CN119849370B - Automobile dynamic wind resistance simulation method and equipment based on artificial intelligence - Google Patents
Automobile dynamic wind resistance simulation method and equipment based on artificial intelligenceInfo
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Abstract
The application discloses an artificial intelligence-based automobile dynamic wind resistance simulation method and equipment, the method comprises the steps of carrying out wind resistance influence degree threshold analysis on a vehicle surface part on three-dimensional parameters of a part to determine the vehicle wind resistance influence part, carrying out part structure feature analysis on the vehicle wind resistance influence part to obtain wind resistance correlation feature parameters of the vehicle surface part, carrying out tire vertical deformation simulation on vehicle tire parameters to obtain vehicle wind resistance simulation height parameters, determining the vehicle wind resistance simulation parameters through vehicle wind resistance feature analysis, inputting the vehicle wind resistance simulation parameters into a preset wind resistance simulation model to obtain a vehicle dynamic wind resistance model to be trained, and carrying out dynamic loss function analysis on the vehicle dynamic wind resistance model to be trained to obtain vehicle wind resistance dynamic change data to dynamically simulate automobile wind resistance. The method solves the technical problem that the simulated wind resistance of the vehicle cannot be determined directly by replacing part of the vehicle in the wind resistance adjusting process.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to an automobile dynamic wind resistance simulation method and equipment based on artificial intelligence.
Background
Windage is one of the key factors affecting vehicle performance, fuel economy and noise level during automobile design and development. Conventionally, in order to evaluate and optimize the windage performance of an automobile, researchers are often required to perform whole-vehicle modeling and analyze the windage performance of the automobile under different working conditions through complex Computational Fluid Dynamics (CFD) simulation.
Because the simulation of the wind resistance of the vehicle is usually realized through the whole vehicle modeling in the prior art, the wind resistance influence among all parts on the surface of the vehicle is interrelated, and the actual performance of a part in the whole vehicle can not be accurately reflected by analyzing the part alone. In the research and development process of vehicles of various types and vehicles of the same type, the difficulty of remodelling for replacing or modifying parts of the vehicles is high, the operation amount and operation time are difficult to meet the production requirement, and in the wind resistance adjustment process, wind resistance simulation data can be timely provided for reference when the parts of the vehicles are partially modified.
Disclosure of Invention
The embodiment of the application provides an artificial intelligence-based automobile dynamic wind resistance simulation method and equipment, which solve the technical problem that in the wind resistance adjustment process in the prior art, the wind resistance of an automobile can not be determined directly by replacing part of components of the automobile.
The embodiment of the application provides an artificial intelligence-based automobile dynamic wind resistance simulation method which is characterized by comprising the steps of obtaining a component three-dimensional parameter of a vehicle surface component, analyzing a wind resistance influence degree threshold value of the vehicle surface component on the component three-dimensional parameter to determine the vehicle wind resistance influence component, analyzing component structural characteristics of the vehicle wind resistance influence component to obtain wind resistance correlation characteristic parameters of the vehicle surface component, obtaining vehicle tire parameters, simulating the tire vertical deformation of the vehicle tire parameters to obtain vehicle wind resistance simulation height parameters, determining the vehicle wind resistance simulation parameters through the vehicle wind resistance characteristic analysis based on the vehicle component wind resistance characteristic parameters and the vehicle wind resistance simulation height parameters, inputting the vehicle wind resistance simulation parameters into a preset wind resistance simulation model to obtain a vehicle dynamic wind resistance model to be trained, analyzing a dynamic loss function of the vehicle dynamic wind resistance model to be trained to obtain vehicle wind resistance dynamic change data, and dynamically simulating automobile wind resistance.
In one implementation mode of the application, wind resistance influence degree threshold analysis of a vehicle surface component is carried out on a component three-dimensional parameter to determine a vehicle wind resistance influence component, and the method specifically comprises the steps of determining space distribution mapping of the vehicle surface parameter through space distribution division of the vehicle component based on the component three-dimensional parameter, carrying out distribution range delineation on the space distribution mapping to obtain a vehicle component distribution space range, determining a wind resistance influence range threshold according to wind resistance influence correlation evaluation of the vehicle component distribution space range, and carrying out model mapping configuration on the wind resistance influence range threshold to determine the vehicle wind resistance influence component.
In one implementation mode of the application, component structural feature analysis is carried out on a vehicle wind resistance influence component to obtain wind resistance correlation characteristic parameters of a vehicle surface component, and the method specifically comprises the steps of carrying out flow field characteristic simulation on the vehicle surface wind resistance influence component to determine an air resistance coefficient set corresponding to the vehicle surface wind resistance influence component, determining air resistance coefficients of the vehicle component through component key field matching based on the air resistance coefficient set, carrying out correlation analysis on the air resistance coefficients of the vehicle component to obtain the vehicle component resistance coefficients, carrying out fluid characteristic change simulation on the vehicle component resistance coefficients, and determining the vehicle component wind resistance characteristic parameters.
In one implementation mode of the application, the vehicle tire parameter is subjected to tire vertical deformation simulation to obtain the vehicle windage simulation height parameter, and the vehicle windage simulation height parameter is obtained by carrying out data preprocessing on the vehicle tire parameter to obtain the tire parameter to be simulated, wherein the tire parameter to be simulated comprises a tire size, a tire material attribute and standard application air pressure, determining the tire vertical stiffness parameter through tire vertical stiffness analysis based on the tire parameter to be simulated, obtaining the vehicle vertical load parameter, and obtaining the vehicle windage simulation height parameter through tire vertical deformation analysis according to the tire vertical stiffness parameter and the vehicle vertical load parameter.
In one implementation mode of the application, vehicle wind resistance simulation parameters are determined through vehicle wind resistance feature analysis based on vehicle component wind resistance feature parameters and vehicle wind resistance simulation height parameters, and the method specifically comprises the steps of determining vehicle wind resistance simulation geometric data based on the vehicle wind resistance simulation height parameters, determining vehicle cavity geometric data according to the vehicle wind resistance simulation geometric data, carrying out boundary fluid influence analysis on the vehicle cavity geometric data to obtain cavity component wind resistance feature parameters, and determining the vehicle wind resistance simulation parameters according to the vehicle component wind resistance feature parameters and the cavity component wind resistance feature parameters.
In one implementation mode of the application, the vehicle wind resistance simulation parameters are input into a preset wind resistance simulation model to obtain a vehicle dynamic wind resistance model to be trained, and the method specifically comprises the steps of dividing training data of the vehicle simulation parameters to obtain a simulation model data set, wherein the simulation model data set comprises a simulation training set and a simulation verification set, inputting the simulation training set into the wind resistance simulation model, and carrying out boundary condition configuration on the wind resistance simulation model to obtain the vehicle dynamic wind resistance model to be trained.
In one implementation mode of the application, the dynamic loss function analysis is carried out on the dynamic wind resistance model of the vehicle to be trained to obtain dynamic change data of the wind resistance of the vehicle so as to dynamically simulate the wind resistance of the vehicle, and the method specifically comprises the steps of training the dynamic wind resistance model of the vehicle to be trained until the model converges based on a simulation verification set so as to obtain the wind resistance model of the vehicle; the method comprises the steps of carrying out loss function iteration on a dynamic wind resistance model of a vehicle to obtain a minimum loss function, updating the wind resistance model weight according to the minimum loss function to obtain the dynamic wind resistance model of the vehicle, obtaining current vehicle information, inputting the vehicle information into the dynamic wind resistance model of the vehicle, and using dynamic change data of the wind resistance of the vehicle.
In one implementation mode of the application, after the dynamic loss function analysis is carried out on the dynamic wind resistance model of the vehicle to be trained to determine the wind resistance coefficient data of the vehicle, the method further comprises the steps of acquiring wind resistance experimental data, determining the dynamic adjustment parameters of the loss function through simulation deviation analysis based on the wind resistance experimental data, and determining the dynamic wind resistance model of the vehicle through wind resistance model optimization according to the dynamic adjustment parameters of the loss function.
In one implementation mode of the application, the dynamic adjustment parameters of the loss function are determined through simulation deviation analysis based on wind resistance experimental data, and the method specifically comprises the steps of comparing the wind resistance experimental data with wind resistance experimental data in a data deviation mode to determine wind resistance simulation deviation data, and carrying out iterative analysis on the dynamic parameters of the loss function through a genetic algorithm based on the wind resistance simulation deviation data to determine the dynamic adjustment parameters of the loss function.
The embodiment of the application also provides an artificial intelligence-based automobile dynamic wind resistance simulation device which is characterized by comprising at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, the instructions are executed by the at least one processor, so that the at least one processor can acquire component three-dimensional parameters of a vehicle surface component, conduct wind resistance influence degree threshold analysis of the vehicle surface component on the component three-dimensional parameters to determine the vehicle wind resistance influence component, conduct component structural feature analysis on the vehicle wind resistance influence component to obtain wind resistance correlation characteristic parameters of the vehicle surface component, acquire vehicle tire parameters, conduct tire vertical deformation simulation on the vehicle tire parameters to obtain vehicle wind resistance simulation height parameters, determine vehicle wind resistance simulation parameters through vehicle wind resistance characteristic analysis based on the vehicle component wind resistance characteristic parameters, input the vehicle wind resistance simulation parameters into a preset wind resistance simulation model to obtain vehicle dynamic wind resistance to be trained, conduct wind resistance dynamic function wind resistance model analysis to obtain vehicle dynamic loss simulation dynamic change data of the vehicle dynamic resistance model to be trained, and dynamically simulate vehicle dynamic change wind resistance function wind resistance data.
The embodiment of the application provides an artificial intelligence-based automobile dynamic wind resistance simulation method and equipment, which are used for mapping a space range corresponding to vehicle components, simulating the automobile height of wind sub-environments of different automobile types and dynamically modeling the automobile wind resistance, so that the technical problem that the automobile simulated wind resistance cannot be determined directly through the replacement of part of the components of the automobile in the wind resistance adjustment process in the prior art is solved, the automobile wind resistance can be simulated without modeling the whole automobile again after the replacement of part of the components of the surface of the automobile, the prediction efficiency of the automobile wind resistance of the automobile is improved, and the operation requirement of the automobile wind resistance simulation in the research and development process is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of an artificial intelligence-based automobile dynamic wind resistance simulation method provided by an embodiment of the application;
Fig. 2 is a schematic diagram of an internal structure of an artificial intelligence-based dynamic wind resistance simulation device for an automobile according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application provides an artificial intelligence-based automobile dynamic wind resistance simulation method and equipment, which are used for mapping a space range corresponding to vehicle components, simulating the automobile height of wind sub-environments of different automobile types and dynamically modeling the automobile wind resistance, so that the technical problem that the automobile simulated wind resistance cannot be determined directly through the replacement of part of the components of the automobile in the wind resistance adjustment process in the prior art is solved, the automobile wind resistance can be simulated without modeling the whole automobile again after the replacement of part of the components of the surface of the automobile, the prediction efficiency of the automobile wind resistance of the automobile is improved, and the operation requirement of the automobile wind resistance simulation in the research and development process is reduced.
The following describes the technical scheme provided by the embodiment of the application in detail through the attached drawings.
FIG. 1 is a flow chart of an artificial intelligence based method for simulating dynamic wind resistance of an automobile according to an embodiment of the application. As shown in fig. 1, the method for simulating the dynamic wind resistance of the automobile based on the artificial intelligence provided by the embodiment of the application specifically comprises the following steps:
and 101, acquiring a component three-dimensional parameter of a vehicle surface component, and analyzing a wind resistance influence degree threshold value of the component three-dimensional parameter to determine the vehicle wind resistance influence component.
The method comprises the steps of determining spatial distribution mapping of vehicle surface parameters through spatial distribution division of vehicle components based on the three-dimensional parameters of the components, carrying out distribution range delineation on the spatial distribution mapping to obtain a vehicle component distribution spatial range, determining a wind resistance influence range threshold value according to wind resistance influence correlation evaluation according to the vehicle component distribution spatial range, and carrying out model mapping configuration on the wind resistance influence range threshold value to determine the vehicle wind resistance influence component.
According to the application, the three-dimensional parameters of the vehicle surface part are obtained, and the wind resistance influence degree threshold analysis is carried out on the three-dimensional parameters of the part, so that the vehicle wind resistance influence part is determined, the influence degree and influence relation analysis of the vehicle surface parts of different automobile types are realized, and the accuracy of the vehicle wind resistance simulation is improved.
In the present embodiment, the following example 1 is used for explanation in detail.
Example 1 due to the large differences in the shape of the vehicle surface components, in the case of simulated windage, it is necessary to analyze the extent to which each component of the vehicle affects windage under certain simulated test conditions.
First, the portion immediately ahead of the vehicle is mainly composed of a wind shielding portion and a vehicle heat exchange portion (determined by a vehicle driving method) as a main windage affecting surface. In order to enable the determination of the main distribution position of each vehicle component, the spatial distribution map needs to be subjected to distribution range delineation so as to obtain a vehicle component distribution spatial range, and the vehicle component distribution spatial range characterizes the main spatial distribution situation of each vehicle component. For example, windshields, rearview mirrors, and the like mainly affect the spatial distribution and connection relationship of components of the vehicle windage.
The largest space range defined by the distribution range is the space formed by the outermost surfaces of the parts corresponding to all the types of the parts which can be recorded currently, wind resistance simulation operation exceeding the range can not occur when the parts are replaced, subsequent wind resistance simulation can be more stably performed, and the distribution range space can be expanded when the larger distribution range occurs when the last name height change is required.
It should be noted that the spatial distribution map may help the model to search the corresponding component data according to the spatial distribution map by importing key fields or preset names of the vehicle and the vehicle component in the wind resistance simulation process.
Then, aerodynamic simulation can be carried out on each part of the vehicle through aerodynamic simulation, wind resistance conditions of each part of the vehicle body under certain fluid configuration conditions are determined through correlation analysis according to the result of the aerodynamic simulation, and a wind resistance influence range threshold value is set according to actual production requirements. The threshold is used to define which components are critical influencing components to the wind resistance of the vehicle section.
And finally, model mapping configuration is carried out on the wind resistance influence range threshold value so as to determine the wind resistance influence component of the vehicle.
And 102, carrying out component structural feature analysis on the wind resistance influence component of the vehicle to obtain wind resistance correlation characteristic parameters of the component on the surface of the vehicle.
The method comprises the steps of carrying out flow field characteristic simulation on a vehicle surface wind resistance influence component to determine an air resistance coefficient set corresponding to the vehicle surface wind resistance influence component, determining the air resistance coefficient of the vehicle component based on the air resistance coefficient set through component key field matching, carrying out correlation analysis among vehicle components on the air resistance coefficient of the vehicle component to obtain the vehicle component resistance coefficient, carrying out fluid characteristic change simulation on the vehicle component resistance coefficient, and determining wind resistance characteristic parameters of the vehicle component.
The application obtains the wind resistance correlation characteristic parameters of the vehicle surface parts by carrying out part structural characteristic analysis on the vehicle wind resistance influence parts, thereby realizing
In the present embodiment, the following example 2 is used for explanation in detail.
Example 2 first, in order to evaluate the wind resistance condition of the wind resistance surface formed by each wind resistance influencing component of the preliminary vehicle in the wind resistance simulation process, the flow field characteristic simulation is performed on the wind resistance influencing component of the vehicle surface to determine the air resistance coefficient set corresponding to the wind resistance influencing component of the vehicle surface.
Then, according to the distribution space range of the vehicle components, a wind resistance influence range threshold value is determined through wind resistance influence correlation evaluation. Since the combination of the individual components of the vehicle and the protrusion of the connecting portions can have an influence on the local air flow, the result of the influence is also influenced by the three-dimensional parameters of the individual components.
Therefore, aerodynamic simulation can be performed on each part of the vehicle through aerodynamic simulation, and according to the result of the aerodynamic simulation, the wind resistance influence degree between the body and the parts of each part of the vehicle can be determined through correlation analysis. According to the wind resistance influence degree, the wind resistance coefficient of each part of the vehicle and the aerodynamic influence condition among the parts are utilized to obtain the resistance coefficient of the vehicle part.
Finally, in order to improve the accuracy of the basic data, fluid characteristic change simulation is carried out on the resistance coefficient of the vehicle part, and the wind resistance characteristic parameter of the vehicle part is determined.
And 103, acquiring vehicle tire parameters, and performing tire vertical deformation simulation on the vehicle tire parameters to obtain vehicle windage simulation height parameters.
The method comprises the steps of carrying out data preprocessing on vehicle tire parameters to obtain tire parameters to be simulated, wherein the tire parameters to be simulated comprise tire sizes, tire material properties and standard application air pressure, determining tire vertical stiffness parameters through tire vertical stiffness analysis based on the tire parameters to be simulated, obtaining vehicle vertical load parameters, and obtaining vehicle windage simulation height parameters through tire vertical deformation analysis according to the tire vertical stiffness parameters and the vehicle vertical load parameters.
According to the application, the vehicle tire parameters are obtained, and the tire vertical deformation quantity simulation is carried out on the vehicle tire parameters so as to obtain the vehicle windage simulation height parameters, so that the height parameter analysis of the vehicle-combined windage simulation is realized, and the accuracy of windage simulation is improved.
In the present embodiment, the following example 3 is explained in detail.
Example 3 vehicle tire parameters including tire size, tire material properties, standard applied air pressure are obtained.
The tire material properties, including the elastic modulus, poisson's ratio, etc. of the tire determine the degree of deformation after being subjected to a force. Tire dimensions include the width, thickness, and tread design of the tire.
The air pressure inside the tire also affects its deformation. In a certain range, as the air pressure increases, the cornering stiffness of the tire increases and the deformation amount decreases. The application is based on the standard applied air pressure of various tires.
Based on the above tire parameters to be simulated, the tire vertical deformation of the tire at a specific pressure (vehicle vertical load parameter) is calculated by Fiala model (a tire mechanical model).
And 104, determining the wind resistance simulation parameters of the vehicle through the wind resistance characteristic analysis of the vehicle based on the wind resistance characteristic parameters of the vehicle components and the wind resistance simulation height parameters of the vehicle.
The method comprises the steps of determining vehicle wind resistance simulation geometric data based on vehicle wind resistance simulation height parameters, determining vehicle cavity geometric data according to the vehicle wind resistance simulation geometric data, analyzing boundary fluid influence of the vehicle cavity geometric data to obtain cavity component wind resistance characteristic parameters, and determining the vehicle wind resistance simulation parameters according to the vehicle component wind resistance characteristic parameters and the cavity component wind resistance characteristic parameters.
According to the application, the wind resistance simulation parameters of the vehicle are determined by building the wind resistance characteristic model of the vehicle based on the wind resistance characteristic parameters of the vehicle parts and the wind resistance simulation height parameters of the vehicle, so that the wind resistance parameter analysis of the vehicle wall part and the non-cavity part is realized, and the accuracy of the wind resistance parameters of the vehicle and the generalization capability of the model are improved.
In the present embodiment, the following example 4 is explained in detail.
Example 4 first, on the basis of the wind resistance characteristic parameters of the vehicle parts and the wind resistance simulation height parameters of the vehicle, the cavity geometry data of the vehicle are extracted. The cavity refers to a closed or semi-closed space formed inside or outside the vehicle, such as an engine compartment, a radiator fan, etc.
Boundary fluid impact analysis is then performed on the vehicle cavity geometry data. This step aims at assessing the flow characteristics of the fluid at the boundary of the cavity and the contribution of the cavity to the total windage. The flow condition of the fluid around the vehicle is simulated through simulation software, and particularly the change of the flow of the fluid in the inlet, the outlet and the interior of the cavity is focused, so that the wind resistance characteristic parameters of the cavity components are obtained.
After the wind resistance characteristic parameters (such as pressure distribution, flow velocity distribution and the like of the surface of the vehicle body) of the vehicle part and the wind resistance characteristic parameters of the cavity part are obtained, the parameters are integrated into a wind resistance simulation model of the vehicle. The parameters provide key input information for simulation, so that the simulation result can more accurately reflect the wind resistance performance of the vehicle in the actual running process.
Step 105, inputting the wind resistance simulation parameters of the vehicle into a preset wind resistance simulation model to obtain a dynamic wind resistance model of the vehicle to be trained.
The method comprises the steps of dividing training data of vehicle simulation parameters to obtain a simulation model data set, wherein the simulation model data set comprises a simulation training set and a simulation verification set, inputting the simulation training set into PINN wind resistance simulation model models, and carrying out boundary condition configuration on the wind resistance simulation model models to obtain the dynamic wind resistance models of the vehicles to be trained.
According to the application, the vehicle wind resistance simulation parameters are input into the preset PINN wind resistance simulation model to obtain the vehicle dynamic wind resistance model to be trained, so that the vehicle dynamic wind resistance model is constructed.
And 106, carrying out dynamic loss function analysis on the dynamic wind resistance model of the vehicle to be trained to obtain dynamic change data of the wind resistance of the vehicle so as to dynamically simulate the wind resistance of the vehicle.
The method comprises the steps of training a vehicle dynamic wind resistance model to be trained until the model converges to obtain a vehicle wind resistance model based on a simulation verification set, carrying out loss function iteration on the vehicle dynamic wind resistance model to obtain a minimum loss function, updating through wind resistance model weights according to the minimum loss function to obtain the vehicle dynamic wind resistance model, obtaining current vehicle information, inputting the vehicle information into the vehicle dynamic wind resistance model, and carrying out vehicle wind resistance dynamic change data.
After the dynamic loss function analysis is carried out on the dynamic wind resistance model of the vehicle to be trained to determine the wind resistance coefficient data of the vehicle, the method further comprises the steps of obtaining wind resistance experimental data, determining dynamic adjustment parameters of the loss function through simulation deviation analysis based on the wind resistance experimental data, and determining the dynamic wind resistance model of the vehicle through wind resistance model optimization according to the dynamic adjustment parameters of the loss function.
The method comprises the steps of determining dynamic adjustment parameters of a loss function through simulation deviation analysis based on wind resistance experimental data, and specifically comprises the steps of comparing the wind resistance experimental data with wind resistance experimental data in a data deviation mode to determine wind resistance simulation deviation data, and carrying out iterative analysis on dynamic parameters of the loss function through a genetic algorithm based on the wind resistance simulation deviation data to determine the dynamic adjustment parameters of the loss function.
According to the method, the dynamic loss function analysis is carried out on the dynamic wind resistance model of the vehicle to be trained so as to determine the vehicle wind resistance coefficient data, so that the vehicle wind resistance can be simulated without modeling the whole vehicle again after part of vehicle surface components are replaced, the prediction efficiency of the vehicle wind resistance of the vehicle is improved, and the operation requirement of the vehicle wind resistance simulation in the research and development process is reduced.
In the examples of the present application, the following example 5 is explained in detail.
Example 5 first, a dynamic wind resistance model of a vehicle to be trained is trained. In the training process, model parameters are continuously adjusted until the model converges, namely, the prediction result of the model is basically consistent with the data in the simulation verification set, so that a preliminary vehicle wind resistance model is obtained.
Then, carrying out loss function iteration on the dynamic wind resistance model of the vehicle. And constructing a loss function by calculating an error between a model predicted value and a simulation verification set true value, and iteratively adjusting model parameters by adopting a gradient descent optimization algorithm so as to minimize a loss function value.
When the loss function value reaches a preset threshold value or does not drop significantly, the model is considered to be trained to an optimal state, and the model obtained at the moment is the dynamic wind resistance model of the vehicle with the minimum loss function.
In order to further improve the accuracy and generalization capability of the model, wind resistance data of a series of vehicles under different working conditions are obtained through wind tunnel experiments or actual road tests. And comparing the wind resistance experimental data with wind resistance data predicted by the model, and calculating the deviation between the wind resistance experimental data and the wind resistance data to obtain wind resistance simulation deviation data. This step helps identify under which conditions the model has a large prediction error.
And carrying out iterative analysis on dynamic parameters of the loss function by adopting a genetic algorithm based on wind resistance simulation deviation data. The genetic algorithm is a global optimization algorithm that searches for optimal solutions in a parameter space by modeling natural selection and genetic mechanisms. In this embodiment, a genetic algorithm is used to adjust the parameters of the loss function, regularization term, etc., to reduce the bias between model predictions and experimental data.
And dynamically adjusting parameters according to the optimal loss function obtained by iterative analysis of the genetic algorithm, and optimizing the dynamic wind resistance model of the vehicle. The optimized model has better prediction performance and generalization capability, and can more accurately reflect the wind resistance characteristics of the vehicle under different working conditions.
The optimized vehicle dynamic wind resistance model is applied to actual vehicle design and performance evaluation, and the accuracy and reliability of the model are verified by comparing the model prediction result with experimental data. Meanwhile, according to new problems and challenges encountered in practical application, the model is continuously iterated and optimized to continuously improve the performance of the model.
The above is a method embodiment of the present application. Based on the same inventive concept, the embodiment of the application also provides an artificial intelligence-based automobile dynamic wind resistance simulation device, and the structure of the device is shown in fig. 2.
Fig. 2 is a schematic diagram of an internal structure of an artificial intelligence-based dynamic wind resistance simulation device for an automobile according to an embodiment of the present application. As shown in fig. 2, the apparatus includes:
at least one processor 201;
And a memory 202 communicatively coupled to the at least one processor;
wherein the memory 202 stores instructions executable by the at least one processor, the instructions being executable by the at least one processor 201 to enable the at least one processor 201 to:
The method comprises the steps of obtaining three-dimensional parameters of components of a vehicle surface component, analyzing wind resistance influence degree threshold values of the vehicle surface component on the three-dimensional parameters of the components to determine a vehicle wind resistance influence component, analyzing component structural features of the vehicle wind resistance influence component to obtain wind resistance correlation feature parameters of the vehicle surface component, obtaining vehicle tire parameters, performing tire vertical deformation simulation on the vehicle tire parameters to obtain vehicle wind resistance simulation height parameters, determining the vehicle wind resistance simulation parameters through the vehicle wind resistance feature analysis based on the vehicle component wind resistance feature parameters and the vehicle wind resistance simulation height parameters, inputting the vehicle wind resistance simulation parameters into a preset wind resistance simulation model to obtain a vehicle dynamic wind resistance model to be trained, and performing dynamic loss function analysis on the vehicle dynamic wind resistance model to be trained to obtain vehicle wind resistance dynamic change data to dynamically simulate the wind resistance of the vehicle. .
The embodiments of the present application are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for the internet of things device and the medium embodiment, since they are substantially similar to the method embodiment, the description is relatively simple, and the relevant points are referred to in the description of the method embodiment.
The system, the medium and the method provided by the embodiment of the application are in one-to-one correspondence, so that the system and the medium also have similar beneficial technical effects to the corresponding method, and the beneficial technical effects of the method are explained in detail above, so that the beneficial technical effects of the system and the medium are not repeated here.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (7)
1. An artificial intelligence-based automobile dynamic wind resistance simulation method is characterized by comprising the following steps:
Acquiring a component three-dimensional parameter of a vehicle surface component, and carrying out wind resistance influence degree threshold analysis on the component three-dimensional parameter to determine a vehicle wind resistance influence component;
Performing component structural feature analysis on the vehicle wind resistance influencing component to obtain wind resistance correlation characteristic parameters of the vehicle surface component;
Acquiring vehicle tire parameters, and performing tire vertical deformation simulation on the vehicle tire parameters to obtain vehicle windage simulation height parameters;
Determining vehicle wind resistance simulation parameters through vehicle wind resistance characteristic analysis based on the vehicle component wind resistance characteristic parameters and the vehicle wind resistance simulation height parameters;
Inputting the vehicle wind resistance simulation parameters into a preset wind resistance simulation model to obtain a vehicle dynamic wind resistance model to be trained;
performing dynamic loss function analysis on the dynamic wind resistance model of the vehicle to be trained to obtain dynamic change data of the wind resistance of the vehicle so as to dynamically simulate the wind resistance of the vehicle;
and carrying out wind resistance influence degree threshold analysis on the three-dimensional parameters of the components to determine the wind resistance influence components of the vehicle, wherein the method specifically comprises the following steps:
Determining a spatial distribution map of the vehicle surface component by spatial distribution partitioning of the vehicle component based on the component three-dimensional parameter;
Carrying out distribution range delineation on the spatial distribution map to obtain a vehicle component distribution spatial range;
according to the distribution space range of the vehicle component, determining a wind resistance influence range threshold value through wind resistance influence correlation evaluation;
Model mapping configuration is carried out on the wind resistance influence range threshold value so as to determine the vehicle wind resistance influence component;
and carrying out component structural feature analysis on the vehicle wind resistance influence component to obtain wind resistance correlation feature parameters of the vehicle surface component, wherein the method specifically comprises the following steps:
Performing flow field characteristic simulation on the vehicle wind resistance influencing component to determine an air resistance coefficient set corresponding to the vehicle wind resistance influencing component;
determining the air resistance coefficient of the vehicle component through component key field matching based on the air resistance coefficient set;
carrying out correlation analysis among vehicle components on the air resistance coefficient of the vehicle component to obtain a vehicle component resistance coefficient;
performing fluid characteristic change simulation on the vehicle component resistance coefficient to determine the vehicle component wind resistance characteristic parameter;
Based on the vehicle component wind resistance characteristic parameter and the vehicle wind resistance simulation height parameter, determining the vehicle wind resistance simulation parameter through vehicle wind resistance characteristic analysis, specifically comprising:
determining vehicle wind resistance simulation geometric data based on the vehicle wind resistance simulation height parameters;
determining vehicle cavity geometric data according to the vehicle wind resistance simulation geometric data, and analyzing boundary fluid influence of the vehicle cavity geometric data to obtain wind resistance characteristic parameters of a cavity component;
And determining the wind resistance simulation parameters of the vehicle according to the wind resistance characteristic parameters of the vehicle part and the wind resistance characteristic parameters of the cavity part.
2. The method for simulating dynamic wind resistance of an automobile based on artificial intelligence according to claim 1, wherein the method for simulating the vertical deformation of the tire of the automobile is characterized by comprising the following steps:
The vehicle tire parameters are subjected to data preprocessing to obtain tire parameters to be simulated, wherein the tire parameters to be simulated comprise tire sizes, tire material properties and standard application air pressure;
determining the tire vertical stiffness parameter through tire vertical stiffness analysis based on the tire parameter to be simulated;
And obtaining a vehicle vertical load parameter, and obtaining the vehicle wind resistance simulation height parameter through tire vertical deformation analysis according to the tire vertical rigidity parameter and the vehicle vertical load parameter.
3. The method for simulating dynamic wind resistance of an automobile based on artificial intelligence according to claim 1, wherein the vehicle wind resistance simulation parameters are input into a preset PINN wind resistance simulation model to obtain the dynamic wind resistance model of the automobile to be trained, specifically comprising:
The vehicle wind resistance simulation parameters are subjected to training data division to obtain a simulation model data set, wherein the simulation model data set comprises a simulation training set and a simulation verification set;
Inputting the simulation training set into the PINN wind resistance simulation model, and carrying out boundary condition configuration on the PINN wind resistance simulation model to obtain the dynamic wind resistance model of the vehicle to be trained.
4. The method for simulating dynamic wind resistance of an automobile based on artificial intelligence according to claim 3, wherein the dynamic loss function analysis is performed on the dynamic wind resistance model of the automobile to be trained to obtain dynamic change data of the wind resistance of the automobile so as to dynamically simulate the wind resistance of the automobile, and the method specifically comprises the following steps:
Training the dynamic wind resistance model of the vehicle to be trained until the model converges based on the simulation verification set so as to obtain a vehicle wind resistance model;
Performing loss function iteration on the vehicle dynamic wind resistance model to obtain a minimum loss function;
According to the minimum loss function, a dynamic wind resistance model of the vehicle is obtained through wind resistance model weight updating;
And acquiring current vehicle information, and inputting the vehicle information into the vehicle dynamic wind resistance model to obtain the vehicle wind resistance dynamic change data.
5. The method for simulating dynamic wind resistance of an automobile based on artificial intelligence according to claim 1, wherein after the dynamic loss function analysis is performed on the dynamic wind resistance model of the automobile to be trained to determine the wind resistance coefficient data of the automobile, the method further comprises:
wind resistance experimental data are obtained, and dynamic loss function adjustment parameters are determined through simulation deviation analysis based on the wind resistance experimental data;
And dynamically adjusting parameters according to the loss function, and determining a dynamic wind resistance model of the vehicle through wind resistance model optimization.
6. The method for simulating dynamic wind resistance of an automobile based on artificial intelligence according to claim 5, wherein the method for determining the dynamic adjustment parameter of the loss function by simulation deviation analysis based on the wind resistance experimental data comprises the following steps:
Performing data deviation comparison on the windage experimental data and the windage experimental data to determine windage simulation deviation data;
and based on the windage simulation deviation data, carrying out iterative analysis on the dynamic parameters of the loss function through a genetic algorithm, and determining the dynamic adjustment parameters of the loss function.
7. An artificial intelligence based automotive dynamic wind resistance simulation device, the device comprising:
at least one processor;
And a memory communicatively coupled to the at least one processor;
Wherein the memory stores instructions executable by the at least one processor, the instructions are executable by the at least one processor to enable the at least one processor to:
Acquiring a component three-dimensional parameter of a vehicle surface component, and carrying out wind resistance influence degree threshold analysis on the component three-dimensional parameter to determine a vehicle wind resistance influence component;
Performing component structural feature analysis on the vehicle wind resistance influencing component to obtain wind resistance correlation characteristic parameters of the vehicle surface component;
Acquiring vehicle tire parameters, and performing tire vertical deformation simulation on the vehicle tire parameters to obtain vehicle windage simulation height parameters;
Determining vehicle wind resistance simulation parameters through vehicle wind resistance characteristic analysis based on the vehicle component wind resistance characteristic parameters and the vehicle wind resistance simulation height parameters;
Inputting the vehicle wind resistance simulation parameters into a preset wind resistance simulation model to obtain a vehicle dynamic wind resistance model to be trained;
performing dynamic loss function analysis on the dynamic wind resistance model of the vehicle to be trained to obtain dynamic change data of the wind resistance of the vehicle so as to dynamically simulate the wind resistance of the vehicle;
and carrying out wind resistance influence degree threshold analysis on the three-dimensional parameters of the components to determine the wind resistance influence components of the vehicle, wherein the method specifically comprises the following steps:
Determining a spatial distribution map of the vehicle surface component by spatial distribution partitioning of the vehicle component based on the component three-dimensional parameter;
Carrying out distribution range delineation on the spatial distribution map to obtain a vehicle component distribution spatial range;
according to the distribution space range of the vehicle component, determining a wind resistance influence range threshold value through wind resistance influence correlation evaluation;
Model mapping configuration is carried out on the wind resistance influence range threshold value so as to determine the vehicle wind resistance influence component;
and carrying out component structural feature analysis on the vehicle wind resistance influence component to obtain wind resistance correlation feature parameters of the vehicle surface component, wherein the method specifically comprises the following steps:
Performing flow field characteristic simulation on the vehicle wind resistance influencing component to determine an air resistance coefficient set corresponding to the vehicle wind resistance influencing component;
determining the air resistance coefficient of the vehicle component through component key field matching based on the air resistance coefficient set;
carrying out correlation analysis among vehicle components on the air resistance coefficient of the vehicle component to obtain a vehicle component resistance coefficient;
performing fluid characteristic change simulation on the vehicle component resistance coefficient to determine the vehicle component wind resistance characteristic parameter;
Based on the vehicle component wind resistance characteristic parameter and the vehicle wind resistance simulation height parameter, determining the vehicle wind resistance simulation parameter through vehicle wind resistance characteristic analysis, specifically comprising:
determining vehicle wind resistance simulation geometric data based on the vehicle wind resistance simulation height parameters;
determining vehicle cavity geometric data according to the vehicle wind resistance simulation geometric data, and analyzing boundary fluid influence of the vehicle cavity geometric data to obtain wind resistance characteristic parameters of a cavity component;
And determining the wind resistance simulation parameters of the vehicle according to the wind resistance characteristic parameters of the vehicle part and the wind resistance characteristic parameters of the cavity part.
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