Equipment aerodynamic heat optimization method and device, equipment and computer readable storage medium

Abstract

The invention relates to the technical field of aircrafts, in particular to an equipment aerodynamic heat optimization method and device, equipment and a computer readable storage medium. The equipment aerodynamic heat optimization method comprises the following steps: determining an initial shape of to-be-optimized equipment and a structural grid of a structural field; determining adjoint variables corresponding to a plurality of moments; wherein the multiple moments are moments corresponding to each changed flow field in a temperature rise period of the equipment under the action of the changed flow fields; determining an objective function gradient according to the adjoint variables corresponding to the multiple moments; and optimizing the coordinates of the structure grid of the structure field based on the target function gradient so as to obtain the shape of the optimized equipment.

Description

Technical Field

[0001] The present disclosure relates to the technical field of aircraft, and particularly to a method, device, equipment and computer-readable storage medium for optimizing aerodynamic heat of equipment. Background Art

[0002] In the technical field of aircraft, related optimization methods are mainly applied to the optimization of aerodynamic forces such as drag reduction and lift-to-drag ratio improvement of low subsonic, transonic and supersonic aircraft.

[0003] However, there are significant differences between aerodynamic force optimization and aerodynamic heat optimization. The difference between the two is that the optimization of aerodynamic force is for the transient quantity of the whole aircraft, while the optimization of aerodynamic heat is for the local cumulative quantity. Since aerodynamic heat is for the local cumulative quantity, a time dimension is introduced, and an optimization method combining the time dimension needs to be considered to optimize the cumulative quantity rather than only the transient quantity. Summary of the Invention

[0004] To overcome the problems existing in the related art, the present disclosure provides a method, device, equipment and computer-readable storage medium for optimizing aerodynamic heat of equipment, which can solve the above problems.

[0005] According to the first aspect of the embodiments of the present disclosure, a method for optimizing aerodynamic heat of equipment is provided. The method includes: determining the initial shape of the equipment to be optimized and the structural field structural grid; determining adjoint variables corresponding to multiple moments; wherein, the multiple moments are the moments corresponding to each changing flow field during the temperature rise period of the equipment under the action of the changing flow field; determining the gradient of the objective function according to the adjoint variables corresponding to the multiple moments; and optimizing the coordinates of the structural field structural grid based on the gradient of the objective function to obtain the optimized shape of the equipment.

[0006] According to the second aspect of the embodiments of the present disclosure, a device for optimizing aerodynamic heat of equipment is provided. The device includes: a structural grid determination unit configured to determine the initial shape of the equipment to be optimized and the structural field structural grid; an adjoint variable determination unit configured to determine adjoint variables corresponding to multiple moments; wherein, the multiple moments are the moments corresponding to each changing flow field during the temperature rise period of the equipment under the action of the changing flow field; a function gradient determination unit configured to determine the gradient of the objective function according to the adjoint variables corresponding to the multiple moments; and a structural grid optimization unit configured to optimize the coordinates of the structural field structural grid based on the gradient of the objective function to obtain the optimized shape of the equipment.

[0007] According to the third aspect of the embodiments of the present disclosure, an electronic device is provided, including: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the method for optimizing aerodynamic heat of equipment as described in the first aspect by calling the computer program.

[0008] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the device aerodynamic heat optimization method described in the first aspect is implemented.

[0009] According to a fifth aspect of the embodiments of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0010] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects:

[0011] The present disclosure can determine the structured grids of the structural field and the flow field, and determine the adjoint variables corresponding to multiple moments. Among them, the multiple moments are the moments corresponding to each changing flow field during the entire heating period when the device to be optimized is under the action of a changing flow field. Since the thermal optimization of aerodynamic heat is based on the cumulative amount of time change, during the entire heating period, the flow field may change multiple times, and each different flow field change corresponds to a different adjoint variable. Therefore, based on the adjoint variables corresponding to the moments of each changing flow field determined by the present disclosure, the cumulative amount of time can be reflected and considered during the optimization process, which is beneficial to optimizing the structural field grid related to the time accumulation amount, for example, the shape optimization based on temperature.

[0012] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings herein are incorporated into the specification and form a part of the present disclosure, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0014] Figure 1 is a schematic flowchart of a method for optimizing the aerodynamic heat of a device shown according to an exemplary embodiment of the present disclosure.

[0015] Figure 2 is a schematic diagram of the flow field structured grid of the initial shape of a device to be optimized shown according to an exemplary embodiment of the present disclosure.

[0016] Figure 3 is a schematic diagram of the structural field structured grid of the initial shape of a device to be optimized shown according to an exemplary embodiment of the present disclosure.

[0017] Figure 4 is a schematic diagram of drawing a control box on a structured grid shown according to an exemplary embodiment of the present disclosure.

[0018] Figure 5It is a flowchart of another device aerodynamic heat optimization based on discrete adjoint shown according to an exemplary embodiment of the present disclosure.

[0019] Figure 6 It is a density cloud map of the initial shape flow field at the end moment shown according to an exemplary embodiment of the present disclosure.

[0020] Figure 7 It is a temperature cloud map of the initial shape structure field at the end moment shown according to an exemplary embodiment of the present disclosure.

[0021] Figure 8 It is a gradient distribution cloud map of the objective function with respect to the design variables in the X direction shown according to an exemplary embodiment of the present disclosure.

[0022] Figure 9 It is a gradient distribution cloud map of the objective function with respect to the design variables in the Y direction shown according to an exemplary embodiment of the present disclosure.

[0023] Figure 10 It is a schematic diagram of the structured grid for the optimized shape flow field calculation shown according to an exemplary embodiment of the present disclosure.

[0024] Figure 11 It is a schematic diagram of the structured grid for the optimized shape structure field calculation shown according to an exemplary embodiment of the present disclosure.

[0025] Figure 12 It is a density cloud map of the optimized shape flow field at the end moment shown according to an exemplary embodiment of the present disclosure.

[0026] Figure 13 It is a temperature cloud map of the optimized shape structure field at the end moment shown according to an exemplary embodiment of the present disclosure.

[0027] Figure 14 It is a schematic diagram for comparing the wall heat fluxes of the initial shape and the optimized shape shown according to an exemplary embodiment of the present disclosure.

[0028] Figure 15 It is a block diagram of a device aerodynamic heat optimization device shown according to an exemplary embodiment of the present disclosure.

[0029] Figure 16 It is a schematic block diagram of a device for a device aerodynamic heat optimization device shown according to an exemplary embodiment of the present disclosure. Detailed implementation manners

[0030] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0031] The terms used in the present disclosure are for the purpose of describing specific embodiments only and are not intended to limit the present disclosure. The singular forms "a", "the", and "said" used in the present disclosure and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0032] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".

[0033] Figure 1 is a schematic flowchart of a method for optimizing the aerodynamic heat of a device according to an embodiment of the present disclosure. The method for optimizing the aerodynamic heat of the device can be executed by a terminal. The method for optimizing the aerodynamic heat of the device can be used to optimize the shape of the device so that the optimized shape meets specific requirements.

[0034] As Figure 1 shown, the method for optimizing the aerodynamic heat of the device includes:

[0035] In step S101, determine the initial shape of the device to be optimized and the structural field structure grid;

[0036] In step S102, determine the adjoint variables corresponding to multiple moments; wherein, the multiple moments are the moments corresponding to each changing flow field during the heating period of the device under the action of the changing flow field;

[0037] In step S103, determine the gradient of the objective function according to the adjoint variables corresponding to the multiple moments;

[0038] In step S104, optimize the coordinates of the structural field structure grid based on the gradient of the objective function to obtain the optimized shape of the device.

[0039] In some embodiments, the device to be optimized can be an aircraft or a vehicle.

[0040] The vehicle can be a vehicle or a ship. During the movement of the aircraft and the vehicle, friction will occur with the flowing medium. Under the influence of the oncoming flow condition, the local temperature of the device will rise, which may affect the stability of the device. For ease of understanding, the device to be optimized is determined as an aircraft in the following embodiments.

[0041] In some embodiments, the method of the present disclosure can also optimize the shape of the local components of the device to be optimized.

[0042] By optimizing the shape of the local components of the device to be optimized, the shape optimization of the entire device to be optimized can be achieved.

[0043] In some embodiments, the initial shape and the structural field structural grid of the device to be optimized are determined.

[0044] The initial shape of the part to be optimized in the device to be optimized can be obtained first. Since the specific structure of the device to be optimized is known, the initial shape of the part to be optimized is also known.

[0045] The performance parameters of the device to be optimized can be obtained, and the oncoming flow condition of the device to be optimized can be determined according to the performance parameters. When the oncoming flow condition of the device to be optimized is known, the oncoming flow condition of the part to be optimized is also known.

[0046] Furthermore, the structural grid required for the aerothermal environment calculation can be drawn according to the initial shape of the part to be optimized, the oncoming flow condition of the part to be optimized, and the heating duration. Specifically, it can include the structural grid of the flow field of the device to be optimized and the structural grid of the structural field of the device to be optimized.

[0047] The above structural grid needs to have good wall grid orthogonality, uniform and smooth transition, the wall normal growth ratio controlled within 1.1, and the distance of the first layer of the wall meets the requirement that the grid Reynolds number is less than 8 (the grid Reynolds number is equal to the product of the wall distance and the Reynolds number per meter of the oncoming flow). Among them, the Reynolds number per meter of the oncoming flow is determined by the oncoming flow condition.

[0048] In some embodiments, the automatic surface grid and automatic volume grid functions of the device to be optimized can be utilized by using Pointwise software; a structural grid can be established around the device, and the generation of the grid can be controlled by setting parameters such as the maximum aspect ratio, mapping options, and subdivision. In another embodiment, ANSYS ICEM CFD software generates the structural grid. Those skilled in the art can select the structural grid drawing method according to the specific scenario, and the corresponding solution falls within the protection scope of the present disclosure.

[0049] See Figure 2, a flow field structure grid 23 is successively drawn outward on the outer surface 22 of the device 21 to be optimized, and the density of the flow field structure grid 23 increases as the distance from the outer surface 22 increases.

[0050] See Figure 3 , a structural field structure grid 34 is drawn between the outer surface 32 and the inner surface (inner wall) 33 of the device 31 to be optimized, and the structural field structure grid 34 can be used to characterize the shape of the device 31 to be optimized.

[0051] It can be achieved by Figure 3 changing the coordinates of the structural field structure grid 34 shown. Adjusting the position of the structural field structure grid 34 is equivalent to adjusting the shape of the device to be optimized, that is, adjusting the shape of the device 31 to be optimized. Thus, the optimization result of the shape of the device to be optimized can be simulated.

[0052] In some embodiments, adjoint variables corresponding to multiple moments are determined; wherein, the multiple moments are the moments corresponding to each changing flow field during the heating period of the device under the action of the changing flow field.

[0053] The adjoint variables are related to the flow field. The adjoint variables can be determined by solving the adjoint equation, and different flow fields correspond to different adjoint variables. Since the aerodynamic heat optimized in the present disclosure is the accumulated amount of heat within a certain time period, during the heating period of this certain time period, the flow field may change due to various factors, and each different flow field corresponds to a different adjoint variable.

[0054] In the embodiments of the present disclosure, it is necessary to separately determine the adjoint variables corresponding to each flow field during the heating period.

[0055] In some embodiments, the objective function gradient is determined according to the adjoint variables corresponding to the multiple moments.

[0056] The objective function gradient is used to characterize the change gradient of the objective function value. Based on this objective function gradient, it is convenient to determine the variable that causes a large change in the objective function value, and this variable is the basis for the change of the position coordinates of the structural field structure grid.

[0057] Specifically, how to determine the adjoint variables and how to determine the objective function gradient will be described in detail in the following text of the present disclosure, and will not be elaborated here.

[0058] In some embodiments, based on the objective function gradient, the coordinates of the structural field structure grid are optimized to obtain the optimized shape of the device.

[0059] Based on the determined objective function gradient, the coordinates of the structural field structure grid can be optimized. For example, it can be Figure 3Modify the coordinates of the edge grid of the structural field structural grid 34 shown in the figure, so as to optimize the outer surface 32 and / or the inner surface 33 of the device, obtain the optimized structural field structural grid of the device, and then determine the optimized shape of the device.

[0060] In some embodiments, the method of the present disclosure can be executed multiple times to optimize the shape of the device to be optimized multiple times.

[0061] The device aerodynamic heat optimization method proposed by the present disclosure can be used to optimize for the cumulative amount with time integration effect as the target, such as the structural field temperature. The steady optimization method for a single flow field in the related art cannot be effectively used in aerodynamic heat optimization.

[0062] The present disclosure can determine the adjoint variables corresponding to each changing flow field during the entire heating period, and determine the objective function gradient based on the multiple adjoint variables, so as to optimize the structural field structural grid. The multiple adjoint variables determined in the present disclosure can respectively reflect the characteristics of the flow field within a small period of time during the heating period, and further can reflect the accumulated amount of the optimization target in time under the cumulative effect of multiple changing flow fields of the device. Therefore, the method of the present disclosure can optimize for time-accumulated amounts such as aerodynamic heat.

[0063] The following is a more detailed description of this method.

[0064] In some embodiments, a control box required for grid deformation of the structural field structural grid can be obtained. The interior of the control box contains grid boundary points to be optimized, and the grid boundary points form the physical surface of the device to be optimized.

[0065] It can be understood that the drawing method of the control box can be the same as that of the structural grid. The difference is that the control box contains grid boundary points to be optimized. And the size of the control box is larger than that of the structural grid, so as to reduce the calculation amount while ensuring the smoothness of the optimized physical surface.

[0066] See Figure 4 , the interior of the control box 41 includes grid boundary points 42 to be optimized. The grid boundary points 42 to be optimized can be the outer surface 32 of the device to be optimized (as shown in the figure), or the inner surface 33 of the device to be optimized (not shown in the figure). It can be understood that when adjusting the position of the control box 41, the position of the grid boundary points 42 inside the control box can be adjusted synchronously; in other words, the adjustment of the position of the grid boundary points 42 is equivalent to adjusting the shape of the outer surface 32 (and / or the inner surface 31) of the device to be optimized, that is, adjusting the shape of the device to be optimized 31.

[0067] In some embodiments, determining the adjoint variables corresponding to multiple moments includes: constructing adjoint equations for the flow fields corresponding to each of the moments to determine an adjoint equation set; based on the adjoint equation set, recursively solving the solutions of the adjoint equations for the flow fields at each of the moments in reverse chronological order to determine the adjoint variables corresponding to the multiple moments.

[0068] After determining the adjoint equation set, the Block-LUSGS iterative method can be used to recursively solve the adjoint equation set in reverse chronological order from the back to the front along the time sequence, thereby determining the adjoint variables of the flow field at each moment.

[0069] In some embodiments, the multiple moments are n moments, and the adjoint equation corresponding to the i-th moment among the n moments is used to characterize the correlation relationship of the right-hand vector at the i-th moment, the adjoint variable at the i-th moment, the first coefficient matrix at the i-th moment, the adjoint variable at the (i + 1)-th moment, and the second coefficient matrix at the i-th moment; where i is greater than 0 and less than or equal to n.

[0070] The adjoint variables corresponding to N moments can be denoted as Λ 1 、Λ 2 、……Λ n 。

[0071] The adjoint equation corresponding to the i-th moment is used to characterize the correlation relationship of the right-hand vector at the i-th moment, the adjoint variable at the i-th moment, the first coefficient matrix at the i-th moment, the adjoint variable at the (i + 1)-th moment, and the second coefficient matrix at the i-th moment.

[0072] Among them, the right-hand vector is used to represent the partial derivative (dependency) of the objective function value F with respect to the flow field variables and the structural field temperature variable W corresponding to the i-th moment;

[0073] The first coefficient matrix corresponding to the i-th moment is used to represent the partial derivative (dependency) of the residual R of the fluid-structure coupling field solution control equation corresponding to the i-th moment with respect to the flow field variables and the structural field temperature variable W;

[0074] The second coefficient matrix corresponding to the (i + 1)-th moment is used to represent the partial derivative (dependency) of the residual R of the fluid-structure coupling field solution control equation corresponding to the (i + 1)-th moment with respect to the flow field variables and the structural field temperature variable W corresponding to the i-th moment.

[0075] For example, the expression (1) of the established adjoint equation set can be as follows:

[0076]

[0077] Among them, R iDenote the residual of the fluid-structure coupling field solution control equation (i.e., the Navier-Stokes equations) at the \(i\)-th moment. The \(i\)-th moment is the information interaction moment between two adjacent flow fields and structure fields in chronological order during the heating period, \(R\). 1 is the information interaction moment between the first flow field and the structure field, \(R\). 2 is the information interaction moment between the first flow field, the second flow field and the structure field, and so on.

[0078] W i Denote the flow field variables and the structure field temperature variables at the \(i\)-th moment.

[0079] Λ i Denote the adjoint variable at the \(i\)-th moment. (\(Λ\) i ) T Denote the transpose of the adjoint variable.

[0080] In the above adjoint equation set, Denote the right-hand vector group, where \(F\) is the objective function value, which is related to the optimization objective and will be introduced in detail later.

[0081] Denote the first coefficient matrix group obtained by explicitly calculating using automatic differentiation, Denote the second coefficient matrix group. It should be noted that there are \(n\) first coefficient matrix groups, corresponding to \(n\) moments, while there are \(n - 1\) second coefficient matrix groups. When \(i=n\), that is, there is no second coefficient matrix at the \(n\)-th moment.

[0082] Based on this adjoint equation set, it can be shown that during the entire heating period, the influence of multiple changing flow fields on the objective function value \(F\) of the structure field at different stages can be reflected. This adjoint equation set can be solved by using the Block-LUSGS iterative method, recursively in reverse chronological order from the end, that is, first determine the adjoint variable \(Λ\) n , and then determine the adjoint variable \(Λ\) n-1 , until all adjoint variables are determined.

[0083] In the adjoint equation set, the objective function value \(F\) is required. Now, the objective function value \(F\) will be introduced in detail.

[0084] In some embodiments, the right-hand vector at the \(i\)-th moment is the partial derivative of the objective function value \(F\) with respect to the flow field variables and the structure field temperature variables \(W\) at the \(i\)-th moment, where: the objective function value \(F\) is used to characterize the optimization degree of the device shape in the current optimization scenario; the objective function value is a value determined according to the objective function; the objective function is determined based on the following two parameters: the ratio of the optimized optimization parameter to the pre-optimized optimization parameter, and the ratio of the optimized constraint parameter to the pre-optimized constraint parameter.

[0085] The optimization parameters can be determined according to the optimization objectives in the current optimization scenario. For example, it can be temperature.

[0086] Optimizing the external shape (structural field grid) of the device will affect the optimization parameters, causing the optimized optimization parameters to change. Therefore, based on the ratio of the optimized optimization parameters to the pre-optimization parameters, the optimization degree in the current optimization scenario can be characterized, and the optimization degree is numerically represented.

[0087] During the optimization process, there are certain limiting conditions, which will affect the limiting parameters. For example, the limiting condition can be the volume of the device or the degree of deformation of the device. Without exceeding the limiting conditions, the ratio of the optimized limiting parameters to the pre-optimization limiting parameters is within the preset range; if the limiting conditions are exceeded (such as a large change in the device volume after optimization), the ratio of the optimized limiting parameters to the pre-optimization limiting parameters will exceed the preset range. Technicians can determine whether the current optimization exceeds the limiting conditions by observing the objective function value based on the preset range.

[0088] For example, a weight can be added to the optimized limiting parameters and the pre-optimization limiting parameters, so that when the limiting conditions are exceeded, the objective function will significantly exceed the reasonable range.

[0089] In some embodiments, the limiting parameters include the volume of the structural field; the optimization parameters include at least one of the following: when the optimization objective in the current optimization scenario is the highest temperature during the heating process of the structural field, the optimization parameter is the highest temperature of the structural field during the entire heating period; when the optimization objective in the current optimization scenario is the maximum temperature gradient during the heating process of the structural field, the optimization parameter is the temperature of the structural field at the position corresponding to the maximum temperature gradient when the maximum temperature gradient appears during the entire heating period; when the optimization objective in the current optimization scenario is the highest temperature of the inner wall surface during the heating process of the structural field, the optimization parameter is the highest temperature of the inner wall surface of the structural field during the entire heating period.

[0090] The limiting parameters can include the volume of the structural field, that is, the increase in the structural field volume does not exceed a preset value.

[0091] The optimization parameters are related to the specific optimization scenario and optimization objective. The corresponding optimization objective can be set for the optimization scenario according to the requirements of the optimization scenario, and then the optimization parameters can be determined.

[0092] For example, when the current optimization scenario is to prevent the device from being burned due to excessive local temperature, and the optimization objective is to reduce the highest temperature during the entire heating process of the structural field, the optimization parameter can be the highest temperature of the structural field during the entire heating period The formula of the objective function is as follows:

[0093]

[0094] Among them, is the highest temperature of the optimized structural field during the entire heating process, is the highest temperature of the initial external shape structural field during the entire heating process, t is the moment when the highest temperature appears during the entire heating process, that is, the serial number of the structural grid at the position where the highest temperature appears corresponding to the entire temperature rise process; V is the volume of the optimized structural field, V′ is the volume of the initial structural field, and a is the proportion restricting the increase in the volume of the structural field during the optimization process.

[0095] It should be noted that the numerical parts in the above formula can be adjusted accordingly according to the actual application situation. For example, The 10 multiplied before the parameter and the minuend 1 can be used to represent that the goal of this optimization is to reduce the highest temperature to 10% (i.e., 0.1) of the original highest temperature. When the highest temperature is reduced to 10% of the original highest temperature, the first part of the objective function value is 0. Therefore, the closer the objective function value is to 0, the closer this optimization is to the optimization goal, and the better the optimization effect.

[0096] In the second part of the above formula, the optimized volume is multiplied by a. Among them, the value of a is the proportion restricting the volume increase, and its role is similar to that of the 10 multiplied before the parameter in the first part. The other parameter values such as 40.5, 100000, 1, etc. are all used to determine the limit parameter as two fixed values. For example, when the limit condition is not exceeded, the value obtained by the tanh function is close to -1, making the second part of the value 0; while when the limit condition is exceeded, the value obtained by the tanh function is close to 1, making the second part of the value 81. Thus, it is convenient to determine whether this optimization exceeds the limit condition according to the magnitude of the objective function value F.

[0097] For example, in the current optimization scenario where it is to prevent the device from being torn due to excessive thermal stress, the optimization goal is to reduce the maximum temperature gradient during the entire heating process of the structural field. Then the formula of the objective function can be as follows:

[0098]

[0099] Among them:

[0100]

[0101] Among them, is the temperature of the structural field at the position corresponding to the maximum temperature gradient at the moment when the maximum temperature gradient appears during the entire heating process of the initial external shape structural field, To optimize the structural field temperature at the position corresponding to the maximum temperature gradient at the moment of the maximum temperature gradient during the entire heating process of the optimized outer shape structure field, t is the moment corresponding to the highest temperature gradient during the entire temperature rise process, I and J are the structural grid numbers corresponding to the position of the highest temperature gradient during the entire temperature rise process, and X and Y respectively represent two directions in the two-dimensional space; V is the volume of the optimized structural field, V′ is the volume of the initial structural field, and a is the ratio restricting the increase in the volume of the structural field during the optimization process.

[0102] Among them, the explanations and functions of some parameters in the above objective function can be referred to the explanations of some parameters in the previous objective function.

[0103] For example, when the current optimization scenario is to prevent the instruments carried on the equipment from being damaged due to excessive temperature in the equipment cabin, and the optimization goal is the highest temperature on the inner wall surface during the structural field heating process, the formula of the objective function can be as follows:

[0104]

[0105] Among them, is the highest temperature on the inner wall surface of the initial outer shape structure field during the entire heating process, is the highest temperature on the inner wall surface of the optimized outer shape structure field during the entire heating process, t is the moment corresponding to the highest temperature on the inner wall surface during the entire temperature rise process, I and J are the numbers of the structural grids at the position corresponding to the highest temperature on the inner wall surface during the entire temperature rise process; V is the volume of the optimized structural field, V′ is the volume of the initial structural field, and a is the ratio restricting the increase in the volume of the structural field during the optimization process.

[0106] In some embodiments, an optimization termination condition can be set to determine whether the optimization terminates.

[0107] When the objective function value of the candidate outer shape meets the optimization termination condition, it can be determined that the candidate outer shape is the final optimized outer shape of the equipment.

[0108] Specifically, the difference between the objective function value of the current optimized outer shape and the objective function value of the previous round of optimized outer shape can be determined. If this difference is less than or equal to 1% of the objective function value of the initial outer shape, it can be determined to terminate the optimization.

[0109] In some scenarios, it may not be possible to optimize the outer shape of the equipment to achieve the set optimization goal. Therefore, after each optimization, the difference between the objective function value of this optimization and the previous optimization can be determined. This difference in the objective function value can represent the optimization effect of this optimization. When this difference is less than the preset threshold, it indicates that the optimization effect of this optimization is small. If optimization continues, more optimization times may be required to achieve the optimization goal. Considering various factors such as cost, it can be determined to terminate the optimization.

[0110] In some embodiments, the optimization objective can be determined according to the unsteady coupling effect.

[0111] Specifically, a hypersonic aerodynamic heat and / or heat transfer coupling simulation program can be used to solve the unsteady coupling process of the hypersonic flow field and the structural temperature field, obtain the entire temperature rise data of the structural field over time, and then the optimization objective under different optimization scenarios can be calculated.

[0112] In some embodiments, determining the gradient of the objective function according to the adjoint variables corresponding to the multiple moments includes: determining the gradient of the objective function according to the partial derivative of the objective function value (F) with respect to the grid of the fluid-structure coupling field, the partial derivative of the residual (R) of the fluid-structure coupling field solution control equation corresponding to each moment with respect to the grid of the fluid-structure coupling field, the derivative of the grid of the fluid-structure coupling field with respect to the variables of the structural grid of the structural field, and the adjoint variable corresponding to each moment.

[0113] The gradient of the objective function can be calculated by using the objective function gradient calculation formula and the determined adjoint variables. For example, the objective function gradient calculation formula can be:

[0114]

[0115] Where:

[0116]

[0117] In the above formula, X represents the grid of the fluid-structure coupling field, β k represents the kth design variable, Δβ k represents the small change amount of the kth design variable, and specifically can be taken as 10 -5 .

[0118] In some embodiments, optimizing the coordinates of the structural grid of the structural field based on the gradient of the objective function includes: calculating the optimization direction of the structural grid of the structural field according to the gradient of the objective function and the steepest descent method; calculating the optimization step size of the structural grid of the structural field according to the golden section method; and optimizing the coordinates of the structural grid of the structural field according to the optimization direction and the optimization step size.

[0119] The gradient of the objective function determined by the above calculation can be used as an input condition, and the optimization direction of the structural grid of the structural field can be calculated by using the steepest descent method.

[0120] The optimization step size can be calculated by using the golden section method.

[0121] Based on the determined optimization direction and optimization step size, the design variables are optimized and updated, that is, the coordinates of the structural grid of the structural field are optimized and updated.

[0122] In some embodiments, the optimization and update of the coordinates of the structural field structure grid can be achieved by optimizing the coordinates of the control box.

[0123] According to the updated coordinates of the control box, the FFD method (Free Form Deformation) can be used to complete the deformation of the device's outer shape and the corresponding computational grids of the flow field and structural field.

[0124] In some embodiments, a hypersonic aerothermal and / or thermal coupling simulation program can be used to simulate and solve the fluid-structure coupling field of the new outer shape, so as to obtain the entire temperature rise data of the structural field of the new outer shape over time, thereby further determining the optimization objectives of the design under the current optimization scenario.

[0125] In some embodiments, it is determined whether the optimized objective function value satisfies the optimization termination condition. If the optimization termination condition is satisfied, the optimization terminates.

[0126] If the optimization termination condition is not satisfied, the optimization method proposed in this disclosure can be repeatedly implemented until the objective function value of the new outer shape satisfies the optimization termination condition.

[0127] This disclosure can establish a coupled adjoint method to obtain the gradient of the objective function based on the temperature of the device's structural field in the case of a large number of design variables, and use the gradient of the objective function to optimize the outer shape of the heat protection structure of the device. Since the influence of the time scale is considered in the optimization process and the optimization objective is directly the temperature of the structural field, the optimization model proposed in this disclosure is closer to the real situation, and the optimization effect can be significantly improved.

[0128] The following further illustrates the method of this disclosure through a specific example.

[0129] The device to be optimized is an aircraft.

[0130] Based on the solution of the above embodiments, the computational process for the aerothermal and / or heat transfer coupled adjoint optimization of the spherical head shape of an aircraft can be as Figure 5 shown.

[0131] Among them, the outer radius of the spherical head shape is 10 mm, the inner radius is 7.5 mm, the structural material of the aircraft is stainless steel, the flight condition is 40 km, Mach number 5, angle of attack 0, laminar flow, the heating time is 4.6 seconds, and the initial temperature of the structural field is 300 K.

[0132] Step 1, the computational grid of the flow field thermal environment drawn according to the spherical head shape size and flight condition is as Figure 2 shown, and the computational grid of the structural temperature field drawn according to the size of the spherical head structure is asFigure 3 as shown

[0133] Step 2: Set the optimization objective as reducing the highest temperature during the entire heating process of the structural field, and the constraint condition as the volume increase not exceeding 20% of the volume of the original outer shape structural field. The corresponding objective function is:

[0134]

[0135] Step 3: Set the optimization termination condition as the difference between the current optimized outer shape objective function value and the previous round's optimized outer shape objective function value being less than or equal to 1% of the original outer shape objective function value.

[0136] Step 4: Draw the deformation control box required for the optimization of the ball head outer shape. It is a 2×10 control box. The boundary points of the object surface inside the control box are the boundary points that need to be deformed. The coordinates of the control box are the optimized design variables. The control box is as Figure 4 as shown

[0137] Step 5: Use the hypersonic aerodynamic heat and / or heat transfer coupling simulation program to solve the coupling process of the flow field and / or structural field of the original outer shape, calculate the highest temperature during the entire heating process of the structural field, and substitute it into the objective function calculation formula to calculate the objective function value of the original outer shape. The original outer shape flow field density cloud map at the end of 4.6 seconds obtained by the coupling solution is as Figure 6 as shown, and the structural field temperature cloud map is as Figure 7 as shown. In this embodiment, the highest temperature during the entire heating process of the original outer shape structural field is 425.2K.

[0138] Step 6: Use automatic differentiation to find the first coefficient matrix group where i ranges from 1 to n; the second coefficient matrix group where i ranges from 1 to n - 1; and the right-hand vector group where i ranges from 1 to n, and then establish the adjoint equations.

[0139] Step 7: Use the Block-LUSGS iterative method to sequentially solve the adjoint equations in the previous step from back to front in time, and sequentially obtain the adjoint variables Λ i where i ranges from 1 to n.

[0140] Step 8: Calculate the objective function gradient using the objective function gradient calculation formula. The distribution cloud map of the projection of the objective function gradient in the X direction is as Figure 8 as shown, and the distribution cloud map of the projection in the Y direction is as Figure 9 as shown.

[0141] Step 9: Use the steepest descent method to calculate the optimization direction, and the optimization direction is the opposite direction of the objective function gradient direction.

[0142] Step ten, use the golden section method to calculate the optimized step size.

[0143] Step eleven, update the coordinates of the control box according to the optimization direction and the optimized step size.

[0144] Step twelve, according to the latest coordinates of the control box, use the FFD method to complete the deformation of the structural grid of the aircraft shape and the corresponding flow field and structural field.

[0145] Step thirteen, use the hypersonic aerodynamic heat and / or heat transfer coupling simulation program to solve the coupling process of the flow field and / or structural field of the optimized shape, calculate the highest temperature during the entire heating process of the structural field of the optimized shape, and substitute it into the objective function calculation formula to calculate the objective function value of the optimized shape.

[0146] Step fourteen, perform an optimization termination judgment according to the objective function value of the new shape. If the optimization termination condition is not yet satisfied, repeat steps five to thirteen until the optimized shape or the objective function value satisfies the optimization termination condition. In this embodiment, the optimization termination condition is satisfied after the seventh round of optimization.

[0147] Step fifteen, through seven rounds of optimization, the structural grid for calculating the flow field thermal environment of the final optimized shape is as Figure 10 shown, and the structural grid for calculating the structural temperature field is as Figure 11 shown. Through seven rounds of optimization, the density cloud map of the flow field of the optimized shape at the end of 4.6 seconds obtained by coupling solution of the final optimized shape is as Figure 12 shown, the structural temperature field is as Figure 13 shown, and the highest temperature during the entire heating process of the structural field of the final optimized shape is 405.9K.

[0148] Figure 14 shows the comparison of the wall heat flux at the initial moment between the original shape and the final optimized shape. Although the highest heat flux of the optimized shape has increased, the highest temperature of the structural field during the entire heating process has decreased from 425.2K of the original shape to 405.9K of the optimized shape, and the maximum temperature rise has decreased from 125.2K to 105.9K, with a 15.4% decrease in the temperature rise.

[0149] Corresponding to the embodiment of the device aerodynamic heat optimization method of the present disclosure, the present disclosure also provides an embodiment of the corresponding device aerodynamic heat optimization device.

[0150] Please refer to Figure 15 , Figure 15 which is a block diagram of the device aerodynamic heat optimization device in an embodiment of the present disclosure. As Figure 15 shown, the device aerodynamic heat optimization device includes:

[0151] A structural grid determination unit 1510, configured to determine the initial shape of the device to be optimized and the structural grid of the structural field;

[0152] An adjoint variable determination unit 1520, configured to determine adjoint variables corresponding to multiple moments; wherein, the multiple moments are moments corresponding to each changing flow field during the temperature rise period of the device under the action of the changing flow field;

[0153] A function gradient determination unit 1530, configured to determine an objective function gradient according to the adjoint variables corresponding to the multiple moments;

[0154] A structural grid optimization unit 1540, configured to optimize the coordinates of the structural grid of the structural field based on the objective function gradient to obtain the optimized shape of the device.

[0155] In some embodiments, determining the adjoint variables corresponding to multiple moments includes: constructing adjoint equations for the flow fields corresponding to each of the moments to determine an adjoint equation set; based on the adjoint equation set, recursively solving the solutions of the adjoint equations for the flow fields at each moment in reverse time order to determine the adjoint variables corresponding to the multiple moments.

[0156] In some embodiments, the multiple moments are n moments, and the adjoint equation corresponding to the i-th moment among the n moments is used to characterize the correlation relationship between the right-hand vector at the i-th moment, the adjoint variable at the i-th moment, the first coefficient matrix at the i-th moment, the adjoint variable at the (i + 1)-th moment, and the second coefficient matrix at the i-th moment; wherein, i is greater than 0 and less than or equal to n.

[0157] In some embodiments, the right-hand vector at the i-th moment is the partial derivative of the objective function value with respect to the flow field variables and the structural field temperature variables at the i-th moment, where: the objective function value is used to characterize the optimization degree of the device shape in the current optimization scenario; the objective function value is a value determined according to the objective function; the objective function is determined based on the following two parameters: the ratio of the optimized optimization parameter to the pre-optimization parameter, and the ratio of the optimized constraint parameter to the pre-optimization constraint parameter.

[0158] In some embodiments, the constraint parameter includes the volume of the structural field; the optimization parameter includes at least one of the following: when the optimization objective in the current optimization scenario is the highest temperature during the structural field temperature rise process, the optimization parameter is the highest temperature of the structural field during the entire temperature rise period; when the optimization objective in the current optimization scenario is the maximum temperature gradient during the structural field temperature rise process, the optimization parameter is the structural field temperature at the position of the maximum temperature gradient when the maximum temperature gradient appears during the entire temperature rise period of the structural field; when the optimization objective in the current optimization scenario is the highest temperature of the inner wall surface during the structural field temperature rise process, the optimization parameter is the highest temperature of the inner wall surface of the structural field during the entire temperature rise period.

[0159] In some embodiments, determining the gradient of the objective function based on the adjoint variables corresponding to the multiple moments includes: determining the gradient of the objective function based on the partial derivative of the objective function value (F) with respect to the mesh of the fluid-structure coupling field, the partial derivative of the residual (R) of the fluid-structure coupling field solution control equation corresponding to each moment with respect to the mesh of the fluid-structure coupling field, the derivative of the mesh of the fluid-structure coupling field with respect to the variables of the structural mesh of the structure field, and the adjoint variable corresponding to each moment.

[0160] In some embodiments, optimizing the coordinates of the structural mesh of the structure field based on the gradient of the objective function includes: calculating the optimization direction of the structural mesh of the structure field according to the gradient of the objective function and the steepest descent method; calculating the optimization step size of the structural mesh of the structure field according to the golden section method; and optimizing the coordinates of the structural mesh of the structure field according to the optimization direction and the optimization step size.

[0161] The implementation processes of the functions and roles of each unit in the above device are specifically described in detail in the implementation processes of the corresponding steps in the above method, and will not be elaborated here.

[0162] An embodiment of the present disclosure also proposes an electronic device, including: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the device aerodynamic heat optimization method according to any one of the above embodiments by calling the computer program.

[0163] An embodiment of the present disclosure also proposes a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the device aerodynamic heat optimization method according to any one of the above embodiments.

[0164] An embodiment of the present disclosure also proposes a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method described in any one of the above embodiments.

[0165] Figure 16 FIG. 19 is a schematic block diagram of a device 1600 for device aerodynamic heat optimization according to an embodiment of the present disclosure. For example, device 1600 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0166] Refer to Figure 16 , device 1600 may include one or more of the following components: processing component 1602, memory 1604, power supply component 1606, multimedia component 1608, audio component 1610, input / output (I / O) interface 1612, sensor component 1614, and communication component 1616.

[0167] The processing component 1602 generally controls the overall operation of the device 1600, such as operations associated with display, telephone calls, data communication, camera operations, and recording operations. The processing component 1602 may include one or more processors 1620 to execute instructions to complete all or part of the steps of the above-described device aerodynamic heat optimization method. In addition, the processing component 1602 may include one or more modules to facilitate the interaction between the processing component 1602 and other components. For example, the processing component 1602 may include a multimedia module to facilitate the interaction between the multimedia component 1608 and the processing component 1602.

[0168] The memory 1604 is configured to store various types of data to support the operation of the device 1600. Examples of such data include instructions for any application or method operating on the device 1600, contact data, phone book data, messages, pictures, videos, etc. The memory 1604 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0169] The power component 1606 provides power to various components of the device 1600. The power component 1606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 1600.

[0170] The multimedia component 1608 includes a screen that provides an output interface between the device 1600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may not only sense the boundaries of the touch or swipe actions, but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 1608 includes a front camera and / or a rear camera. When the device 1600 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each of the front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0171] The audio component 1610 is configured to output and / or input audio signals. For example, the audio component 1610 includes a microphone (MIC) that is configured to receive external audio signals when the device 1600 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 1604 or transmitted via the communication component 1616. In some embodiments, the audio component 1610 further includes a speaker for outputting audio signals.

[0172] The I / O interface 1612 provides an interface between the processing component 1602 and a peripheral interface module, and the peripheral interface module can be a keyboard, a click wheel, buttons, etc. These buttons can include but are not limited to: a home button, a volume button, a power button, and a lock button.

[0173] The sensor component 1614 includes one or more sensors for providing an assessment of the state of the device 1600 in various aspects. For example, the sensor component 1614 can detect the on / off state of the device 1600, the relative positioning of components, such as the display and keypad of the device 1600. The sensor component 1614 can also detect a change in the position of the device 1600 or a component of the device 1600, the presence or absence of user contact with the device 1600, the orientation or acceleration / deceleration of the device 1600, and the temperature change of the device 1600. The sensor component 1614 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 1614 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 1614 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0174] The communication component 1616 is configured to facilitate communication between the device 1600 and other devices in a wired or wireless manner. The device 1600 can access a wireless network based on communication standards, such as WiFi, 2G, 3G, 4G LTE, 5G NR, or a combination thereof. In an exemplary embodiment, the communication component 1616 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1616 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0175] In an exemplary embodiment, the apparatus 1600 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-mentioned method for optimizing the aerodynamic heat of the device.

[0176] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1604 including instructions, and the above instructions can be executed by a processor 1620 of the apparatus 1600 to complete the above-mentioned method for optimizing the aerodynamic heat of the device. For example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0177] Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the specification and practicing the disclosure herein. The present disclosure aims to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0178] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

[0179] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0180] The above has introduced the methods and devices provided by the embodiments of the present disclosure in detail. Specific examples are used in this article to elaborate on the principles and implementation manners of the present disclosure. The description of the above embodiments is only used to help understand the method of the present disclosure and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present disclosure, there will be changes in the specific implementation manners and application scope. In summary, the content of this specification should not be construed as a limitation to the present disclosure.

Claims

1. A method for aerodynamic thermal optimization of equipment, characterized in that: The method comprises: Determine the initial shape of the equipment to be optimized and the structural field structure grid; Determining accompanying variables corresponding to a plurality of moments; wherein the plurality of moments are moments corresponding to each changing flow field during a heating period of the device under the action of a changing flow field; Determining the objective function gradient according to the accompanying variables corresponding to the multiple moments; The coordinates of the structure field structure grid are optimized based on the objective function gradient to obtain the optimized shape of the device.

2. The method according to claim 1, characterized in that The determining of the accompanying variables corresponding to the multiple moments includes: Constructing adjoint equations corresponding to the flow field at each of the moments to determine an adjoint equation group; Based on the adjoint equation group, the solution of the adjoint equation of the flow field at each of the moments is recursively solved in reverse chronological order to determine the adjoint variables corresponding to the multiple moments.

3. The method according to claim 2, characterized in that The multiple moments are n moments, and the adjoint equation corresponding to the i-th moment among the n moments is used to characterize the relationship between the right-hand side vector of the i-th moment, the adjoint variable of the i-th moment, the first coefficient matrix of the i-th moment, the adjoint variable of the i+1-th moment, and the second coefficient matrix of the i-th moment; wherein, i is greater than 0 and less than or equal to n.

4. The method according to claim 3, characterized in that The right-hand vector at the i-th moment is the partial derivative of the objective function value and the flow field variables and structural field temperature variables at the i-th moment, where: The objective function value is used to represent the degree of optimization of the shape of the device in the current optimization scenario; The objective function value is a value determined according to the objective function; The objective function is determined based on the following two parameters: The ratio of the optimized parameters after optimization to the optimized parameters before optimization, as well as the ratio of the restricted parameters after optimization to the restricted parameters before optimization.

5. The method according to claim 4, characterized in that The limiting parameters include the volume of the structural field; The optimization parameters include at least one of the following: When the optimization target of the current optimization scenario is the highest temperature during the heating process of the structure field, the optimization parameter is the highest temperature of the structure field during the entire heating period; When the optimization target of the current optimization scenario is the maximum temperature gradient during the heating process of the structure field, the optimization parameter is the maximum temperature gradient of the structure field during the entire heating period; When the optimization target of the current optimization scenario is the maximum temperature of the inner wall surface during the heating process of the structure field, the optimization parameter is the maximum temperature of the inner wall surface of the structure field during the entire heating period.

6. The method according to claim 4, characterized in that The determining the objective function gradient according to the accompanying variables corresponding to the multiple moments includes: The objective function gradient is determined based on the partial derivative of the objective function value with respect to the grid of the fluid-structure coupling field, the partial derivative of the residual of the control equation solved for the fluid-structure coupling field at each moment with respect to the grid of the fluid-structure coupling field, the derivative of the grid of the fluid-structure coupling field with respect to the variable of the structural grid of the structural field, and the adjoint variable corresponding to each moment.

7. The method according to claim 1, characterized in that The optimizing the coordinates of the structure grid of the structure field based on the objective function gradient includes: Calculating the optimization direction of the structural grid of the structural field according to the objective function gradient and the steepest descent method; Calculating the optimized step length of the structural grid of the structural field according to the golden section method; The coordinates of the structure grid of the structure field are optimized according to the optimization direction and the optimization step size.

8. An equipment aerodynamic thermal optimization device, characterized in that: The device comprises: a structural grid determination unit configured to determine an initial shape of the device to be optimized and a structural field structural grid; An accompanying variable determination unit is configured to determine accompanying variables corresponding to a plurality of moments; wherein the plurality of moments are moments corresponding to each changed flow field during a heating period of the device under the action of a changed flow field; a function gradient determining unit, configured to determine the objective function gradient according to the accompanying variables corresponding to the multiple moments; The structural grid optimization unit is configured to optimize the coordinates of the structural grid of the structural field based on the objective function gradient to obtain the optimized shape of the device.

9. An electronic device, characterized in that: include: Processor, memory; The memory is used to store computer programs; The processor is configured to execute the equipment aerodynamic thermal optimization method according to any one of claims 1 to 7 by calling the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the equipment aerodynamic thermal optimization method according to any one of claims 1 to 7 is implemented.

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