface behavior.

[0113] Two-dimensional verification was performed on the initial interlayer model. Stress transfer verification confirmed the correct stress transfer between different layers, as stress transfer between material layers is crucial in actual tire operation; incorrect stress transfer can lead to tire structural failure or performance degradation. Heat generation during tire operation, especially in the shoulder area, was verified through thermal conduction verification to check the accuracy of heat conduction between layers. Thermal conduction behavior not only affects the tire's temperature distribution but can also significantly impact its material properties and service life. After these verifications, the final pixel-level interlayer model accurately describes the coupling behavior between different material layers, including mechanical stress and thermal conduction.

[0114] In summary, the aircraft tire shoulder heat generation optimization method based on interlayer pixel-level modeling provided in this application has the following technical effects:

[0115] By performing pixel-level modeling between layers and outputting pixel-level models, the mechanical behavior and thermal conductivity characteristics between different layers of aerospace radial tires can be accurately simulated. This modeling method breaks through the limitations of traditional macroscopic hierarchical models. By refining to the individual pixel level, it can more accurately capture the microscopic physical properties between different layers. This not only greatly improves the accuracy of tire heat generation analysis but also makes the simulation results more realistic and reliable, providing a scientific basis for subsequent optimization. By extracting basic parameters of the tire shoulder region from the initial hierarchical composite structure design, a detailed physical parameter foundation is provided for tire shoulder heat generation simulation and optimization. These basic parameters are highly accurate. The simulation accurately describes the geometric characteristics of the tire shoulder region and ensures the accuracy of the thermal generation simulation results. This makes the simulation results more realistic and can effectively reflect the tire's performance in actual flight. Using high-speed, heavy-load conditions as the application scenario constraint, the simulation performs tire shoulder thermal generation under extreme conditions. Under these extreme conditions, the tire's thermal stress and temperature changes have a significant impact on performance. Traditional simulations often fail to reflect the true situation of the tire under extreme conditions, especially during high temperature and pressure, high speed (critical speed), and intense impact. Through optimized thermal generation simulation, the temperature distribution cloud map of the tire shoulder region can be accurately obtained, thereby locating the highest temperature extreme value and high-temperature accumulation area. This provides a clear direction for further thermodynamic optimization, ensuring tire performance near critical speeds. Iterative optimization of the basic parameters in the tire shoulder region using a particle swarm optimization algorithm allows for adaptive parameter adjustment during the optimization process, finding the optimal solution in a complex multidimensional space. The inclusion of a cross-parameter combination optimization strategy with random perturbation enhances the algorithm's exploratory and convergent capabilities, overcoming the limitations of traditional optimization methods and quickly finding the most suitable parameter combination. This optimization method enables the finding of optimal tire shoulder thermodynamic parameters in the shortest possible time, maximizing tire heat generation. Minimize and avoid the impact of high-temperature accumulation areas on tire durability and performance; by optimizing the maximum temperature as the heat generation temperature, and setting the area of ​​high-temperature accumulation areas to be less than 15% of the total area of ​​the tire shoulder region as the heat generation range constraint, the heat distribution in the tire shoulder region can be directly controlled during the optimization process. By optimizing the thermodynamic properties of the tire shoulder region, the tire's tolerance under high-speed and heavy-load conditions is improved. Through this optimization, the service life of aviation tires can be significantly extended, and reliable performance can be maintained in high-stress and high-temperature environments, thereby achieving the goal of improving the tire's critical speed and making it more adaptable to more complex flight scenarios.

[0116] Example 2: Based on the same inventive concept as the aircraft tire shoulder heat generation optimization method based on interlayer pixel-level modeling in the previous examples, this application provides an aircraft tire shoulder heat generation optimization system based on interlayer pixel-level modeling. See [link to relevant documentation]. Figure 6 The system includes:

[0117] The interlayer pixel-level modeling module 10 is used to perform interlayer pixel-level modeling based on the initial layered composite structure design of the aviation radial tire, and output the interlayer pixel-level model.

[0118] The basic parameter extraction module 20 is used to extract basic parameters of the shoulder region from the initial hierarchical composite structure design, wherein the basic parameters of the shoulder region include the initial radius of curvature of the shoulder curve, the initial layering angle sequence of the belt layer, and the initial thickness ratio of the shoulder.

[0119] The shoulder heat generation simulation module 30 is used to load the basic parameters of the shoulder region into the interlayer pixel-level model, and then perform shoulder heat generation simulation under extreme conditions with high-speed heavy-load working conditions as the application scenario constraint, and output the temperature distribution cloud map of the shoulder region.

[0120] The coordinate positioning module 40 is used to traverse the temperature distribution cloud map of the tire shoulder area and locate the coordinate positions of the highest temperature extreme value and the high temperature accumulation area, wherein the coordinate positions of the high temperature accumulation area are marked with the area of ​​the high temperature accumulation area.

[0121] The parameter adjustment vector array setting module 50 is used to set the parameter adjustment vector array of the basic parameters of the tire shoulder region with the goal of minimizing the maximum temperature extreme value as the heat generation temperature optimization target and with the heat generation range constraint condition that the area of ​​the high temperature accumulation zone is less than 15% of the total area of ​​the tire shoulder region.

[0122] The particle swarm optimization module 60 is used to perform particle swarm optimization of the basic parameters of the shoulder region in the interlayer pixel-level model using the parameter tuning vector array as the set of optimization variables, and output the shoulder optimization correlation parameters.

[0123] The cross-parameter combination optimization module 70 is used to perform cross-parameter combination optimization with random perturbation on the shoulder optimization related parameters and output the shoulder thermodynamic optimization parameters that satisfy the extreme threshold of the heat generation temperature.

[0124] In one implementation, the inter-layer pixel-level modeling module 10 is used to perform the following operation steps:

[0125] The initial hierarchical composite structure design analyzes the hierarchical characteristic parameters, which include the number of carcass ply layers, the number of belt layers, and the hierarchical spatial distribution. Based on the hierarchical characteristic parameters, a multi-layer foundation finite element model is constructed, wherein the carcass ply layers, carcass belt layers, and carcass rubber layers in the multi-layer foundation finite element model are connected according to the carcass spatial topology. Using the bead wire ring as the reference point, single-pixel-level mesh generation within each layer and multi-pixel-level interface coupling modeling between layers are performed layer by layer in the multi-layer foundation finite element model, and the inter-layer pixel-level model is output.

[0126] In one implementation, the particle swarm optimization module 60 is used to perform the following steps:

[0127] Using the model ID of the radial aviation tire and the index composition of the basic parameters of the tire shoulder region as two-dimensional search conditions, the historical parameter set, historical temperature extreme value record set, and historical high temperature region distribution set of the tire shoulder region are retrieved online. After constructing the particle swarm optimization space based on the index composition of the basic parameters of the tire shoulder region, Q historical particle points are located in the particle swarm optimization space according to the historical parameter set of the tire shoulder region. According to the mapping relationship between the historical temperature extreme value record set and the historical high temperature region distribution set, Q temperature extreme value records and Q historical high temperature region distributions are mapped and filled in the Q historical particle points to complete the initialization of the particle swarm optimization space. After filtering the particle point effectiveness by traversing the Q historical particle points using the highest temperature extreme value and the area of ​​the high temperature cluster, a feasible envelope space is fitted based on the filtering results to obtain the parameter tuning optimization space.

[0128] In one implementation, the particle swarm optimization module 60 is used to perform the following steps:

[0129] The high-temperature accumulation area is projected onto the interlayer pixel-level model to obtain the volume coverage ratio of the high-temperature accumulation area on the tire carcass ply, tire carcass belt layer, and tire carcass rubber layer. Influence weight reassemblies for the ply, belt, and rubber layers are set. Based on these weights, the standard parameter tuning scales are corrected to obtain parameter tuning scale groups for the ply, belt, and rubber layers. Using the parameter tuning vector array as the optimization variable set, the first round of updates to the basic parameters of the tire shoulder region is performed in the parameter tuning optimization space mapping according to the ply, belt, and rubber layer parameter tuning scale groups, thus locating the first update. Particle point; after parsing the first updated particle point and extracting the update parameters of the first shoulder region, the update parameters of the first shoulder region are loaded into the interlayer pixel-level model. Under the constraint of high-speed heavy-load working conditions, the shoulder heat generation simulation under extreme working conditions is performed, and the first updated temperature distribution cloud map is output. If the first updated temperature extreme value of the first updated temperature distribution cloud map is less than the highest temperature extreme value, and the first updated high temperature area is less than the area of ​​the high temperature accumulation area, then the first updated particle point is used as the update starting point, and the search range is iteratively shrunk in the parameter tuning optimization space until the updated temperature extreme value is ≤120℃ and the updated high temperature area is less than 15% of the total area of ​​the shoulder region, and the shoulder optimization correlation parameters are output.

[0130] In one implementation, the cross-parameter combination optimization module 70 is used to perform the following operation steps:

[0131] In the parameter tuning and optimization space, the parameter tuning scale groups of the ply layer, belt layer, and rubber layer are used as perturbation scale constraints. Orthogonal perturbation sampling with cross-parameter combinations is performed around the tire shoulder optimization correlation parameters to obtain multiple perturbation sampling correlation parameters. After loading the multiple perturbation sampling correlation parameters into the interlayer pixel-level model, tire shoulder heat generation simulation under extreme conditions is performed with high-speed heavy-load working conditions as the application scenario constraint, and multiple perturbation temperature distribution cloud maps are output. According to the temperature optimization characteristics of the multiple perturbation temperature distribution cloud maps, the first perturbation optimization parameter is located. With the first perturbation optimization parameter as the update starting point, the orthogonal perturbation sampling with cross-parameter combinations and tire shoulder heat generation simulation are iteratively performed until the obtained updated temperature extreme value is ≤115℃ and the updated high temperature area is less than 12% of the total area of ​​the tire shoulder region. The tire shoulder thermodynamic optimization parameters are then output, wherein the extreme value threshold of the heat generation temperature is a tire shoulder temperature extreme value ≤115℃ and a tire shoulder high temperature area less than 12% of the total area of ​​the tire shoulder region.

[0132] In one implementation, the inter-layer pixel-level modeling module 10 is used to perform the following operation steps:

[0133] Using the bead wire ring as a reference point, the tire carcass belt layer is separated into inner independent sub-layer groups, middle independent sub-layer groups, and outer independent sub-layers according to radial radial positioning. Using the single cord diameter as the initial mesh scale, the N inner independent sub-layers in the inner independent sub-layer group are meshed into N initial sub-layer mesh unit sets. A predefined stress gradient limit is used to perform intra-layer mesh iterative optimization on the N initial sub-layer mesh unit sets, resulting in N inner pixel-level models, which serve as the inner pixel-level model group. Similarly, the middle pixel-level model group and the outer pixel-level model are constructed. Inter-layer multi-pixel-level interface coupling modeling is performed on the inner pixel-level model group, the middle pixel-level model group, and the outer pixel-level model to output the belt layer pixel-level model.

[0134] In one implementation, the inter-layer pixel-level modeling module 10 is used to perform the following operation steps:

[0135] Based on the initial mesh scale, the carcass ply is meshed into an initial carcass mesh unit set; the interlayer interface regions of the initial carcass mesh unit set and the interlayer pixel-level model are traversed to calculate and output the interlayer stress gradient distribution; the interlayer stress gradient distribution is compared with the stress gradient limit value to locate W high-risk mesh units in the initial carcass mesh unit set; the mesh scale is iteratively refined for the W high-risk mesh units until the updated stress gradient of the W high-risk mesh units meets the stress gradient limit value, and W updated carcass mesh unit sets are output; the W updated carcass mesh unit sets are used to map and replace the W high-risk mesh units in the initial carcass mesh unit set to generate the ply ply pixel-level model; and so on, using the interlayer pixel-level model as a reference, the rubber layer pixel-level model is constructed.

[0136] In one implementation, the inter-layer pixel-level modeling module 10 is used to perform the following operation steps:

[0137] By embedding two layers of Cohesive units between adjacent models within the inner pixel-level model group, inner-layer band coupling is achieved, resulting in an inner-layer pixel-level coupled model. By embedding one layer of Cohesive units between adjacent models within the middle pixel-level model group, middle-layer band coupling is achieved, resulting in a middle-layer pixel-level coupled model. By embedding three layers of Cohesive units between the inner and middle pixel-level coupled models, and two layers of Cohesive units between the middle and outer pixel-level coupled models, full-layer band coupling is achieved, resulting in the band-layer pixel-level model.

[0138] In one implementation, the inter-layer pixel-level modeling module 10 is used to perform the following operation steps:

[0139] Three layers of Cohesive elements are implanted at the interlayer interfaces of the pixel-level models of the fabric layer and the belt layer, as well as the interlayer interfaces of the pixel-level models of the belt layer and the adhesive layer. A bilinear constitutive relation is defined to obtain an initial interlayer model. Using MPC multi-point constraint equations, the mesh nodes of adjacent layer interfaces are forced to align to common nodes. Then, a two-dimensional verification is performed on the initial interlayer model to output the interlayer pixel-level model. The two-dimensional verification includes stress transfer verification and heat conduction verification.

[0140] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing heat generation on the shoulder of aircraft tires based on interlayer pixel-level modeling, characterized in that, The method includes: Based on the initial layered composite structure design of the aviation radial tire, interlayer pixel-level modeling is performed, and interlayer pixel-level model is output. Basic parameters of the shoulder region are extracted from the initial hierarchical composite structure design, wherein the basic parameters of the shoulder region include the initial radius of curvature of the shoulder curve, the initial layering angle sequence of the belt layer, and the initial thickness ratio of the shoulder. After loading the basic parameters of the tire shoulder region into the interlayer pixel-level model, the tire shoulder heat generation simulation under extreme conditions is performed with high-speed heavy-load working conditions as the application scenario constraint, and the temperature distribution cloud map of the tire shoulder region is output. Traverse the temperature distribution cloud map of the tire shoulder area to locate the highest temperature extreme value and the coordinate position of the high temperature accumulation area, wherein the coordinate position of the high temperature accumulation area is marked with the area of ​​the high temperature accumulation area; The optimization objective is to minimize the maximum temperature extreme value, and the constraint condition is that the area of ​​the high temperature accumulation zone is less than 15% of the total area of ​​the tire shoulder region. The parameter adjustment vector array of the basic parameters of the tire shoulder region is set. Using the parameter tuning vector array as the set of optimization variables, particle swarm optimization of the basic parameters of the shoulder region is performed in the inter-layer pixel-level model, and the shoulder optimization correlation parameters are output. The shoulder optimization parameters are subjected to random perturbation and cross-parameter combination optimization to output the shoulder thermodynamic optimization parameters that satisfy the extreme threshold of heat generation temperature.

2. The method for optimizing heat generation on the shoulder of aircraft tires based on inter-layer pixel-level modeling as described in claim 1, characterized in that, Based on the initial layered composite structure design of the aviation radial tire, interlayer pixel-level modeling is performed, and the interlayer pixel-level model is output, including: The initial hierarchical composite structure design analyzes the hierarchical characteristic parameters, wherein the hierarchical characteristic parameters include the number of carcass ply layers, the number of belt layers, and the hierarchical spatial distribution. A multi-layered finite element model is constructed based on the hierarchical feature parameters, wherein the tire carcass ply, tire carcass belt layer and tire carcass rubber layer in the multi-layered finite element model are connected according to the tire carcass spatial topology. Using the tire bead wire ring as a reference point, the multi-layer foundation finite element model is subjected to single-pixel-level mesh generation within each layer and multi-pixel-level interface coupling modeling between layers, and the inter-layer pixel-level model is output.

3. The method for optimizing heat generation on the shoulder of aircraft tires based on inter-layer pixel-level modeling as described in claim 2, characterized in that, Using the parameter tuning vector array as the optimization variable set, particle swarm optimization of the basic parameters of the shoulder region is performed in the inter-layer pixel-level model, and the shoulder optimization correlation parameters are output. Before this, the following steps are included: Using the model ID of the aviation radial tire and the index composition of the basic parameters of the tire shoulder area as two-dimensional search conditions, the historical parameter set, historical temperature extreme value record set, and historical high temperature area distribution set of the tire shoulder area are searched online. After constructing the particle swarm optimization space based on the index of the basic parameters of the shoulder region, Q historical particle points are located in the particle swarm optimization space according to the historical parameter set of the shoulder region. Based on the mapping relationship between the historical temperature extreme value record set and the historical high temperature region distribution set, Q temperature extreme value records and Q historical high temperature region distributions are mapped and filled in the Q historical particle points to complete the initialization of the particle swarm optimization space; After filtering the effectiveness of the Q historical particle points by using the highest temperature extreme value and the area of ​​the high temperature cluster, a feasible envelope space is fitted based on the filtering results to obtain the parameter tuning optimization space.

4. The method for optimizing heat generation on the shoulder of aircraft tires based on inter-layer pixel-level modeling as described in claim 3, characterized in that, Using the parameter tuning vector array as the set of optimization variables, particle swarm optimization of the basic parameters of the shoulder region is performed in the inter-layer pixel-level model, and the shoulder optimization correlation parameters are output, including: Project the high-temperature accumulation area onto the interlayer pixel-level model to obtain the volume coverage ratio of the high-temperature accumulation area on the tire carcass ply, tire carcass belt layer and tire carcass rubber layer, and set the influence weights of the ply, belt, and rubber layers. Based on the aforementioned influence weight reorganization of the ply layer, influence weight reorganization of the belt layer, and influence weight correction standard for the rubber layer, the following parameter adjustment scales are obtained: ply layer parameter adjustment scale group, belt layer parameter adjustment scale group, and rubber layer parameter adjustment scale group. Using the parameter adjustment vector array as the set of optimization variables, and based on the parameter adjustment scale group of the ply layer, the parameter adjustment scale group of the belt layer, and the parameter adjustment scale group of the rubber layer, the first round of updating of the basic parameters of the tire shoulder area is performed in the parameter adjustment optimization space mapping, and the first update particle point is located. After analyzing the first updated particle point and extracting the first tire shoulder region update parameters, the first tire shoulder region update parameters are loaded into the interlayer pixel-level model. Then, with high-speed heavy-load working conditions as the application scenario constraint, tire shoulder heat generation simulation under extreme working conditions is performed, and the first updated temperature distribution cloud map is output. If the first updated temperature extreme value of the first updated temperature distribution cloud map is less than the highest temperature extreme value, and the first updated high temperature area is less than the area of ​​the high temperature cluster, then the search range is iteratively shrunk in the parameter optimization space with the first updated particle point as the update starting point until the updated temperature extreme value is ≤120℃ and the updated high temperature area is less than 15% of the total area of ​​the tire shoulder region, and the tire shoulder optimization correlation parameters are output.

5. The method for optimizing heat generation on the shoulder of aircraft tires based on inter-layer pixel-level modeling as described in claim 4, characterized in that, Perform cross-parameter combination optimization with random perturbation on the aforementioned shoulder optimization correlation parameters, and output shoulder thermodynamic optimization parameters that satisfy the extreme threshold of heat generation temperature, including: In the parameter tuning optimization space, the parameter tuning scale group of the ply layer, the parameter tuning scale group of the belt layer, and the parameter tuning scale group of the rubber layer are used as perturbation scale constraints. Orthogonal perturbation sampling with cross-parameter combination is performed around the tire shoulder optimization correlation parameters to obtain multiple perturbation sampling correlation parameters. After loading the multiple disturbance sampling correlation parameters into the interlayer pixel-level model, the tire shoulder heat generation simulation under extreme conditions is performed with high-speed heavy-load working conditions as the application scenario constraint, and multiple disturbance temperature distribution cloud maps are output. Based on the temperature optimization characteristics of the multiple perturbation temperature distribution cloud maps, the first perturbation optimization parameter is located; Starting from the first perturbation optimization parameter, iteratively perform orthogonal perturbation sampling and shoulder heat generation simulation across parameter combinations until the obtained updated temperature extreme value is ≤115℃ and the updated high temperature area is less than 12% of the total area of ​​the shoulder region. Output the shoulder thermodynamic optimization parameters, wherein the heat generation temperature extreme value threshold is the shoulder temperature extreme value ≤115℃ and the high temperature area of ​​the shoulder is less than 12% of the total area of ​​the shoulder region.

6. The method for optimizing heat generation on the shoulder of aircraft tires based on inter-layer pixel-level modeling as described in claim 2, characterized in that, Also includes: Using the bead wire ring as a reference point, the tire carcass belt layer is separated into an inner independent sub-layer group, a middle independent sub-layer group, and an outer independent sub-layer group in a radial radial sequence. Using the diameter of a single curtain wire as the initial grid scale, the N independent sub-layers in the inner layer independent sub-layer group are meshed into N initial sub-layer grid cell sets; The N initial sub-layer mesh element sets are subjected to intra-layer mesh iterative optimization using a predefined stress gradient limit to obtain N inner-layer pixel-level models, which are used as the inner-layer pixel-level model group. By analogy, construct the middle-layer pixel-level model group and the outer-layer pixel-level model; Interlayer multi-pixel level interface coupling modeling is performed on the inner layer pixel-level model group, the middle layer pixel-level model group and the outer layer pixel-level model to output the band layer pixel-level model.

7. The method for optimizing heat generation on the shoulder of aircraft tires based on inter-layer pixel-level modeling as described in claim 6, characterized in that, Also includes: Based on the initial grid scale, the carcass ply is meshed into an initial carcass grid cell set; Traverse the initial set of matrix mesh elements and the interlayer interface region of the interlayer pixel-level model, and calculate and output the interlayer stress gradient distribution; By comparing the stress gradient limit with the interlayer stress gradient distribution, W high-risk mesh elements are located in the initial matrix mesh element set; Perform iterative refinement of the mesh scale for the W high-risk mesh elements until the updated stress gradient of the W high-risk mesh elements satisfies the stress gradient limit, and output a set of W updated matrix mesh elements; The W updated tire carcass mesh cell sets are used to map and replace the W high-risk mesh cells in the initial tire carcass mesh cell set to generate a pixel-level model of the ply layer. Similarly, based on the interlayer pixel-level model, a glue layer pixel-level model is constructed.

8. The method for optimizing heat generation on the shoulder of aircraft tires based on inter-layer pixel-level modeling as described in claim 6, characterized in that, Interlayer multi-pixel level interface coupling modeling is performed on the inner layer pixel-level model group, the middle layer pixel-level model group, and the outer layer pixel-level model to output a bundled layer pixel-level model, including: By embedding two layers of Cohesive units between adjacent models within the inner pixel-level model group, inner-layer band coupling is performed to obtain an inner pixel-level coupled model. By implanting one layer of Cohesive units between adjacent models within the middle-layer pixel-level model group, and performing band-bundled middle-layer coupling, a middle-layer pixel-level coupled model is obtained. By implanting three layers of Cohesive units between the inner and middle pixel-level coupling models, and two layers of Cohesive units between the middle and outer pixel-level coupling models, full-level band coupling is performed to obtain the band-layer pixel-level model.

9. The method for optimizing heat generation on the shoulder of aircraft tires based on inter-layer pixel-level modeling as described in claim 7, characterized in that, Also includes: Three layers of Cohesive units are implanted at the interlayer interfaces of the pixel-level models of the fabric layer and the belt layer, as well as the interlayer interfaces of the pixel-level models of the belt layer and the adhesive layer, respectively. Bilinear constitutive relations are defined to obtain the initialized interlayer model. Using the MPC multi-point constraint equation, after forcing common node alignment of adjacent layer interface mesh nodes in the initialized inter-layer model, two-dimensional verification is performed on the initialized inter-layer model, and the inter-layer pixel-level model is output. The two-dimensional verification includes stress transfer verification and heat conduction verification.

10. An optimization system for heat generation on the shoulder of an aircraft tire based on interlayer pixel-level modeling, characterized in that, The system is used to implement the aircraft tire shoulder heat generation optimization method based on interlayer pixel-level modeling as described in any one of claims 1-9, the system comprising: The interlayer pixel-level modeling module is used to perform interlayer pixel-level modeling based on the initial layered composite structure design of the aviation radial tire, and output the interlayer pixel-level model. The basic parameter extraction module is used to extract basic parameters of the shoulder region from the initial hierarchical composite structure design, wherein the basic parameters of the shoulder region include the initial radius of curvature of the shoulder curve, the initial layering angle sequence of the belt layer, and the initial thickness ratio of the shoulder. The tire shoulder heat generation simulation module is used to load the basic parameters of the tire shoulder region into the interlayer pixel-level model, and then perform tire shoulder heat generation simulation under extreme conditions with high-speed heavy-load working conditions as the application scenario constraint, and output the temperature distribution cloud map of the tire shoulder region. The coordinate location module is used to traverse the temperature distribution cloud map of the tire shoulder area, locate the coordinate location of the highest temperature extreme value and the high temperature accumulation area, wherein the coordinate location of the high temperature accumulation area is marked with the area of ​​the high temperature accumulation area; The parameter tuning vector array setting module is used to set the parameter tuning vector array of the basic parameters of the tire shoulder region with the goal of minimizing the maximum temperature extreme value and the constraint condition that the area of ​​the high temperature accumulation zone is less than 15% of the total area of ​​the tire shoulder region. The particle swarm optimization module is used to perform particle swarm optimization of the basic parameters of the shoulder region in the inter-layer pixel-level model using the parameter tuning vector array as the set of optimization variables, and output the shoulder optimization correlation parameters. The cross-parameter combination optimization module is used to perform random perturbation cross-parameter combination optimization on the shoulder optimization related parameters and output the shoulder thermodynamic optimization parameters that satisfy the extreme threshold of the heat generation temperature.

Images