WO2024050982A1 - 基于拓扑优化的导冷结构设计方法和装置 - Google Patents

基于拓扑优化的导冷结构设计方法和装置 Download PDF

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WO2024050982A1
WO2024050982A1 PCT/CN2022/134503 CN2022134503W WO2024050982A1 WO 2024050982 A1 WO2024050982 A1 WO 2024050982A1 CN 2022134503 W CN2022134503 W CN 2022134503W WO 2024050982 A1 WO2024050982 A1 WO 2024050982A1
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model
optimization
cooling structure
conductive cooling
objective
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French (fr)
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邵晴
王爱彬
胡浩
于淼
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中车长春轨道客车股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation

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  • the present invention relates to the technical field of conductive cooling structure design, and in particular to a conductive cooling structure design method and device based on topology optimization.
  • Superconducting magnet refers to an electromagnet that uses a second type of superconductor with a high transition temperature and a particularly high critical magnetic field to make a coil under ultra-deep cooling.
  • the ultra-deep cold, strong magnetic field, and high vacuum conditions where superconducting magnets are located are maintained by a conductive cooling structure.
  • the cooling structure when designing the cooling structure, a single physical field design iteration method is generally used. For example, the cooling structure is first designed based on the stress field to obtain the stress field design index, and then designed based on the temperature field to meet the temperature field design index. However, based on the temperature field, It is easy to change the parameters previously designed based on the stress field during design, resulting in the cooling structure not meeting the design indicators of the stress field. Therefore, existing design methods require repeated iterations between different physical fields, resulting in low design efficiency.
  • the present invention provides a method and device for designing a cooling structure based on topology optimization to provide an efficient cooling structure solution.
  • the first aspect of this application provides a conductive cooling structure design method based on topology optimization, including:
  • the lightweight model is a lightweight topology optimization model of the conductive cooling structure
  • the heat dissipation model is a heat dissipation topology optimization model of the conductive cooling structure
  • topological configuration of the conductive cooling structure does not meet the design indicators, return to the multi-objective thermal coupling topology optimization of the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure until the topological configuration is obtained that satisfies the design. So far as the topological configuration of the conductive cooling structure of the index is determined.
  • determining the optimization conditions of the lightweight model includes:
  • determining the optimization conditions of the heat dissipation model includes:
  • the method before checking whether the topological configuration of the conductive cooling structure meets the design specifications, the method also includes:
  • the verification of whether the topological configuration of the conductive cooling structure meets the design indicators includes:
  • the multi-objective thermal coupling topology optimization is performed on the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure, including:
  • multi-objective thermal coupling topology optimization is performed on the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure; where the optimization tolerance is 0.001 and the maximum number of iterations for 100 times.
  • the second aspect of this application provides a conductive cooling structure design device based on topology optimization, including:
  • the lightweight model is a lightweight topology optimization model of a conductive cooling structure
  • the heat dissipation model is a heat dissipation topology optimization model of a conductive cooling structure
  • a determination unit configured to determine the optimization conditions of the lightweight model and determine the optimization conditions of the heat dissipation model
  • a weighting unit used to weight the lightweight model and the heat dissipation model according to a preset weighting coefficient to obtain a multi-objective weighted optimization model
  • An optimization unit is used to perform multi-objective thermal coupling topology optimization on the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure;
  • a checking unit used to check whether the topological configuration of the conductive cooling structure meets the design indicators
  • topological configuration of the conductive cooling structure does not meet the design index, return to the optimization unit to perform the multi-objective thermal coupling topology optimization of the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure until the Until the topological configuration of the conductive cooling structure meets the design indicators.
  • the determination unit determines the optimization conditions of the lightweight model, it is specifically used to:
  • the determination unit determines the optimization conditions of the heat dissipation model, it is specifically used to:
  • the verification unit is also used to:
  • the checking unit checks whether the topological configuration of the conductive cooling structure meets the design indicators, it is specifically used for:
  • the optimization unit performs multi-objective thermal coupling topology optimization on the multi-objective weighted optimization model, and when obtaining the topological configuration of the conductive cooling structure, it is specifically used for:
  • multi-objective thermal coupling topology optimization is performed on the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure; where the optimization tolerance is 0.001 and the maximum number of iterations for 100 times.
  • This application provides a design method and device for a conductive cooling structure based on topology optimization.
  • the method includes establishing a lightweight model and a heat dissipation model; the lightweight model is a lightweight topology optimization model of the conductive cooling structure, and the heat dissipation model is a heat dissipation topology optimization model of the conductive cooling structure. model; respectively determine the optimization conditions of the lightweight model and the optimization conditions of the heat dissipation model; perform multi-objective thermal coupling topology optimization on the multi-objective weighted optimization model obtained by weighting the lightweight model and the heat dissipation model, and obtain a conductive cooling structure topology that meets the design indicators. type.
  • This solution uses a weighted lightweight model and a heat dissipation model to achieve coupling optimization of the stress field and temperature field, thereby obtaining a configuration that meets multiple physical field design indicators at the same time and improving design efficiency.
  • Figure 1 is a schematic diagram of a high-temperature superconducting magnet structure provided by an embodiment of the present application
  • FIG. 2 is a schematic diagram of a cooling structure provided by an embodiment of the present application.
  • Figure 3 is a flow chart of a topology optimization-based conductive cooling structure design method provided by an embodiment of the present application
  • Figure 4 is a schematic diagram of a cold power curve provided by an embodiment of the present application.
  • Figure 5 is a schematic diagram of a temperature field simulation of a cooling structure provided by an embodiment of the present application.
  • Figure 6 is a schematic structural diagram of a device for designing a conductive cooling structure based on topology optimization provided by an embodiment of the present application.
  • This application provides an imaging method and an imaging system for improving the comprehensiveness of image display.
  • Ultra-deep cold generally refers to an environment where the operating temperature is below -180°C, about -200°C.
  • Liquid nitrogen equipment can usually be used to refrigeration to achieve an ultra-deep cold environment.
  • Strong magnetic field and ultra-strong magnetic field refer to magnetic fields above 5T (Tesla) generated by superconducting technology, and also include ultra-high magnetic fields generated by pulse technology, hybrid magnet technology or ultra-high power electromagnet technology.
  • High vacuum the environment when the vacuum degree is lower than 1.333 ⁇ 10-1 ⁇ 1.333 ⁇ 10-6Pa is called high vacuum.
  • Superconducting magnet refers to an electromagnet that uses a second type of superconductor with a high transition temperature and a particularly high critical magnetic field to make a coil at low temperatures. Its main feature is that there is no electrical loss caused by wire resistance, and there is no magnetic loss caused by the presence of the iron core.
  • Traction force also known as suspension force or guiding force, refers to the electromagnetic force generated by the mutual repulsion between superconducting magnets and ground traction coils and figure-8 coil magnetic fields.
  • Lorentz force a term in electromagnetism, refers to the force exerted by moving charges in a magnetic field, that is, the force exerted by a magnetic field on moving charges.
  • thermomechanical coupling the thermomechanical coupling process is the process of mutual influence between the two physical fields of stress field and temperature field, that is, temperature has an impact on force deformation, and force deformation also has an influence on temperature change.
  • Superconducting magnet refers to an electromagnet that uses a second-type superconductor with a high transition temperature and a particularly high critical magnetic field to make a coil under ultra-deep cooling. Its critical transition temperature is around 35K, so it is necessary to ensure that the conductive cooling structure can Cooling energy is most efficiently conducted to the location of the superconducting magnet. Since superconducting magnets work under ultra-cold conditions, strong magnetic fields, and high vacuum conditions, it is necessary to ensure that the conductive cooling structure can withstand the traction force, suspension force, guiding force, and Lorentz force, as well as thermal stress caused by changes in temperature distribution, and ensuring the lightest weight of the structure, so that the superconducting magnet can achieve higher magnetic field strength while consuming lower electrical energy.
  • Figure 1 is a schematic diagram of a typical superconducting magnet structure. It can be seen that the superconducting magnet structure includes a plate-shaped member, which is the conductive cooling structure of the superconducting magnet. Figure 1 The schematic diagram of the three-dimensional model of the conductive cooling structure can be seen in Figure 2.
  • the existing technology When designing the conductive cooling structure of a superconducting magnet, the existing technology generally adopts an iterative approach of single physical field design, verification, parameter modification, and redesign. For example, when separately checking the influence of the stress field on the strength of the superconducting magnet, through repeated iterative calculations, the design indicators of the stress field are finally met. However, in the subsequent temperature field design, it may involve changes to the structural parameters that originally satisfied the stress field, resulting in the previous physical field, that is, the design index of the stress field not being satisfied. In addition, the existing technology relies heavily on the designer's experience when checking the structural strength of superconducting magnets, which often leads to design redundancy. Therefore, the design process of the existing technology is cumbersome, low in efficiency, and poor in accuracy, making it difficult to ensure the design indicators of multiple physical fields at the same time.
  • this application provides a design method of conductive cooling structure based on topology optimization.
  • the structural parameters output after optimization can satisfy both the stress field and the temperature field. design indicators to improve design efficiency.
  • Figure 3 is a flow chart of a conductive cooling structure design method based on topology optimization (hereinafter referred to as the design method) provided by an embodiment of the present application.
  • the method may include the following steps:
  • the design method provided by the embodiment of the present application aims to use numerical simulation technology to optimize the conductive cooling structure and obtain a suitable topological configuration of the conductive cooling structure that meets the design indicators. Therefore, before executing the design method of this application, the cooling structure that needs to be optimized must first be divided into multiple grid nodes.
  • the shape and size of each grid node can be set according to actual needs. This embodiment No restrictions.
  • Meshing nodes can be implemented using any existing finite element analysis software (such as ANSYS).
  • the lightweight model is the lightweight topology optimization model of the conductive cooling structure
  • the heat dissipation model is the heat dissipation topology optimization model of the conductive cooling structure.
  • the form of the lightweight model and the heat dissipation model in this embodiment is not unique.
  • the established lightweight model and heat dissipation model may have different forms, which is not limited in this embodiment.
  • the lightweight model established in S301 can be expressed by the following formula (1):
  • the heat dissipation model can be expressed by the following formula (2):
  • x can be regarded as a vector
  • xi can be regarded as the i-th component of the vector x
  • the vector x can be recorded as the design parameter of the conductive cooling structure.
  • Each component in the vector x represents the material properties of a certain grid node in the cooling structure.
  • the material properties of the grid nodes include Young's modulus, thermal conductivity and heat capacity.
  • the component x 1 in the vector x can represent the Young's modulus, thermal conductivity and thermal conductivity of grid node 1.
  • Heat capacity, x 2 can represent the Young's modulus, thermal conductivity and heat capacity of grid node 2...and so on.
  • U represents the displacement matrix of the conductive cooling structure
  • K represents the stiffness matrix of the conductive cooling structure
  • K is determined by the Young's modulus of each grid node in the conductive cooling structure.
  • ⁇ represents the area occupied by the cooling structure
  • represents the overall area or volume of the cooling structure
  • n is the condensation factor, the larger n is, the closer the geometric mean temperature of the cooling structure is to the maximum temperature, the specific value of n Can be set as needed.
  • n can be set to 50.
  • T(x) represents the temperature of grid node x in the design domain (that is, the area occupied by the conductive cooling structure).
  • T(x 1 ) can represent the temperature of grid node 1
  • T(x 2 ) can represent the temperature of grid node 2.
  • S302 Determine the optimization conditions of the lightweight model and determine the optimization conditions of the heat dissipation model.
  • determining the optimization conditions for lightweight models can include:
  • the topology optimization domain of the lightweight model can be the area occupied by the cooling structure shown in Figure 2, which specifically consists of the cold head connection area and the cold transfer area.
  • the diameter of the cold head connection area is D
  • the cold conduction area is related to the size of the superconducting coil inside the superconducting magnet, that is, the length, width, and height of the superconducting coil inside the superconducting magnet.
  • the static Lorentz forces FLO1 and FLO2 generated by the electromagnetic field in the two coils are calculated through the calculation of the internal operating current of the superconducting magnet and the geometric parameters of the coil. The above forces are respectively applied to the positions where the conductive cooling structure is used to support the coil of the superconducting magnet. These forces are the mechanical boundary conditions of the conductive cooling structure.
  • the cold head power curve is loaded at the input end of the conductive cooling structure, and the minimum operating temperature of the coil is 35K at the end of the conductive cooling structure.
  • the influence of material thermal expansion is activated as the thermal coupling boundary condition of the conductive cooling structure.
  • the penalty function can be defined as ⁇ design (X), where X represents each grid node of the cooling structure.
  • each grid node corresponds to a penalty function value, and the penalty function value is 1 or 0, among which, if the penalty function value of a grid node is 0, it means that the grid node corresponds to the cavity part of the cooling structure, and the grid node is not considered in the optimization calculation. If the penalty function of a grid node A value of 1 indicates that the grid node corresponds to the solid part of the cooling structure and needs to be considered during optimization calculations.
  • the penalty function can be determined using a variety of different topology optimization algorithms.
  • This embodiment is not limited to this.
  • various orthogonal penalty material variable density methods Solid isotropic material with penalization
  • model, SIMP SIMP
  • E 0 , k 0 and C 0 are the real Young’s modulus, real thermal conductivity and real heat capacity of each grid node of the cooling structure.
  • the optimization goal is to maximize the stiffness of the conductive cooling structure.
  • maximizing stiffness is equivalent to minimizing flexibility.
  • the conductive cooling structure Minimizing the compliance is equivalent to minimizing the total strain energy. Therefore, minimizing the total strain energy is determined as the optimization goal of the lightweight model. Set the constraint condition to the volume fraction of 20%, and the maximum Mises stress (Von-mises) of the grid nodes does not exceed 80% of the material yield limit.
  • the topology optimization domain of the heat dissipation model is the same as the topology optimization domain of the lightweight model. They are both areas occupied by the cooling structure and will not be described again.
  • a preset cooling power curve f(t) is loaded at the cold head position according to the cooling capacity of the refrigerator as the cooling capacity input end.
  • the loaded cooling power curve can be the curve shown in Figure 4.
  • the cold transmission terminal is generally the position of the supporting coil on the cooling structure.
  • the cooling structure shown in Figure 2 has the position of the cold transmission terminal as shown in Figure 5.
  • the temperature field boundary condition can be set to the cold transfer terminal coil temperature of 19K.
  • Multiplying the thermal conductivity and heat capacity by the penalty function can obtain the penalty Young's modulus E(X), the penalty thermal conductivity k(X), and the penalty heat capacity C(X). Specifically, it can be expressed by the following formula:
  • k 0 and C 0 are the real thermal conductivity and real heat capacity of each grid node of the cooling structure.
  • the constraints are that the volume fraction is 20% and the cold transmission terminal temperature is less than 30K.
  • the objective function is the geometric mean temperature of the conductive cooling structure, and the optimization goal is to minimize the objective function, that is, to minimize the geometric mean temperature of the conductive cooling structure.
  • the objective function of the multi-objective weighted optimization model can be expressed by the following formula:
  • f 1 (x) is the aforementioned lightweight model
  • f 2 (x) is the aforementioned heat dissipation model
  • w 1 and w 2 are weighting coefficients.
  • the specific value of the weighting coefficient can be set according to actual engineering needs, which is not limited in this embodiment.
  • the weighting coefficient can be set to 50% for both w 1 and w 2.
  • the corresponding engineering significance is to maximize the cooling capacity and load-bearing capacity of the integrated cooling load-bearing structure at the same time.
  • the multi-objective weighted optimization model generated after weighting can be expressed by the following formula.
  • max Vonmises is the maximum Mises stress, and ⁇ s is the material yield strength.
  • T out represents the temperature of the cold transfer terminal.
  • the design parameters x ⁇ x 1 , x 2 , x 3 ,... ..., x N ⁇ , is regarded as the topological configuration of the conductive cooling structure, and the design parameters are determined, which also determines the topological configuration of the conductive cooling structure.
  • perform multi-objective thermal coupling topology optimization on the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure including:
  • multi-objective thermal coupling topology optimization is performed on the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure.
  • the optimization tolerance is 0.001
  • the maximum number of iterations is 100.
  • topological configuration of the conductive cooling structure does not meet the design indicators, return to the multi-objective thermal coupling topology optimization of the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure until the topological configuration of the conductive cooling structure that meets the design indicators is obtained.
  • the topology of the conductive cooling structure meets the design specifications, it also includes:
  • the verification of the topological configuration of the conductive cooling structure in S305 can be as follows:
  • ⁇ max is the maximum Mises stress in key parts.
  • w t is the weight of the optimized topological configuration of the conductive cooling structure
  • w 0 is the weight of the original conductive cooling structure
  • the stress field and temperature field are coupled and optimized to achieve the thermal coupling design of the conductive cooling structure.
  • Using this solution does not require the designer's experience and repeated iterative optimization between multiple models. Obtain a configuration that meets multiple physical field design indicators at the same time, greatly improve design efficiency, simplify the design process, and improve design accuracy.
  • the embodiment of the present application also provides a topology optimization-based cooling structure design device. See Figure 6.
  • the device may include the following units.
  • the establishment unit 601 is used to establish a lightweight model and a heat dissipation model.
  • the lightweight model is the lightweight topology optimization model of the conductive cooling structure
  • the heat dissipation model is the heat dissipation topology optimization model of the conductive cooling structure.
  • the determination unit 602 is used to determine the optimization conditions of the lightweight model and determine the optimization conditions of the heat dissipation model.
  • the weighting unit 603 is used to weight the lightweight model and the heat dissipation model according to the weighting coefficient to obtain a multi-objective weighted optimization model.
  • the optimization unit 604 is used to perform multi-objective thermal coupling topology optimization on the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure.
  • the checking unit 605 is used to check whether the topological configuration of the conductive cooling structure meets the design indicators.
  • topological configuration of the conductive cooling structure does not meet the design indicators, return to the optimization unit to perform multi-objective thermal coupling topology optimization on the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure until the topological configuration of the conductive cooling structure that meets the design indicators is obtained. until.
  • the determination unit 602 is specifically used to:
  • the determination unit 602 is specifically used to:
  • check unit 605 is also used to:
  • the checking unit checks whether the topology of the conductive cooling structure meets the design indicators, it is specifically used for:
  • the optimization unit 604 performs multi-objective thermal coupling topology optimization on the multi-objective weighted optimization model, and when obtaining the topological configuration of the conductive cooling structure, it is specifically used for:
  • multi-objective thermal coupling topology optimization is performed on the multi-objective weighted optimization model to obtain the topological configuration of the conductive cooling structure; where the optimization tolerance is 0.001 and the maximum number of iterations is 100 Second-rate.
  • topology optimization-based cooling structure design device provided in this embodiment can be found in the topology optimization-based cooling structure design method provided in the previous embodiments, and will not be described again here.

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Abstract

公开了一种基于拓扑优化的导冷结构设计方法和装置，方法包括，建立轻量化模型和散热模型；轻量化模型为导冷结构轻量化拓扑优化模型，散热模型为导冷结构散热拓扑优化模型；分别确定轻量化模型的优化条件和散热模型的优化条件；对轻量化模型和散热模型加权得到的多目标加权优化模型进行多目标热力耦合拓扑优化，获得满足设计指标的导冷结构拓扑构型。本方案通过加权轻量化模型和散热模型，实现应力场和温度场耦合优化，从而获得同时满足多个物理场设计指标的构型，提高设计效率。

Description

基于拓扑优化的导冷结构设计方法和装置
本申请要求在2022年9月5日提交中华人民共和国国家知识产权局，申请号为202211077621.0，发明名称为“基于拓扑优化的导冷结构设计方法和装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及导冷结构设计技术领域，特别涉及一种基于拓扑优化的导冷结构设计方法和装置。
背景技术
超导磁体是指超深冷下用具有高转变温度和临界磁场特别高的第二类超导体制成线圈的一种电磁体。超导磁体所在的超深冷、强磁场、高真空工况由导冷结构维持。要使超导磁体在消耗更低的电能的同时，实现更高的磁场强度，就需要尽量提高导冷结构的散热效率，并尽量减轻导冷结构的质量。
目前设计导冷结构时一般采用单一物理场设计迭代的方法，例如先基于应力场设计，得到应力场设计指标的导冷结构，再基于温度场设计，以满足温度场设计指标，然而基于温度场设计时容易改动之前基于应力场设计出的参数，导致导冷结构又不满足应力场的设计指标。因此，现有的设计方法需要在不同物理场之间反复迭代，设计效率低。
发明内容
针对上述现有技术的缺点，本发明提供一种基于拓扑优化的导冷结构设计方法和装置，以提供一种高效的导冷结构方案。
本申请第一方面提供一种基于拓扑优化的导冷结构设计方法，包括：
建立轻量化模型和散热模型；其中，所述轻量化模型为导冷结构轻量化拓扑优化模型，所述散热模型为导冷结构散热拓扑优化模型；
确定所述轻量化模型的优化条件，并确定所述散热模型的优化条件；
按预设的加权系数将所述轻量化模型和所述散热模型加权，得到多目标加权优化模型；
对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构 拓扑构型；
校核所述导冷结构拓扑构型是否满足设计指标；
若所述导冷结构拓扑构型不满足所述设计指标，返回执行所述对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型，直至获得满足所述设计指标的导冷结构拓扑构型为止。
可选的，所述确定所述轻量化模型的优化条件，包括：
确定所述轻量化模型的拓扑优化域；
加载力学边界条件和热力耦合边界条件；
在材料属性中将杨氏模量、导热系数及热容乘以罚函数；
确定所述轻量化模型的约束条件和最小化总应变能的优化目标。
可选的，所述确定所述散热模型的优化条件，包括：
确定所述散热模型的拓扑优化域；
确定冷量输入端和冷量传输终端，加载温度场边界条件；
在材料属性中将导热系数和热容乘以罚函数；
确定所述散热模型的约束条件和最小化域内几何平均温度的优化目标。
可选的，所述校核所述导冷结构拓扑构型是否满足设计指标之前，还包括：
对所述导冷结构拓扑构型进行平滑处理；
所述校核所述导冷结构拓扑构型是否满足设计指标，包括：
校核平滑处理后的所述导冷结构拓扑构型是否满足设计指标。
可选的，所述对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型，包括：
基于移动渐近线算法(The method of moving asymptotes，MMA)对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型；其中，优化容差为0.001，最大迭代次数为100次。
本申请第二方面提供一种基于拓扑优化的导冷结构设计装置，包括：
建立单元，用于建立轻量化模型和散热模型；其中，所述轻量化模型为导冷结构轻量化拓扑优化模型，所述散热模型为导冷结构散热拓扑优化模型；
确定单元，用于确定所述轻量化模型的优化条件，并确定所述散热模型的优化条件；
加权单元，用于按预设的加权系数将所述轻量化模型和所述散热模型加权，得到多目标加权优化模型；
优化单元，用于对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型；
校核单元，用于校核所述导冷结构拓扑构型是否满足设计指标；
若所述导冷结构拓扑构型不满足所述设计指标，返回所述优化单元执行所述对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型，直至获得满足所述设计指标的导冷结构拓扑构型为止。
可选的，所述确定单元确定所述轻量化模型的优化条件时，具体用于：
确定所述轻量化模型的拓扑优化域；
加载力学边界条件和热力耦合边界条件；
在材料属性中将杨氏模量、导热系数及热容乘以罚函数；
确定所述轻量化模型的约束条件和最小化总应变能的优化目标。
可选的，所述确定单元确定所述散热模型的优化条件时，具体用于：
确定所述散热模型的拓扑优化域；
确定冷量输入端和冷量传输终端，加载温度场边界条件；
在材料属性中将导热系数和热容乘以罚函数；
确定所述散热模型的约束条件和最小化域内几何平均温度的优化目标。
可选的，所述校核单元还用于：
对所述导冷结构拓扑构型进行平滑处理；
所述校核单元校核所述导冷结构拓扑构型是否满足设计指标时，具体用于：
校核平滑处理后的所述导冷结构拓扑构型是否满足设计指标。
可选的，所述优化单元对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型时，具体用于：
基于移动渐近线算法(The method of moving asymptotes，MMA)对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型；其中，优化容差为0.001，最大迭代次数为100次。
本申请提供一种基于拓扑优化的导冷结构设计方法和装置，方法包括，建立轻量化模型和散热模型；轻量化模型为导冷结构轻量化拓扑优化模型，散热 模型为导冷结构散热拓扑优化模型；分别确定轻量化模型的优化条件和散热模型的优化条件；对轻量化模型和散热模型加权得到的多目标加权优化模型进行多目标热力耦合拓扑优化，获得满足设计指标的导冷结构拓扑构型。本方案通过加权轻量化模型和散热模型，实现应力场和温度场耦合优化，从而获得同时满足多个物理场设计指标的构型，提高设计效率。
附图说明
图1为本申请实施例提供的一种高温超导磁体结构的示意图；
图2为本申请实施例提供的一种导冷结构的示意图；
图3为本申请实施例提供的一种基于拓扑优化的导冷结构设计方法的流程图；
图4为本申请实施例提供的一种冷功率曲线的示意图；
图5为本申请实施例提供的一种导冷结构的温度场仿真示意图；
图6为本申请实施例提供的一种基于拓扑优化的导冷结构设计方装置的结构示意图。
具体实施方式
本申请提供一种成像方法以及成像系统，用于提高图像显示的全面性。
本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的实施例能够以除了在这里图示或描述的内容以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
为了便于理解本申请的技术方案，首先简要介绍本申请可能涉及的部分术语。
超深冷，一般指工作温度在-180℃以下的环境，约-200℃左右，通常可以 用液氮设备制冷来实现超深冷环境。
强磁场，超强磁场是指采用超导技术产生的5T(Tesla)以上的磁场，同时也包括采用脉冲技术、或者混合磁体技术或者超高功率电磁铁技术产生的超高强磁场。
高真空，真空度低于1.333×10-1～1.333×10-6Pa时的环境称为高真空。
超导磁体，超导磁体是指低温下用具有高转变温度和临界磁场特别高的第二类超导体制成线圈的一种电磁体。它的主要特点是无导线电阻产生的电损耗，也没有因铁芯存在而产生的磁损耗。
牵引力，又称悬浮力或导向力，是指超导磁体与地面牵引线圈、8字线圈磁场相斥产生的电磁力。
洛伦兹力，电磁学名词，指运动电荷在磁场中所受到的力，即磁场对运动电荷的作用力。
热力耦合，热力耦合过程是应力场与温度场两个物理场之间相互影响的过程，即温度对受力变形有影响，同时受力变形对温度变化也有影响。
超导磁体，是指超深冷下用具有高转变温度和临界磁场特别高的第二类超导体制成线圈的一种电磁体，其临界转变温度在35K左右，因此需要保证导冷结构可以将冷量效率最大地传导至超导磁体位置。由于超导磁体在超深冷、强磁场、高真空工况下工作，因此需要保证导冷结构可以承受由超导磁体与外界牵引和8字线圈相互作用产生的牵引力、悬浮力、导向力及洛伦兹力，以及由于温度分布变化产生的热应力，并且保证结构自重最轻，以便使超导磁体在消耗更低的电能的同时，实现更高的磁场强度。
示例性的，请参见图1，为一种典型的超导磁体结构的示意图，可以看到，该超导磁体结构包含一板状构件，该构件就是这个超导磁体的导冷结构，图1中的导冷结构的三维模型示意图可以参见图2。
现有技术在设计超导磁体的导冷结构时，一般采用单一物理场设计、校核、修改参数再设计的迭代方式进行设计。例如在单独校核应力场对超导磁体强度影响时，通过反复迭代计算，最终满足应力场的设计指标。但在后续进行温度场设计时，可能涉及到原本满足应力场的结构参数改动，导致不满足前一物理场，即应力场的设计指标。另外，现有技术在校核超导磁体结构强度时，非常 依赖设计师经验，常常导致设计冗余。因此现有技术设计过程繁琐、效率低、精度差，难以同时保证多个物理场的设计指标。
针对上述现有技术存在的问题，本申请提供一种基于拓扑优化的导冷结构设计方法，通过在优化阶段耦合应力场和温度场，使得优化后输出的结构参数能够同时满足应力场和温度场的设计指标，提高设计效率。
请参见图3，为本申请实施例提供的基于拓扑优化的导冷结构设计方法(以下简称设计方法)的流程图，该方法可以包括如下步骤：
首先需要说明的是，本申请实施例提供的设计方法，旨在利用数值仿真技术对导冷结构进行优化，得到合适的满足设计指标的导冷结构拓扑构型。因此，在执行本申请的设计方法前，首先要将需要优化的导冷结构分割为多个网格节点，每个网格节点的形状和大小等等可以根据实际需要进行设定，本实施例不做限定。网格节点的位置可以用横纵坐标表示，即网格节点X＝(x，y，z)。划分网格节点可以利用任意一款现有的有限元分析软件(如ANSYS)实现。
S301，建立轻量化模型和散热模型。
其中，轻量化模型为导冷结构轻量化拓扑优化模型，散热模型为导冷结构散热拓扑优化模型。
需要说明的是，本实施例中轻量化模型和散热模型的形式并不唯一，根据建立模型的方法的不同，建立的轻量化模型和散热模型可以有不同形式，本实施例对此不作限定。
作为一个示例，S301中建立的轻量化模型可以用如下公式(1)表示：
f 1(x)＝U TKU
散热模型可以用如下公式(2)表示：
Figure PCTCN2022134503-appb-000001
上述公式(1)和(2)中，x可以视为一个向量，xi可以视为向量x的第i个分量，向量x可以记为：x＝{x 1，x 2，x 3，……，x N}，向量x可以记为导冷结构的设计参数。
向量x中的每一个分量，均表示导冷结构中某一网格节点的材料属性，本申请中网格节点的材料属性包括杨氏模量，导热系数和热容。
示例性的，假设导冷结构被拆分为N个网格节点，依次记为网格节点1至N，则向量x中分量x 1可以表示网格节点1的杨氏模量，导热系数和热容，x 2可以表示网格节点2的杨氏模量，导热系数和热容……等等。
U表示导冷结构的位移矩阵，K表示导冷结构的刚度矩阵，K由导冷结构中各个网格节点的杨氏模量决定。
Ω表示导冷结构所占的区域，|Ω|表示导冷结构整体的面积或体积，n为凝聚因子，n越大，导冷结构的几何平均温度越接近于最高温度，n的具体取值可以按需设定。作为一个示例，n可以设定为50。
T(x)表示设计域(即导冷结构所占的区域)中网格节点x的温度。续接前述示例，T(x 1)可以表示网格节点1的温度，T(x 2)可以表示网格节点2的温度。
S302，确定轻量化模型的优化条件，并确定散热模型的优化条件。
其中，确定轻量化模型的优化条件，可以包括：
确定轻量化模型的拓扑优化域。
具体的，以图2所示的导冷结构为例，轻量化模型的拓扑优化域可以是图2所示的导冷结构所占的区域，具体由冷头连接区域及冷量传导区域构成，其中冷头连接区域直径为D，冷量传导区域与超导磁体内部超导线圈尺寸，也就是与超导磁体内部超导线圈的长、宽、高有关。
加载力学边界条件和热力耦合边界条件。
具体的，通过与超导磁体内部线圈配合的牵引线圈、8字线圈计算获得由磁场相互作用产生的两个线圈的牵引力FT1(t),FT2(t)、悬浮力FLE1(t),FLE2(t)、导向力FG1(t),FG2(t)。通过超导磁体内部运行电流及线圈几何参数计算获得两个线圈由电磁场产生的静态洛伦兹力FLO1,FLO2。分别将上述力加载在导冷结构用于支撑超导磁体的线圈的位置，这些力即为导冷结构的力学边界条件。
在导冷输入端加载冷头功率曲线，导冷结构末端加载线圈最低工作温度35K，并激活材料热膨胀影响作为导冷结构的热力耦合边界条件。
在材料属性中将杨氏模量、导热系数及热容乘以罚函数。
本实施例中，罚函数可以定义为ρ design(X)，X表示导冷结构的各个网格节点，在罚函数中，每一个网格节点都对应一个罚函数值，且罚函数值为1或0，其中，若一个网格节点的罚函数值为0，表示该网格节点对应于导冷结构的空 洞部分，在优化计算时不考虑该网格节点，若一个网格节点的罚函数值为1，表示该网格节点对应于导冷结构的实体部分，在优化计算时需要考虑该网格节点。
通过定义罚函数，可以在优化计算时筛选出有用的网格节点，而忽略对设计结果无没有影响的网格节点。
可选的，罚函数可以采用多种不同的拓扑优化算法确定，本实施例对此不作限定，作为一个示例，本实施例中可以采用各项正交惩罚材料变密度法(Solid isotropic material with penalization model，SIMP)算法确定罚函数。
在结构力学和热学仿真中，将杨氏模量、导热系数及热容乘以罚函数可以得到罚杨氏模量E(X)、罚导热系数k(X)和罚热容C(X)，具体可以用如下公式表示：
E(X)＝ρ design(X)E 0
k(X)＝ρ design(X)k 0
C(X)＝ρ design(X)C 0
其中E 0，k 0,C 0为导冷结构各个网格节点的真实杨氏模量、真实导热系数和真实热容。
确定轻量化模型的约束条件和最小化总应变能的优化目标。
对于导冷结构轻量化拓扑优化模型，优化的目标是希望最大化导冷结构的刚度，而在结构力学问题中，最大化刚度等同于最小化柔度，从能量的角度来说，导冷结构的柔度最小化，相当于总应变能最小化，因此，将最小化总应变能确定为轻量化模型的优化目标。设置约束条件为体积分数20％，网格节点的最大米塞思应力(Von-mises)不超过材料屈服极限的80％。
确定散热模型的优化条件，可以包括：
确定散热模型的拓扑优化域。
散热模型的拓扑优化域和轻量化模型的拓扑优化域相同，均为导冷结构所占区域，不再赘述。
确定冷量输入端和冷量传输终端，加载温度场边界条件。
具体的，根据制冷机制冷能力在冷头位置加载预设的冷功率曲线f(t)作为冷量输入端，示例性的，加载的冷功率曲线可以为图4所示的曲线。
冷量传输终端一般为导冷结构上支撑线圈的位置，示例性的，如图2所示 的导冷结构，其冷量传输终端的位置如图5所示。
根据超导磁体的线圈的特性，温度场边界条件可以设定为，冷量传输终端线圈温度19K。
在材料属性中将导热系数和热容乘以罚函数。
将导热系数及热容乘以罚函数可以得到罚杨氏模量E(X)、罚导热系数k(X)和罚热容C(X)，具体可以用如下公式表示：
E(X)＝ρ design(X)E 0
k(X)＝ρ design(X)k 0
C(X)＝ρ design(X)C 0
其中k 0,C 0为导冷结构各个网格节点的真实导热系数和真实热容。
确定散热模型的约束条件和最小化域内几何平均温度的优化目标。
具体的，约束条件为体积分数20％，冷量传输终端温度小于30K。
目标函数为导冷结构的几何平均温度，优化目标为使目标函数最小化，也就是最小化导冷结构的几何平均温度。
S303，按预设的加权系数将轻量化模型和散热模型加权，得到多目标加权优化模型。
多目标加权优化模型的目标函数可以用如下公式表示：
f(x)＝[f 1(x)，f 2(x)]＝w 1f 1(x)+w 2f 2(x)
其中，f 1(x)为前述轻量化模型，f 2(x)为前述散热模型，w 1和w 2为加权系数。加权系数的具体取值可以根据实际的工程需要而设定，本实施例对此不作限定。
作为示例，加权系数可以设置为，w 1和w 2均为50％，对应的工程意义为一体式导冷承载结构的导冷能力和承载能力同时最大化。
加权后产生的多目标加权优化模型可以用如下公式表示。
find：x＝{x 1，x 2，x 3，……，x N}
min：f(x)＝[f 1(x)，f 2(x)]＝w 1f 1(x)+w 2f 2(x)
Figure PCTCN2022134503-appb-000002
Figure PCTCN2022134503-appb-000003
subject to：V(x)/V 0＝f
max Vonmises<0.8σ s
T out<35K
0≤x i≤1(i＝1，2，3，……，N)
上述公式的含义是，在满足约束条件(即subjectto后面的公式)的前提下，求解出一组能使目标函数f(x)的取值最小化的设计参数x＝{x 1，x 2，x 3，……，x N}。
其中，V(x)/V 0＝f为体积因子，f＝20％，即S302所述的约束条件中20％的体积分数。max Vonmises为最大米塞思应力，σ s为材料屈服强度。T out表示冷量传输终端的温度。
S304，对多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型。
根据S303所述的多目标加权优化模型可以看出，通过求解该模型，可以得到一组满足约束条件，且使目标函数f(x)的取值最小化的设计参数x＝{x 1，x 2，x 3，……，x N}，根据这一组设计参数，可以唯一确定一个导冷结构拓扑构型，因此，也可以将设计参数x＝{x 1，x 2，x 3，……，x N}，视为导冷结构拓扑构型，确定了设计参数，也就确定了导冷结构拓扑构型。
可选的，对多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型，包括：
基于移动渐近线算法(The method of moving asymptotes，MMA)对多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型。
其中，优化容差为0.001，最大迭代次数为100次。
进一步的，在优化过程中还可以采用亥姆霍兹过滤器和双曲正切投影抑制灰度问题，其中过滤半径Rmin＝0.002m，投影参数β＝8，投影点；采用SIMP方法进行插值。
S305，校核导冷结构拓扑构型是否满足设计指标。
若导冷结构拓扑构型不满足设计指标，返回执行对多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型，直至获得满足设计指标 的导冷结构拓扑构型为止。
可选的，校核导冷结构拓扑构型是否满足设计指标之前，还包括：
对导冷结构拓扑构型进行平滑处理；
校核导冷结构拓扑构型是否满足设计指标，包括：
校核平滑处理后的导冷结构拓扑构型是否满足设计指标。
设计指标的数量和数值可以根据实际需要而设定，本实施例对此不做限定。作为一个示例，设计指标可以包括强度安全系数F S＝1.5，轻量化指标lightweight index＝0.5，以及冷量传输终端温度T out小于30K。
基于上述设计指标，S305中对导冷结构拓扑构型的校核具体可以是：
1、根据导冷结构拓扑构型中关键部位的最大米塞思应力判断结构强度是否大于设定的强度安全系数；具体的判断公式如下：
Figure PCTCN2022134503-appb-000004
其中，σ max即关键部位的最大米塞思应力。
2、判断优化后的导冷结构拓扑构型的重量和原始的导冷结构的重量之比是否小于轻量化指标；具体的判断公式如下：
Figure PCTCN2022134503-appb-000005
其中，w t为优化后的导冷结构拓扑构型的重量，w 0为原始的导冷结构的重量。
3、加载冷头导冷功率曲线及相关边界条件后，计算获得优化后的导冷结构拓扑构型的稳态温度场，根据稳态温度场判断其中冷量传输终端温度T out是否不大于30K。
若上述1至3任意一项判断的结果为否，即结构强度不大于设定的强度安全系数，或者重量之比不小于轻量化指标，或者冷量传输终端温度Tout大于30K，则确定本次优化得到的导冷结构拓扑构型不满足设计指标。
反之，若上述1至3每一项判断的结果均为是，即结构强度大于设定的强度安全系数，并且重量之比小于轻量化指标，并且冷量传输终端温度T out不大于30K，则确定本次优化得到的导冷结构拓扑构型满足设计指标。
本实施例的有益效果在于：
通过加权轻量化模型和散热模型，将应力场和温度场耦合优化，从而实现导冷结构的热力耦合设计，利用本方案不需要依赖设计师经验以及在多个模型之间反复迭代优化，就可以获得同时满足多个物理场设计指标的构型，大幅度提高设计效率，简化设计流程，提高设计精度。
根据本申请实施例提供的基于拓扑优化的导冷结构设计方法，本申请实施例还提供一种基于拓扑优化的导冷结构设计装置，请参见图6，该装置可以包括如下单元。
建立单元601，用于建立轻量化模型和散热模型。
其中，轻量化模型为导冷结构轻量化拓扑优化模型，散热模型为导冷结构散热拓扑优化模型。
确定单元602，用于确定轻量化模型的优化条件，并确定散热模型的优化条件。
加权单元603，用于按加权系数将轻量化模型和散热模型加权，得到多目标加权优化模型。
优化单元604，用于对多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型。
校核单元605，用于校核导冷结构拓扑构型是否满足设计指标。
若导冷结构拓扑构型不满足设计指标，返回优化单元执行对多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型，直至获得满足设计指标的导冷结构拓扑构型为止。
可选的，确定单元602确定轻量化模型的优化条件时，具体用于：
确定轻量化模型的拓扑优化域；
加载力学边界条件和热力耦合边界条件；
在材料属性中将杨氏模量、导热系数及热容乘以罚函数；
确定轻量化模型的约束条件和最小化总应变能的优化目标。
可选的，确定单元602确定散热模型的优化条件时，具体用于：
确定散热模型的拓扑优化域；
确定冷量输入端和冷量传输终端，加载温度场边界条件；
在材料属性中将导热系数和热容乘以罚函数；
确定散热模型的约束条件和最小化域内几何平均温度的优化目标。
可选的，校核单元605还用于：
对导冷结构拓扑构型进行平滑处理；
校核单元校核导冷结构拓扑构型是否满足设计指标时，具体用于：
校核平滑处理后的导冷结构拓扑构型是否满足设计指标。
可选的，优化单元604对多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型时，具体用于：
基于移动渐近线算法(The method of moving asymptotes，MMA)对多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型；其中，优化容差为0.001，最大迭代次数为100次。
本实施例所提供的基于拓扑优化的导冷结构设计装置，其具体工作原理和有益效果均可以参见前述实施例所提供的基于拓扑优化的导冷结构设计方法，此处不再赘述。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (10)

  1. 一种基于拓扑优化的导冷结构设计方法，其特征在于，包括：
    建立轻量化模型和散热模型；其中，所述轻量化模型为导冷结构轻量化拓扑优化模型，所述散热模型为导冷结构散热拓扑优化模型；
    确定所述轻量化模型的优化条件，并确定所述散热模型的优化条件；
    按加权系数将所述轻量化模型和所述散热模型加权，得到多目标加权优化模型；
    对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型；
    校核所述导冷结构拓扑构型是否满足设计指标；
    若所述导冷结构拓扑构型不满足所述设计指标，返回执行所述对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型，直至获得满足所述设计指标的导冷结构拓扑构型为止。
  2. 根据权利要求1所述的方法，其特征在于，所述确定所述轻量化模型的优化条件，包括：
    确定所述轻量化模型的拓扑优化域；
    加载力学边界条件和热力耦合边界条件；
    在材料属性中将杨氏模量、导热系数及热容乘以罚函数；
    确定所述轻量化模型的约束条件和最小化总应变能的优化目标。
  3. 根据权利要求1所述的方法，其特征在于，所述确定所述散热模型的优化条件，包括：
    确定所述散热模型的拓扑优化域；
    确定冷量输入端和冷量传输终端，加载温度场边界条件；
    在材料属性中将导热系数和热容乘以罚函数；
    确定所述散热模型的约束条件和最小化域内几何平均温度的优化目标。
  4. 根据权利要求1所述的方法，其特征在于，所述校核所述导冷结构拓扑构型是否满足设计指标之前，还包括：
    对所述导冷结构拓扑构型进行平滑处理；
    所述校核所述导冷结构拓扑构型是否满足设计指标，包括：
    校核平滑处理后的所述导冷结构拓扑构型是否满足设计指标。
  5. 根据权利要求1所述的方法，其特征在于，所述对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型，包括：
    基于移动渐近线算法(The method of moving asymptotes，MMA)对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型；其中，优化容差为0.001，最大迭代次数为100次。
  6. 一种基于拓扑优化的导冷结构设计装置，其特征在于，包括：
    建立单元，用于建立轻量化模型和散热模型；其中，所述轻量化模型为导冷结构轻量化拓扑优化模型，所述散热模型为导冷结构散热拓扑优化模型；
    确定单元，用于确定所述轻量化模型的优化条件，并确定所述散热模型的优化条件；
    加权单元，用于按加权系数将所述轻量化模型和所述散热模型加权，得到多目标加权优化模型；
    优化单元，用于对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型；
    校核单元，用于校核所述导冷结构拓扑构型是否满足设计指标；
    若所述导冷结构拓扑构型不满足所述设计指标，返回所述优化单元执行所述对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型，直至获得满足所述设计指标的导冷结构拓扑构型为止。
  7. 根据权利要求6所述的装置，其特征在于，所述确定单元确定所述轻量化模型的优化条件时，具体用于：
    确定所述轻量化模型的拓扑优化域；
    加载力学边界条件和热力耦合边界条件；
    在材料属性中将杨氏模量、导热系数及热容乘以罚函数；
    确定所述轻量化模型的约束条件和最小化总应变能的优化目标。
  8. 根据权利要求6所述的装置，其特征在于，所述确定单元确定所述散热模型的优化条件时，具体用于：
    确定所述散热模型的拓扑优化域；
    确定冷量输入端和冷量传输终端，加载温度场边界条件；
    在材料属性中将导热系数和热容乘以罚函数；
    确定所述散热模型的约束条件和最小化域内几何平均温度的优化目标。
  9. 根据权利要求6所述的装置，其特征在于，所述校核单元还用于：
    对所述导冷结构拓扑构型进行平滑处理；
    所述校核单元校核所述导冷结构拓扑构型是否满足设计指标时，具体用于：
    校核平滑处理后的所述导冷结构拓扑构型是否满足设计指标。
  10. 根据权利要求6所述的装置，其特征在于，所述优化单元对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型时，具体用于：
    基于移动渐近线算法(The method of moving asymptotes，MMA)对所述多目标加权优化模型进行多目标热力耦合拓扑优化，得到导冷结构拓扑构型；其中，优化容差为0.001，最大迭代次数为100次。
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