Heat-vibration-weight collaborative optimization method of high-power-density hovercar controller

Abstract

The invention is suitable for the technical field of hovercar control, and provides a thermal-vibration-weight collaborative optimization method of a high-power-density hovercar controller, and the method comprises the steps: constructing a three-dimensional housing structure based on fractal mechanics, achieving 35-45% of weight reduction through bionic design, and remarkably reducing the weight while guaranteeing the structural strength; thermal management, electromagnetic compatibility and vibration suppression collaborative optimization layout is established, a high-heating-power module is arranged near a heat dissipation air opening, and a sensitive control unit is arranged in a center damping area of a shell; a gradient material distribution scheme is adopted, a high-heat-conduction composite material is configured in a high-heat-flow area, and a high-damping material is adopted in a vibration sensitive area. Under the high-power working condition, the heat dissipation performance of the controller is improved, vibration transmission is reduced, the overall weight is reduced, and high-reliability control hardware support is provided for the hovercar.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of flying car control system and optimization design, and particularly relates to a thermal-vibration-weight collaborative optimization method for a high-power-density flying car controller. BACKGROUND

[0002] With the rapid development of flying car technology, the flight control system plays a crucial role in ensuring flight safety and stability. As a new type of transportation, the performance of the controller directly affects the accuracy, stability and safety of flight. Since flying cars need to operate in high power density and complex environments, the controller needs to cope with extreme temperature changes, strong vibrations and strict weight requirements. Traditional controller design often cannot meet the high performance requirements in these multiple aspects at the same time, so how to effectively improve the comprehensive performance of the controller has become the core problem of current technology research and development.

[0003] In the design of flying car controller, how to balance thermal management, electromagnetic compatibility, vibration suppression and lightweight design is the key to improving the performance of the controller. First, the high-power control system generates a large amount of heat when working, and the traditional heat dissipation method often leads to uneven system temperature, affecting control accuracy and stability; second, vibration problems also affect flight stability, especially during flight, vibration will be transmitted through the controller, and then affect the response accuracy of flight control; in addition, the weight of the controller is also one of the factors restricting the performance of the flying car, and the heavy controller will increase energy consumption, and then affect the flight time and efficiency. Therefore, how to realize collaborative optimization in terms of heat, vibration, weight, etc. through a scientific design scheme to improve the comprehensive performance of the controller has become an important issue in the development of flying car technology. SUMMARY

[0004] The application proposes a thermal-vibration-weight collaborative optimization method for a high-power-density flying car controller. This method realizes the weight reduction and performance optimization of the controller by constructing a three-dimensional shell structure based on fractal mechanics and adopting bionics design concept; at the same time, collaborative optimization is carried out in terms of thermal management, vibration suppression and electromagnetic compatibility, and reasonable module layout and material selection scheme are proposed, thereby greatly improving the heat dissipation capacity of the controller under high power working condition, reducing vibration transmission, and effectively reducing the overall weight.

[0005] The thermal-vibration-weight collaborative optimization method for a high-power-density flying car controller includes fractal-based structure construction, dynamic optimization of functional partition and gradient material integration of a high-power-density flying car controller, including:

[0006] S1, fractal infrastructure construction: generate a three-level fractal shell structure based on the restricted Voronoi algorithm, optimize the size ratio and wall thickness gradient of the three-level fractal shell structure through a multi-objective optimization algorithm, and establish a fractal infrastructure that meets the target strength and weight ratio as the fractal infrastructure of the high-power-density flying car controller;

[0007] S2, dynamic optimization of functional partition: on the fractal infrastructure of the high-power-density flying car controller generated in S1, determine the functional module layout through thermal-electric-vibration multi-objective collaborative optimization, wherein the power module of the high-power-density flying car controller is arranged at the air inlet side of the shell and forms a continuous heat dissipation channel through the merging of fractal cells, and the centralized control MCU unit of the high-power-density flying car controller is placed in the geometric center of the shell, and a shock absorption isolation area is constructed through fractal node reinforcement;

[0008] S3, gradient material integration: the contact area of the power module of the high-power-density flying car controller adopts copper-diamond composite material, and the centralized control MCU unit area of the high-power-density flying car controller adopts silicone-ceramic composite, and the material performance gradient matching is realized through the fractal interface transition layer.

[0009] Further, the step S1 comprises:

[0010] S11: adopt an improved restricted Voronoi algorithm to construct a fractal structure, and the implementation process includes two key steps: first, generate a uniformly distributed seed point set in the design domain based on the Poisson disc sampling algorithm, and ensure that the point set meets the minimum distance requirement by setting the repulsion radius r1, which is mathematically expressed as

[0011]

[0012] Wherein, p i ,p j is the spatial coordinate vector of the seed point; then introduce the fractal growth constraint function

[0013]

[0014] Wherein V i is the i-th Voronoi cell, Ω is the design domain space, α=0.35 is the fractal coefficient, and f(x) is the spatial weight function;

[0015] S12: On the basis of generating a fractal structure, the key parameters are precisely controlled and optimized through a multi-objective optimization algorithm. The optimization process targets two core parameters: size ratio β and wall thickness gradient dt / dl; for size ratio optimization, a double-objective function is established, which includes stress constraint and mass control:

[0016]

[0017] where β is the size ratio parameter, f1 ensures the structural strength meets the design requirements, f2 controls the weight index close to the target value, σ max is the actual stress, [σ] is the allowable stress, m is the actual mass, m0 is the target mass; in the gradient control aspect, the nonlinear differential equation is adopted:

[0018]

[0019] where t is the local wall thickness, l is the path length along the fractal branch, k t is the material attenuation constant (0.12), C is the integral constant; this equation describes the variation law of the wall thickness along the fractal branch, and the exponential term t 1.5 The local reinforcement in the stress concentration area is realized, while the overall lightweight characteristics are maintained; the two optimization links are coupled through iterative calculation, and the optimal parameter combination that meets the mechanical performance and weight requirements is finally obtained;

[0020] S13: After completing the fractal structure parameter optimization, the preliminary mechanical performance verification of the design is carried out, first, the homogenization method is used to calculate the equivalent elastic tensor:

[0021]

[0022] is the macroscopic equivalent elastic tensor, representing the stiffness characteristics of the homogenized material, |V| is the volume of the representative volume element, V is the integral region, i.e. the representative volume element of the fractal structure, C ijkl is the microscale material constitutive tensor, describing the local material properties, C ijmn is the component of the material stiffness tensor, is the unit test strain field, used to calculate the equivalent performance; this method obtains the macroscopic equivalent stiffness characteristics of the structure, which can accurately reflect the influence of the fractal cell microstructure on the overall mechanical performance; secondly, the vibration characteristic analysis is carried out, and the characteristic equation is solved:

[0023]

[0024] The natural frequency ω i and mode shape of the structure are obtained, where the construction of the stiffness matrix K and the mass matrix M considers the gradient material distribution and geometric nonlinear characteristics of the fractal structure.

[0025] Further, the step S2 specifically comprises:

[0026] S21: Multi-objective collaborative optimization method based on NSGA-III algorithm, by establishing a thermal-electric-vibration three-field coupling optimization model, the systematic improvement of the controller performance is realized; the core optimization objective function is as follows:

[0027]

[0028] Where, T i is the temperature value of the ith temperature monitoring point, T amb is the ambient temperature, n is the number of temperature monitoring points, TR(ω) is the vibration transmissibility at frequency ω, ω is the angular frequency, Z in (f j ) is the input impedance modulus value of the jth frequency point, f j is the jth sampling frequency, m is the number of frequency sampling points, in terms of thermal management, the sum of squares of temperature difference between power module key temperature monitoring point and environment is taken as optimization objective (f thermal ); in terms of vibration control, the vibration transmissibility integral in 50-500Hz working frequency band is selected as evaluation index (f vibration ); in terms of electromagnetic compatibility, the input impedance matching degree (f electrical ) is taken as optimization objective;

[0029] S22: Guiding power module arrangement based on thermal resistance network model: calculating the equivalent thermal resistance of each section of channel:

[0030]

[0031] Where, R th is the total equivalent thermal resistance of the heat dissipation channel, L k is the length of the kth section of channel, A k is the cross-sectional area of the kth section of channel, λ k is the thermal conductivity of the material of the kth section of channel, ensuring that the overall thermal resistance is controlled below 0.38K / W; secondly, computational fluid dynamics simulation is used to analyze the actual heat dissipation effect in the flow channel, and the temperature distribution uniformity of the region with heat flux density >5W / cm 2 ;

[0032] S23: Calculate the dynamic energy density proportion of each node by modal dynamic energy density distribution:

[0033]

[0034] Where, KE i is the modal dynamic energy density of the ith node, representing the vibration energy intensity at this position; ω i is the circular frequency of the ith mode, is the i-th order modal shape vector, describing the vibration mode of the structure; M is the finite element mass matrix; the node is the vibration concentration area that needs to be focused on; stiffness gradient control is implemented on the identified vibration concentration area, combined with the nonlinear stiffness gradient equation:

[0035]

[0036] wherein, represents the change rate of the stiffness k along the radial position r, and k is the local stiffness value.

[0037] Further, the step S3 specifically comprises:

[0038] A gradient material distribution model is established based on a thermoelastic control equation:

[0039]

[0040] wherein, λ(x), λ(y) are the thermal conductivity coefficients of the material in the x and y directions, which are functions of the spatial coordinates; T is the temperature field distribution; x, y are spatial coordinates; the optimal spatial distribution of the thermal conductivity coefficient λ(x,y) is obtained by finite element solution, wherein the contact area of the power module adopts copper-diamond composite material, the concentrated control MCU unit area adopts silicone-ceramic composite, and the transition area changes according to the volume fraction gradient of diamond and is formed by gradient hot pressing process.

[0041] Beneficial effects: The thermal-vibration-weight synergistic optimization method of the high-power-density flying car controller provided in the application realizes 35%-45% weight reduction by constructing a fractal basic structure and using bionics design, while ensuring the structural strength. Through the synergistic optimization of thermal management, electromagnetic compatibility and vibration suppression, the high-heat-power modules and sensitive control units are reasonably arranged, and the heat dissipation performance and anti-vibration ability of the controller are optimized. The gradient material distribution scheme is adopted, high-thermal-conductivity composite materials are used in the high-heat-flow area, and high-damping materials are used in the vibration-sensitive area, which effectively reduces the vibration transmission and improves the stability and reliability of the system. The overall optimization design makes the controller have significantly improved heat dissipation performance, reduced vibration transmission and reduced weight under high-power working condition, further enhances the control performance and reliability of the flying car under complex working conditions, and thus provides more efficient and reliable hardware support for the flying car. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0043] Figure 1 FIG. 1 is a flow diagram of a method for thermal-vibration-weight synergistic optimization of a high-power-density flying car controller according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular sequences of acts, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, and circuits are omitted so as not to obscure the description of the present application with unnecessary detail.

[0045] It is to be understood that the terminology “includes,” “has,” “holds,” “contains,” “comprises,” “comprised of,” “comprising,” or variants thereof does not exclude the presence of additional

[0046] It is to be understood that the terminology “includes,” “has,” “holds,” “contains,” “comprises,” “comprised of,” “comprising,” or variants thereof does not exclude the presence of additional

[0047] As used in the description of the application and the appended claims, the term “if’ can be interpreted to mean “when” or “upon” or “in response to determining” or “in response to detecting” depending on the context. Similarly, the phrase “if it is determined” or “if [a described condition or event] is detected” can be interpreted to mean “upon determining” or “in response to determining” or “upon [the described condition or event] being detected” or “in response to [the described condition or event] being detected,” depending on the context.

[0048] In addition, in the description of the application and the appended claims, the terms “first,” “second,” “third,” etc. are used merely as labels for the convenience of the reader and do not necessarily connote an ordering, or a relative importance.

[0049] Reference within the specification of this application to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" or "in some embodiments" in various places within specified

[0050] The high-power-density flying car controller thermal-vibration-weight synergistic optimization method provided by the embodiments of the application is described in detail below with reference to the accompanying drawings.

[0051] Figure 1 A flowchart of the high-power-density flying car controller thermal-vibration-weight synergistic optimization method provided by the embodiments of the application is shown.

[0052] As Figure 1 shown, the high-power-density flying car controller thermal-vibration-weight synergistic optimization method includes the following steps:

[0053] S1, fractal basic structure construction: generate a three-level fractal shell structure based on a restricted Voronoi algorithm, achieve 35%-45% weight reduction by adjusting the spatial distribution of fractal cells, and control the wall thickness to change along the fractal branches in a gradient manner to establish a basic load-bearing frame that meets the strength-weight ratio;

[0054] Further, in the embodiments of the application, the above step S1 includes:

[0055] S11: The improved restricted Voronoi algorithm is used to construct the fractal structure, and the implementation process includes two key steps: first, a uniform distribution of seed point set is generated in the design domain based on the Poisson disc sampling algorithm, and by setting the repulsion radius r1 (1 / 8-1 / 10 of the minimum size of the design domain), it is ensured that the point set meets the minimum spacing requirement, which is mathematically expressed as

[0056]

[0057] Where p i ,p j is the spatial coordinate vector of the seed point; then a fractal growth constraint function

[0058]

[0059] Where V iFor the ith Voronoi cell, Ω is the design domain space, α = 0.35 is the fractal coefficient, and f(x) is the spatial weight function. By adjusting these parameters, the fractal characteristics and mechanical properties of the generated structure can be accurately controlled. This method achieves a balance between structural self-similarity and functional requirements by adjusting the fractal coefficient.

[0060] S12: Based on the generation of fractal structures, the key parameters are precisely controlled and optimized through a multi-objective optimization algorithm. The optimization process mainly focuses on two core parameters: size ratio β and wall thickness gradient dt / dl. For size ratio optimization, a dual-objective function is established, which includes stress constraints and mass control:

[0061]

[0062] where β is the size ratio parameter, f1 ensures that the structural strength meets the design requirements, f2 controls the weight index close to the target value, σ max is the actual stress, [σ] is the allowable stress, m is the actual mass, and m0 is the target mass. In terms of gradient control, a nonlinear differential equation is used:

[0063]

[0064] where t is the local wall thickness, l is the path length along the fractal branch, k t is the material attenuation constant (value 0.12), and C is the integral constant (determined by boundary conditions). This equation describes the variation of wall thickness along the fractal branch, and by introducing the exponential term t 1.5 achieves local strengthening in stress concentration areas while maintaining overall lightweight characteristics. These two optimization steps are coupled through iterative calculations, ultimately obtaining the optimal parameter combination that meets both mechanical performance and weight requirements.

[0065] S13: After completing the fractal structure parameter optimization, preliminary mechanical performance verification of the design is performed. First, the homogenization method is used to calculate the equivalent elastic tensor:

[0066]

[0067] is the macroscopic equivalent elastic tensor, representing the stiffness characteristics of the homogenized material, |V| is the volume of the representative volume element, V is the integral region, i.e., the representative volume element of the fractal structure, C ijkl is the microscale material constitutive tensor, describing the local material properties, C ijmn is the component of the material stiffness tensor, is the unit test strain field, used to calculate the equivalent performance. This method obtains the macroscopic equivalent stiffness characteristics of the structure, accurately reflecting the influence of fractal cell microstructure on overall mechanical performance; secondly, vibration characteristic analysis is performed, solving the characteristic equation:

[0068]

[0069] Obtaining the natural frequency ω of the structure i and mode shape Where the construction of the stiffness matrix K and the mass matrix M takes into account the gradient material distribution and the geometric nonlinear characteristics of the fractal structure.

[0070] S2, dynamic optimization of functional partition: on the basis of the fractal structure generated in S1, the layout of the functional module is determined through multi-objective collaborative optimization of heat-electricity-vibration, wherein the power module is arranged at the air inlet side of the shell and forms a continuous heat dissipation channel through the merging of fractal cells; the centralized control MCU unit is placed in the geometric center area of the shell, and a shock absorption and isolation area is constructed through the fractal node reinforcement; further, in the embodiment of the application, the above step S2 comprises:

[0071] S21: multi-objective collaborative optimization method based on NSGA-III algorithm, through the establishment of an optimization model of heat-electricity-vibration three-field coupling, the systematic improvement of the controller performance is realized. The core optimization objective function is as follows:

[0072]

[0073] Where, T i is the temperature value of the ith temperature measuring point, T amb is the environmental temperature, n is the number of temperature monitoring points, TR(ω) is the vibration transmissibility at frequency ω, ω is the angular frequency, Z in (f j ) is the input impedance modulus value of the jth frequency point, f j is the jth sampling frequency, m is the number of frequency sampling points, in terms of heat management, the sum of the squares of the temperature difference between the key temperature measuring points of the power module and the environment is taken as the optimization objective (f thermal ), to ensure the uniformity of temperature distribution; in terms of vibration control, the integral of the vibration transmissibility in the working frequency band of 50-500Hz is selected as the evaluation index (f vibration ), to effectively suppress the main vibration energy; in terms of electromagnetic compatibility, the input impedance matching degree (f electrical ) is taken as the optimization objective to ensure the signal transmission quality. The three objective functions are collaboratively optimized through the NSGA-III algorithm, while maintaining the lightweight advantage of the fractal structure, the comprehensive performance of the controller is improved.

[0074] S22: power module arrangement based on thermal resistance network model: calculate the equivalent thermal resistance of each section of the channel:

[0075]

[0076] Where, R thL is the total equivalent thermal resistance of the heat dissipation channel k Lk is the length of the kth channel segment k Ak is the cross-sectional area of the kth channel segment k λk is the thermal conductivity of the material of the kth channel segment, and the overall thermal resistance is controlled to be less than or equal to 0.38 K / W; secondly, computational fluid dynamics simulation is used to analyze the actual heat dissipation effect in the flow channel, and the heat flux density > 5 W / cm 2 The temperature distribution uniformity of the region. When the results show local overheating, readjust the merging angle of the fractal cell (controlled within ± 15°) or optimize the channel cross-sectional parameters until the theoretical thermal resistance requirement and the actual heat dissipation demand are met at the same time.

[0077] S23: Calculate the dynamic energy density proportion of each node by modal dynamic energy density distribution:

[0078]

[0079] wherein, KE i is the modal dynamic energy density of the ith node, representing the vibration energy intensity at the position; ω i is the circular frequency of the ith mode, is the ith mode shape vector, describing the vibration form of the structure; M is the finite element mass matrix; accurately locate the vibration energy concentration area (KE i > 15% of the total kinetic energy of the key nodes), which are the vibration concentration areas that need to be strengthened. The identified vibration concentration areas are subjected to stiffness gradient control, combined with the nonlinear stiffness gradient equation:

[0080]

[0081] wherein, represents the change rate of the stiffness k along the radial position r, k is the local stiffness value, realizing the exponential decay of the stiffness from the core area to the outside, blocking the vibration propagation path, and the damping layer adopts a silicone rubber-ceramic composite, realizing the optimal balance between the thickness (2.5±0.3mm) of the damping layer and the overall weight.

[0082] S3, gradient material integration: copper-diamond composite material is used in the contact area of the power module, silicone rubber-ceramic composite is used in the concentrated control MCU unit area, and material performance gradient matching is realized through a fractal interface transition layer.

[0083] Further, in the embodiment of the present application, the above step S3 comprises:

[0084] A gradient material distribution model is established based on the thermoelastic control equation:

[0085]

[0086] Wherein, λ(x), λ(y) are the thermal conductivity coefficients of the material in x, y directions, which are functions of spatial coordinates; T is the temperature field distribution, and x, y are spatial coordinates. The optimal spatial distribution of the thermal conductivity coefficient λ(x, y) is obtained by finite element solution, wherein the copper-diamond composite material is used in the contact area of the power module, the silicone rubber-ceramic composite is used in the concentrated control MCU unit area, and the transition area is formed by gradient hot pressing process with the diamond volume fraction changing in a gradient manner.

[0087] The copper-diamond composite material belongs to the prior art, such as the existing CDW-45-30 material, which is from Zhurong Technology; the silicone rubber-ceramic composite material belongs to the prior art, such as the existing CS-70 material, which is from Guangmai Electronics.

[0088] The high-power-density flying car controller thermal-vibration-weight synergistic optimization method provided in the application first achieves a weight reduction effect of 35%-45% by constructing a three-dimensional shell structure based on fractal mechanics and combining bionics design, thereby ensuring that the flying car controller is reduced in overall weight while ensuring strength. In the optimization process, the arrangement of the high-heat-power module is accurately designed through synergistic optimization of thermal management, electromagnetic compatibility and vibration suppression, and the high-heat-power module is reasonably arranged near the heat dissipation air outlet, thereby effectively improving the heat dissipation efficiency. At the same time, the sensitive control unit is placed in the damping area at the center of the shell, thereby reducing the influence of vibration on the controller. Through this layout, the control system of the flying car can stably and efficiently operate under high-power load, thereby avoiding the negative influence of overheating and vibration interference on the control performance. The application also adopts a gradient material distribution scheme, selects a high-thermal-conductivity composite material in the high-heat-flow area, thereby ensuring rapid heat dissipation, and selects a high-damping material in the vibration-sensitive area, thereby significantly reducing vibration transmission. This fine material distribution not only improves the heat dissipation performance, but also effectively enhances the anti-interference ability of the controller, thereby improving the reliability and stability of the flying car under complex working conditions.

[0089] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0090] The above-described embodiments are only used to illustrate the technical solutions of the application, rather than limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application, and should be included in the protection scope of the application.

Claims

1. A heat-vibration-weight synergistic optimization method for a high-power-density flying car controller, comprising fractal infrastructure construction, dynamic optimization of functional partitioning, and gradient material integration of the high-power-density flying car controller, characterized in that: include: S1. Fractal base structure construction: Generate a three-level fractal shell structure based on the restricted Voronoi algorithm. Optimize the size ratio and wall thickness gradient of the three-level fractal shell structure through a multi-objective optimization algorithm to establish a fractal base structure that meets the target strength and weight ratio. This will serve as the fractal base structure for the high-power density flying car controller. S2. Dynamic optimization of functional zoning: Based on the fractal infrastructure of the high-power-density flying car controller generated in S1, the functional module layout is determined through thermal-electrical-vibration multi-objective collaborative optimization. Specifically, the power module of the high-power-density flying car controller is arranged on the air inlet side of the housing and a continuous heat dissipation channel is formed by merging fractal cells. The centralized control MCU unit of the high-power-density flying car controller is placed in the geometric center of the housing, and a shock-absorbing isolation zone is constructed by strengthening fractal nodes. S3. Gradient material integration: Copper-diamond composite materials are used in the power module contact area of ​​the high-power density flying car controller, and silicone rubber-ceramic composite materials are used in the centralized control MCU unit area of ​​the high-power density flying car controller. Gradient matching of material properties is achieved through a fractal interface transition layer.

2. The heat-vibration-weight coordinated optimization method for a high-power density flying car controller according to claim 1, characterized in that: The step S1 comprises: S11: The fractal structure is constructed using the improved restricted Voronoi algorithm. The implementation process includes two key steps: first, a uniformly distributed seed point set is generated in the design domain based on the Poisson disk sampling algorithm, and the exclusion radius r1 is set to ensure that the point set meets the minimum spacing requirement. Its mathematical expression is: Among them, p i ,p j is the spatial coordinate vector of the seed point; then the fractal growth constraint function is introduced Where V i is the i-th Voronoi cell, Ω is the design domain space, α = 0.35 is the fractal coefficient, and f(x) is the spatial weight function; S12: Based on the generated fractal structure, a multi-objective optimization algorithm is used to precisely control key parameters. The optimization process targets two core parameters: the dimension ratio β and the wall thickness gradient dt / dl. For the dimension ratio optimization, a dual-objective function is established that includes stress constraints and quality control: Where β is the size ratio parameter, f1 ensures that the structural strength meets the design requirements, f2 controls the weight index to be close to the target value, and σ max is the actual stress, [σ] is the allowable stress, m is the actual mass, and m0 is the target mass. In terms of gradient control, a nonlinear differential equation is used: Where t is the local wall thickness, l is the path length along the fractal branch, and k t is the material attenuation constant (valued at 0.12), C is the integral constant. This equation describes the variation of wall thickness along the fractal branch. By introducing the exponential term t 1.5 This achieves local strengthening in stress concentration areas while maintaining overall lightweight properties. These two optimization steps are coupled with each other through iterative calculations, ultimately obtaining the optimal parameter combination that meets both mechanical performance and weight requirements. S13: After completing the fractal structure parameter optimization, the equivalent elastic tensor is first calculated using the homogenization method: is the macroscopic equivalent elastic tensor, which characterizes the stiffness characteristics of the material after homogenization, |V| is the volume of the representative volume unit, V is the integration area, that is, the representative volume unit of the fractal structure, C ijkl is the constitutive tensor of the microscopic material, describing the local material properties, C ijmn are the components of the material stiffness tensor, The unit test strain field is used to calculate the equivalent performance. This method obtains the macroscopic equivalent stiffness characteristics of the structure, which can accurately reflect the influence of the fractal cell microstructure on the overall mechanical properties. Secondly, the vibration characteristics analysis is carried out to solve the characteristic equation: Get the natural frequency ω of the structure i Harmonic vibration mode The construction of the stiffness matrix K and mass matrix M takes into account the gradient material distribution and geometric nonlinear characteristics of the fractal structure.

3. The heat-vibration-weight coordinated optimization method for a high power density flying car controller according to claim 1, characterized in that: The step S2 specifically includes: S21: A multi-objective collaborative optimization method based on the NSGA-III algorithm achieves a systematic improvement in controller performance by establishing an optimization model for the thermal, electrical, and vibration coupling. The core optimization objective function is as follows: Among them, T i is the temperature value of the i-th temperature measurement point, T amb is the ambient temperature, n is the number of temperature monitoring points, TR(ω) is the vibration transmissibility at frequency ω, ω is the angular frequency, Z in (f j ) is the input impedance modulus at the jth frequency point, f j is the jth sampling frequency, m is the number of frequency sampling points, and in terms of thermal management, the square sum of the temperature difference between the key temperature measurement points of the power module and the environment is used as the optimization target (f thermal ); In terms of vibration control, the vibration transmissibility integral within the 50-500Hz working frequency band is selected as the evaluation index (f vibration ); in terms of electromagnetic compatibility, the input impedance matching (f electrical ) is the optimization objective; S22: Guide the layout of power modules based on the thermal resistance network model: Calculate the equivalent thermal resistance of each channel: Among them, R th is the total equivalent thermal resistance of the heat dissipation channel, L k is the length of the kth channel, A k is the cross-sectional area of ​​the kth channel, λ k The thermal conductivity of the channel material in the kth section is used to ensure that the overall thermal resistance is controlled below 0.38K / W. Computational fluid dynamics simulation is then used to analyze the actual heat dissipation effect in the flow channel, and the heat flux density is > 5W / cm 2 Uniformity of temperature distribution in the region; S23: Calculate the kinetic energy density ratio of each node through the modal kinetic energy density distribution: Among them, KE i is the modal kinetic energy density of the i-th node, representing the vibration energy intensity at that position; ω i is the circular frequency of the i-th mode, is the i-th order modal vibration vector, which describes the structural vibration form; M is the finite element mass matrix; the vibration energy concentration area is accurately located, and the node is the vibration concentration area that needs to be strengthened; the stiffness gradient control is implemented on the identified vibration concentration area, combined with the nonlinear stiffness gradient equation: in, It represents the rate of change of stiffness k along radial position r, where k is the local stiffness value.

4. The heat-vibration-weight coordinated optimization method for a high-power density flying car controller according to claim 1, characterized in that: The step S3 specifically includes: Establish a gradient material distribution model based on the thermoelastic governing equations: Where λ(x) and λ(y) are the thermal conductivities of the material in the x and y directions, respectively, and are functions of the spatial coordinates; T is the temperature field distribution; x and y are the spatial coordinates; the optimal spatial distribution of the thermal conductivity λ(x,y) is obtained through finite element solution. The power module contact area adopts a copper-diamond composite material, the centralized control MCU unit area adopts a silicone rubber-ceramic composite, and the transition zone varies according to the diamond volume fraction gradient and is formed using a gradient hot pressing process.

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