A vehicle aerodynamic optimization method, system and electronic device

Abstract

The present invention provides a vehicle aerodynamic optimization method, system and electronic device, including step 1: based on the vehicle three-dimensional CAD model, conduct vehicle aerodynamic simulation analysis; step 2: import the vehicle aerodynamic analysis results into the aerodynamic sensitivity analysis software to obtain the external aerodynamic sensitivity distribution information; step 3: with reference to the sensitivity magnitude, use the IDW method to perform grid deformation and complete the three-dimensional grid update; step 4: verify the optimization effect. By considering the characteristics of the vehicle shape's sensitivity to aerodynamic resistance, the present invention selects high-sensitivity regions for deformation, uses the IDW algorithm to achieve corresponding grid deformation, and tries to ensure the quality of the deformed grid, especially the boundary layer grid quality in the near-wall region. The present invention can expand the scope of the vehicle aerodynamic optimization region and significantly improve the optimization efficiency at the same time.

Description

Technical Field

[0001] The present invention relates to the field of automotive aerodynamic shape optimization, and particularly to a vehicle aerodynamic optimization technology based on sensitivity information and mesh deformation. Background Art

[0002] The aerodynamic performance of an automobile directly affects its energy consumption performance, and is directly related to the energy consumption / emission of traditional fuel vehicles and the driving range of electric vehicles. Reducing aerodynamic drag through shape optimization is an important part of vehicle development. Simulation technology is an important means of shape optimization. Optimizing the aerodynamic shape through simulation methods usually includes the following steps: selecting parameters - adjusting the shape - deforming the mesh - simulating and verifying. Among them, the adjustment of the shape and the mesh deformation technology have a significant impact on the effect and efficiency of optimization. How to adjust the shape directly affects whether the optimization scheme is effective; if the mesh deformation technology cannot ensure the quality of the deformed mesh, it will lead to poor accuracy or even computational divergence in the simulation verification, resulting in the failure of the simulation verification and the failure of this round of optimization.

[0003] Due to the extremely complex and irregular shape of an automobile, in actual engineering applications, it is very difficult to adjust the shape and deform the mesh. Therefore, the optimization scheme usually selects areas with relatively smooth shapes based on experience. This brings two problems. One is that usually, the selection of the deformed area and the magnitude of the deformation amount both depend on the experience of engineers, and it is not certain that the aerodynamic drag can be reduced. A large number of trial calculations are required to find a truly effective optimization scheme. The other is that when deforming the mesh accordingly after adjusting the shape, the problem of deteriorated mesh quality often occurs. In severe cases, it will lead to computational divergence in the simulation verification, and manual adjustment of the mesh is required, causing the entire automated shape optimization process to collapse. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above-mentioned defects existing in the prior art, and provide a vehicle aerodynamic optimization method, system and electronic device, which consider the characteristics of the sensitivity of the vehicle shape to aerodynamic drag, select areas with high sensitivity for deformation, use the IDW algorithm to achieve the corresponding mesh deformation, and try to ensure the quality of the deformed mesh, especially the boundary layer mesh quality in the near-wall region. Through this method, the area range of vehicle aerodynamic optimization is expanded, and the optimization efficiency is significantly improved.

[0005] The technical solution of the present invention is as follows:

[0006] The present invention provides a vehicle aerodynamic optimization method in the first aspect, including the following steps:

[0007] Step 1: Vehicle aerodynamic simulation analysis

[0008] Mainly based on the vehicle three-dimensional CAD model, according to the analysis specifications, perform vehicle aerodynamic simulation analysis.

[0009] Step 2: Aerodynamic Sensitivity Analysis of the Whole Vehicle Shape

[0010] Import the aerodynamic simulation analysis results of the whole vehicle into the aerodynamic sensitivity analysis calculation program to obtain the aerodynamic sensitivity distribution information of the whole vehicle shape.

[0011] Step 3: Mesh Deformation

[0012] Taking the magnitude of the aerodynamic sensitivity of the shape as a reference, use the IDW method for mesh deformation to automatically complete the update of the 3D mesh.

[0013] Step 4: Verification of Optimization Effect

[0014] Based on the updated mesh, re - conduct the aerodynamic simulation analysis of the whole vehicle to evaluate the optimization effect. If the aerodynamic target is not achieved, return to Step 2 for iterative analysis.

[0015] The present invention provides a whole - vehicle aerodynamic optimization system in a second aspect, which includes:

[0016] Simulation Analysis Module: Based on the 3D CAD model of the whole vehicle, conduct aerodynamic simulation analysis of the whole vehicle;

[0017] Sensitivity Analysis Module: Import the aerodynamic analysis results of the whole vehicle into the aerodynamic sensitivity analysis calculation program, solve the adjoint equations by the coupling method to obtain the aerodynamic sensitivity distribution information of the shape, and conduct aerodynamic sensitivity analysis of the whole vehicle shape;

[0018] Mesh Deformation Module: Taking the magnitude of the sensitivity as a reference, use the IDW method for mesh deformation to complete the update of the 3D mesh;

[0019] Verification Module: Conduct verification of the optimization effect.

[0020] The present invention provides an electronic device in a third aspect, which includes:

[0021] One or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the electronic device to implement the whole - vehicle aerodynamic optimization method described in the above - mentioned first aspect.

[0022] The advantages of the present invention are as follows:

[0023] The present invention conducts whole - vehicle aerodynamic optimization based on sensitivity information and IDW mesh deformation. By considering the characteristics of the sensitivity of the whole - vehicle shape to aerodynamic resistance, select high - sensitivity regions for deformation, and use the IDW algorithm to achieve corresponding mesh deformation, which can better ensure the quality of the deformed mesh, especially the boundary - layer mesh quality in the near - wall region. Conducting mesh deformation based on sensitivity information can ensure that the aerodynamic coefficient of the deformed model is less than that before deformation. The entire optimization process is stable and can be driven by a script program to be automatically executed.

[0024] The present invention can expand the scope of vehicle aerodynamic optimization and significantly improve the optimization efficiency at the same time.

[0025] Other features and advantages of the present invention will be described in detail in the following specific implementation section. Brief Description of the Drawings

[0026] Figure 1 is the implementation flowchart of the present invention.

[0027] Figure 2 is the vehicle flow field and sensitivity analysis calculation domain.

[0028] Figure 3 is the sensitivity analysis result of a certain vehicle model.

[0029] Figure 4 is the schematic diagram of the vehicle aerodynamic optimization system of the present invention. Specific Embodiments

[0030] The following will illustrate the embodiments of the present application through specific examples. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0031] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0032] This embodiment will elaborate on the vehicle aerodynamic optimization method based on sensitivity information and IDW grid deformation. The overall process is as Figure 1 shown, including the following steps:

[0033] Step 1: Vehicle aerodynamic resistance simulation analysis.

[0034] First, according to the aerodynamic analysis specification, the three-dimensional CAD model of the vehicle is imported into the simulation analysis software. In this embodiment, the STAR-CCM+ software is selected. STAR-CCM+ is a new generation CFD solver developed by CD-adapco using the most advanced computational continuum mechanics algorithms.

[0035] Then, geometric cleaning is carried out. After the geometric cleaning is completed, the vehicle components are grouped and named according to their geometric characteristics.

[0036] Establish the external flow field domain of the vehicle, as Figure 2 shown. The size of the external flow field of the vehicle is designed according to the wind tunnel size at a ratio of 1:1. The length of the flow field domain is 12 times the vehicle length L, the width is 10 times the vehicle width W, and the height is 10 times the vehicle height H. The front end of the flow field domain is the velocity inlet, and the rear end is the pressure outlet. The vehicle flow field calculation domain includes all vehicle components except for accessories such as the passenger compartment seat. The volume mesh generation, turbulent model setting, and boundary condition setting are completed.

[0037] Finally, the steady-state vehicle flow field model is solved, that is, the vehicle flow field analysis is completed using a steady-state flow field solver.

[0038] Step 2: Aerodynamic sensitivity analysis of the vehicle shape.

[0039] The aerodynamic sensitivity is defined as the gradient of the aerodynamic resistance with respect to a small change in the shape. Based on existing research, the sensitivity can be described as

[0040]

[0041] where A is the area of the mesh element on the surface of the optimization object; n i is the component of the outer normal direction of the mesh element on the surface of the optimization object in the i direction; υ and υ t are the laminar and turbulent kinematic viscosity coefficients respectively; u i and v i are the components of the flow velocity and the adjoint velocity in the i direction respectively; q is the adjoint pressure; the adjoint velocity and the adjoint pressure satisfy the following equations

[0042]

[0043]

[0044] and boundary conditions:

[0045] At the wall surface of the optimization object:

[0046] u i =-1

[0047] At the inlet and the wall surface that does not need to be optimized:

[0048] u i =0

[0049]

[0050] At the outlet:

[0051]

[0052] As mentioned above, equations (2) and (3) can usually be solved by the pressure correction method represented by SIMPLE. However, this method often diverges when applied to vehicle analysis. Therefore, the present invention couples the accompanying pressure and the accompanying velocity into one equation for simultaneous solution, and rewrites equation (3) as

[0053]

[0054] Where D is the inverse of the convection coefficient in equation (2); u is the accompanying velocity; the overline in the second term indicates the interpolation of the relevant quantity from the grid center to the grid surface; the gradient of the accompanying pressure in the third term is It is obtained by the difference of the adjoint pressures in adjacent grids. The last two terms on the left side of equation (4) introduce the adjoint pressure into the adjoint continuity equation, avoiding the "saddle point problem" where some diagonal elements in the integrated linear equation system are zero. It also avoids the "checkerboard error problem" caused by the unrelatedness of the adjoint continuity equation of any unit and the adjoint pressure of this unit. This is the key to the coupled solution of the adjoint equation.

[0055] It can be seen that equations (2) and (4) are linear partial differential equations about unknown variables (i.e., the accompanying pressure q and the accompanying velocity u), which can be discretized and converted into a set of algebraic equations to be solved. In the present invention, the finite volume method is used to discretize the terms on the left side of equations (2) and (4). The basic idea of the finite volume method is to integrate the terms of the partial differential equation in the control volume (i.e., each discrete grid unit); using the Gaussian integral formula, the volume integral can be converted into the surface integral to obtain; the variable values on the grid unit surface can be obtained by interpolating the variable values in the adjacent control volume. Through the discretization process of the finite volume method, linear algebraic equations can be constructed for the accompanying pressure q and the accompanying velocity u in each control volume, and then equations (2) and (4) can be converted into a set of simultaneous linear algebraic equations in the entire calculation domain.

[0056] The linear algebraic equations obtained by the above process are usually difficult to solve using an iterative method because of the large condition number of the coefficient matrix. In the present invention, a direct method-iterative method coupling method is used to solve the linear algebraic equations. The specific method is as follows:

[0057] 1. Divide the computational domain into many sub-regions;

[0058] 2. Perform direct LU decomposition on the submatrix of the coefficient matrix corresponding to the subregion;

[0059] 3. Based on LU decomposition, construct the Schur Complement linear equations for the unknown variables at the junction of the subregions;

[0060] 4. The iterative method is used to solve the Schur Complement linear equations, and then the solutions of the entire system of linear algebraic equations are obtained.

[0061] As described above, when the new coupled solution algorithm is adopted, the discrete forms of Equation (2) and Equation (4) can be integrated into a system of linear equations for simultaneous solution, thus greatly improving the stability of the solution.

[0062] After the adjoint pressure q and the adjoint velocity u are solved, they can be substituted into Equation (1) to calculate the wind resistance sensitivity distribution.

[0063] In this embodiment, the computational domain for solving Equations (2) and (4) is the same as that in Step 1, as Figure 2 shown.

[0064] Using the above method, the sensitivity analysis results obtained are as Figure 3 shown, where the positive and negative values respectively indicate that the concave and convex deformations are more beneficial to the wind resistance, and the magnitude thereof reflects the degree to which the deformation affects the wind resistance.

[0065] Step 3: Mesh deformation.

[0066] The wind resistance sensitivity distribution obtained in the previous step gives guidance on the deformation amount of the outer surface mesh. After the deformation amount of the outer surface mesh is given, the three-dimensional volume mesh of the computational domain needs to be deformed to generate a computational model to verify the optimization effect. Compared with remeshing, mesh deformation can greatly save time; at the same time, the deformed flow field can use the flow field before deformation as the initial condition, greatly saving the simulation time of the deformed flow field.

[0067] The mesh deformation algorithm only adjusts the coordinates of the mesh points and does not change the connection relationship between the mesh points. Among the various existing mesh deformation algorithms, the IDW (Inverse Distance Weighted) algorithm can better maintain the orthogonality of the boundary layer mesh. Therefore, the IDW algorithm is adopted in the present invention.

[0068] First, based on the sensitivity analysis results in Step 2, n points (referred to as control points) are selected in the high-sensitivity area of the outer surface:

[0069]

[0070] For any mesh point within the computational domain calculate the distance between it and the control points:

[0071]

[0072] Secondly, determine the deformation amount at each control point The magnitude of the deformation is proportional to the sensitivity, and at the same time, the maximum deformation does not exceed the limit allowed in engineering. To avoid excessive differences in the deformation at adjacent control points, 1 to 2 smoothing processes may be required, that is, the deformation is taken as the algebraic average of the deformations at adjacent control points.

[0073] After determining the control points and the deformation at each control point the calculation method of the deformation of the internal grid points in the computational domain is given by the conventional IDW algorithm, which is briefly described as follows:

[0074] Calculate the local rigid rotation deformation: For control point i, the rigid rotation deformation of the points near it can be represented by the rotation matrix M i and the local deformation after rigid rotation can be made to be consistent with the deformation of the points near point i / the change in the normal of the surface element to the greatest extent. Furthermore, its contribution to the global deformation can be expressed as

[0075]

[0076] Use the inverse distance weighting method to obtain the deformation of any grid point in the computational domain:

[0077]

[0078] where

[0079]

[0080] A i is the area of the surface element at control point i; L def is the maximum distance of the grid point; a, b, α are constant parameters and can be taken as 3, 0, 0.

[0081] Step 4: Verification of the optimization effect

[0082] Based on the deformed grid obtained in Step 3, with other conditions unchanged, use the method described in Step 1 to conduct a wind resistance simulation analysis again to obtain the vehicle wind resistance coefficient and verify the optimization effect.

[0083] Example 2

[0084] The following example gives a vehicle aerodynamic optimization system. Refer to Figure 4 , this system includes a simulation analysis module, a sensitivity analysis module, a grid deformation module, and a verification module. The specific execution content of each functional module is as follows:

[0085] Simulation analysis module: Based on the vehicle three-dimensional CAD model, conduct a vehicle wind resistance simulation analysis.

[0086] Sensitivity analysis module: Import the vehicle aerodynamic resistance analysis results into the aerodynamic resistance sensitivity analysis calculation program, solve the adjoint equations by the coupling method, obtain the aerodynamic resistance sensitivity distribution information of the vehicle shape, and conduct the aerodynamic resistance sensitivity analysis of the vehicle shape.

[0087] Mesh deformation module: Referring to the sensitivity magnitude, use the IDW method to perform mesh deformation and complete the update of the three-dimensional mesh.

[0088] Verification module: Verify the optimization effect.

[0089] The preferred embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0090] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any appropriate manner without conflict. To avoid unnecessary repetition, the present invention will not separately describe various possible combination methods. In addition, any combination can be made between different embodiments of the present invention, as long as it does not violate the disclosed idea of the present invention, it should also be regarded as the content disclosed by the present invention.

Claims

1. A vehicle aerodynamic optimization method, characterized in that, It includes the following steps: Step 1: Based on the three-dimensional CAD model of the whole vehicle, conduct a wind resistance simulation analysis of the whole vehicle; Step 2: Wind resistance sensitivity analysis of the whole vehicle shape: According to the wind resistance analysis results of the whole vehicle, adopt the wind resistance sensitivity analysis algorithm, solve the adjoint equations by the coupling method, and obtain the distribution information of the shape wind resistance sensitivity; Step 3: Mesh deformation: Taking the sensitivity magnitude as a reference, use the IDW method to perform mesh deformation and complete the update of the three-dimensional mesh; Step 4: Verify the optimization effect; The method of the wind resistance sensitivity analysis in Step 2 is as follows: The wind resistance sensitivity is defined as the gradient of the wind resistance with respect to the small change in the shape, and the sensitivity is described as where A is the area of the mesh element on the surface of the object to be optimized; n i is the component of the outer normal direction of the mesh element on the surface of the object to be optimized in the i direction; υ and υ t are the laminar and turbulent kinematic viscosity coefficients respectively; u i and v i are the components of the flow velocity and the adjoint velocity in the i direction respectively; q is the adjoint pressure; the adjoint velocity and the adjoint pressure satisfy the following equations and boundary conditions: At the optimized object wall surface: u i = -1 At the inlet and on the walls that do not require optimization: u i = 0 At the exit: Rewrite Equation (3) as where D is the reciprocal of the convection term coefficient in Equation (2); u is the adjoint velocity; the overline in the second term indicates that the relevant quantity is interpolated from the grid center to the grid surface; the gradient of the adjoint pressure in the third term is obtained by the difference of the adjoint pressure in adjacent grids; Couple the adjoint pressure and adjoint velocity, namely Equation (2) and (4), and solve them simultaneously in a linear algebraic equation system.

2. The vehicle aerodynamic optimization method according to claim 1, characterized in that, Adopt the method of coupling the direct method and the iterative method to solve the linear algebraic equation system, including: Perform graph partitioning on the computational domain to form many sub-regions; Directly perform LU decomposition on the sub-matrix of the coefficient matrix corresponding to the interior of the sub-region; Based on the LU decomposition, construct the Schur Complement linear equation system for the unknown variables at the boundaries of the sub-regions; Use the iterative method to solve the Schur Complement linear equation system, and then obtain the solution of the entire linear algebraic equation system.

3. The vehicle aerodynamic optimization method according to claim 1, wherein In Step 3, the wind resistance sensitivity is the gradient of the wind resistance with respect to the small change in the shape, that is, when a small deformation along the normal direction occurs at any position of the shape, how much change in the wind resistance will be caused.

4. The vehicle aerodynamic optimization method according to claim 1, wherein Step 3 specifically includes: First, select n points as control points in the high-sensitivity area of the outer surface: For any grid point within the computational domain Calculate the distance between it and the control point: Secondly, determine the deformation amount at each control point is proportional to the magnitude of the sensitivity; the maximum deformation amount does not exceed the limit allowed in engineering; Finally, based on the control points and the deformation amounts at each control point calculate the deformation amounts of the internal grid points in the computational domain.

5. The vehicle aerodynamic optimization method according to claim 4, characterized in that The method for calculating the deformation amount of the internal grid points of the computational domain includes: Calculate the local rigid rotation deformation: For control point i, the rigid rotation deformation of its neighboring points is represented by rotation matrix M i The local deformation after rigid rotation Can be maximally consistent with the deformation of points near point i / the change in the normal of the surface element, and thus its contribution to the global deformation is expressed as Use the inverse distance weighting method to obtain the deformation amount of any grid point in the computational domain: Where A i is the area of the surface element at control point i; L def is the maximum distance of the grid points; a, b, and α are constant parameters.

6. The vehicle aerodynamic optimization method according to any one of claims 1-5, characterized in that, In Step 4, the verification of the optimization effect is based on the updated grid, re-conduct the wind resistance simulation analysis of the whole vehicle, evaluate the optimization effect, and if the wind resistance target is not achieved, return to Step 2 for iteration.

7. The vehicle aerodynamic optimization method according to any one of claims 1-5, characterized in that The whole vehicle wind resistance simulation analysis in Step 1 includes: Establish a three-dimensional CAD model of the whole vehicle; Conduct geometric cleaning, group and name the whole vehicle parts according to geometric features; Establish the external flow field computational domain of the whole vehicle, complete the volume mesh division, turbulence model setting, and boundary condition setting; Solve the steady-state working condition whole vehicle flow field model.

8. A vehicle pneumatic optimization system, characterized in that, It includes: Simulation analysis module: Based on the three-dimensional CAD model of the whole vehicle, conduct a wind resistance simulation analysis of the whole vehicle; Sensitivity analysis module: Import the wind resistance analysis results of the whole vehicle into the wind resistance sensitivity analysis calculation program, solve the adjoint equations by the coupling method, obtain the distribution information of the shape wind resistance sensitivity, and conduct the wind resistance sensitivity analysis of the whole vehicle shape; Mesh deformation module: Taking the sensitivity magnitude as a reference, use the IDW method to perform mesh deformation and complete the update of the three-dimensional mesh; Verification module: Verify the optimization effect; The wind resistance sensitivity analysis module conducts analysis through the following method: The wind resistance sensitivity is defined as the gradient of the wind resistance with respect to the small change in the shape, and the sensitivity is described as where A is the area of the mesh element on the surface of the object to be optimized; n i is the component of the outer normal direction of the mesh element on the surface of the object to be optimized in the i direction; υ and υ t are the laminar and turbulent kinematic viscosity coefficients respectively; u i and v i are the components of the flow velocity and the adjoint velocity in the i direction respectively; q is the adjoint pressure; the adjoint velocity and the adjoint pressure satisfy the following equations and boundary conditions: At the optimized object wall surface: u i = -1 At the inlet and on the walls that do not require optimization: u i = 0 At the exit: Rewrite Equation (3) as where D is the reciprocal of the convective term coefficient in Equation (2); u is the adjoint velocity; the overline in the second term indicates that the relevant quantity is interpolated from the grid center to the grid face; the gradient of the adjoint pressure in the third term is obtained from the difference of the adjoint pressures in adjacent grids; Couple the adjoint pressure and adjoint velocity, namely Equation (2) and (4), and solve them simultaneously in an equation.

9. The vehicle aerodynamic optimization system according to claim 8, characterized in that, The mesh deformation module is configured to specifically perform the following process: First, select n points as control points in the highly sensitive area of the outer surface: For any grid point within the computational domain Calculate the distance between it and the control point: Secondly, determine the amount of deformation at each control point is proportional to the magnitude of the sensitivity; the maximum amount of deformation does not exceed the limit allowed in engineering; Finally, based on the control points and the deformation amounts at each control point calculate the deformation amounts of the internal grid points in the computational domain.

10. The vehicle aerodynamic optimization system according to claim 9, characterized in that, The calculation method for the deformation amount of the internal grid points of the calculation domain is as follows Calculate the local rigid rotation deformation: For control point i, the rigid rotation deformation of its nearby points is represented by the rotation matrix M i The local deformation after rigid rotation Can be maximally consistent with the deformation of the points near point i / the change in the surface element normal, and then its contribution to the global deformation is expressed as The inverse distance weighting method is used to obtain the deformation amount of any grid point in the calculation domain: Where A i is the area of the surface element at control point i; L def is the maximum distance of the grid points; a, b, α are constant parameters.

11. An electronic device, characterized in that, Includes: One or more processors; A storage device for storing one or more programs, which when executed by the one or more processors cause the electronic device to implement the method described in any one of claims 1 to 7.

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