Multi-scene optimization design method for anti-collision structure at end part of metro vehicle

Abstract

The invention provides a multi-scene optimization design method and system for an anti-collision structure at the end of a metro vehicle, and belongs to the technical field of rail transit vehicle design. According to the design method, two typical collision working conditions of middle loading and corner loading are set, a structure response simulation model based on the steel-carbon fiber composite structure is established, and a plurality of performance indexes are used as evaluation output. By performing sensitivity analysis on different structure parameters, the influence weight of the different structure parameters on each performance index is quantified, and a multi-scene collaborative optimization model oriented to a multi-performance target is constructed. Wherein the design parameters are globally optimized by adopting a multi-objective genetic algorithm. The precision and consistency of the established simulation model are verified through an entity loading test, the reliability and feasibility of an optimization result are ensured, the performance balance and lightweight design of the anti-collision structure under different loading working conditions are achieved, and the method is suitable for engineering optimization and product development of the metro vehicle end structure.

Description

Technical Field

[0001] The present invention belongs to the technical field of rail transit vehicle design, and in particular relates to a multi-scenario optimization design method for an anti-collision structure at the end of a subway vehicle. Background Art

[0002] Existing anti-collision structures at the ends of subway vehicles mostly use all-steel frames or hollow steel tube structures, which absorb part of the impact energy through structural yielding and crushing when encountering a collision. However, such structures have problems such as low energy absorption efficiency per unit mass, large overall mass, and uncontrollable plastic deformation process, making it difficult to achieve lightweight goals while ensuring collision safety. In recent years, steel-carbon fiber reinforced composite (Steel / CFRP) hybrid square tubes have been proposed as a new type of energy-absorbing structural unit because they combine the plastic deformation ability of metal materials with the high specific strength characteristics of fiber composite materials. They have good structural stability and quality control advantages. Although this type of structure shows excellent performance in static loading and local crushing, in rail transit applications, there is still a lack of unified modeling and performance optimization design methods for various typical collision conditions such as actual mid-section loading and corner loading. At the same time, the existing design path lacks quantitative analysis of the energy absorption response law of Steel / CFRP hybrid structural parameters, and has not formed a complete design system for multi-indicator, multi-objective, and multi-scenario collaborative optimization. Therefore, it is urgent to establish a structural response modeling and optimization method for typical collision conditions to fully tap the performance potential of Steel / CFRP hybrid square tubes in subway vehicle anti-collision structures.

[0003] According to relevant public technologies, the technical solution with publication number CN107914728A proposes a front-end energy absorption device for subway vehicles, which implements sequential energy absorption and cushioning effects by setting up multiple movable mechanisms. The technical solution with publication number WO2024148950A1 proposes an anti-collision structure that enhances the strength of the vehicle chassis through optimized structural component design. The technical solution with publication number PH12019500998A1 proposes an anti-collision connection structure that strengthens the overall rigidity of the vehicle frame by adding multiple reinforcing ribs to the vehicle frame.

[0004] The above technical solutions all propose several technical solutions for improving the structural strength and rigidity of vehicles. However, there are currently few relevant technical solutions for the anti-collision structure of rail vehicles and related optimization design methods.

[0005] The foregoing discussion of the background art is intended only to facilitate an understanding of the present invention. This discussion does not acknowledge or admit that any of the material referred to is part of the common general knowledge. Summary of the Invention

[0006] The purpose of the present invention is to provide a multi-scenario optimization design method and a design system for the anti-collision structure at the end of a subway vehicle. The design method establishes a structural response simulation model based on a steel-carbon fiber composite structure by setting two typical collision conditions of middle loading and corner loading, and uses multiple performance indicators as evaluation outputs. By performing sensitivity analysis on different structural parameters, the weights of their influence on each performance indicator are quantified, and a multi-scenario collaborative optimization model for multiple performance objectives is constructed. A multi-objective genetic algorithm is used to perform global optimization on the design parameters. The accuracy and consistency of the established simulation model are verified through physical loading tests to ensure the reliability and feasibility of the optimization results, and to achieve performance balance and lightweight design of the anti-collision structure under different loading conditions, which is suitable for engineering optimization and product development of the front end structure of subway vehicles.

[0007] The present invention adopts the following technical solution: a multi-scenario optimization design method for the anti-collision structure of the end of a subway vehicle, the design method comprising the following steps:

[0008] S100: Setting typical loading conditions for the end anti-collision structure of a subway vehicle, including at least a middle loading scenario for a middle anti-collision column and a corner loading scenario for a corner anti-collision column;

[0009] S200: Establishing a simulation model of the structural response of the anti-collision structure; the simulation model includes multiple design parameters based on the steel-carbon fiber composite structure as input and multiple post-collision performance index responses as output;

[0010] S300: Performing a design parameter sensitivity analysis based on the simulation model to identify design parameters that affect performance indicators under different loading scenarios and the weights corresponding to each design parameter;

[0011] S400: Based on the weights of various design parameters, optimization objectives and constraints are set to build a multi-scenario collaborative optimization model for multiple performance objectives.

[0012] S500: performing collaborative optimization on the multi-scenario collaborative optimization model to output an optimal structural parameter combination solution that meets the design requirements of the center loading and corner loading scenarios;

[0013] The design parameters include at least the thickness of the outer steel plate, the thickness of the carbon fiber composite material, the thickness of the upper beam and the thickness of the lower beam; the performance indicators include energy absorption EA, specific energy absorption SEA, peak force PCF, intrusion area S i and the maximum deformation Di max .

[0014] Preferably, the optimization goal is to reduce the invasion area S i and increase the energy absorption EA; the constraints include:

[0015] The first constraint condition is that the MCF of the crash frame during plastic collision must be higher than the elastic design load of the entire vehicle body;

[0016] The second constraint condition: After the collapse, the intrusion amount D at the middle height of each anti-collision column mid It should be no less than 1 / 3 of the longitudinal dimension of the column;

[0017] The third constraint is that the peak force PCF should be as small as possible.

[0018] Preferably, in step S200, after establishing the simulation model of the structural response of the anti-collision structure, the simulation model is subjected to consistency verification using a physical vehicle body to determine whether the simulation model meets the validity requirements; wherein the consistency verification includes the following steps:

[0019] E100: Set up a physical subway end anti-collision structure and set quasi-static bending loading conditions for the middle loading scenario and the corner loading scenario respectively;

[0020] E200: Setting a force sensor and a displacement sensor to collect a plurality of measurement values ​​including at least the performance indicator;

[0021] E300: Compare the load-displacement curves, energy-displacement curves, and displacement sensor measurements obtained from the test with the simulation output results of the simulation model to verify the numerical accuracy of the simulation model in multiple performance indicators, as well as the consistency of the structural crushing process and final deformation morphology with the test results.

[0022] Preferably, in step S500, a multi-objective genetic algorithm is used for collaborative optimization of the multi-scenario collaborative optimization model, including the following sub-steps:

[0023] S510: Determine the objective function, design parameters, and constraints based on the design standards;

[0024] S520: Determine the sample distribution of the design parameters;

[0025] S530: Generate an approximate model of the simulation model to perform multi-objective optimization calculation;

[0026] S540: Generate multiple Pareto front solutions using a non-dominated sorting strategy;

[0027] S550: In the iterative process, selection, crossover, and mutation are performed based on genetic evolution operations, and the fitness of the solutions in the population is updated until the termination condition is reached or the optimal solution set converges, and finally the optimal design parameter combination scheme is output.

[0028] At the same time, a multi-scenario optimization design system for a subway vehicle end anti-collision structure is proposed. The design system is applied to the multi-scenario optimization design method for a subway vehicle end anti-collision structure. The design system includes a memory, a processor, and machine-readable instructions stored in the memory and executable on the processor. When the machine-readable instructions are executed by the processor, the following are performed:

[0029] Set typical loading conditions for the end anti-collision structure of subway vehicles, including at least a middle loading scenario for the middle anti-collision column and a corner loading scenario for the corner anti-collision column;

[0030] Establishing a simulation model of the structural response of the crashworthy structure; the simulation model includes multiple design parameters based on the steel-carbon fiber composite structure as input and multiple post-crash performance index responses as output;

[0031] Performing a sensitivity analysis of design parameters based on the simulation model to identify design parameters that affect performance indicators under different loading scenarios and the weights corresponding to each design parameter;

[0032] Based on the weights of various design parameters, we set optimization goals and constraints to build a multi-scenario collaborative optimization model for multiple performance objectives.

[0033] Performing collaborative optimization on the multi-scenario collaborative optimization model to output an optimal structural parameter combination solution that meets the design requirements of the center loading and corner loading scenarios;

[0034] The design parameters include at least the thickness of the outer steel plate, the thickness of the carbon fiber composite material, the thickness of the upper beam and the thickness of the lower beam; the performance indicators include energy absorption EA, specific energy absorption SEA, peak crushing force PCF, intrusion area S i and the maximum deformation Di max .

[0035] The beneficial effects achieved by the present invention are:

[0036] 1. The design approach of this technical solution takes into account two typical anti-collision conditions: mid-load and corner loading. By constructing a unified structural response model and collaborative optimization mechanism, it avoids the problem of local performance excellence but overall imbalance caused by traditional design optimization for a single scenario. It achieves a balance between energy absorption capacity and structural stability under different impact directions, and improves the anti-collision system's adaptability to all working conditions.

[0037] 2. The design method of this technical solution uses steel-carbon fiber composite materials to form the anti-collision column structure, giving full play to the plasticity of steel and the high specific strength of CFRP, achieving higher energy absorption efficiency per unit mass, lower intrusion volume and enhanced crushing stability, effectively meeting the dual design requirements of lightweight and safety of rail vehicles.

[0038] 3. The design approach of this technical solution was verified through full-scale quasi-static loading tests to verify the response consistency of the simulation model, ensuring the accuracy of the model in indicators such as load-displacement curves, deformation patterns, and energy absorption. This verification process enhances the credibility of subsequent optimization results in engineering implementation, making the optimization process not only theoretically complete but also practically applicable.

[0039] 4. The software and hardware parts of the design system of this technical solution adopt a modular design. The various working modules and components of the hardware part of the system, as well as the instructions, parameters, and algorithms of the software part can be easily replaced and / or upgraded at a later stage, thereby reducing the construction cost and maintenance cost of this system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but rather the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0041] Explanation of the accompanying drawings: 1- chassis; 2- hydraulic drive device; 3- displacement meter fixture; 4- transverse support frame; 5- rigid wall; 6- vehicle body; 7- back plate; 8- hydraulic cylinder; 9- anti-collision structure; 10- rigid pressure plate; 500- computing architecture; 502- bus; 504- processor; 506- main memory; 508- read-only memory; 510- storage device; 512- display; 514- input device; 516- cursor control device; 518- network device;

[0042] Figure 1 A diagram showing the steps of the design method of the present invention;

[0043] Figure 2 Schematic diagram of a subway car body used in an embodiment of the present invention;

[0044] Figure 3 Schematic diagram of various parts of a subway car body used in an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the locations of multiple displacement sensors provided on the central anti-collision column in an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the locations of multiple displacement sensors provided on the corner anti-collision column in an embodiment of the present invention;

[0047] Figure 6 A schematic diagram of establishing a simulation model of an anti-collision structure in an embodiment of the present invention;

[0048] Figure 7Schematic diagram of the speed-time curve of the pressure head loading in the simulation experiment in an embodiment of the present invention;

[0049] Figure 8 This is a flowchart of a multi-objective and multi-scenario collaborative optimization design optimization method according to an embodiment of the present invention;

[0050] Figure 9 Schematic diagram of the spatial response surface of various design parameters and performance indicators of the central anti-collision column loading scenario in an embodiment of the present invention;

[0051] Figure 10 Schematic diagram of the spatial response surface of various design parameters and performance indicators of the corner anti-collision column loading scenario in an embodiment of the present invention;

[0052] Figure 11 Schematic diagram of Pareto front statistics formed by two design objectives EA and Si after multi-objective optimization in an embodiment of the present invention;

[0053] Figure 12 Schematic diagram of the framework of the computer system used in the embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with its embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. For those skilled in the art, other systems, methods and / or features of the present embodiment will become apparent after reviewing the following detailed description. It is intended that all such additional systems, methods, features and advantages are included in this specification. Included within the scope of the present invention and protected by the appended claims. Additional features of the disclosed embodiments are described in the following detailed description, and these features will be apparent from the following detailed description.

[0055] The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components. In the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", etc. indicating an orientation or positional relationship, they are based on the orientation or positional relationship shown in the drawings. This is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or component referred to must have a specific orientation. The terms used in the drawings to describe the positional relationship are only for illustrative purposes and cannot be understood as limiting this patent. For those skilled in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0056] Example 1: Exemplary, a multi-scenario optimization design method for a subway vehicle end anti-collision structure, the design method comprising the following steps:

[0057] S100: Setting typical loading conditions for the end anti-collision structure of a subway vehicle, including at least a middle loading scenario for a middle anti-collision column and a corner loading scenario for a corner anti-collision column;

[0058] S200: Establishing a simulation model of the structural response of the anti-collision structure; the simulation model includes multiple design parameters based on the steel-carbon fiber composite structure as input and multiple post-collision performance index responses as output;

[0059] S300: Performing a design parameter sensitivity analysis based on the simulation model to identify design parameters that affect performance indicators under different loading scenarios and the weights corresponding to each design parameter;

[0060] S400: Based on the weights of various design parameters, optimization objectives and constraints are set to build a multi-scenario collaborative optimization model for multiple performance objectives.

[0061] S500: performing collaborative optimization on the multi-scenario collaborative optimization model to output an optimal structural parameter combination solution that meets the design requirements of the center loading and corner loading scenarios;

[0062] The design parameters include at least the thickness of the outer steel plate, the thickness of the carbon fiber composite material, the thickness of the upper beam and the thickness of the lower beam; the performance indicators include energy absorption EA, specific energy absorption SEA, peak force PCF, intrusion area S i and the maximum deformation Di max .

[0063] Preferably, the optimization goal is to reduce the invasion area S i and increase the energy absorption EA; the constraints include:

[0064] The first constraint condition is that the MCF of the crash frame during plastic collision must be higher than the elastic design load of the entire vehicle body;

[0065] The second constraint condition: After the collapse, the intrusion amount D at the middle height of each anti-collision column mid It should be no less than 1 / 3 of the longitudinal dimension of the column;

[0066] The third constraint is that the peak force PCF should be as small as possible.

[0067] Preferably, in step S200, after establishing the simulation model of the structural response of the anti-collision structure, the simulation model is subjected to consistency verification using a physical vehicle body to determine whether the simulation model meets the validity requirements; wherein the consistency verification includes the following steps:

[0068] E100: Set up a physical subway end anti-collision structure and set quasi-static bending loading conditions for the middle loading scenario and the corner loading scenario respectively;

[0069] E200: Setting a force sensor and a displacement sensor to collect a plurality of measurement values ​​including at least the performance indicator;

[0070] E300: Compare the load-displacement curves, energy-displacement curves, and displacement sensor measurements obtained from the test with the simulation output results of the simulation model to verify the numerical accuracy of the simulation model in multiple performance indicators, as well as the consistency of the structural crushing process and final deformation morphology with the test results.

[0071] Preferably, in step S500, a multi-objective genetic algorithm is used for collaborative optimization of the multi-scenario collaborative optimization model, including the following sub-steps:

[0072] S510: Determine the objective function, design parameters, and constraints based on the design standards;

[0073] S520: Determine the sample distribution of the design parameters;

[0074] S530: Generate an approximate model of the simulation model to perform multi-objective optimization calculation;

[0075] S540: Generate multiple Pareto front solutions using a non-dominated sorting strategy;

[0076] S550: In the iterative process, selection, crossover, and mutation are performed based on genetic evolution operations, and the fitness of the solutions in the population is updated until the termination condition is reached or the optimal solution set converges, and finally the optimal design parameter combination scheme is output.

[0077] At the same time, a multi-scenario optimization design system for a subway vehicle end anti-collision structure is proposed. The design system is applied to the multi-scenario optimization design method for a subway vehicle end anti-collision structure. The design system includes a memory, a processor, and machine-readable instructions stored in the memory and executable on the processor. When the machine-readable instructions are executed by the processor, the following are performed:

[0078] Set typical loading conditions for the end anti-collision structure of subway vehicles, including at least a middle loading scenario for the middle anti-collision column and a corner loading scenario for the corner anti-collision column;

[0079] Establishing a simulation model of the structural response of the crashworthy structure; the simulation model includes multiple design parameters based on the steel-carbon fiber composite structure as input and multiple post-crash performance index responses as output;

[0080] Performing a sensitivity analysis of design parameters based on the simulation model to identify design parameters that affect performance indicators under different loading scenarios and the weights corresponding to each design parameter;

[0081] Based on the weights of various design parameters, we set optimization goals and constraints to build a multi-scenario collaborative optimization model for multiple performance objectives.

[0082] Performing collaborative optimization on the multi-scenario collaborative optimization model to output an optimal structural parameter combination solution that meets the design requirements of the center loading and corner loading scenarios;

[0083] The design parameters include at least the thickness of the outer steel plate, the thickness of the carbon fiber composite material, the thickness of the upper beam and the thickness of the lower beam; the performance indicators include energy absorption EA, specific energy absorption SEA, peak crushing force PCF, intrusion area S i and the maximum deformation Di max .

[0084] First, in order to systematically evaluate the crashworthiness performance of the end anti-collision structure of subway vehicles, this technical solution will use the following key crashworthiness indicators as the performance indicators, namely energy absorption EA, peak force PCF, average force MCF, specific energy absorption SEA, crushing force efficiency CFE, maximum intrusion Di max And the intrusion area S i The definitions of the above performance indicators are as follows:

[0085] (1) Energy absorption EA: It is obtained by integrating the load-displacement curve formed during bending loading. The energy absorption EA index is widely used to evaluate the ability of a structure to absorb energy during deformation. Its expression equation is:

[0086]

[0087] Where F(s) represents the crushing force when the loading displacement is D, and D represents the displacement of the indenter during the loading stage.

[0088] (2) Peak force (PCF): This represents the maximum crushing force generated during structural deformation. Excessively high PCF may reduce the survival rate of drivers and passengers. It can be expressed as: PCF = max(F(s)).

[0089] (3) Mean force MCF: It is the average value of the force of the structure during the entire bending loading stage and can be defined by the following formula.

[0090]

[0091] (4) Specific energy absorption SEA: It can be understood as the ability of the verified structure to absorb energy per unit mass. It is an important evaluation index for structural lightweighting. Its calculation method is:

[0092]

[0093] Where M represents the mass of the structure. The larger the SEA value, the higher the energy absorption efficiency of the structure.

[0094] (5) Crushing force efficiency (CFE): This is the ratio of the average crushing force to the peak crushing force. It is usually used to evaluate the stability of a structure during the crushing process. An ideal energy-absorbing structure always pursues a high CFE, which is expressed as follows:

[0095] CFE=MCF / PCF.

[0096] (6) Maximum intrusion Di max : After the pressure head is completely unloaded, the final deformation of the rear surface of the structure after elastic recovery. Excessive intrusion will seriously squeeze the internal space of the subway end, so the intrusion after the structure collapses should be minimized.

[0097] (7) Intrusion area S i : Under the action of bending load, the anti-collision structure not only needs to have excellent plastic mechanical properties, but also should have a certain elastic recovery ability, and be able to release a certain amount of intrusion space after bending and crushing to protect the safe space of the cab.

[0098] Therefore, the intrusion area S i The parameter is selected as an evaluation metric. This parameter is defined as the area swept by the structure before and after bending and crushing. The smaller the intrusion area, the less space the structure occupies after crushing, the smaller the residual deformation, and helps to increase the remaining survival space of the vehicle body.

[0099] To evaluate and optimize the energy absorption performance of subway car end impact protection structures under typical collision scenarios, this technical solution first conducted physical loading tests on full-scale subway car end impact protection structures, accompanied by the development of a corresponding finite element simulation model. During the tests, relevant performance data was measured and recorded in detail. After verification with the simulation model's test data, the reliability of the simulation model was confirmed. The simulation model was then used to conduct multi-scenario, multi-parameter experiments.

[0100] In a preferred embodiment, a quasi-static bending loading test of the anti-collision structure of the subway car body end is performed in the following manner to obtain real test data.

[0101] Preferably, the car body 6 used in the test is a 1678mm long substructure cut from the end of the first car body of the subway, and is structurally welded to the car body 6 using two back plates 7. The structural diagram is shown in the attached figure. Figure 2 shown.

[0102] Furthermore, to capture the detailed bending response of the crash structure, three high-definition cameras were deployed to record the entire test process. These cameras focused on damage to key areas of the crash bollard from different angles. Furthermore, to meet testing requirements, all test equipment required detailed setup and commissioning prior to the test.

[0103] Preferably, the main steps of the test arrangement are as follows:

[0104] (1) Test device configuration:

[0105] The test scenario of the quasi-static bending loading test of the anti-collision structure at the end of the subway car body is as shown in the attached figure. Figure 3 As shown in the figure, the back plate 7 is bolted to the rigid wall 5. The mass of the vehicle body 1 is 1.581 tons, and the mass of the back plate 7 is 1.101 tons. Considering that the loading load is offset relative to the longitudinal centerline of the vehicle body 6, two transverse support frames 4 are installed along the length of the vehicle body 6 to provide lateral constraints and prevent deflection of the test structure during loading.

[0106] The back of vehicle body 6 is supported by rigid wall 5, while the front is longitudinally loaded by hydraulic cylinder 8 driven by hydraulic drive unit 2. To record the amount of space intrusion by the center and corner bumpers before and after the test, displacement meter fixtures 3 were designed behind the two types of bumpers to facilitate installation and testing of LVDT displacement meters.

[0107] Preferably, the anti-collision structure of the subway vehicle is subjected to quasi-static loading tests in the following two typical loading scenarios in accordance with the requirements of the ASMERT-2-2014 standard: (1) middle anti-collision bar loading scenario; (2) corner anti-collision bar loading scenario.

[0108] Furthermore, a force sensor LC0 was installed at the front end of hydraulic cylinder 8 to record the load generated during the loading process. During the loading test, the longitudinal crushing load was applied by hydraulic cylinder 8 driven by hydraulic drive unit 2 at a uniform loading rate of 20 mm / min and an unloading rate of 50 mm / min. During the test, the longitudinal loading distances of the hydraulic cylinder 8's pressure head against the center and corner bumper posts were 90 mm and 130 mm, respectively.

[0109] To measure the lateral deformation and lateral load of vehicle body 3 after a frontal load, a lateral support frame 4 is installed on either side of the vehicle body 3, outside the anti-collision bar. Transverse force sensors, including a left lateral force sensor LC1 and a right lateral force sensor LC2, are installed between the lateral support frame 4 and the vehicle body 3 to record the lateral load generated during the compression process.

[0110] Furthermore, in a preferred embodiment, in order to measure the displacement and deformation of various components and devices during the experiment, a plurality of linear variable differential transformer (LVDT) displacement sensors are used to measure the displacement data.

[0111] In order to measure the longitudinal displacement of the pressure head of the hydraulic cylinder 8, a wire displacement sensor is set up, one end of the wire displacement sensor is constrained on the movable hydraulic cylinder, and the other end is constrained on the fixed tooling bracket.

[0112] Furthermore, in order to determine the final intrusion amount of the center anti-collision column and the corner anti-collision column after the test, two sets of ejector displacement sensors are set, which are respectively arranged on the rear surface of the center anti-collision column and the corner anti-collision column. Each tested anti-collision column is set to test the displacement of at least five positions. For example, as shown in the attached figure Figure 4 As shown in the figure, it is a schematic diagram of the arrangement of multiple displacement sensors in the central anti-collision column bending loading test; as shown in the attached figure, Figure 5 The figure shows the layout of multiple displacement sensors in the corner anti-collision column bending loading test.

[0113] Preferably, ejector displacement sensors are respectively provided at the top, bottom, middle, load application point and other relatively important measurement points, as shown in the attached example. Figure 4 and attached Figure 5 As shown in the figure, 6 detection points are set on the central anti-collision column and 5 detection points are set on the corner anti-collision column.

[0114] In addition, the indenter load application locations for the center and corner impact bollards are located 457mm and 607.5mm above the base, respectively, and the intermediate height locations are located 1120mm and 961.1mm above the base, respectively. Preferably, a displacement meter with a relatively large range should be used for locations with large intrusions, such as the intermediate area of ​​the center impact bollard and the loading area of ​​the corner impact bollards.

[0115] Furthermore, in order to collect sufficient experimental data in a timely manner, the relevant acquisition instruments are reasonably configured. All test sensors, including force sensors and displacement sensors, need to cooperate with data acquisition equipment to implement data acquisition. The data acquisition system for the quasi-static bending loading test of the anti-collision structure adopts a self-made data acquisition subsystem and an IMCDataWorks subsystem (IMC system for short). The two subsystems run simultaneously through a computer. The data acquisition subsystem operates at a working voltage of 5V. Each data acquisition subsystem provides 32 data acquisition channels. These channels can be configured as data inputs provided by the ejector displacement sensor. The working potential of the IMC system is 24V. Each IMC system processes 8 channels of mixed data input from force sensors and wire displacement sensors.

[0116] Both subsystems sample at a rate of 100 samples per second. The collected data can be further filtered and analyzed in post-processing. Furthermore, to ensure that the time signatures within all data samples are aligned, a trigger is installed between the IMC system and the data acquisition subsystem to synchronize the start times. This trigger provides a trigger signal by flipping a shared switch.

[0117] In general, the specific experiments were carried out through the following steps.

[0118] (1) Install the test fixture and mark the specific location of the test point.

[0119] (2) Install the sensors required for the test and connect the data acquisition system and trigger.

[0120] (3) Measure and record the original physical position of the center / corner anti-collision pillars on the vehicle body.

[0121] (4) Before the test begins, record the initial values ​​of all sensors and reset all test instruments and sensors to zero.

[0122] (5) While continuously recording instrument data, apply load according to the test standard.

[0123] (6) After the test is completed, measure and record the physical position of the center / corner anti-collision pillars on the vehicle body after crushing.

[0124] Furthermore, to verify the multi-scenario optimized design, it is necessary to establish an accurate digital simulation model to conduct a large number of multi-parameter experiments digitally. This process includes establishing the simulation model and conducting experimental verification and comparison of the above-mentioned quasi-static bending loading test of the anti-collision structure with the simulation model to verify the reliability of the simulation model.

[0125] Specifically, a digital simulation model was created using digital modeling software based on the anti-collision structure at the end of the subway. The various dimensions and manufacturing processes in the structure were variable parameters, allowing for modification during subsequent simulation experiments.

[0126] Furthermore, in order to verify the validity and calculation accuracy of the established anti-collision structure simulation model, the explicit nonlinear finite element software LS-DYNA was first used to establish the finite element model of the subway end anti-collision structure under bending loading conditions, as shown in the attached figure. Figure 6 As shown in Figure 1, the finite element model includes at least the following structural simulation components: the vehicle body 6, the anti-collision structure 9, and the rigid pressure plate 10. The rigid pressure head 10 simulates the load applied to the pressure head. Under this operating condition, plastic deformation primarily occurs in the loaded anti-collision structure. Finite element modeling was performed based on the actual dimensions of the test vehicle body end.

[0127] The loading and unloading process of the anti-collision structure is achieved by applying a specified velocity-time curve along the positive X direction to the rigid indenter 10. In order to avoid introducing significant dynamic effects in the finite element calculation, a smooth velocity curve is used to achieve this requirement. Figure 7 shown.

[0128] To thoroughly and comprehensively study the mechanical properties of the anti-collision structure during bending loading, the *MAT_PIECEVISE_LINEAR_PLASTICITY material model (material MAT24) was selected from the LS-DYNA material library to define the steel material. The engineering stress-strain curve obtained from the uniaxial tensile test in the previous section was converted into a true stress-strain curve and input into this material model. Because the indenter undergoes almost no plastic deformation during loading, the rigid indenter 10 was treated as a rigid body during the modeling process and simulated using the *MAT_RIGID material model (material MAT20).

[0129] In this model, the *AUTOMATIC_SURFACE_TO_SURFACE surface-to-surface contact algorithm is used between the indenter and the anti-collision structure. For the vehicle body, the *AUTOMATIC_SINGLE_SURFACE self-contact algorithm is employed to prevent interpenetration of the vehicle structure during bending and crushing. In both contact relationships, the static friction coefficient is defined as 0.3, and the dynamic friction coefficient is defined as 0.2.

[0130] During the test, the rigid indenter 10 first contacts the outer surface of the center / corner bumper and is loaded in the positive X direction to a specified maximum displacement within approximately 0.17 seconds. After reaching maximum displacement, the indenter moves in the negative X direction, gradually removing the applied load until it completely disengages from and moves away from the center / corner bumper. To simulate the rigid wall's fixed constraint on the vehicle structure, all end nodes of the vehicle structure are constrained in six directions using the keyword *BOUNDRY_SPC_NODE. The boundary conditions in the finite element model of the crash structure are identical to those used in the test.

[0131] Furthermore, considering the impact of mesh size on computational accuracy and efficiency, a mesh sensitivity analysis was performed. Given the relatively large overall structural dimensions of the vehicle body and the fact that the primary plastic deformation is concentrated in the crash structure, different mesh sizes were set for different parts of the finite element model of the entire vehicle body. To determine the optimal mesh size, multiple finite element simulations with different mesh sizes were performed. Calculations show that when the crash structure's mesh size is less than 8 mm, the associated MCF and PCF converge. Therefore, to balance accuracy and computational cost, the mesh size of the center crash column, corner crash columns, and the auxiliary crossbeam connected to them was determined to be 8 mm. As for the rear end of the vehicle body, which did not undergo significant plastic deformation during loading, its mesh size was assigned to 20 mm. The mesh size of the connecting area between the front and rear ends of the vehicle body gradually and evenly transitioned from 8 mm to 20 mm.

[0132] Furthermore, to verify the validity of the finite element model of the crash structure, real-time deformation sequence images collected during quasi-static loading tests of the center and corner bumpers were compared with the corresponding finite element simulation results. The finite element simulations of the center and corner bumpers were examined to determine whether the deformation processes during the tests were consistent with those during the tests.

[0133] Furthermore, the force-displacement curves and energy-displacement curves of the finite element simulation of the central and corner anti-collision columns were compared with the test results to verify the comparison of the mechanical properties of the real experiment and the finite element simulation model experiment, and confirm the consistency of the two.

[0134] Furthermore, the intrusion of the finite element model's crash barrier structure during bending loading was verified. To evaluate the maximum intrusion at each measurement point of the center and corner bumpers after the rigid indenter was unloaded, as well as the residual permanent deformation after elastic recovery, the LVDT displacement-indenter displacement curves of the center and corner bumpers were compared with the actual experimental test results to confirm their consistency.

[0135] Furthermore, to verify the accuracy of the finite element model of the crashworthiness structure, the relative error between the quantified real-world and finite element simulation crashworthiness indicators was calculated, resulting in the following table. The maximum error between the center crash bollard test and simulation results is 2.77%, while the maximum error between the corner crash bollard test and simulation results is 3.48%. This data demonstrates that the error between the experimental results using the simulation model of the crashworthiness structure and the real-world experimental values ​​is within the expected 5%, indicating that the constructed finite element model of the crashworthiness structure has high accuracy and can be used for subsequent research.

[0136]

[0137] Based on the design requirements, the design goal is to minimize the intrusion area S i Therefore, in order to make the anti-collision structure have the best crash resistance performance, that is, S i In order to ensure that EA can meet the designer's requirements under a set of design variables, it is necessary to perform multi-objective crashworthiness optimization of Steel / CFRP anti-collision structures under multiple loading scenarios.

[0138] However, in actual subway collisions, collision scenarios are not unique. Furthermore, the central and corner crash barriers at subway ends are often connected by auxiliary crossbeams, forming a crash-resistant structure that prevents foreign object intrusion. Therefore, the optimization problem must consider the multi-coupling factors of different components within the same system. This allows for a holistic optimization design based on multiple structural factors, synergistically enhancing the bending deformation resistance and energy absorption performance of the Steel / CFRP crash barrier structure at subway ends under multiple loading scenarios without increasing mass.

[0139] At the same time, considering that in subway vehicle collision accidents, the central anti-collision column is the main protective device for subway vehicles to prevent foreign object intrusion, and its protection level is generally stronger than that of the corner anti-collision column. Preferably, in an exemplary embodiment, the sensitivity analysis of each parameter of the anti-collision structure under the central anti-collision column loading scenario and the corner anti-collision column loading scenario is first performed; then, the influence mechanism of each structural parameter on the bending mechanical properties of the anti-collision structure under the two loading scenarios is analyzed one by one; finally, the collaborative enhancement optimization design of the anti-collision structure under multiple loading scenarios is implemented. Based on the idea of ​​hierarchical optimization and combined with the multi-criteria decision-making method, the multi-scenario collaborative optimization problem is decoupled step by step.

[0140] Preferably, multiple linear regression fitting is performed on several design parameters and bending loading responses based on the design sample points to determine the relationship between each design parameter on the bending response of the anti-collision structure and its related influencing parameters under multiple loading scenarios, find the main effect and direction of each independent variable on the dependent variable, establish a multiple regression equation between the response and each design parameter, and thus obtain the contribution rate of the design parameter to each response.

[0141] Preferably, for the central anti-collision column, the following design parameters are selected: the thickness of the upper beam T u , thickness of lower beam T l , thickness of middle beam T m , the thickness of the outer steel plate of the central anti-collision column T as and the inner CFRP thickness T ac , to analyze the effect on the bending loading response.

[0142] For the corner anti-collision column loading scenario, the same method is used to study the thickness of the upper beam T u , thickness of lower beam T l , the thickness of the outer steel plate of the corner anti-collision column Tcs and the inner CFRP thickness T cc Contribution rate to each response.

[0143] The contribution rate is calculated based on the following method:

[0144] Assuming there are n variables, for any response y, its approximate multivariate polynomial expression is as follows:

[0145]

[0146] In the above formula, y(x1,x2…,x n ) is the target parameter value, μ is a constant; is the main effect of the design variable; is the interaction effect between any two variables; ε is the interaction effect or error of multiple design variables. Usually the main effect of the design variable can be approximated by the following formula:

[0147]

[0148] In the above formula, is the linear main effect coefficient obtained by least squares fitting, It can reflect the contribution of design variables to the response and After normalization through the following formula, it can be converted into contribution percentage, and the contribution of any design variable to the target parameter value can be obtained:

[0149]

[0150] Based on the above calculation method, the contribution rate of each design parameter can be further calculated:

[0151] (1) For energy absorbing EA:

[0152] In the central anti-collision column loading scenario, T as >T ac >T l >T u , and T m The contribution rate is less than 2%, and the energy absorption EA is positively correlated with the five design parameters.

[0153] In the corner anti-collision column loading scenario, T cs >T cc >T l >T u , and the energy absorption EA is positively correlated with these four design parameters.

[0154] (2) For the intrusion area S i ,

[0155] In the central anti-collision column loading scenario, T as >T ac >T l >T u , and T m The contribution rate is less than 2%, and the intrusion area S i With this T ac 、T l 、T u 、T m Positively correlated with T as There is a negative correlation.

[0156] In the corner anti-collision column loading scenario, T cs >T cc >T l >T u , and the intrusion area S i With this T cc 、T l 、T u Positively correlated with T cs There is a negative correlation.

[0157] In summary, in the two loading scenarios, the thickness of the outer steel plate has a significant effect on the energy absorption EA and the intrusion area S. i , the peak force PCF has the largest contribution rate, and the change of the inner layer CFRP thickness also has an important influence on the bending loading response; in the corner anti-collision column loading scenario, the lower beam thickness T l The change of has a greater impact on the bending loading response than that in the middle anti-collision column loading scenario. Through analysis, it can be seen that in the corner anti-collision column loading scenario, the lower crossbeam position is closer to the corner anti-collision column buckling position, so it has a greater impact on its bending response; for the middle anti-collision column loading scenario, the middle crossbeam thickness T m The influence on each bending response is small, so in subsequent studies, the thickness of the beam T m Remain unchanged.

[0158] Furthermore, by setting different design parameters separately and fixing other design parameters for simulation analysis, the following conclusions can be drawn:

[0159] T as and T cs Increasing will significantly improve the EA and PCF of the structure, but will not significantly improve the SEA, and will make the mass increase faster; Di max and S i There is a certain degree of reduction, but the marginal benefits are diminishing. In summary, the thickness of the outer steel plate is the main control parameter for improving stiffness and strength, but it is not conducive to lightweighting, so a trade-off is needed.

[0160] The increase in CFRP thickness contributes significantly to SEA, is beneficial to lightweighting, and has a certain effect on EA improvement. PCF changes slightly. max The decreasing trend is clear, indicating that it is effective in controlling the deformation. i The impact is minor, and the intrusion distribution pattern does not change much. In summary, CFRP thickness is a key variable in controlling deformation and improving energy absorption per unit mass; increasing CFRP thickness should be prioritized under mass-constrained working conditions.

[0161] Upper beam thickness T u The EA of the corner loading scene has a significant impact, but has a smaller impact on the middle loading scene; the SEA changes slightly, and its main role is to suppress local deformation and share the load; Di max It decreases significantly in the corner scene, indicating that it has a strengthening effect on structural stability; S i It also decreases slightly but not significantly. Therefore, T u Structural reinforcement parameters related to corner response; they should be appropriately increased when corner conditions are the dominant design condition.

[0162] Lower beam thickness T l EA and Di in the middle loading condition max The control effect is better than that of the upper beam, and the SEA variation trend is close to the thickness of the outer steel plate. i It has an inhibitory effect and a balanced effect; in summary, T l It is an important structural element for energy absorption control and stability enhancement under central loading scenarios, and makes a significant contribution to improving the overall coordinated response capability of the system.

[0163] Therefore, the weights of each parameter vary across scenarios, necessitating a sensitivity analysis to rationally balance their values. Furthermore, considering that in subway car collisions, the central bollard is the primary protective device against foreign object intrusion, and its level of protection is generally stronger than that of the corner bollards, this collaborative optimization problem is decoupled step by step, based on the concept of hierarchical optimization and combined with a multi-criteria decision-making approach.

[0164] For ease of explanation, Figure 8 The flowchart further illustrates the steps of the proposed multi-objective and multi-scenario collaborative optimization design method for Steel / CFRP anti-collision structures. In the optimization method, the design parameters of the center anti-collision column and the auxiliary beam should first be realized to achieve the optimal crashworthiness performance under the center anti-collision column loading scenario, and then the optimal auxiliary beam T l and T u The value of is assigned to the multi-objective optimization problem in the corner anti-collision column loading scenario, thereby achieving the optimal design of the corner anti-collision column.

[0165] In order to meet the requirements of anti-bending and energy absorption coordinated design of anti-collision structure, the intrusion area S i The design objectives are to improve the energy absorption EA. In addition, the assessment requirements specified in the crashworthiness standard ASMERT-2-2014 are used as design requirements, and three constraints are set:

[0166] The first constraint is that the MCF of the crash frame during plastic collision must be higher than the elastic design load of the entire vehicle body to meet the static strength requirements.

[0167] The second constraint condition: After the collapse, the intrusion amount D at the middle height of each anti-collision column mid It should be no less than 1 / 3 of the longitudinal dimension of the column;

[0168] The third constraint condition: The peak force PCF should be as small as possible to reduce the risk of fatal damage to the driver and passengers caused by the peak force PCF.

[0169] Therefore, we can set the following two loading constraint expressions:

[0170] For the central crash column loading scenario:

[0171]

[0172] For the corner bumper loading scenario:

[0173]

[0174] In the above two equations, X L and X U It refers to the lower and upper bounds of the design parameter vector X, which limits the value range of the design variables during the optimization process. At the same time, the value range of each design parameter is also listed in the constraints.

[0175] Furthermore, when optimizing a design, in order to balance the computational power and time consumption of finite element simulations with the accuracy of the optimization results to achieve optimal optimization efficiency, an approximate model approach can be adopted. This approach uses mathematical models to replace complex physical relationships to reduce computational costs and improve computational efficiency, ultimately capturing the correlation between responses and design parameters in a more efficient manner.

[0176] During the optimization calculation, the calculation program compares the performance by simulating and setting multiple sampling points. However, the uneven distribution of sampling points may miss certain areas of the design space, resulting in sampling gaps in the design space, which in turn makes it impossible to accurately construct the optimization model. In order to avoid the appearance of gap areas, the distribution of sampling points in the design space should be as uniform as possible. The present technical solution preferably applies the optimal Latin hypercube experimental design (OLHS) to generate sample points to capture higher-order effects. The OLHS experimental design has been widely used in structural optimization because of its good uniformity in space filling. For example, for two different loading scenarios, 25 evenly distributed sample points are generated respectively using the OLHS sampling method.

[0177]

[0178] Furthermore, for the same sample points, different approximation methods will result in approximation models of different precisions. In a preferred embodiment, an approximate calculation method of a radial basis function (RBF) neural network model is used to obtain the desired approximate model.

[0179] The RBF approximate model is an approximate model based on neural networks, which has the characteristics of fast learning speed, strong fault tolerance, and the ability to approximate complex nonlinear functions well.

[0180] For n-dimensional input variables [x1, x2, ..., x n ] T , whose response value is [y1,y2,…,y n ] T , if the number of sample points is m, for any input variable x, the corresponding output response y can be expressed as:

[0181]

[0182] Among them, vector Φ=[φ1,φ2,…,φ n ] T is the radial basis function of the input variables, Ω=[ω1,ω2,…,ω n ] T is the weighting coefficient of the radial basis function; and φ i =φ(│xx i │), that is, any input variable x to a known sample point x i The Euclidean distance of .

[0183] Furthermore, the root mean square error (RSME) and the coefficient of determination (R 2 ), evaluate the accuracy of the approximate model. Overall, R 2 The closer the value is to 1, the higher the fitting accuracy of the approximate model is; the closer the RSME is to 0, the more accurate the approximate model is.

[0184] By using the OLHS method, a test matrix containing 5 sample points is formed to evaluate the fitting accuracy of the RBF approximation model. The calculation results are:

[0185] For the central crash column loading scenario:

[0186]

[0187] For the corner anti-collision column loading scenario:

[0188]

[0189] Moreover, the RBF model can be used to describe the relationship between the design parameter variables and EA, S under the central anti-collision column loading scenario and the corner anti-collision column loading scenario. i and the spatial response surface of PCF, Figure 9 and attached Figure 10 shown.

[0190] Furthermore, in an exemplary embodiment, the MOGA algorithm is used to solve the proposed multi-objective, multi-scenario collaborative optimization problem. The multi-objective genetic algorithm (MOGA) has attracted much attention due to its high efficiency and high accuracy, and has been successfully applied to crashworthiness optimization design. Preferably, the detailed parameters of the MOGA algorithm are as follows:

[0191] Maximum number of iterations: 200; Minimum number of iterations: 50; Population size: 84; Constraint violation rate: 0.1%; Number of non-dominated points: 500; Mutation rate: 0.01; Elite size: 10%.

[0192] Furthermore, in an exemplary embodiment, in order to solve the abnormal solutions of the objective function that often conflict with each other, the Top-of-Success-to-Ideal-Solutions (TOPSIS) method is introduced to weigh the best ideal design scheme. The TOPSIS method is widely used to solve multi-attribute decision-making problems because it can handle quantitative and qualitative data well. In addition, in the multi-criteria decision-making method, weight allocation is a key stage in determining the best choice. The entropy weight method can avoid subjective bias and make the results more objective. Therefore, this paper adopts the entropy weight method to determine the weights of different responses to disperse the influence of weights on subjective intervention.

[0193] Specifically, the entropy weight method is first used to determine the weight of each response; the second step is to use the TOPSIS method to select and rank all optimal designs.

[0194] Preferably, in using the entropy weight method, the following steps are included:

[0195] (1) Data standardization: For multi-criteria decision-making problems, the initial decision matrix is ​​described as:

[0196]

[0197] Where X is the decision matrix and x represents the raw response value. Because the units and quantities of evaluation criteria often vary, direct comparison of different criteria is not possible. Therefore, the raw responses need to be standardized and converted into comparable standard values. The cost-based indicator P is defined as follows, with the goal of minimizing the value:

[0198]

[0199] The profit indicator is defined as follows, with the goal of getting the larger the value, the better:

[0200]

[0201] Among them, min j and max j Represent the minimum and maximum values ​​of the j-th response respectively. The standardized decision matrix can be expressed as P = (p ij ) DK

[0202] (2) Perform normalization processing on the decision matrix, and the normalized result r ij Used to calculate the weight of the i-th sample under each indicator:

[0203]

[0204] (3) Calculate the information entropy e of the jth indicator j :

[0205]

[0206] Finally, get the weight factor for each target:

[0207]

[0208] Preferably, in the TOPSIS method, the following steps are included:

[0209] (1) Construct a weighted decision matrix:

[0210] Z=P*W=(z ij ) DK ;

[0211] Among them, z ij represents the weighted normalized response value.

[0212] (2) Obtain positive and negative ideal solutions, namely:

[0213]

[0214] Among them, Z + and Z -represent positive ideal solution and negative positive ideal solution respectively.

[0215] (3) Calculate the Euclidean distance of each alternative:

[0216]

[0217] Among them, D i + and D i - is the Euclidean distance of each alternative from the positive ideal solution and the negative ideal solution.

[0218] (4) Determine the comprehensive evaluation index H of each scheme i :

[0219]

[0220] (5) Sort the comprehensive evaluation indicators:

[0221] According to the calculated comprehensive evaluation index H i Sort each option and establish the relative order of each alternative option. i The higher the value, the better the performance of the scheme.

[0222] Finally, through the above method, based on the hierarchical optimization idea, the multi-objective optimization of the central anti-collision column loading scenario was first carried out. After the multi-objective optimization, the two design objectives EA and S i The Pareto front formed is shown in the following figure. Figure 11 As shown. Figure 11 It can be seen that EA and S i They always conflict with each other and cannot achieve the optimization goals at the same time. As EA increases, S i The Pareto frontier is the optimal configuration of the Steel / CFRP anti-collision structure under strict constraints. In order to obtain the optimal design under the anti-collision loading scenario, the EA and S are first determined by the entropy weight method. i The weight values ​​of EA and S are 0.5676 and 0.4324 respectively. It can be seen that in the central anti-collision column loading scenario, the weight of EA is greater than that of S. i , which means that EA has a more significant impact on crashworthiness. Then the weight values ​​of the two performance objectives are combined with the TOPSIS method to obtain the optimal performance configuration of the Steel / CFRP anti-collision structure under the anti-collision loading scenario. At this time, the obtained set of optimal designs is T as =4.22mm, T ac =10mm, T u =5.70mm, T l =5.67mm.

[0223] The crashworthiness results of the initial design and optimized Steel / CFRP anti-collision structure under the central anti-collision column loading scenario are summarized in the table below. Compared with the initial design and the results before optimization, the energy absorption EA of the optimized Steel / CFRP anti-collision structure increased by 17.52% and 39.76% respectively, and the intrusion area S i The longitudinal displacement of the center anti-collision column at the middle height position after unloading is 80.23 mm, which is greater than the 65.0 mm required by the evaluation criteria.

[0224]

[0225] Embodiment 2: This embodiment should be understood to include at least all the features of any of the above embodiments and further improve upon them;

[0226] For example, as shown in the attached Figure 12 As shown, an implementation method of the computer system 500 adopted in the present technical solution is exemplarily described; the computer system 500 can be applied to the data storage, calculation and result output process of each working module in the identification and judgment system.

[0227] Illustratively, computer system 500 includes a bus 502 or other communication mechanism for communicating information, and one or more processors 504 coupled with bus 502 for processing information; processor 504 may be, for example, one or more general-purpose microprocessors;

[0228] The computer system 500 also includes a main memory 506, such as a random access memory (RAM), a cache, and / or other dynamic storage device, coupled to the bus 502 for storing information and instructions to be executed by the processor 504; the main memory 506 may also be used to store temporary variables or other intermediate information during execution of instructions to be executed by the processor 504; these instructions, when stored in a storage medium accessible to the processor 504, present the computer system 500 as a special-purpose machine customized to perform the operations specified in the instructions;

[0229] The computer system 500 may also include a read-only memory (ROM) 508 or other static storage device coupled to the bus 502 for storing static information and instructions for the processor 504; a storage device 510 such as a magnetic disk, an optical disk, or a USB drive (flash drive) is coupled to the bus 502 for storing information and instructions;

[0230] And further, coupled to the bus 502 may also include a display 122 for displaying various information, data, media, etc., an input device 514 for allowing a user of the computer system 500 to control, manipulate, and / or interact with the computer system 500;

[0231] A preferred way of interacting with the management system may be through a cursor control device 516, such as a computer mouse or similar control / navigation mechanism;

[0232] Furthermore, the computer system 500 may further include a network device 518 coupled to the bus 502; wherein the network device 518 may include, for example, a wired network card, a wireless network card, a switching chip, a router, a switch, and other components;

[0233] In general, the terms "engine," "component," "system," "database," and the like as used herein may refer to logic embodied in hardware or firmware, or to a collection of software instructions, possibly with entry and exit points, written in a programming language such as Java, C, or C++; software components may be compiled and linked into executable programs, installed in a dynamic link library, or may be written in an interpreted programming language (e.g., BASIC, Perl, or Python); it will be understood that software components may be callable from other components or from themselves, and / or may be called in response to detected events or interrupts;

[0234] Software components configured to execute on a computing device may be provided on a computer-readable medium, such as a compact disc, digital video disc, flash drive, magnetic disk, or any other tangible medium, or as a digital download (and may be initially stored in a compressed or installable format that requires installation, decompression, or decryption prior to execution); such software code may be stored in part or in whole on a memory device of the executing computing device for execution by the computing device; software instructions may be embedded in firmware, such as an EPROM; it will also be understood that hardware components may be composed of connected logic units (such as gates and flip-flops), and / or may be composed of programmable units (such as a programmable gate array or processor);

[0235] Computer system 500 includes a processor that can implement the techniques described herein using custom hard-wired logic, one or more ASICs or FPGAs, firmware, and / or program logic that, in combination with the computer system, renders computer system 500 a special-purpose computing device;

[0236] According to one or more embodiments, the techniques herein are performed by computer system 500 in response to processor 504 executing one or more sequences of one or more instructions contained in main memory 506; such instructions may be read into main memory 506 from another storage medium, such as storage device 510; execution of the sequences of instructions contained in main memory 506 causes processor 504 to perform the process steps described herein; in alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions;

[0237] As used herein, the term "non-transitory media" and similar terms refer to any media that store data and / or instructions that cause a machine to operate in a specific fashion; such non-transitory media may include non-volatile media and / or volatile media; non-volatile media include, for example, optical or magnetic disks, such as storage device 510; volatile media include dynamic memory, such as main memory 506;

[0238] Among these, common forms of non-transitory media include, for example, floppy disks, diskettes, hard disks, solid-state drives, magnetic tape or any other magnetic data storage medium, CD-ROMs, any other optical data storage medium, any physical medium having a pattern of holes, RAM, PROM and EPROM, FLASH-EPROM, NVRAM, any other memory chip or cartridge, and networked versions thereof;

[0239] Non-transient media are distinct from, but may be used in conjunction with, transmission media; transmission media participate in the transmission of information between non-transient media; for example, transmission media include coaxial cables, copper wires, and optical fibers, including the wires that comprise bus 502; transmission media may also take the form of sound or light waves, such as radio waves and infrared data communications.

[0240] Although the present invention has been described above with reference to various embodiments, it will be understood that many changes and modifications may be made without departing from the scope of the invention. That is, the methods, systems, and devices discussed above are examples. Various configurations may omit, replace, or add various processes or components as appropriate. For example, in alternative configurations, the methods may be performed in an order different from that described, and / or various components may be added, omitted, and / or combined. Moreover, features described with respect to certain configurations may be combined in various other configurations, such as different aspects and elements of the configurations may be combined in a similar manner. Furthermore, as technology develops, the elements therein may be updated, i.e., many of the elements are examples and do not limit the scope of the present disclosure or the claims.

[0241] Specific details are given in the description to provide a thorough understanding of the exemplary configurations, including implementations. However, the configurations can be practiced without these specific details. For example, well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail to avoid obscuring the configurations. This description provides only example configurations and does not limit the scope, applicability, or configurations of the claims. Instead, the foregoing description of the configurations will provide those skilled in the art with an enabling description for implementing the described techniques. Various changes may be made to the function and arrangement of the elements without departing from the spirit or scope of the present disclosure.

[0242] In summary, it is intended that the above detailed description be considered illustrative rather than restrictive, and it should be understood that the above embodiments are intended to be merely illustrative of the present invention and not to limit the scope of protection of the present invention. After reading the contents of the present invention, a skilled person may make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A multi-scenario optimization design method for the anti-collision structure of a subway vehicle end, characterized in that: The design method comprises the following steps: S100: Setting typical loading conditions for the end anti-collision structure of a subway vehicle, including at least a middle loading scenario for a middle anti-collision column and a corner loading scenario for a corner anti-collision column; S200: Establishing a simulation model of the structural response of the anti-collision structure; the simulation model includes multiple design parameters based on the steel-carbon fiber composite structure as input and multiple post-collision performance index responses as output; S300: Performing a design parameter sensitivity analysis based on the simulation model to identify design parameters that affect performance indicators under different loading scenarios and the weights corresponding to each design parameter; S400: Based on the weights of various design parameters, optimization objectives and constraints are set to build a multi-scenario collaborative optimization model for multiple performance objectives. S500: performing collaborative optimization on the multi-scenario collaborative optimization model to output an optimal structural parameter combination solution that meets the design requirements of the center loading and corner loading scenarios; The design parameters include at least the thickness of the outer steel plate, the thickness of the carbon fiber composite material, the thickness of the upper beam and the thickness of the lower beam; the performance indicators include energy absorption EA, specific energy absorption SEA, peak force PCF, intrusion area S i and the maximum deformation Di max .

2. The design method according to claim 1, wherein: The optimization goal is to reduce the invasion area S i and increase the energy absorption EA; the constraints include: The first constraint condition is that the MCF of the crash frame during plastic collision must be higher than the elastic design load of the entire vehicle body; The second constraint condition: After the collapse, the intrusion amount D at the middle height of each anti-collision column mid It should be no less than 1 / 3 of the longitudinal dimension of the column; The third constraint is that the peak force PCF should be as small as possible.

3. The design method according to claim 1, wherein: In step S200, after establishing the simulation model of the structural response of the anti-collision structure, the simulation model is subjected to consistency verification using a physical vehicle body to determine whether the simulation model meets the validity requirements. The consistency verification includes the following steps: E100: Set up a physical subway end anti-collision structure and set quasi-static bending loading conditions for the middle loading scenario and the corner loading scenario respectively; E200: Setting a force sensor and a displacement sensor to collect a plurality of measurement values ​​including at least the performance indicator; E300: Compare the load-displacement curves, energy-displacement curves, and displacement sensor measurements obtained from the test with the simulation output results of the simulation model to verify the numerical accuracy of the simulation model in multiple performance indicators, as well as the consistency of the structural crushing process and final deformation morphology with the test results.

4. The design method according to claim 1, wherein: In step S500, a multi-objective genetic algorithm is used for collaborative optimization of the multi-scenario collaborative optimization model, including the following sub-steps: S510: Determine the objective function, design parameters, and constraints based on the design standards; S520: Determine the sample distribution of the design parameters; S530: Generate an approximate model of the simulation model to perform multi-objective optimization calculation; S540: Generate multiple Pareto front solutions using a non-dominated sorting strategy; S550: In the iterative process, selection, crossover, and mutation are performed based on genetic evolution operations, and the fitness of the solutions in the population is updated until the termination condition is reached or the optimal solution set converges, and finally the optimal design parameter combination scheme is output.

5. A multi-scenario optimization design system for the anti-collision structure of subway vehicle ends, characterized by: The design system is applied to a multi-scenario optimization design method for a subway vehicle end anti-collision structure according to any one of claims 1 to 4; the design system includes a memory, a processor, and machine-readable instructions stored in the memory and executable on the processor, wherein when the machine-readable instructions are executed by the processor, the following are performed: Set typical loading conditions for the end anti-collision structure of subway vehicles, including at least a middle loading scenario for the middle anti-collision column and a corner loading scenario for the corner anti-collision column; Establishing a simulation model of the structural response of the crashworthy structure; the simulation model includes multiple design parameters based on the steel-carbon fiber composite structure as input and multiple post-crash performance index responses as output; Performing a sensitivity analysis of design parameters based on the simulation model to identify design parameters that affect performance indicators under different loading scenarios and the weights corresponding to each design parameter; Based on the weights of various design parameters, we set optimization goals and constraints to build a multi-scenario collaborative optimization model for multiple performance objectives. Performing collaborative optimization on the multi-scenario collaborative optimization model to output an optimal structural parameter combination solution that meets the design requirements of the center loading and corner loading scenarios; The design parameters include at least the thickness of the outer steel plate, the thickness of the carbon fiber composite material, the thickness of the upper beam and the thickness of the lower beam; the performance indicators include energy absorption EA, specific energy absorption SEA, peak crushing force PCF, intrusion area S i and the maximum deformation Di max .

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