Vehicle wind tunnel simulation model establishment method and device, equipment and storage medium

Abstract

The invention discloses a vehicle wind tunnel simulation model establishment method and device, equipment and a storage medium, and relates to the technical field of automobile aerodynamics. The method comprises the steps of determining an initial simulation model of a target vehicle according to geometric data of the target vehicle and a historical wind tunnel simulation result; setting pavement simulation parameters and pavement simulation working conditions for the initial simulation model to obtain a candidate simulation model of the target vehicle; inputting the collected sample simulation data of the target vehicle into the candidate simulation model to obtain wind tunnel simulation data of the target vehicle; according to the wind tunnel actual measurement data and the wind tunnel simulation data, adjusting the candidate simulation model to obtain a target simulation model; the wind tunnel actual measurement data is acquired by the target vehicle in the actual driving process. According to the technical scheme, on the basis of wind tunnel simulation, real road surface conditions are fully considered, and the wind tunnel simulation model of the vehicle on the open road surface is established.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of automobile aerodynamics technology, in particular to the field of automobile wind tunnel test technology, and specifically to a method, device, equipment and storage medium for establishing a vehicle wind tunnel simulation model. Background Art

[0002] During vehicle driving, the main resistance to overcome is air resistance, rolling resistance, transmission resistance, etc. Studies have shown that when driving at high speeds, wind resistance can account for more than 50% of total energy consumption. At present, the requirements for vehicle energy conservation and emission reduction are becoming more and more stringent. In order to improve the aerodynamic performance of vehicles, accurate commercial vehicle wind resistance simulation and correction methods are needed to ensure the target of reducing wind resistance in the development process.

[0003] The current technologies for evaluating the air resistance of a vehicle mainly include wind tunnel tests and numerical wind tunnel simulations, coasting tests and open road simulations. On the one hand, affected by the blockage ratio, the existing wind tunnel tests for commercial vehicles have large errors, and corrections are required during the establishment of numerical wind tunnel simulations. On the other hand, the coasting test environment deviates greatly from the actual driving environment, making it difficult to accurately simulate the impact of the actual environment. Summary of the invention

[0004] The present application provides a method, device, equipment and storage medium for establishing a vehicle wind tunnel simulation model, so as to realize the establishment of a wind tunnel simulation model of a vehicle on an open road.

[0005] According to one aspect of the present application, a method for establishing a vehicle wind tunnel simulation model is provided, the method comprising:

[0006] Determining an initial simulation model of the target vehicle based on geometric data of the target vehicle and historical wind tunnel simulation results;

[0007] Setting road simulation parameters and road simulation conditions for the initial simulation model to obtain a candidate simulation model of the target vehicle;

[0008] Inputting the collected sample simulation data of the target vehicle into the candidate simulation model to obtain wind tunnel simulation data of the target vehicle;

[0009] The candidate simulation model is adjusted according to the wind tunnel measured data and the wind tunnel simulation data to obtain a target simulation model; the wind tunnel measured data is collected during the actual driving of the target vehicle.

[0010] According to another aspect of the present application, a vehicle wind tunnel simulation model establishment device is provided, the device comprising:

[0011] A first model building module, used for determining an initial simulation model of the target vehicle according to geometric data of the target vehicle and historical wind tunnel simulation results;

[0012] A second model building module is used to set road simulation parameters and road simulation conditions for the initial simulation model to obtain a candidate simulation model of the target vehicle;

[0013] A model simulation module, used for inputting the collected sample simulation data of the target vehicle into the candidate simulation model to obtain wind tunnel simulation data of the target vehicle;

[0014] The model adjustment module is used to adjust the candidate simulation model according to the wind tunnel measured data and the wind tunnel simulation data to obtain the target simulation model; the wind tunnel measured data is collected during the actual driving of the target vehicle.

[0015] According to another aspect of the present application, an electronic device is provided, the electronic device comprising:

[0016] one or more processors;

[0017] A memory for storing one or more programs;

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement any one of the vehicle wind tunnel simulation model establishment methods provided in the embodiments of the present application.

[0019] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, any one of the vehicle wind tunnel simulation model establishment methods provided in the embodiments of the present application is implemented.

[0020] This application determines the initial simulation model of the target vehicle based on the geometric data of the target vehicle and the historical wind tunnel simulation results; sets the road simulation parameters and road simulation conditions for the initial simulation model to obtain the candidate simulation model of the target vehicle; inputs the collected sample simulation data of the target vehicle into the candidate simulation model to obtain the wind tunnel simulation data of the target vehicle; adjusts the candidate simulation model based on the wind tunnel measured data and the wind tunnel simulation data to obtain the target simulation model; the wind tunnel measured data is collected by the target vehicle during actual driving. The above technical solution, by fully considering the real road conditions on the basis of wind tunnel simulation, realizes the establishment of a wind tunnel simulation model of the vehicle on an open road. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of a method for establishing a vehicle wind tunnel simulation model provided in accordance with Embodiment 1 of the present application;

[0022] Figure 2 is a flow chart of a method for establishing a vehicle wind tunnel simulation model provided in accordance with Embodiment 2 of the present application;

[0023] Figure 3 It is a structural schematic diagram of a vehicle wind tunnel simulation model establishment device provided according to the third embodiment of the present application;

[0024] Figure 4 It is a structural schematic diagram of an electronic device for implementing the vehicle wind tunnel simulation model establishment method of an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] In addition, it should be noted that the collection, storage, use, processing, transmission, provision and disclosure of relevant data such as geometric data and historical wind tunnel simulation results involved in the technical solution of this application are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0028] Embodiment 1

[0029] Figure 1This is a flow chart of a vehicle wind tunnel simulation model establishment method according to the first embodiment of the present application. This embodiment can be applied to the case of establishing a simulation model for a vehicle wind tunnel test on an open road, and can be executed by a vehicle wind tunnel simulation model establishment device. The vehicle wind tunnel simulation model establishment device can be implemented in the form of hardware and / or software, and the vehicle wind tunnel simulation model establishment device can be configured in a computer device, such as a server. Figure 1 As shown, the method includes:

[0030] S110: Determine an initial simulation model of the target vehicle according to geometric data of the target vehicle and historical wind tunnel simulation results.

[0031] Among them, the target vehicle refers to a specific vehicle that needs to be subjected to wind tunnel simulation analysis; illustratively, the vehicle can be a commercial vehicle. Historical wind tunnel simulation results refer to data information describing the shape, size and structure of the target vehicle; these data may include the vehicle's outer contour, length, width and height, body curve, aerodynamic characteristics, etc.; geometric data usually exist in CAD (Computer-Aided Design) models or other formats for subsequent simulation and analysis. Historical wind tunnel simulation results refer to simulation data and results obtained in previous traditional wind tunnel experiments or computational fluid dynamics analysis; these results provide information about the aerodynamic performance of the vehicle under different flow field conditions, such as drag, lift and airflow distribution. The initial simulation model refers to a mathematical model that is initially established to simulate the aerodynamic characteristics of the target vehicle during wind tunnel simulation; the initial model is usually based on the geometric data of the target vehicle and combined with historical wind tunnel simulation results to quickly generate a reasonable starting point, providing a basis for subsequent optimization and precise adjustment.

[0032] Exemplarily, an initial simulation model of the target vehicle is established based on CFD (Computational Fluid Dynamics) software, and the initial simulation model is modified based on traditional wind tunnel test results.

[0033] It is understandable that using traditional wind tunnel tests and simulations as the basis for model establishment has a certain degree of reliability compared to direct modeling, and saves the adjustment process of the initial model to a certain extent.

[0034] S120: Setting road surface simulation parameters and road surface simulation conditions for the initial simulation model to obtain a candidate simulation model of the target vehicle.

[0035] Among them, road simulation parameters refer to various parameter settings related to road surface characteristics during the simulation process, which may include road surface type, friction coefficient, slope, inclination, road surface temperature, etc.; the settings of these parameters are crucial for simulating the aerodynamic and power performance of vehicles under different road conditions. Road simulation conditions refer to specific operating conditions or environmental parameters set in the simulation, which affect the aerodynamic performance and driving state of the vehicle; they may include speed, wind speed, wind direction, load state, etc.; by setting different conditions, the performance of the vehicle in various driving scenarios can be simulated. The candidate simulation model is an improvement on the initial model, reflecting the aerodynamic characteristics of the vehicle under specific road conditions and conditions, specifically referring to the open road simulation model of the target vehicle.

[0036] Exemplarily, based on the initial simulation model, appropriate road simulation parameters and road calculation conditions are set, and the aerodynamic characteristics of the target vehicle on the actual road are preliminarily simulated to obtain a candidate simulation model.

[0037] It should be noted that in traditional commercial vehicle wind tunnel tests, simulation numerical wind tunnel models all use wind tunnel size as boundary conditions for modeling, and corrections are made on this basis; the present application directly establishes boundary conditions that are much larger than the wind tunnel size in the simulation model to obtain a candidate simulation model, which approximately simulates the characteristics of commercial vehicle road tests.

[0038] S130: Input the collected sample simulation data of the target vehicle into the candidate simulation model to obtain wind tunnel simulation data of the target vehicle.

[0039] Among them, sample simulation data refers to data on the performance of the target vehicle obtained through various methods (such as computational fluid dynamics simulation, experimental testing, etc.), which may include multi-dimensional information such as sample vehicle data, driving data, road conditions, other model input parameters, etc. Wind tunnel simulation data refers to data on the performance of the vehicle in the airflow obtained through wind tunnel testing or simulation technology, which may include aerodynamic drag, lift coefficient, flow field characteristics, vortex and separation phenomena, etc.

[0040] Optionally, before inputting the collected sample simulation data of the target vehicle into the candidate simulation model, a large amount of wind resistance simulation data and actual test data of the target vehicle under different open road conditions are collected; these data include multi-dimensional information such as sample vehicle data, driving data, road conditions, other model input parameters, etc.; the collected data are preprocessed, including data cleaning, outlier processing, data normalization, etc., to obtain sample simulation data.

[0041] Among them, the sample vehicle data refers to various information and parameters related to the target vehicle, which may include vehicle collective parameters, power system information, suspension system characteristics, weight and center position, etc. Driving data refers to the data related to the vehicle's operating status and performance collected during the actual driving of the target vehicle, which may include speed, acceleration and braking performance, fuel consumption, energy efficiency, wind speed, wind direction, load, etc. Road conditions refer to the road environment and conditions that affect the vehicle's driving performance, which may include road surface type, road surface state, slope, curvature, etc.

[0042] It should be noted that the method of collecting data is not limited to road tests, but also can collect indicator data of real commercial vehicle users during driving.

[0043] S140, adjusting the candidate simulation model according to the wind tunnel measured data and the wind tunnel simulation data to obtain a target simulation model; the wind tunnel measured data is collected during the actual driving of the target vehicle.

[0044] Among them, wind tunnel measured data refers to data related to the target vehicle during actual driving process obtained through wind tunnel experiments, and may include at least one of the vehicle performance data and vehicle dynamic data of the target vehicle; the vehicle performance data may be fuel consumption, gliding distance, etc.; the vehicle dynamic data may be the impact of acceleration, deceleration, turning, etc. on wind resistance.

[0045] Exemplarily, the collected vehicle performance data is compared with the wind tunnel simulation data, and the vehicle dynamic data during vehicle driving is additionally considered, and a sensitivity analysis is performed on the sample simulation data to screen out the parameters that mainly affect the accuracy of the drag coefficient, and use the parameters as the main correction parameters of the model; a suitable algorithm is selected, and the candidate simulation model is trained using the collected real data, and the real data is used as a training sample. The mapping relationship between the real data and the simulation data is learned by the algorithm, and the correction parameters and structure of the model are adjusted; the trained algorithm is applied to the simulation model, the simulation results are corrected, and the target simulation model is obtained.

[0046] Furthermore, the algorithm can be tuned, such as adjusting the learning rate, number of iterations and other parameters, to obtain better correction effects.

[0047] In an optional implementation, after adjusting the candidate simulation model according to the wind tunnel measured data and the wind tunnel simulation data to obtain the target simulation model, the accuracy of the target simulation model is verified, and if the accuracy verification fails, the wind tunnel simulation data of the target vehicle is re-determined.

[0048] Among them, accuracy verification refers to the verification of the generalization ability and robustness of the target simulation model.

[0049] The embodiment of the present application determines the initial simulation model of the target vehicle according to the geometric data of the target vehicle and the historical wind tunnel simulation results; sets the road simulation parameters and road simulation conditions for the initial simulation model to obtain the candidate simulation model of the target vehicle; inputs the collected sample simulation data of the target vehicle into the candidate simulation model to obtain the wind tunnel simulation data of the target vehicle; adjusts the candidate simulation model according to the wind tunnel measured data and the wind tunnel simulation data to obtain the target simulation model; the wind tunnel measured data is collected by the target vehicle during actual driving. The above technical scheme realizes the establishment of a wind tunnel simulation model of the vehicle on an open road by fully considering the real road conditions on the basis of wind tunnel simulation.

[0050] Embodiment 2

[0051] Figure 2 This is a flow chart of a method for establishing a vehicle wind tunnel simulation model provided in accordance with the second embodiment of the present application. Based on the technical solutions of the above embodiments, this embodiment refines "adjusting the candidate simulation model according to the wind tunnel measured data and the wind tunnel simulation data to obtain the target simulation model" into "comparing the vehicle performance data with the wind tunnel simulation data for consistency to obtain performance comparison data; adjusting the candidate simulation model according to the performance comparison data and the vehicle dynamic data to obtain the target simulation model". It should be noted that for the parts not described in detail in the embodiments of the present application, please refer to the relevant descriptions of other embodiments. Figure 2 As shown, the method includes:

[0052] S210: Determine an initial simulation model of the target vehicle according to geometric data of the target vehicle and historical wind tunnel simulation results.

[0053] S220: Setting road surface simulation parameters and road surface simulation conditions for the initial simulation model to obtain a candidate simulation model of the target vehicle.

[0054] S230: Input the collected sample simulation data of the target vehicle into the candidate simulation model to obtain wind tunnel simulation data of the target vehicle.

[0055] S240: Compare the vehicle performance data with the wind tunnel simulation data for consistency to obtain performance comparison data.

[0056] Performance comparison data refers to the analysis results obtained by comparing actual vehicle performance data with wind tunnel simulation data. These data are used to evaluate and verify the accuracy and reliability of wind tunnel simulation models.

[0057] For example, the vehicle performance data and wind tunnel simulation data are compared to analyze their performance under the same conditions, such as numerical comparison, directly comparing the differences in values ​​such as lift and drag; graphical analysis, drawing graphs such as airflow distribution and pressure distribution, and observing the similarities and differences in flow fields; error analysis, calculating the error between the predicted value and the measured value, and evaluating the accuracy of the simulation model.

[0058] It should be noted that the performance comparison data serves as a bridge between wind tunnel simulation and actual testing, providing an important basis for evaluating and verifying the effectiveness of the simulation model. By analyzing the consistency between the two, engineers can be guided to optimize the design and improve the vehicle's aerodynamic performance and actual performance.

[0059] S250: Adjust the candidate simulation model according to the performance comparison data and the vehicle dynamic data to obtain a target simulation model.

[0060] Optionally, a sensitivity analysis is performed on the sample simulation data based on the performance comparison data and the vehicle dynamic data to obtain correlation parameters of the candidate simulation model and parameter weights corresponding to the correlation parameters; the candidate simulation model is adjusted based on the correlation parameters and the parameter weights to obtain the target simulation model.

[0061] Among them, sensitivity analysis refers to evaluating the sensitivity of the output results to these input changes by systematically changing the model input parameters; its purpose is to determine which parameters have the greatest impact on the simulation results, thereby identifying the correlation parameters. Correlation parameters refer to the parameters that have a significant impact on the simulation results determined by sensitivity analysis; these parameters can significantly change the model output and are the focus of further adjustment and optimization of the model. Parameter weight refers to the numerical value assigned to each correlation parameter in sensitivity analysis, reflecting the relative importance of the parameter to the model output; the weight value is usually determined by comparing the contribution of each parameter to the change in the model output.

[0062] It should be noted that the model adjustment process is actually the process of correcting the model, which is a step of modifying the candidate simulation model based on the sensitivity analysis results and the weights of the correlation parameters; this process may include parameter optimization, model reconstruction, etc.

[0063] Specifically, the candidate simulation models are adjusted according to the correlation parameters and parameter weights to obtain the target simulation model, which may be: the parameter weights are sorted, and the correlation parameters with higher parameter weights are determined as the target parameters; the parameter type of the target parameter is determined, and based on the correspondence between the parameter type and the adjustment method, the target adjustment method of the target parameter is determined according to the parameter type; based on the target adjustment method, the target parameters in the candidate simulation models are adjusted to obtain the target simulation model.

[0064] Among them, parameter weight sorting refers to sorting the correlation parameters from high to low according to the weight value of each parameter in the sensitivity analysis; this process helps identify the parameters that have the greatest impact on the model output. Target parameters refer to the correlation parameters that rank high in the parameter weight sorting; these parameters are considered to have the most significant impact on the output of the candidate simulation model, so they will be the focus of attention in the subsequent adjustment process. Parameter type refers to the correlation type of the target parameter, which can include positive correlation, negative correlation, etc. The target adjustment method refers to the specific adjustment strategy for the target parameter determined according to the parameter type; by identifying the type of the target parameter, the appropriate adjustment method is selected to ensure that the improvement of the simulation model achieves the expected effect.

[0065] Specifically, a sensitivity analysis is performed on the sample simulation data based on the performance comparison data and the vehicle dynamic data to obtain the correlation parameters of the candidate simulation model. The vehicle parameters in the sample simulation data are verified for correlation through wind tunnel simulation based on the performance comparison data and the vehicle dynamic data, and the vehicle parameters that have passed the correlation verification through wind tunnel simulation are determined as correlation parameters; the correlation parameters are verified for positive correlation with wind tunnel simulation data to obtain the parameter type of the correlation parameters.

[0066] Among them, wind tunnel simulation correlation verification refers to confirming whether there is a statistical correlation between certain parameters through experiments or data analysis methods; this usually involves comparing different data sets (such as wind tunnel simulation data and vehicle dynamics data) to determine which parameters are significantly correlated with vehicle performance. Positive correlation verification refers to the process of detecting whether vehicle performance or output results increase when a certain parameter increases; this type of verification helps to confirm which parameters should be regarded as positive correlation parameters, so that their improvement effects can be considered when optimizing the design. Parameter type usually refers to the classification of parameters into positive correlation parameters, negative correlation parameters, or irrelevant parameters based on the direction and nature of the parameter's impact on the model output; this classification helps to select appropriate adjustment strategies during model optimization and decision-making.

[0067] Further, the parameter type of the correlation parameter that has passed the positive correlation verification of the wind tunnel simulation data is determined as a positive correlation parameter; and the parameter type of the correlation parameter that has not passed the positive correlation verification of the wind tunnel simulation data is determined as a negative correlation parameter.

[0068] It can be understood that by performing sensitivity analysis on sample simulation data, parameters related to vehicle performance can be identified, and the relevance and type of these parameters can be verified through wind tunnel simulation; this process provides important data support for vehicle design and optimization, helping to improve performance and efficiency.

[0069] The embodiment of the present application determines the initial simulation model of the target vehicle according to the geometric data of the target vehicle and the historical wind tunnel simulation results; sets the road simulation parameters and road simulation conditions for the initial simulation model to obtain the candidate simulation model of the target vehicle; inputs the collected sample simulation data of the target vehicle into the candidate simulation model to obtain the wind tunnel simulation data of the target vehicle; compares the consistency of the vehicle performance data with the wind tunnel simulation data to obtain the performance comparison data; adjusts the candidate simulation model according to the performance comparison data and the vehicle dynamic data to obtain the target simulation model. The above technical scheme realizes the establishment of a wind tunnel simulation model of the vehicle on an open road by fully considering the real road conditions on the basis of wind tunnel simulation.

[0070] Embodiment 3

[0071] Figure 3 This is a schematic diagram of the structure of a vehicle wind tunnel simulation model building device provided in accordance with the third embodiment of the present application, which can be applied to the case of building a simulation model for a vehicle wind tunnel test on an open road. The vehicle wind tunnel simulation model building device can be implemented in the form of hardware and / or software, and the vehicle wind tunnel simulation model building device can be configured in a computer device, such as a server. Figure 3 As shown, the device comprises:

[0072] A first model building module 310 is used to determine an initial simulation model of a target vehicle based on geometric data of the target vehicle and historical wind tunnel simulation results;

[0073] A second model building module 320 is used to set road simulation parameters and road simulation conditions for the initial simulation model to obtain a candidate simulation model of the target vehicle;

[0074] A model simulation module 330 is used to input the collected sample simulation data of the target vehicle into the candidate simulation model to obtain wind tunnel simulation data of the target vehicle;

[0075] The model adjustment module 340 is used to adjust the candidate simulation model according to the wind tunnel measured data and the wind tunnel simulation data to obtain the target simulation model; the wind tunnel measured data is collected during the actual driving of the target vehicle.

[0076] The embodiment of the present application determines the initial simulation model of the target vehicle according to the geometric data of the target vehicle and the historical wind tunnel simulation results; sets the road simulation parameters and road simulation conditions for the initial simulation model to obtain the candidate simulation model of the target vehicle; inputs the collected sample simulation data of the target vehicle into the candidate simulation model to obtain the wind tunnel simulation data of the target vehicle; adjusts the candidate simulation model according to the wind tunnel measured data and the wind tunnel simulation data to obtain the target simulation model; the wind tunnel measured data is collected by the target vehicle during actual driving. The above technical scheme realizes the establishment of a wind tunnel simulation model of the vehicle on an open road by fully considering the real road conditions on the basis of wind tunnel simulation.

[0077] Optionally, the wind tunnel measured data includes vehicle performance data and vehicle dynamic data of the target vehicle; accordingly, the model adjustment module 340 includes:

[0078] A data comparison unit, used for comparing the vehicle performance data with the wind tunnel simulation data for consistency, to obtain performance comparison data;

[0079] The model adjustment unit is used to adjust the candidate simulation model according to the performance comparison data and the vehicle dynamic data to obtain the target simulation model.

[0080] Optional, model adjustment unit, including:

[0081] A data analysis subunit is used to perform sensitivity analysis on the sample simulation data according to the performance comparison data and the vehicle dynamic data, and obtain correlation parameters of the candidate simulation model and parameter weights corresponding to the correlation parameters;

[0082] The model adjustment subunit is used to adjust the candidate simulation model according to the correlation parameter and the parameter weight to obtain the target simulation model.

[0083] Optionally, the model adjustment subunit is used to:

[0084] The parameter weights are sorted, and the correlation parameters with the highest parameter weights are determined as the target parameters;

[0085] Determine a parameter type of the target parameter, and based on a correspondence between the parameter type and the adjustment method, determine a target adjustment method of the target parameter according to the parameter type;

[0086] Based on the target adjustment method, the target parameters in the candidate simulation model are adjusted to obtain the target simulation model.

[0087] Optional, data analysis subunit, specifically used for:

[0088] According to the performance comparison data and the vehicle dynamic data, the vehicle parameters in the sample simulation data are verified by wind tunnel simulation for correlation, and the vehicle parameters verified by wind tunnel simulation for correlation are determined as correlation parameters;

[0089] The positive correlation of the correlation parameters is verified by wind tunnel simulation data to obtain the parameter type of the correlation parameters.

[0090] Optional, data analysis subunit, specifically used for:

[0091] Determine the parameter type of the correlation parameter verified by the positive correlation of wind tunnel simulation data as a positive correlation parameter;

[0092] The parameter type of the correlation parameter that has not been verified by the positive correlation of the wind tunnel simulation data is determined as a negative correlation parameter.

[0093] Optionally, the device further comprises a model verification module, wherein the model verification module is used to:

[0094] After adjusting the candidate simulation models according to the wind tunnel measured data and the wind tunnel simulation data to obtain the target simulation model, the accuracy of the target simulation model is verified, and if the accuracy verification fails, the wind tunnel simulation data of the target vehicle is re-determined.

[0095] The vehicle wind tunnel simulation model establishment device provided in the embodiment of the present application can execute the vehicle wind tunnel simulation model establishment method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects for executing each vehicle wind tunnel simulation model establishment method.

[0096] Embodiment 4

[0097] Figure 4 4 is a schematic diagram of the structure of an electronic device 410 for implementing the vehicle wind tunnel simulation model establishment method of the embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0098] like Figure 4As shown, the electronic device 410 includes at least one processor 411, and a memory connected to the at least one processor 411 in communication, such as a read-only memory (ROM) 412, a random access memory (RAM) 413, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 411 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 412 or the computer program loaded from the storage unit 418 to the random access memory (RAM) 413. In RAM413, various programs and data required for the operation of the electronic device 410 can also be stored. The processor 411, ROM412 and RAM413 are connected to each other via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0099] Multiple components in the electronic device 410 are connected to the I / O interface 415, including: an input unit 416, such as a keyboard, a mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a disk, an optical disk, etc.; and a communication unit 419, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 419 allows the electronic device 410 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0100] The processor 411 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 411 executes the various methods and processes described above, such as a vehicle wind tunnel simulation model establishment method.

[0101] In some embodiments, the vehicle wind tunnel simulation model establishment method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 410 via the ROM 412 and / or the communication unit 419. When the computer program is loaded into the RAM 413 and executed by the processor 411, one or more steps of the vehicle wind tunnel simulation model establishment method described above may be performed. Alternatively, in other embodiments, the processor 411 may be configured as a vehicle wind tunnel simulation model establishment method in any other appropriate manner (e.g., by means of firmware).

[0102] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] The computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable vehicle wind tunnel simulation model building device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0104] In the context of the present application, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0105] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0106] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0107] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0108] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0109] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for establishing a vehicle wind tunnel simulation model, characterized in that: include: Determining an initial simulation model of the target vehicle based on geometric data of the target vehicle and historical wind tunnel simulation results; Setting road simulation parameters and road simulation conditions for the initial simulation model to obtain a candidate simulation model of the target vehicle; Inputting the collected sample simulation data of the target vehicle into the candidate simulation model to obtain wind tunnel simulation data of the target vehicle; The candidate simulation model is adjusted according to the wind tunnel measured data and the wind tunnel simulation data to obtain a target simulation model; the wind tunnel measured data is collected during the actual driving of the target vehicle.

2. The method according to claim 1, characterized in that The wind tunnel measured data includes vehicle performance data and vehicle dynamic data of the target vehicle; accordingly, the candidate simulation model is adjusted according to the wind tunnel measured data and the wind tunnel simulation data to obtain a target simulation model, including: Comparing the vehicle performance data with the wind tunnel simulation data for consistency to obtain performance comparison data; The candidate simulation model is adjusted according to the performance comparison data and the vehicle dynamic data to obtain a target simulation model.

3. The method according to claim 2, characterized in that The step of adjusting the candidate simulation model according to the performance comparison data and the vehicle dynamic data to obtain a target simulation model includes: Performing sensitivity analysis on the sample simulation data according to the performance comparison data and the vehicle dynamic data to obtain correlation parameters of the candidate simulation model and parameter weights corresponding to the correlation parameters; The candidate simulation model is adjusted according to the correlation parameter and the parameter weight to obtain a target simulation model.

4. The method according to claim 3, characterized in that: The step of adjusting the candidate simulation model according to the correlation parameter and the parameter weight to obtain a target simulation model includes: Sorting the parameter weights, and determining the correlation parameters with the highest parameter weights as target parameters; Determining a parameter type of the target parameter, and based on a correspondence between the parameter type and the adjustment method, determining a target adjustment method of the target parameter according to the parameter type; Based on the target adjustment method, the target parameters in the candidate simulation model are adjusted to obtain a target simulation model.

5. The method according to claim 3, characterized in that: According to the performance comparison data and the vehicle dynamic data, a sensitivity analysis is performed on the sample simulation data to obtain correlation parameters of the candidate simulation model, including: According to the performance comparison data and the vehicle dynamic data, performing wind tunnel simulation correlation verification on the vehicle parameters in the sample simulation data, and determining the vehicle parameters that pass the wind tunnel simulation correlation verification as correlation parameters; A positive correlation verification of wind tunnel simulation data is performed on the correlation parameter to obtain a parameter type of the correlation parameter.

6. The method according to claim 5, characterized in that The correlation parameter is verified by positive correlation of wind tunnel simulation data to obtain parameter types of the correlation parameter, including: Determining the parameter type of the correlation parameter verified by the positive correlation of the wind tunnel simulation data as a positive correlation parameter; The parameter type of the correlation parameter that fails to pass the positive correlation verification of the wind tunnel simulation data is determined as a negative correlation parameter.

7. The method according to claim 1, characterized in that After the candidate simulation model is adjusted according to the wind tunnel measured data and the wind tunnel simulation data to obtain the target simulation model, the method further includes: The target simulation model is verified for accuracy, and if the accuracy verification fails, the wind tunnel simulation data of the target vehicle is re-determined.

8. A vehicle wind tunnel simulation model building device, characterized in that: include: A first model building module, used for determining an initial simulation model of the target vehicle according to geometric data of the target vehicle and historical wind tunnel simulation results; A second model building module is used to set road simulation parameters and road simulation conditions for the initial simulation model to obtain a candidate simulation model of the target vehicle; A model simulation module, used for inputting the collected sample simulation data of the target vehicle into the candidate simulation model to obtain wind tunnel simulation data of the target vehicle; The model adjustment module is used to adjust the candidate simulation model according to the wind tunnel measured data and the wind tunnel simulation data to obtain the target simulation model; the wind tunnel measured data is collected during the actual driving of the target vehicle.

9. The device according to claim 8, characterized in that The wind tunnel measured data includes vehicle performance data and vehicle dynamic data of the target vehicle; accordingly, the model adjustment module includes: A data comparison unit, used for performing consistency comparison between the vehicle performance data and the wind tunnel simulation data to obtain performance comparison data; A model adjustment unit is used to adjust the candidate simulation model according to the performance comparison data and the vehicle dynamic data to obtain a target simulation model.

10. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle wind tunnel simulation model establishment method according to any one of claims 1 to 7.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle wind tunnel simulation model establishment method according to any one of claims 1 to 7 is implemented.

12. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for establishing a vehicle wind tunnel simulation model according to any one of claims 1 to 7 is implemented.

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