A multi-objective thermal concentrator structure design method based on topology optimization

Abstract

The present application relates to a multi-objective method based on topology optimization. The method comprises: first, establishing an initial topology optimized thermal concentrator model; then, performing iterative optimization using an optimization algorithm based on the initial topology optimized thermal concentrator model to obtain an optimized thermal concentrator topology structure unit; then, performing array formation based on the thermal concentrator topology structure unit to obtain a thermal concentrator array; then, performing numerical simulation based on the thermal concentrator array to obtain numerical simulation data; finally, determining a heat flow control scheme based on the numerical simulation data, optimizing the thermal concentrator topology structure unit, and determining a target thermal concentrator.

Description

Technical Field

[0001] The present application relates to the field of thermal management technology, and in particular to a multi-objective thermal concentrator structure design method based on topology optimization. Background Art

[0002] In recent years, thermal concentrators, as a new type of thermal functional material, have attracted widespread attention due to their unique capabilities in heat flux manipulation and energy harvesting. However, existing technologies still face many challenges in achieving high heat flux density and effectiveness of thermal concentrators, especially in multi-point thermal manipulation. Although traditional design methods can achieve optimization to a certain extent, they are time-consuming and inefficient, which limits their application in the design of complex thermal concentrators. Especially when considering the influence and interaction of multiple parameters, traditional methods are stretched. Therefore, it is particularly important to develop a comprehensive optimization design method that can deeply analyze the thermal characteristics of thermal concentrators from multiple perspectives.

[0003] In the prior art, the heat transfer efficiency of the thermal concentrator is usually improved under given conditions. By optimizing the thermal properties of the material and adjusting the heat flux distribution, the heat transfer mechanism (conduction and convection) can be amplified respectively, and the heat transfer driving force (temperature difference) can be better distributed. Topology optimization directly acts on the topology of the geometric shape by spatially optimizing the heat flux within a defined domain, thereby achieving the minimization or maximization of the optimization goal under certain constraints. Under different non-uniform heat flux conditions, the topology optimized thermal concentrator has significant temperature control robustness. Compared with traditional designs, the design of the optimized thermal concentrator in high temperature areas becomes more arbitrary and free. However, despite the high overall heat transfer efficiency, complex heat transfer phenomena may also occur in the topology optimized thermal concentrator. For example, heat flow turbulence may occur in some local areas, resulting in the inability to effectively regulate the heat flow.

[0004] Therefore, in the related art, there is an urgent need for a method that can effectively control and adjust the heat flow path to avoid local turbulence. Summary of the invention

[0005] Based on this, it is necessary to provide a multi-objective thermal concentrator structure design method based on topology optimization, which can effectively control and adjust the heat flow path to avoid local turbulence phenomena in response to the above technical problems.

[0006] In a first aspect, the present application provides a multi-objective thermal concentrator structure design method based on topology optimization. The method comprises:

[0007] Establish an initial topology optimized thermal concentrator model;

[0008] Based on the initial topology optimized thermal concentrator model, an optimization algorithm is used to perform iterative optimization to obtain an optimized thermal concentrator topology structure unit;

[0009] Arraying the thermal concentrator topological structure units to obtain a thermal concentrator array;

[0010] Performing numerical simulation based on the thermal concentrator array to obtain numerical simulation data;

[0011] A heat flow control scheme is determined based on the numerical simulation data, the thermal concentrator topology unit is optimized, and a target thermal concentrator is determined.

[0012] Optionally, in one embodiment of the present application, establishing an initial topology optimized thermal concentrator model includes:

[0013] According to the design objectives and parameters of the thermal concentrator, the thermal conductivity is set and an initial optimized thermal concentrator model is established.

[0014] Optionally, in one embodiment of the present application, the optimization algorithm is used to perform iterative optimization based on the initial topology optimization thermal concentrator model, and the optimized thermal concentrator topology structure unit includes:

[0015] Performing finite element meshing according to the initial topology optimized thermal concentrator model;

[0016] An optimization algorithm is selected, and based on the optimization algorithm, the divided initial topology optimized thermal concentrator model is iteratively optimized, and when the iteration reaches a preset convergence standard, an optimized thermal concentrator topology structure unit is obtained.

[0017] Optionally, in one embodiment of the present application, the iterative optimization of the divided initial topology optimized thermal concentrator model based on the optimization algorithm further includes:

[0018] The adjoint method is used to solve the sensitivity.

[0019] Optionally, in one embodiment of the present application, the iterative optimization of the divided initial topology optimized thermal concentrator model based on the optimization algorithm further includes:

[0020] Add Helmholtz filter function to perform boundary demarcation during the convergence process.

[0021] Optionally, in one embodiment of the present application, the numerical simulation data includes temperature gradient, heat flux density and temperature concentration index.

[0022] Optionally, in one embodiment of the present application, determining the heat flow control scheme based on the numerical simulation data, optimizing the thermal concentrator topology unit, and determining the target thermal concentrator includes:

[0023] Comparing the numerical simulation data with the standard data to determine a target high temperature gradient and a corresponding target heat flux density;

[0024] The thermal concentrator topology unit is optimized based on the target high temperature gradient and the corresponding target heat flux density to determine a target thermal concentrator.

[0025] In a second aspect, the present application also provides a multi-objective thermal concentrator structure design device based on topology optimization. The device comprises:

[0026] Initial model building module, used to build the initial topology optimized thermal concentrator model;

[0027] A topology unit determination module is used to perform iterative optimization based on the initial topology optimized thermal concentrator model using an optimization algorithm to obtain an optimized thermal concentrator topology unit;

[0028] An array module, used for performing array formation based on the thermal concentrator topological structure unit to obtain a thermal concentrator array;

[0029] A numerical simulation module, used for performing numerical simulation based on the thermal concentrator array to obtain numerical simulation data;

[0030] The target thermal concentrator determination module is used to determine a heat flow control scheme based on the numerical simulation data, optimize the thermal concentrator topology structure unit, and determine a target thermal concentrator.

[0031] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the methods described in the above embodiments.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in each of the above embodiments are implemented.

[0033] The above-mentioned multi-objective thermal concentrator structure design method based on topology optimization first establishes an initial topology optimized thermal concentrator model; then, based on the initial topology optimized thermal concentrator model, an optimization algorithm is used to perform iterative optimization to obtain an optimized thermal concentrator topology structure unit; then, an array is formed based on the thermal concentrator topology structure unit to obtain a thermal concentrator array; then, a numerical simulation is performed based on the thermal concentrator array to obtain numerical simulation data; finally, a heat flow control scheme is determined based on the numerical simulation data, the thermal concentrator topology structure unit is optimized, and the target thermal concentrator is determined. In other words, when designing the thermal concentrator structure, the design process is made more sophisticated and complex by adopting a topology optimization method, combined with a variable density topology optimization algorithm and numerical simulation technology. By studying the influence of different thermal conductivity ranges on the thermal concentrator design, the mechanism of heat flow changes on the thermal equilibrium state and heat transfer efficiency is revealed, and the heat flow path can be effectively controlled and adjusted to avoid local turbulence phenomena, which helps researchers better understand the behavior of multi-objective thermal concentrators. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of a flow chart of a multi-objective thermal concentrator structure design method based on topology optimization in one embodiment;

[0035] Figure 2 is a schematic diagram of a geometric model of a thermal concentrator in one embodiment;

[0036] Figure 3 is a schematic diagram of a topology optimization structural unit in one embodiment;

[0037] Figure 4 is a schematic diagram of numerically simulated temperature field distribution in a single-target thermal concentrator in one embodiment;

[0038] Figure 5 A schematic diagram of numerically simulated temperature field distribution in a multi-objective thermal concentrator in one embodiment;

[0039] Figure 6 A schematic diagram of temperature gradient comparison of a target area of ​​a single target thermal concentrator in one embodiment;

[0040] Figure 7 A schematic diagram of temperature gradient comparison of target areas of a multi-target thermal concentrator in one embodiment;

[0041] Figure 8 A structural block diagram of a multi-objective thermal concentrator structure design device based on topology optimization in one embodiment;

[0042] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] In one embodiment, Figure 1 As shown, a multi-objective thermal concentrator structure design method based on topology optimization is provided, including the following steps:

[0045] S101: Establish an initial topology optimized thermal concentrator model.

[0046] In the embodiment of the present application, firstly, a topology optimization mathematical model of multiple heat flow control units with different thermal conductivity ranges is established according to the parameters of the thermal concentrator. Figure 2 Shown is a schematic diagram of the established thermal concentrator geometric model.

[0047] S103: performing iterative optimization using an optimization algorithm based on the initial topology optimized thermal concentrator model to obtain an optimized thermal concentrator topology structure unit.

[0048] In the embodiment of the present application, after establishing the initial topology optimization thermal concentrator model, an optimization algorithm is used to perform iterative optimization according to the topology optimization mathematical models corresponding to different target areas to obtain the optimized thermal concentrator topology structure unit. Figure 3 As shown, it is a schematic diagram of the topology optimization structural unit, which indicates the unit size, the change of unit distribution, and the arrangement of high thermal conductivity materials and low thermal conductivity materials of different units.

[0049] S105: arranging an array based on the thermal concentrator topological structure units to obtain a thermal concentrator array.

[0050] In the embodiment of the present application, the optimized thermal concentrator topological structure units are respectively arrayed to obtain a thermal concentrator array. Specifically, by placing topological optimization units in a 10×10 grid, different units are set on the grid in COMSOL in conjunction with MATLAB to generate different arrays, such as a single-objective thermal concentrator and a multi-objective thermal concentrator.

[0051] S107: Perform numerical simulation based on the thermal concentrator array to obtain numerical simulation data.

[0052] In the embodiments of the present application, numerical simulation is performed based on different thermal concentrator arrays to obtain corresponding numerical simulation data, which include temperature field distribution, temperature gradient, heat flux density data and temperature concentration index.

[0053] S109: Determine a heat flow control scheme based on the numerical simulation data, optimize the thermal concentrator topology unit, and determine a target thermal concentrator.

[0054] In the embodiment of the present application, a better heat flow control scheme is determined according to the numerical simulation data corresponding to different target areas, and the design of the thermal concentrator is optimized according to the heat flow control scheme to obtain the final optimized target thermal concentrator, and its corresponding parameters can be used to construct a physical thermal concentrator.

[0055] In the above-mentioned multi-objective thermal concentrator structure design method based on topology optimization, first, an initial topology optimized thermal concentrator model is established; then, an optimization algorithm is used to perform iterative optimization based on the initial topology optimized thermal concentrator model to obtain an optimized thermal concentrator topology structure unit; then, an array is formed based on the thermal concentrator topology structure unit to obtain a thermal concentrator array; then, numerical simulation is performed based on the thermal concentrator array to obtain numerical simulation data; finally, a heat flow control scheme is determined based on the numerical simulation data, the thermal concentrator topology structure unit is optimized, and the target thermal concentrator is determined. In other words, when designing the thermal concentrator structure, the design process is made more sophisticated and complex by adopting a topology optimization method, combined with a variable density topology optimization algorithm and numerical simulation technology. By studying the influence of different thermal conductivity ranges on the thermal concentrator design, the mechanism of heat flow changes on the thermal equilibrium state and heat transfer efficiency is revealed, and the heat flow path can be effectively controlled and adjusted to avoid local turbulence phenomena, which helps researchers better understand the behavior of multi-objective thermal concentrators.

[0056] In one embodiment of the present application, establishing an initial topology optimized thermal concentrator model includes:

[0057] According to the design objectives and parameters of the thermal concentrator, the thermal conductivity is set and an initial optimized thermal concentrator model is established.

[0058] In one embodiment of the present application, first, a heat source and boundary conditions are set to establish a simplified geometric model. Then, according to the design goal of the thermal concentrator, an objective function is established, and boundary conditions of different positions and widths are set at the boundary of the unit, while changing the volume factor ratio. Then, according to the parameters of the thermal concentrator, the thermal conductivity range is set, and a topological optimization mathematical model of a thermal concentrator with different thermal conductivities is established based on a topological optimization method. Optionally, the thermal conductivity range is 0.1-200W / mK, the main material in the model uses aluminum alloy, and the material in the pores uses PDMS. The boundary conditions are set as follows: the left side of the thermal concentrator is a high temperature of 363K, and the right side is a low temperature of 283K. The thermal conductivity of aluminum alloy is 201W / mK, and the heat capacity is 897J / kgK; the thermal conductivity of PDMS is 0.17W / mK, and the heat capacity is 1460J / kgK.

[0059] In this embodiment, by setting the thermal conductivity according to the design objectives and parameters of the thermal concentrator, an initial optimized thermal concentrator model is established to simulate the heat flow and heat transfer under actual working conditions, thereby providing a reliable basis for evaluating the thermal characteristics of the optimized design.

[0060] In one embodiment of the present application, the optimization algorithm is used to perform iterative optimization based on the initial topology optimization thermal concentrator model to obtain an optimized thermal concentrator topology structure unit including:

[0061] S201: Perform finite element meshing according to the initial topology optimized thermal concentrator model.

[0062] S203: selecting an optimization algorithm, and iteratively optimizing the divided initial topology optimized thermal concentrator model based on the optimization algorithm, and obtaining an optimized thermal concentrator topology structure unit when the iteration reaches a preset convergence standard.

[0063] In one embodiment of the present application, first, finite element meshing is performed according to the mathematical model of thermal concentrator topology optimization corresponding to each thermal conductivity. Afterwards, an optimization algorithm is selected, the maximum number of iteration steps is set, and the convergence criterion is set, and iterative optimization is performed until the preset convergence criterion is met to obtain an optimized thermal concentrator topology structural unit. Optionally, the optimization algorithms include MMA (Method of Moving Asymptotes) and SIMP (Solid Isotropic Material with Penalization). MMA updates the upper and lower bounds of the design variables through a step-by-step optimization algorithm, and SIMP introduces a penalty function to map the continuous distribution of material density to discrete 0-1 design variables to optimize structural performance.

[0064] In this embodiment, finite element meshing is performed according to the initial topology optimized thermal concentrator model, an optimization algorithm is selected, and the divided initial topology optimized thermal concentrator model is iteratively optimized based on the optimization algorithm. When the iteration reaches a preset convergence standard, an optimized thermal concentrator topology structure unit is obtained, which can ensure the stability and convergence of the optimization process.

[0065] In one embodiment of the present application, the iterative optimization of the divided initial topology optimized thermal concentrator model based on the optimization algorithm further includes:

[0066] The adjoint method is used to solve the sensitivity.

[0067] In one embodiment of the present application, the adjoint method is used to efficiently calculate the sensitivity of the design variables to the objective function or constraint conditions. The sensitivity calculation is converted into solving the adjoint equation by introducing the adjoint equation to guide the optimization algorithm to adjust the design variables.

[0068] In this embodiment, the accuracy of sensitivity calculation is ensured by using the adjoint method to solve the sensitivity.

[0069] In one embodiment of the present application, the iterative optimization of the divided initial topology optimized thermal concentrator model based on the optimization algorithm further includes:

[0070] Add Helmholtz filter function to perform boundary demarcation during the convergence process.

[0071] In one embodiment of the present application, a Helmholtz filter is used to smooth the design variables, avoid numerical instability and grid dependence, make the material distribution smoother, and effectively define the boundaries of the solid area and the cavity area.

[0072] In this embodiment, the clarity of the boundary division is ensured by adding the Helmholtz filter function to perform boundary division during the convergence process.

[0073] In one embodiment of the present application, the numerical simulation data includes temperature gradient, heat flux density and temperature concentration index.

[0074] In one embodiment of the present application, the numerical simulation data includes temperature gradient, heat flux density and temperature concentration index. The temperature gradient refers to the rate of change of temperature with position, which is usually expressed as the formula shown below.

[0075]

[0076] Here, T is temperature and x is position.

[0077] Heat flux density refers to the heat flow per unit area and is usually expressed as the following formula.

[0078]

[0079] Where k refers to thermal conductivity or thermal conductivity coefficient, Refers to the temperature gradient. In one dimension, this formula can be simplified to the following formula.

[0080] q=-kdT / dx

[0081] The temperature concentration index is used to measure the concentration of temperature distribution of a system or material. Specifically, the temperature concentration index can be expressed by temperature deviation, and the formula is as follows.

[0082] ΔT=Tmax-Tmin

[0083] Among them, Tmax represents the maximum temperature, Tmin represents the minimum temperature, and the larger ΔT is, the better the aggregation is at the same size. Through the above derivation, it can be known that there is a positive correlation between temperature gradient, heat flux density and temperature aggregation. Figure 4 As shown in FIG. 1 , it is a schematic diagram of the numerical simulation temperature field distribution in a single-target thermal concentrator in an embodiment, showing the temperature distribution of the single-target thermal concentrator after the optimized design, verifying the effectiveness of the optimized design. Through numerical simulation, the heat flux aggregation effect in the single-target area can be clearly observed, achieving the purpose of high temperature aggregation. Figure 5 FIG. 1 is a schematic diagram of numerically simulated temperature field distribution in a multi-objective thermal concentrator in an embodiment, showing the temperature distribution in different target areas, verifying the effectiveness of the optimization design under multi-objective conditions. Through numerical simulation, the distribution and aggregation of heat flux in multi-objective areas can be understood.

[0084] In one embodiment of the present application, determining the heat flow control scheme based on the numerical simulation data, optimizing the thermal concentrator topology unit, and determining the target thermal concentrator includes:

[0085] S301: Compare the numerical simulation data with the standard data to determine a target high temperature gradient and a corresponding target heat flux density.

[0086] S303: Optimizing the thermal concentrator topology structure unit based on the target high temperature gradient and the corresponding target heat flux density to determine a target thermal concentrator.

[0087] In one embodiment of the present application, first, the numerical simulation data corresponding to each thermal conductivity obtained by numerical simulation is compared with the standard data to determine the target high temperature gradient and the corresponding target heat flux density. The standard data refers to the relevant measured data of the thermal concentrator obtained by 3D printing and casting pores with polydimethylsiloxane PMDS. When the temperature gradient is greater than the standard temperature gradient 1 of the uniform medium, the target area achieves the target high temperature gradient compared to the uniform medium. According to the formula conversion, the heat flux density is also increased compared to the uniform medium. Similarly, the temperature aggregation index is relatively improved. Afterwards, the topological structure unit of the thermal concentrator is further optimized based on the determined target high temperature gradient and the corresponding target heat flux density to obtain the final optimized target thermal concentrator, and its corresponding parameters can be used to construct the thermal concentrator. Figure 6 As shown in FIG. 1 , a schematic diagram of the temperature gradient comparison of the target area of ​​a single target thermal concentrator in one embodiment is shown. By comparison, it can be seen that the temperature gradient changes of the optimized design simulation and the actual measurement are basically consistent. The change in temperature gradient reflects the quality of the heat flux shielding effect. Figure 7 FIG. 1 is a schematic diagram of temperature gradient comparison of target areas of a multi-target thermal concentrator in one embodiment. By comparing, the temperature gradient variation of the optimized design under different conditions can be seen. The variation of temperature gradient shows the heat flux distribution and aggregation in the multi-target area.

[0088] In this embodiment, the practical effect and reliability of the optimized design are further verified by combining experimental testing with simulation analysis.

[0089] The following is a specific implementation example to illustrate the specific implementation steps of a multi-objective thermal concentrator structure design method based on topology optimization of the present application. First, S401, establish an initial topology optimized thermal concentrator model. Specifically, S403, according to the design objectives of the thermal concentrator and the parameters of the thermal concentrator, set the thermal conductivity and establish the initial optimized thermal concentrator model. Afterwards, S405, based on the initial topology optimized thermal concentrator model, an optimization algorithm is used to perform iterative optimization to obtain an optimized thermal concentrator topological structural unit. Specifically, S407-S409, finite element meshing is performed according to the initial topology optimized thermal concentrator model; an optimization algorithm is selected, and the divided initial topology optimized thermal concentrator model is iteratively optimized based on the optimization algorithm, and when the iteration reaches the preset convergence standard, an optimized thermal concentrator topological structural unit is obtained. S411, the sensitivity is solved by the adjoint method. S413, a Helmholtz filter function is added during the convergence process to perform boundary division.

[0090] Then, S415, arraying is performed based on the thermal concentrator topological structure units to obtain a thermal concentrator array. Then, S417, numerical simulation is performed based on the thermal concentrator array to obtain numerical simulation data. S419, the numerical simulation data includes temperature gradient, heat flux density and temperature aggregation index.

[0091] Finally, S421, based on the numerical simulation data, determine the heat flow control scheme, optimize the thermal concentrator topology unit, and determine the target thermal concentrator. Specifically, S423-S425, compare the numerical simulation data with the standard data, determine the target high temperature gradient and the corresponding target heat flux density; optimize the thermal concentrator topology unit based on the target high temperature gradient and the corresponding target heat flux density, and determine the target thermal concentrator.

[0092] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0093] Based on the same inventive concept, the embodiment of the present application also provides a multi-objective thermal concentrator structure design device based on topology optimization for implementing the multi-objective thermal concentrator structure design method based on topology optimization mentioned above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the multi-objective thermal concentrator structure design device based on topology optimization provided below can refer to the above limitations on the multi-objective thermal concentrator structure design method based on topology optimization, and will not be repeated here.

[0094] In one embodiment, Figure 8 As shown, a multi-objective thermal concentrator structure design device 800 based on topology optimization is provided, comprising: an initial model building module 801, a topology structure unit determination module 803, an array module 805, a numerical simulation module 807 and a target thermal concentrator determination module 809, wherein:

[0095] An initial model building module 801 is used to build an initial topology optimized thermal concentrator model;

[0096] A topology unit determination module 803 is used to perform iterative optimization based on the initial topology optimized thermal concentrator model using an optimization algorithm to obtain an optimized thermal concentrator topology unit;

[0097] An array module 805 is used to form an array based on the thermal concentrator topology structure unit to obtain a thermal concentrator array;

[0098] A numerical simulation module 807, used for performing numerical simulation based on the thermal concentrator array to obtain numerical simulation data;

[0099] The target thermal concentrator determination module 809 is used to determine a heat flow control scheme based on the numerical simulation data, optimize the thermal concentrator topology structure unit, and determine a target thermal concentrator.

[0100] In one embodiment of the present application, the initial model building module is also used to:

[0101] According to the design objectives and parameters of the thermal concentrator, the thermal conductivity is set and an initial optimized thermal concentrator model is established.

[0102] In one embodiment of the present application, the topology structure unit determination module is further used to:

[0103] Performing finite element meshing according to the initial topology optimized thermal concentrator model;

[0104] An optimization algorithm is selected, and based on the optimization algorithm, the divided initial topology optimized thermal concentrator model is iteratively optimized, and when the iteration reaches a preset convergence standard, an optimized thermal concentrator topology structure unit is obtained.

[0105] In one embodiment of the present application, the topology structure unit determination module is further used to:

[0106] The adjoint method is used to solve the sensitivity.

[0107] In one embodiment of the present application, the topology structure unit determination module is further used to:

[0108] Add Helmholtz filter function to perform boundary demarcation during the convergence process.

[0109] In one embodiment of the present application, the numerical simulation data includes temperature gradient, heat flux density and temperature concentration index.

[0110] In one embodiment of the present application, the target thermal concentrator determination module is further used to:

[0111] Comparing the numerical simulation data with the standard data to determine a target high temperature gradient and a corresponding target heat flux density;

[0112] The thermal concentrator topology unit is optimized based on the target high temperature gradient and the corresponding target heat flux density to determine a target thermal concentrator.

[0113] Each module in the above-mentioned multi-objective thermal concentrator structure design device based on topology optimization can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0114] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a multi-objective thermal concentrator structure design method based on topology optimization is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0115] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0116] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0117] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0118] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0119] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0120] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0121] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A multi-objective thermal concentrator structure design method based on topology optimization, characterized in that: The method comprises: Establish an initial topology optimized thermal concentrator model; Based on the initial topology optimized thermal concentrator model, an optimization algorithm is used to perform iterative optimization to obtain an optimized thermal concentrator topology structure unit; The optimization algorithm is used to perform iterative optimization based on the initial topology optimization thermal concentrator model, and the optimized thermal concentrator topology structure unit includes: Performing finite element meshing according to the initial topology optimized thermal concentrator model; Selecting an optimization algorithm, and iteratively optimizing the initial topology optimized thermal concentrator model after the division based on the optimization algorithm, and obtaining an optimized thermal concentrator topology structure unit when the iteration reaches a preset convergence standard; Arraying the thermal concentrator topological structure units to obtain a thermal concentrator array; Performing numerical simulation based on the thermal concentrator array to obtain numerical simulation data, wherein the numerical simulation data includes temperature gradient, heat flux density and temperature aggregation index; A heat flow control scheme is determined based on the numerical simulation data, the thermal concentrator topology unit is optimized, and a target thermal concentrator is determined.

2. The multi-objective thermal concentrator structure design method based on topology optimization according to claim 1 is characterized in that: The establishing of the initial topology optimization thermal concentrator model comprises: According to the design objectives and parameters of the thermal concentrator, the thermal conductivity is set and an initial optimized thermal concentrator model is established.

3. The multi-objective thermal concentrator structure design method based on topology optimization according to claim 1 is characterized in that: The iterative optimization of the divided initial topology optimized thermal concentrator model based on the optimization algorithm further comprises: The adjoint method is used to solve the sensitivity.

4. The multi-objective thermal concentrator structure design method based on topology optimization according to claim 1 is characterized in that: The iterative optimization of the divided initial topology optimized thermal concentrator model based on the optimization algorithm further comprises: Add Helmholtz filter function to perform boundary demarcation during the convergence process.

5. The multi-objective thermal concentrator structure design method based on topology optimization according to claim 1 is characterized in that: The determining of the heat flow control scheme based on the numerical simulation data, optimizing the thermal concentrator topology unit, and determining the target thermal concentrator comprises: Comparing the numerical simulation data with the standard data to determine a target high temperature gradient and a corresponding target heat flux density; The thermal concentrator topology unit is optimized based on the target high temperature gradient and the corresponding target heat flux density to determine a target thermal concentrator.

6. A multi-objective thermal concentrator structure design device based on topology optimization, characterized in that: The device comprises: Initial model building module, used to build the initial topology optimized thermal concentrator model; A topology unit determination module is used to perform iterative optimization based on the initial topology optimized thermal concentrator model using an optimization algorithm to obtain an optimized thermal concentrator topology unit; The optimization algorithm is used to perform iterative optimization based on the initial topology optimization thermal concentrator model, and the optimized thermal concentrator topology structure unit includes: Performing finite element meshing according to the initial topology optimized thermal concentrator model; Selecting an optimization algorithm, and iteratively optimizing the initial topology optimized thermal concentrator model after the division based on the optimization algorithm, and obtaining an optimized thermal concentrator topology structure unit when the iteration reaches a preset convergence standard; An array module, used for performing array formation based on the thermal concentrator topological structure unit to obtain a thermal concentrator array; A numerical simulation module, used for performing numerical simulation based on the thermal concentrator array to obtain numerical simulation data, wherein the numerical simulation data includes temperature gradient, heat flux density and temperature aggregation index; The target thermal concentrator determination module is used to determine a heat flow control scheme based on the numerical simulation data, optimize the thermal concentrator topology structure unit, and determine a target thermal concentrator.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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