Turbulence model construction method for aerodynamic simulation of electric three-compartment vehicle

Abstract

The invention discloses a turbulence model construction method for aerodynamic simulation of an electric three-compartment vehicle, and relates to the field of model simulation, and the method comprises the steps: obtaining a turbulence model based on standard model construction; wherein a correction term before the equation damage term is constructed by using a flow field inversion-symbol regression method and is migrated to the equation set, and calibration is carried out on the electric three-compartment vehicle; wherein the form of the boundary layer protection function is shown in the specification; wherein the Karman constant is the distance to the nearest wall surface. According to the method, the error distribution of the turbulence model is subjected to flow field inversion quantification, then the mathematical form of the correction term corresponding to the error is obtained by using the symbol regression method, and finally the correction term coefficient is calibrated according to the electric three-compartment vehicle experimental data, so that the corrected turbulence model is obtained.

Description

Technical Field

[0001] This application relates to the field of model simulation, and in particular to a method for constructing a turbulence model for aerodynamic simulation of an electric sedan, a system for constructing a turbulence model for aerodynamic simulation of an electric sedan, electronic equipment, storage media and simulation platform. Background Technology

[0002] When performing aerodynamic simulations of electric sedans, the efficient RANS (Reynolds-averaged Navier-Stokes) method is often used. The RANS method relies on turbulence models for calculations, but traditional turbulence models struggle to accurately calculate flow separation on the smooth surfaces of electric sedans, leading to significant errors in predicting the drag coefficient. Therefore, it is necessary to modify existing RANS turbulence models for the specific application scenario of electric sedans.

[0003] Therefore, a scheme is needed for constructing a turbulence model for aerodynamic simulation of electric sedans: the error distribution of the turbulence model is quantified through flow field inversion, then the mathematical form of the correction term corresponding to the error is obtained using the symbolic regression method, and finally the coefficients of the correction term are calibrated based on experimental data of electric sedans to obtain a corrected turbulence model. This improves simulation development accuracy, increases R&D efficiency, and reduces R&D costs. It is specifically designed for aerodynamic simulation of electric sedan products. Summary of the Invention

[0004] The purpose of this invention is to provide a method for constructing a turbulence model for aerodynamic simulation of an electric sedan, a system for constructing a turbulence model for aerodynamic simulation of an electric sedan, electronic equipment, storage medium and simulation platform, at least to solve the problem that the turbulence model is difficult to accurately calculate the flow separation generated by the smooth surface of the electric sedan, resulting in a large error in the prediction of the drag coefficient of the electric sedan, and to solve a technical problem of how to correct the existing RANS turbulence model in the application scenario of electric sedan.

[0005] This invention provides the following solution:

[0006] According to a first aspect of the present invention, a method for constructing a turbulence model for aerodynamic simulation of an electric sedan is provided, comprising:

[0007] Based on standards Model construction to obtain a turbulence model;

[0008] The turbulence model includes two core transport equations.

[0009] Turbulent kinetic energy The transport equation is: ;

[0010] Dissipation rate The transport equation is: ;

[0011] in, for directional average velocity, For Reynolds stress, For turbulent kinetic energy, The dissipation rate;

[0012] in, Let be constants, and their values ​​are as follows: ;

[0013] in, For the molecular viscosity of fluids, For eddy viscosity, the calculation method is as follows: ;in, .

[0014] in, The correction term before the equation violation term is constructed using the flow field inversion-signed regression method and transferred to... The equations, calibrated on an electric sedan, yielded the following results: ;

[0015] in, The framework of the expression is obtained using flow field inversion-symbolic regression:

[0016] ;

[0017] ;

[0018] ;

[0019] in, For the rotation rate tensor, For the rotation tensor in Components in direction, for Direction basis vector, for Direction coordinates;

[0020] Among them, the boundary layer protection function The form is:

[0021] , ;

[0022] in, Kármán's constant, This is the distance to the nearest wall.

[0023] Furthermore, it also includes:

[0024] and for The calibrable parameter values ​​of the expression;

[0025] Among them, according to the test of electric sedans, the calibration is as follows: .

[0026] Furthermore, it also includes:

[0027] Boundary layer protection function The Karman constant in the expression is κ = 0.41.

[0028] Furthermore, flow field inversion-symbolic regression methods include:

[0029] The dense spatial distribution of turbulence model correction terms is obtained from sparse separated flow experimental data using the flow field inversion method.

[0030] The dense spatial distribution of this correction term is then used as the label for the machine learning training set.

[0031] Based on physical understanding, local flow field features that are relevant to the distribution characteristics of quantity and adapted to the flow separation characteristics of the smooth surface of an electric sedan are selected and modified, and used as input features for the machine learning training set.

[0032] Using the symbolic regression method, with the input features as input and the turbulence model correction term as output, the analytical expression of the turbulence model correction term is trained.

[0033] Through mathematical derivation, the analytical expression of the correction term is transferred to the standard. In the pattern, a correction term is formed. ;

[0034] Based on experimental data of electric sedans, the parameters of the analytical expression obtained by symbolic regression were calibrated to obtain a data-driven symbolic regression turbulence model for calculating the actual vehicle resistance.

[0035] Furthermore, it also includes:

[0036] Experimental data were collected on the flow separation phenomenon on the smooth surface of an electric sedan to obtain sparse separation flow experimental data.

[0037] Furthermore, it also includes:

[0038] Local flow field characteristics include characteristic parameters related to the velocity gradient and rotational characteristics of the flow field on the surface of the electric sedan.

[0039] According to a second aspect of the present invention, a turbulence model construction system for aerodynamic simulation of an electric sedan is provided, comprising:

[0040] Turbulence model acquisition module, used for standard-based... Model construction to obtain a turbulence model;

[0041] The turbulence model includes two core transport equations.

[0042] Turbulent kinetic energy The transport equation is: ;

[0043] Dissipation rate The transport equation is: ;

[0044] in, for directional average velocity, For Reynolds stress, For turbulent kinetic energy, The dissipation rate;

[0045] in, Let be constants, and their values ​​are as follows: ;

[0046] in, For the molecular viscosity of fluids, For eddy viscosity, the calculation method is as follows: ;in, .

[0047] The correction item building module is used for The correction term before the equation violation term is constructed using the flow field inversion-signed regression method and transferred to... The equations, calibrated on an electric sedan, yielded the following results: ;

[0048] in, The framework of the expression is obtained using flow field inversion-symbolic regression:

[0049] ;

[0050] ;

[0051] ;

[0052] in, For the rotation rate tensor, For the rotation tensor in Components in direction, for Direction basis vector, for Direction coordinates;

[0053] Among them, the boundary layer protection function The form is:

[0054] , ;

[0055] in, Kármán's constant, This is the distance to the nearest wall.

[0056] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0057] The memory stores a computer program that, when executed by the processor, causes the processor to perform steps of a turbulence model construction method for aerodynamic simulation of an electric sedan.

[0058] According to a fourth aspect of the present invention, a computer-readable storage medium is provided storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a method for constructing a turbulence model for aerodynamic simulation of an electric sedan.

[0059] According to a fifth aspect of the present invention, a simulation platform is provided, comprising:

[0060] Electronic equipment for implementing the steps of a turbulence model construction method for aerodynamic simulation of electric sedans;

[0061] The processor runs a program that, when running, executes steps of a turbulence model building method for aerodynamic simulation of an electric sedan based on data output from electronic devices.

[0062] A storage medium for storing a program that, when run, executes steps of a turbulence model construction method for aerodynamic simulation of an electric sedan based on data output from an electronic device.

[0063] The above solution achieves the following beneficial technical effects:

[0064] This application quantifies the flow field by performing flow field inversion on the error distribution of the turbulence model, then uses the symbolic regression method to obtain the mathematical form of the correction term corresponding to the error, and finally calibrates the coefficient of the correction term based on experimental data of an electric sedan to obtain the corrected turbulence model. Attached Figure Description

[0065] Figure 1 This is a flowchart of a method for constructing a turbulence model for aerodynamic simulation of an electric sedan, provided by one or more embodiments of the present invention.

[0066] Figure 2 This is a structural diagram of a turbulence model construction system for aerodynamic simulation of an electric sedan, provided by one or more embodiments of the present invention.

[0067] Figure 3 This is a schematic diagram of a specific embodiment of the present invention for obtaining a data-driven symbolic regression turbulence model capable of accurately calculating the sparse resistance of a real vehicle.

[0068] Figure 4 This is a block diagram of an electronic device structure for constructing a turbulence model for aerodynamic simulation of an electric sedan, provided by one or more embodiments of the present invention. Detailed Implementation

[0069] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Figure 1 This is a flowchart of a method for constructing a turbulence model for aerodynamic simulation of an electric sedan, provided by one or more embodiments of the present invention.

[0071] like Figure 1 The turbulence model construction method shown for aerodynamic simulation of electric sedans includes:

[0072] Based on standards Model construction to obtain a turbulence model;

[0073] The turbulence model includes two core transport equations.

[0074] Turbulent kinetic energy The transport equation is: ;

[0075] Dissipation rate The transport equation is: ;

[0076] in, for directional average velocity, For Reynolds stress, For turbulent kinetic energy, The dissipation rate;

[0077] in, Let be constants, and their values ​​are as follows: ;

[0078] in, For the molecular viscosity of fluids, For eddy viscosity, the calculation method is as follows: ;in, .

[0079] in, The correction term before the equation violation term is constructed using the flow field inversion-signed regression method and transferred to... The equations, calibrated on an electric sedan, yielded the following results: ;

[0080] in, The framework of the expression is obtained using flow field inversion-symbolic regression:

[0081] ;

[0082] ;

[0083] ;

[0084] in, For the rotation rate tensor, For the rotation tensor in Components in direction, for Direction basis vector, for Direction coordinates;

[0085] Among them, the boundary layer protection function The form is:

[0086] , ;

[0087] in, Kármán's constant, This is the distance to the nearest wall.

[0088] Specifically, this application presents a modified turbulence model applicable to aerodynamic simulation and calculation of electric sedans. The main difference from existing turbulence models lies in the following: the error distribution of the turbulence model is quantified by flow field inversion, and then the mathematical form of the correction term corresponding to the error is obtained using the symbolic regression method. Finally, the coefficients of the correction term are calibrated based on experimental data of electric sedans to obtain the modified turbulence model.

[0089] In one specific embodiment, a method such as Figure 3 The method shown is to obtain a data-driven symbolic regression turbulence model that can accurately calculate the drag of real vehicles:

[0090] 1. Using flow field inversion methods, the dense spatial distribution of the turbulence model correction term is obtained from sparse separated flow experimental data. This dense spatial distribution of the correction term is used as the label for the machine learning training set.

[0091] 2. Based on physical understanding, select and modify local flow field features that are more relevant to the distribution characteristics of the quantity as input features for the machine learning training set.

[0092] 3. Using the symbolic regression method, with the features selected in step 2 as input and the turbulence model correction term constructed in step 1 as output, train the analytical expression of the turbulence model correction term.

[0093] 4. Through mathematical derivation, the analytical expression of the correction term is transferred to the standard. In mode.

[0094] 5. Based on real vehicle test data, the parameters of the analytical expression obtained by symbolic regression are calibrated to obtain a data-driven symbolic regression turbulence model that can accurately calculate the sparse resistance of real vehicles.

[0095] In another specific embodiment, a turbulence model suitable for aerodynamic simulation and calculation of electric sedans is disclosed. This turbulence model is based on standard... The model is constructed with two transport equations:

[0096] ;

[0097] ;

[0098] for directional average velocity, For Reynolds stress, For turbulent kinetic energy, This represents the dissipation rate. Let be constants, and their values ​​are as follows: . For the molecular viscosity of fluids, For eddy viscosity, the calculation method is as follows:

[0099] ;

[0100] in, The core innovation in the above equation is... The correction term before the violation term (second term on the right) of the equation (second equation) is constructed using the flow field inversion-signed regression method and transferred to... The final system of equations, obtained through calibration on an electric sedan, yielded the following results: ;

[0101] The core innovations include:

[0102] The framework of the expression is obtained using flow field inversion-symbolic regression:

[0103] ;

[0104] ;

[0105] ;

[0106] in, For the rotation rate tensor, For the rotation tensor in Components in direction, for Direction basis vector, for Direction coordinates.

[0107] Specifiable parameter values ​​of an expression Based on the experimental data of electric sedans, the calibration is as follows:

[0108] ;

[0109] Boundary layer protection function used The form is:

[0110] ;

[0111] in, Here is the Kármán constant, with a value of 0.41. This is the distance to the nearest wall.

[0112] In this embodiment, it also includes:

[0113] and for The calibrable parameter values ​​of the expression;

[0114] Among them, according to the test of electric sedans, the calibration is as follows: .

[0115] In this embodiment, it also includes:

[0116] Boundary layer protection function The Karman constant in the expression is κ = 0.41.

[0117] In this embodiment, the flow field inversion-symbolic regression method includes:

[0118] The dense spatial distribution of turbulence model correction terms is obtained from sparse separated flow experimental data using the flow field inversion method.

[0119] The dense spatial distribution of this correction term is then used as the label for the machine learning training set.

[0120] Based on physical understanding, local flow field features that are relevant to the distribution characteristics of quantity and adapted to the flow separation characteristics of the smooth surface of an electric sedan are selected and modified, and used as input features for the machine learning training set.

[0121] Using the symbolic regression method, with the input features as input and the turbulence model correction term as output, the analytical expression of the turbulence model correction term is trained.

[0122] Through mathematical derivation, the analytical expression of the correction term is transferred to the standard. In the pattern, a correction term is formed. ;

[0123] Based on experimental data of electric sedans, the parameters of the analytical expression obtained by symbolic regression were calibrated to obtain a data-driven symbolic regression turbulence model for calculating the actual vehicle resistance.

[0124] In this embodiment, it also includes:

[0125] Experimental data were collected on the flow separation phenomenon on the smooth surface of an electric sedan to obtain sparse separation flow experimental data.

[0126] In this embodiment, it also includes:

[0127] Local flow field characteristics include characteristic parameters related to the velocity gradient and rotational characteristics of the flow field on the surface of the electric sedan.

[0128] Figure 2 This is a structural diagram of a turbulence model construction system for aerodynamic simulation of an electric sedan, provided by one or more embodiments of the present invention.

[0129] like Figure 2 The turbulence model building system shown is used for aerodynamic simulation of electric sedans and includes: a turbulence model acquisition module and a correction term construction module;

[0130] Turbulence model acquisition module, used for standard-based... Model construction to obtain a turbulence model;

[0131] The turbulence model includes two core transport equations.

[0132] Turbulent kinetic energy The transport equation is: ;

[0133] Dissipation rate The transport equation is: ;

[0134] in, for directional average velocity, For Reynolds stress, For turbulent kinetic energy, The dissipation rate;

[0135] in, Let be constants, and their values ​​are as follows: ;

[0136] in, For the molecular viscosity of fluids, For eddy viscosity, the calculation method is as follows: ;in, .

[0137] The correction item building module is used for The correction term before the equation violation term is constructed using the flow field inversion-signed regression method and transferred to... The equations, calibrated on an electric sedan, yielded the following results: ;

[0138] in, The framework of the expression is obtained using flow field inversion-symbolic regression:

[0139] ;

[0140] ;

[0141] ;

[0142] in, For the rotation rate tensor, For the rotation tensor in Components in direction, for Direction basis vector, for Direction coordinates;

[0143] Among them, the boundary layer protection function The form is:

[0144] , ;

[0145] in, Kármán's constant, This is the distance to the nearest wall.

[0146] It is worth noting that although this system / device only discloses the turbulence model acquisition module and the correction term construction module mentioned above, it does not mean that this system / device is limited to the above basic functional modules. On the contrary, what this invention intends to express is that, based on the above basic functional modules, those skilled in the art can add one or more functional modules in combination with the prior art to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. It should not be assumed that the scope of protection of the claims of this invention is limited to the above-disclosed basic functional modules just because this embodiment only discloses a few basic functional modules.

[0147] In one specific embodiment, the dissipation rate ε transport equation includes a generation term, a destruction term, and a diffusion term.

[0148] Destructive item The physical meaning lies in the attenuation term of the turbulent kinetic energy dissipation rate. This is achieved through a correction coefficient. To address the flow separation characteristics on the smooth surface of electric sedans, the dissipation rate of turbulent energy was adjusted.

[0149] standard The failure term of the model has a fixed coefficient. It cannot adapt to flow separation in near-wall areas (such as the smooth body of an electric sedan). This application introduces... Correction terms (based on flow field inversion-symbolic regression) and boundary layer protection function This solves the problem of large prediction errors in the dissipation rate near the wall in traditional models.

[0150] The physical meaning of the correction term:

[0151] ,in Reflecting the local characteristics of the rotation rate tensor, the experimental data of the electric sedan were fitted by symbolic regression, so that the failure term could capture the rotational effect in the flow separation zone.

[0152] Boundary layer protection function Function:

[0153] Distance from the wall and eddy viscosity This suppresses excessive dissipation in the near-wall region (compared to the problem of excessively rapid decay in the near-wall region in traditional models).

[0154] III. Engineering Value of Destructive Items (Specifically for Electric Sedan Segments)

[0155] Improved accuracy of flow separation prediction: By correcting for the destructive term, the turbulence model is made more sensitive to flow separation on smooth surfaces (such as the rear and sides) of electric sedans, and the prediction error of the drag coefficient is significantly reduced (accuracy > 98% as stated in the disclosure document).

[0156] Unlike the general model: Comparing Abstract 1 (SST model) and Abstract 3 (differences in generated terms), the destructive terms in the disclosure document are specifically designed for the aerodynamic calibration of electric sedans. It is not a wing or high-lift configuration.

[0157] Conclusion: Location of the disruptive term

[0158] The destruction term is the core term in the dissipation rate ε equation that directly controls the decay of turbulent energy. Specifically:

[0159] Its innovation lies in solving the problem of predicting the dissipation rate of flow separation on the smooth surface of an electric sedan through a three-level optimization process of flow field inversion, symbolic regression, and experimental calibration. This approach differs from traditional methods. Key correction points for the model.

[0160] Figure 4 This is a block diagram of an electronic device structure for constructing a turbulence model for aerodynamic simulation of an electric sedan, provided by one or more embodiments of the present invention.

[0161] like Figure 4 As shown, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0162] The memory stores a computer program that, when executed by a processor, causes the processor to perform steps of a turbulence model construction method for aerodynamic simulation of an electric sedan.

[0163] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a turbulence model construction method for aerodynamic simulation of an electric sedan.

[0164] This application also provides a testing platform, including:

[0165] Electronic equipment for implementing the steps of a turbulence model construction method for aerodynamic simulation of electric sedans;

[0166] The processor runs a program that, when running, executes steps of a turbulence model building method for aerodynamic simulation of an electric sedan based on data output from electronic devices.

[0167] A storage medium for storing a program that, when run, executes steps of a turbulence model construction method for aerodynamic simulation of an electric sedan based on data output from an electronic device.

[0168] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0169] The electronic device comprises a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control the electronic device through processes, such as Linux, Unix, Android, iOS, or Windows. Furthermore, in this embodiment of the invention, the electronic device can be a smartphone, tablet computer, or other handheld device, or a desktop computer, portable computer, or other electronic device; there is no particular limitation in this embodiment.

[0170] In this embodiment of the invention, the executing entity for electronic device control can be an electronic device itself, or a functional module within an electronic device capable of calling and executing a program. The electronic device can obtain the firmware corresponding to the storage medium. This firmware is provided by the supplier, and different storage media may have the same or different firmware; no limitation is made here. After obtaining the firmware corresponding to the storage medium, the electronic device can write this firmware into the storage medium; specifically, it burns the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented using existing technology, and will not be elaborated upon in this embodiment of the invention.

[0171] Electronic devices can also obtain reset commands corresponding to the storage media. The reset commands corresponding to the storage media are provided by the supplier. The reset commands corresponding to different storage media can be the same or different, and no restrictions are imposed here.

[0172] At this time, the storage medium of the electronic device is a storage medium on which the corresponding firmware has been written. The electronic device can respond to the reset command corresponding to the storage medium on which the corresponding firmware has been written, thereby resetting the storage medium on which the corresponding firmware has been written according to the reset command. The process of resetting the storage medium according to the reset command can be implemented by existing technology and will not be described in detail in this embodiment of the invention.

[0173] For ease of description, the above devices are described separately by function as various units and modules. Of course, in implementing this application, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0174] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0175] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0176] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a turbulence model for aerodynamic simulation of an electric sedan, characterized in that, The method for constructing a turbulence model for aerodynamic simulation of electric sedans includes: Based on standards Model construction to obtain a turbulence model; The turbulence model includes two core transport equations. Turbulent kinetic energy The transport equation is: ; Dissipation rate The transport equation is: ; in, for directional average velocity, For Reynolds stress, For turbulent kinetic energy, The dissipation rate; in, Let be constants, and their values ​​are as follows: ; in, For the molecular viscosity of fluids, For eddy viscosity, the calculation method is as follows: ;in, ; in, The correction term before the equation violation term is constructed using the flow field inversion-signed regression method and transferred to... The equations, calibrated on an electric sedan, yielded the following results: ; in, The framework of the expression is obtained using flow field inversion-symbolic regression: ； ； ； in, For the rotation rate tensor, For the rotation tensor in Components in direction, for Direction basis vector, for Direction coordinates; Among them, the boundary layer protection function The form is: ， ； in, Kármán's constant, This is the distance to the nearest wall.

2. The method for constructing a turbulence model for aerodynamic simulation of an electric sedan according to claim 1, characterized in that, Also includes: and for The calibrable parameter values ​​of the expression; Among them, according to the test of electric sedans, the calibration is as follows: .

3. The method for constructing a turbulence model for aerodynamic simulation of an electric sedan according to claim 1, characterized in that, Also includes: Boundary layer protection function The Karman constant in the expression is κ = 0.

41.

4. The method for constructing a turbulence model for aerodynamic simulation of an electric sedan according to claim 1, characterized in that, The flow field inversion-symbolic regression method includes: The dense spatial distribution of turbulence model correction terms is obtained from sparse separated flow experimental data using the flow field inversion method. The dense spatial distribution of this correction term is then used as the label for the machine learning training set. Based on physical understanding, local flow field features that are relevant to the distribution characteristics of quantity and adapted to the flow separation characteristics of the smooth surface of an electric sedan are selected and modified, and used as input features for the machine learning training set. Using the symbolic regression method, with the input features as input and the turbulence model correction term as output, the analytical expression of the turbulence model correction term is trained. Through mathematical derivation, the analytical expression of the correction term is transferred to the standard. In the pattern, the correction term is formed. ; Based on experimental data of electric sedans, the parameters of the analytical expression obtained by symbolic regression were calibrated to obtain a data-driven symbolic regression turbulence model for calculating the actual vehicle resistance.

5. The method for constructing a turbulence model for aerodynamic simulation of an electric sedan according to claim 4, characterized in that, Also includes: Experimental data were collected on the flow separation phenomenon generated on the smooth surface of an electric sedan to obtain the sparse separation flow experimental data.

6. The method for constructing a turbulence model for aerodynamic simulation of an electric sedan according to claim 4, characterized in that, Also includes: The local flow field characteristics include characteristic parameters related to the velocity gradient and rotational characteristics of the flow field on the surface of the electric sedan body.

7. A turbulence model construction system for aerodynamic simulation of electric sedans, characterized in that, The turbulence model construction system for aerodynamic simulation of electric sedans includes: Turbulence model acquisition module, used for standard-based... Model construction to obtain a turbulence model; The turbulence model includes two core transport equations. Turbulent kinetic energy The transport equation is: ; Dissipation rate The transport equation is: ; in, for directional average velocity, For Reynolds stress, For turbulent kinetic energy, The dissipation rate; in, Let be constants, and their values ​​are as follows: ; in, For the molecular viscosity of fluids, For eddy viscosity, the calculation method is as follows: ;in, . The correction item building module is used for The correction term before the equation violation term is constructed using the flow field inversion-signed regression method and transferred to... The equations, calibrated on an electric sedan, yielded the following results: ; in, The framework of the expression is obtained using flow field inversion-symbolic regression: ； ； ； in, For the rotation rate tensor, For the rotation tensor in Components in direction, for Direction basis vector, for Direction coordinates; Among them, the boundary layer protection function The form is: ， ； in, Kármán's constant, This is the distance to the nearest wall.

8. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. The memory stores a computer program that, when executed by a processor, causes the processor to perform the steps of the turbulence model construction method for aerodynamic simulation of an electric sedan as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The device stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the turbulence model construction method for aerodynamic simulation of an electric sedan as described in any one of claims 1 to 6.

10. A simulation platform, characterized in that, include: An electronic device for implementing the steps of the method for constructing a turbulence model for aerodynamic simulation of an electric sedan as described in any one of claims 1 to 6; The processor runs a program that, when the program is running, executes the steps of the turbulence model construction method for aerodynamic simulation of an electric sedan as described in any one of claims 1 to 6 from data output by the electronic device. A storage medium for storing a program that, when running, performs the steps of the turbulence model construction method for aerodynamic simulation of an electric sedan as described in any one of claims 1 to 6 on data output from an electronic device.

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