Vehicle wading simulation analysis method and device and storage medium
By simulating and analyzing the vehicle, obtaining a model and simulating the liquid flow path, the high-cost and high-cycle vehicle wading analysis problem in existing technologies was solved, and early assessment of safety risks and problem discovery were achieved.
Patent Information
- Application Number
- CN202410294821.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-14
- Publication Date
- 2025-09-16
AI Technical Summary
In the existing technology, vehicle wading analysis methods usually conduct tests after production, resulting in high testing costs and long testing cycles, and are unable to effectively evaluate the safety of vehicles in wading conditions.
By obtaining the vehicle's preset part model, water storage container model and grid parameters, meshing processing and physical field parameter construction are performed, and the target solver is used to simulate the flow path of the liquid in the vehicle to assess potential safety risks in advance.
It reduces testing costs, shortens development cycles, and can discover and resolve potential water safety issues before vehicles are produced.
Smart Images
Figure CN120654582A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of simulation testing technology, and in particular to a vehicle wading simulation analysis method, device and storage medium. Background Art
[0002] Passengers in a vehicle may feel thirsty after driving or riding for a long time, and may bring a cup of water or a drink into the vehicle. If the vehicle suddenly stops, the water in the cup holder may spill out due to inertia. The spilled water may flow through gaps in the vehicle's interior panels and into the vehicle's electrical components, thus affecting vehicle safety.
[0003] Currently, vehicle wading analysis typically involves conducting a wading test on the vehicle after production to identify safety issues. However, this analysis method is typically costly and time-consuming. Summary of the Invention
[0004] In order to solve the above technical problems, the present application provides a vehicle wading simulation analysis method, device and storage medium, which can reduce testing costs and shorten the development cycle.
[0005] In the first aspect, the present application provides a vehicle wading simulation analysis method, including: obtaining a preset part model, a water storage container model and grid parameters of a vehicle to be simulated; gridding the preset part model according to the grid parameters to obtain a grid model; obtaining physical field parameters and motion parameters, the physical field parameters at least including the physical field parameters of the grid model, the physical field parameters of the water storage container model, and the physical field parameters of the liquid in the water storage container model; constructing a simulation scene of the vehicle to be simulated according to the physical field parameters, and controlling the vehicle to be simulated to perform simulated motion in the simulation scene according to the motion parameters; using a target solver to solve the simulation data of the liquid in the water storage container model in the grid model; the water storage container model is placed at the bracket position of the preset part model, and the bracket is set as a support device for the water storage container in the vehicle to be simulated; the simulation data includes the distribution of the liquid in the grid model; and according to the distribution of the liquid in the grid model, determining the flow path of the liquid in the vehicle to be simulated.
[0006] In the second aspect, the present application provides a vehicle wading simulation analysis device, including: an acquisition module for acquiring a preset part model, a water storage container model and grid parameters of a vehicle to be simulated; a processing module for gridding the preset part model according to the grid parameters to obtain a grid model; a setting module for acquiring physical field parameters and motion parameters, the physical field parameters at least including the physical field parameters of the grid model, the physical field parameters of the water storage container model, and the physical field parameters of the liquid in the water storage container model; a determination module for constructing a simulation scene of the vehicle to be simulated according to the physical field parameters, and controlling the simulated movement of the vehicle to be simulated in the simulation scene according to the motion parameters; a simulation module for using a target solver to solve the simulation data of the liquid in the water storage container model in the grid model; the water storage container model is placed at the bracket position of the preset part model, and the bracket is set as a support device for the water storage container in the vehicle to be simulated; the simulation data includes the distribution of the liquid in the grid model; and an output module for determining the flow path of the liquid in the vehicle to be simulated according to the distribution of the liquid in the grid model.
[0007] In a third aspect, the present application provides an electronic device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the vehicle wading simulation analysis method according to the first aspect is implemented.
[0008] In a fourth aspect, the present application provides a computer-readable storage medium, comprising: a computer program stored on the computer-readable storage medium, and when the computer program is executed by a processor, the vehicle wading simulation analysis method as in the first aspect is implemented.
[0009] In a fifth aspect, the present application provides a computer program product, comprising: when the computer program product is run on a computer, the computer implements the vehicle wading simulation analysis method as in the first aspect.
[0010] The technical solution provided by the present application has the following advantages over the prior art: obtaining a preset part model, a water storage container model and grid parameters of a vehicle to be simulated; gridding the preset part model according to the grid parameters to obtain a grid model; obtaining physical field parameters and motion parameters, the physical field parameters at least including the physical field parameters of the grid model, the physical field parameters of the water storage container model, and the physical field parameters of the liquid in the water storage container model; constructing a simulation scene of the vehicle to be simulated according to the physical field parameters, and controlling the simulated motion of the vehicle to be simulated in the simulation scene according to the motion parameters; using a target solver to solve the simulation data of the liquid in the water storage container model in the grid model; the water storage container model is placed at the bracket position of the preset part model, and the bracket is set as a support device for the water storage container in the vehicle to be simulated; the simulation data includes the distribution of the liquid in the grid model; and determining the flow path of the liquid in the vehicle to be simulated according to the distribution of the liquid in the grid model. In this way, when the simulated vehicle is performing simulated movement in the simulation scene, the state of the liquid in the water storage container model in the grid model can be simulated, that is, the risk of water contact of the preset part structure of the simulated vehicle can be evaluated in advance before the vehicle is produced, and problems existing in the preset part structure of the actual vehicle corresponding to the vehicle to be simulated in the vehicle wading simulation process in the water wading scene corresponding to the simulation scene can be discovered, thereby saving test costs and shortening the development cycle. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0013] Figure 1 One of the flow charts of the vehicle wading simulation analysis method is provided for the embodiment of the present application;
[0014] Figure 2 A second flow chart of a vehicle wading simulation analysis method is provided for an embodiment of the present application;
[0015] Figure 3 A schematic diagram of a vehicle wading simulation analysis scenario provided in an embodiment of the present application;
[0016] Figure 4A This is one of the speed-time correlation curve diagrams provided in the embodiments of the present application;
[0017] Figure 4B This is the second schematic diagram of the speed-time correlation curve provided in the embodiment of the present application;
[0018] Figure 4C The third diagram of the speed-time correlation curve provided in the embodiment of the present application;
[0019] Figure 5 The third flow chart of the vehicle wading simulation analysis method is provided for the embodiment of the present application;
[0020] Figure 6 A fourth flow chart of a vehicle wading simulation analysis method is provided for an embodiment of the present application;
[0021] Figure 7 A schematic diagram of a range for solving a restriction domain provided in an embodiment of the present application;
[0022] Figure 8 A fifth flow chart of a vehicle wading simulation analysis method is provided for an embodiment of the present application;
[0023] Figure 9 A schematic structural diagram of a vehicle wading simulation analysis device provided in an embodiment of the present application;
[0024] Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0025] In order to more clearly understand the above-mentioned objectives, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.
[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application can also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present application, not all of the embodiments.
[0027] The vehicle wading simulation analysis method provided in the embodiment of the present application can be implemented by a vehicle wading simulation analysis device, and the vehicle wading simulation analysis device provided in the embodiment of the present application can be hardware or software. When the vehicle wading simulation analysis device is hardware, it can be various electronic devices with the function of running vehicle wading simulation analysis, including but not limited to vehicle-mounted equipment, smart vehicles, mobile phones, computers, etc. When the vehicle wading simulation analysis device is software, it can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules, or it can be implemented as a single software or software module, which is not specifically limited here.
[0028] Figure 1 A flow chart of the vehicle wading simulation analysis method provided in the embodiment of the present application is shown as follows: Figure 1 As shown, the vehicle wading simulation analysis method may include the following steps.
[0029] S1. Obtain a preset part model, a water storage container model, and grid parameters of a vehicle to be simulated.
[0030] First, obtain the preset part model of the vehicle to be simulated.
[0031] In some embodiments, as Figure 2 As shown, the method of obtaining the preset part model of the vehicle to be simulated may include the following steps:
[0032] S11. Construct a whole vehicle model of the vehicle to be simulated.
[0033] Among them, the whole vehicle model may include vehicle interior, vehicle exterior, vehicle electrical appliances, vehicle chassis, body-in-white, vehicle opening and closing parts and other components.
[0034] In some embodiments, the whole vehicle model of the vehicle to be simulated can be constructed by receiving the whole vehicle model of the vehicle to be simulated from other devices in response to user operations; or the whole vehicle model can be constructed through relevant software, such as Catia, AutoCAD, etc., in response to user operations.
[0035] S12. Extracting a preset part model of the vehicle to be simulated from the whole vehicle model.
[0036] Specifically, the method for extracting the model of the preset part of the vehicle to be simulated from the full vehicle model can be to extract the geometric structure of the preset part from the full vehicle model based on the components that make up the vehicle to be simulated, thereby obtaining the preset part model. For example, the preset part can be the vehicle interior. For another example, the preset part can include the passenger compartment interior, electronic appliances (vehicle controllers and connectors), carpet, air conditioning thermal management system, front doors, front seats, autonomous driving controller, etc.
[0037] In the above scheme, a whole vehicle model of the vehicle to be simulated can be constructed, and a preset part model of the vehicle to be simulated can be extracted from the whole vehicle model, so as to perform vehicle wading simulation analysis on the preset parts of the vehicle to be simulated, estimate safety risks in advance, and eliminate safety hazards.
[0038] In some embodiments, when the preset part model of the vehicle to be simulated is extracted from the whole vehicle model and structural distortions such as deformation and distortion appear, the deformation and distortion in the whole vehicle model are repaired in the pre-processing software (model building software, such as Catia, AutoCAD), and then the preset part model of the vehicle to be simulated is extracted again from the whole vehicle model until the extracted preset part model no longer has a distorted structure to obtain the final preset part model.
[0039] Afterwards, obtain the water storage container model.
[0040] Specifically, the water storage container model can be obtained by receiving the water storage container model from other devices in response to user operations; or by constructing the water storage container model through relevant software, such as Catia, AutoCAD, etc., in response to user operations.
[0041] In some embodiments, using the initial volume flow mode, a volume source (Volumesource) is used in Preonlab to define the initial water level height in the water storage container model, a Seedpoint is established in the water storage container model and connected to the Volumesource to generate water source particles.
[0042] Finally, get the grid parameters.
[0043] The grid parameters include at least one of a grid type, a grid size, a grid skewness, a ratio of the longest side length to the shortest side length of the grid, and a minimum orthogonal quality of the grid.
[0044] In some embodiments, the mesh parameters can be set by the user in real time or by default. For example, the mesh type can be a triangular mesh, the mesh size can be controlled between 0.5 mm and 1 mm, the mesh skewness can be less than 0.6, the ratio of the longest side length to the shortest side length can be less than 20, and the minimum orthogonality quality can be greater than 0.6.
[0045] S2. Meshing the preset part model according to the mesh parameters to obtain a mesh model.
[0046] Specifically, the preset part model can be meshed according to mesh parameters using relevant software, such as ANSA, Hypermesh, Femap and other pre-processing software, to obtain a mesh model.
[0047] In some embodiments, when the obtained mesh model has structural distortion, the corresponding distorted structure in the whole vehicle model is repaired in the pre-processing software (model building software, such as Catia, AutoCAD), and then the preset part model of the vehicle to be simulated is extracted from the whole vehicle model again, and the preset part model is meshed according to the mesh parameters to obtain the mesh model, until the mesh model no longer has a distorted structure to obtain the final mesh model.
[0048] S3. Obtain physical field parameters and motion parameters.
[0049] First, obtain the physics parameters.
[0050] The physical field parameters include at least the physical field parameters of the grid model, the physical field parameters of the water storage container model, and the physical field parameters of the liquid in the water storage container model.
[0051] In some embodiments, the physical field parameters of the mesh model include at least the state of a predetermined portion of the vehicle, the gap size of the predetermined portion of the vehicle, the adhesion coefficient of the predetermined portion, the roughness of the predetermined portion, and the friction coefficient of the predetermined portion. The physical field parameters of the water storage container model include at least the state of the water storage container model, the capacity of the water storage container model, the positional relationship between the water storage container model and the vehicle to be simulated, the adhesion coefficient of the water storage container model, the roughness of the water storage container model, and the friction coefficient of the water storage container model. The physical field parameters of the liquid in the water storage container model include at least the liquid particle size, the liquid cohesion coefficient, the liquid adhesion coefficient, the liquid shear viscosity, and the liquid bulk viscosity.
[0052] In some embodiments, the physical field parameters may be obtained by responding to a setting operation by the user to obtain the physical field parameters set by the user. That is, the physical field parameters obtained by the electronic device are all set by the user in real time.
[0053] In some embodiments, the method for obtaining physical field parameters may also be to obtain a first physical field parameter set by the user in response to a user setting operation, and then obtain the first physical field parameter from a preset physical field relationship table. That is, when the electronic device obtains the physical field parameters, it can obtain a portion of the physical field parameters according to the method set by the user, and then query the other portion of the physical field parameters through the preset physical field relationship table. The first physical field parameters may include the state of a preset portion of the vehicle, the gap size of the preset portion of the vehicle, the state of the water storage container model, the capacity of the water storage container model, the positional relationship between the water storage container model and the vehicle to be simulated, and the size of the liquid particles. The second physical field parameters may include the adhesion coefficient of the preset portion, the roughness of the preset portion, the friction coefficient of the preset portion, the adhesion coefficient of the water storage container model, the roughness of the water storage container model, the friction coefficient of the water storage container model, the cohesion coefficient of the body, the adhesion coefficient of the liquid, the shear viscosity of the liquid, and the volumetric viscosity of the liquid.
[0054] For example, the user can set the state of the preset part of the vehicle to a solid wall surface, and control the gap size of the preset part of the vehicle to be between 1-1.5mm (a gap size of 1.5mm is recommended, so that spilled water can more easily enter the gap and can predict more severe situations); the state of the water storage container model is a solid wall surface, and the capacity of the water storage container model is controlled so that the liquid water level height is between 15-20mm from the upper edge of the water storage container model. The positional relationship between the water storage container model and the vehicle to be simulated is that the water storage container model is placed in the vehicle's on-board cup holder; the particle size of the liquid in the water storage container model is half of the minimum gap size in the preset part of the vehicle. For example, when the minimum gap size of the preset part is 1mm, the particle size of the liquid is 0.5mm.
[0055] When the preset location is the vehicle's center console, the adhesion coefficient is 0.65 N / m, the roughness is 0.8, and the friction coefficient is 0.8. When the preset location is another vehicle component, the adhesion coefficient is 0.8 N / m, the roughness is 0.8, and the friction coefficient is 0.8. The adhesion coefficient of the water container model is 1 N / m, the roughness is 0.6, and the friction coefficient is 0.6. The cohesion coefficient of the liquid is 0.072 N / m, the adhesion coefficient is 0.072 N / m, the shear viscosity is 0.001 Pa·s, and the bulk viscosity is 0.003 Pa·s.
[0056] For example, Figure 3 A schematic diagram of a vehicle wading simulation analysis scenario provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the vehicle wading simulation analysis scene includes a preset part 22 and a water storage container 21. The preset part 22 includes a cup holder 222, and the water storage container 21 is placed in the cup holder 222, and the water storage container 21 contains liquid; the preset part 22 is made up of multiple solid pieces, and there is a gap 221 between two adjacent solid pieces. Figure 3In the illustrated vehicle wading simulation analysis scenario, the user can set the preset location 22 to a solid wall and control the size of the gap 221 within the preset location 22 to be between 1 and 1.5 mm. The water container 21 is a solid wall, with the liquid level within the container 21 controlled to be between 15 and 20 mm from its top edge. The container 21 is positioned relative to the simulated vehicle in one of the vehicle's cup holders 222. The particle size of the liquid in the container 21 is half the minimum gap size within the preset location 221. For example, when the minimum gap size within the preset location 22 is 1 mm, the particle size is 0.5 mm. If the preset location 22 is the vehicle's center console, the adhesion coefficient of the preset location 22 is 0.65 N / m, the roughness of the preset location 22 is 0.8, and the friction coefficient of the preset location 22 is 0.8. When predetermined location 22 is another vehicle component, the adhesion coefficient of predetermined location 22 is 0.8 N / m, the roughness of predetermined location 22 is 0.8, and the friction coefficient of predetermined location 22 is 0.8. The adhesion coefficient of water storage container 21 is 1 N / m, the roughness of water storage container 21 is 0.6, and the friction coefficient of water storage container 21 is 0.6. The cohesion coefficient of the liquid is 0.072 N / m, the adhesion coefficient of the liquid is 0.072 N / m, the shear viscosity of the liquid is 0.001 Pa·s, and the bulk viscosity of the liquid is 0.003 Pa·s.
[0057] certainly, Figure 3 An exemplary vehicle wading simulation analysis scenario diagram is provided only for the purpose of clarifying the setting of physical field parameters in the embodiment of this application. The preset parts in this application may include all vehicle interiors or a combination of part of the vehicle interiors, and this application does not limit this.
[0058] Afterwards, the motion parameters are obtained.
[0059] The motion parameters include at least a speed-time correlation. In some embodiments, the speed-time correlation may be a curve showing the speed changing over time. Figures 4A-4C This is a schematic diagram of the speed-time correlation curve provided in the embodiment of the present application, wherein: Figure 4A is the curve of vehicle speed changing with time when the vehicle speed is 20km / h; Figure 4B This is the curve of vehicle speed changing with time when the vehicle speed is 40km / h; Figure 4C This is the curve of vehicle speed changing with time when the vehicle speed is 60km / h.
[0060] In some embodiments, the method for obtaining motion parameters can be directly receiving the speed-time correlation relationship imported by the user; or it can be querying the speed-time correlation relationship corresponding to the speed input by the user from a preset interface based on the speed input by the user, where the preset interface can be an Internet interface, or a database interface that stores multiple speed-time correlation relationships, etc., and this application does not limit this.
[0061] In some embodiments, the motion parameters can also be obtained by creating a transform group for all solid walls and computational domains in the Preonlab software, selecting the X-axis in the Velocity option bar, and right-clicking to open the show curve to obtain the motion parameters.
[0062] S4. Construct a simulation scene of the vehicle to be simulated according to the physical field parameters, and control the vehicle to be simulated to perform simulated motion in the simulation scene according to the motion parameters.
[0063] First, a simulation scenario of the vehicle to be simulated is constructed based on the physical field parameters.
[0064] In some embodiments, as Figure 5 As shown, the method of constructing a simulation scene of a vehicle to be simulated according to physical field parameters may include the following steps:
[0065] S41. Determine the gap size of a preset portion of the vehicle to be simulated, the adhesion coefficient of the preset portion, the roughness of the preset portion, and the friction coefficient of the preset portion based on the physical field parameters of the grid model.
[0066] S42. Determine the capacity of the water storage container model, the positional relationship between the water storage container model and the vehicle to be simulated, the adhesion coefficient of the water storage container model, the roughness of the water storage container model, and the friction coefficient of the water storage container model based on the physical field parameters of the water storage container model.
[0067] S43. Determine the liquid particle size, the cohesion coefficient, the adhesion coefficient, the shear viscosity, and the volume viscosity of the liquid based on the physical field parameters of the liquid in the water storage container model.
[0068] S44. Determine, based on the physical field parameters of the grid model and the physical field parameters of the water storage container model, that the preset portion of the vehicle to be simulated and the surface of the water storage container are solid walls.
[0069] In the above scheme, the simulation scene of the preset part of the vehicle to be simulated can be simulated according to the physical field parameters of the grid model, the simulation scene of the water storage container model can be simulated according to the physical field parameters of the water storage container model, and the simulation scene of the liquid in the water storage container model can be simulated according to the physical field parameters of the liquid in the water storage container model. The construction of the simulation environment is realized to facilitate the subsequent implementation of the vehicle wading simulation analysis method in this application, save experimental costs, and shorten the development cycle.
[0070] Afterwards, the simulated motion of the vehicle to be simulated is determined according to the motion parameters.
[0071] In some embodiments, the motion parameters include a correlation between speed and time. Controlling the simulated motion of the simulated vehicle in the simulation scene based on the motion parameters may include controlling the simulated vehicle to simulate motion at a preset speed at a preset time based on the correlation. The preset time is any time in the correlation, and the preset speed is the speed corresponding to the preset time in the correlation.
[0072] For example, when the relationship is Figure 4A When the speed of the vehicle is 20 km / h and the speed changes with time, the simulation motion of the vehicle to be simulated is determined to be the braking and deceleration motion of the vehicle to be simulated at a speed of 20 km / h until the speed drops to 0 in 8.2 seconds. Figure 4B When the speed of the vehicle is 40 km / h and the speed changes with time, the simulation motion of the vehicle to be simulated is determined to be the vehicle to be simulated braking and decelerating at a speed of 40 km / h until the speed drops to 0 in 9 seconds. Figure 4C When the speed curve of the vehicle is 60 km / h and changes with time, the simulated motion of the vehicle to be simulated is determined to be the vehicle to be simulated performing braking and deceleration motion at a speed of 60 km / h until the speed drops to 0 in 15 seconds.
[0073] S5. Use the target solver to solve the simulation data of the liquid in the water storage container model in the grid model.
[0074] The water storage container model is placed at the bracket position of the preset part model, and the bracket is set as a supporting device for the water storage container in the vehicle to be simulated; for example, Figure 3 In the preset location 22 shown, the water storage container 21 can be placed in the cup holder 222 of the preset location 22; that is, the water storage container model can be placed at the cup holder position corresponding to the preset location model. The simulation data includes the distribution of the liquid in the grid model.
[0075] In some embodiments, before using the target solver for solving, the convergence condition number (CFL) and maximum time step of the target solver are obtained in response to the user's setting operation on the target solver. For example, the CFL number is 1 and the maximum time step is 0.0001s.
[0076] In some embodiments, the target solver may be a PreonSolver. In some embodiments, the particle size of the liquid in the water storage container model may also be set in the target solver. Specifically, in response to a user setting operation for the particle size, the user-entered value is read to obtain the user-set particle size of the liquid.
[0077] In some embodiments, as Figure 6 As shown, the method of using the target solver to solve the simulation data of the liquid in the water storage container model in the grid model when the simulated vehicle performs simulated motion in the simulation scene can include the following steps:
[0078] S51. Determine the size of a preset part of the vehicle to be simulated.
[0079] In order to improve simulation efficiency and computing efficiency, and reduce computing power costs, when using the target solver to solve the simulation data of the liquid in the water storage container model in the grid model when the simulated vehicle performs simulated movement in the simulation scene, it also responds to the user's restriction domain setting operation, determines the part size of the preset part of the vehicle to be simulated, and constructs a solution restriction domain based on the part size to limit the solution range of the target solver.
[0080] S52. Construct a solution restriction domain according to the part size.
[0081] In some embodiments, the solution restriction domain may be constructed according to the part size by taking the preset part as the center and determining an area larger than the preset range of the part size as the solution restriction domain.
[0082] For example, Figure 7 A schematic diagram of solving the restriction domain provided in the embodiment of this application. Figure 6 As shown in FIG, the solution restriction domain includes the position boundaries in three dimensions: X, Y, and Z.
[0083] Among them, in the X direction: the front boundary of the solution restriction domain is the X-direction position of the front end of the front panel of the simulated vehicle, and extends forward 200 mm. The rear boundary is the X-direction position of the rear end of the front seat, and extends backward 200 mm.
[0084] In the Y direction: the left and right boundaries of the restricted domain are taken as the maximum Y positions on both sides of the front seats (including the main driver's seat and the co-driver's seat) of the vehicle to be simulated, and extend 400mm along the Y direction.
[0085] In the Z direction: the upper boundary of the solution restriction domain is the Z position of the highest point of the front seat, and the lower boundary is the Z position of the lowest end of the front floor of the vehicle to be simulated.
[0086] In the above scheme, a solution restriction domain is constructed according to the size of the parts of the vehicle to be simulated to limit the solution range of the target solver. This can prevent the problem of excessive speed of water under the action of gravity, which leads to slow calculation speed. It improves simulation efficiency and calculation efficiency and reduces computing power costs.
[0087] S53. Use the target solver to solve the simulation data of the liquid in the water storage container model within the solution restriction domain.
[0088] In some embodiments, the grid model includes a plurality of grid cells. Using a target solver to solve the simulation data of the liquid in the water storage container model within the grid model may also involve using the target solver to determine, for each grid cell, simulation data of the interaction between the liquid in the water storage container model and the grid model during simulated motion of the simulated vehicle in the simulation scene.
[0089] In the above scheme, the simulation data of the interaction between the liquid in the water storage container model and the grid model during the simulated movement of the simulated vehicle in the simulation scene can be determined with each grid unit as a unit, so as to facilitate the subsequent analysis of the simulation data to obtain the simulation results, and adjust the simulated vehicle according to the simulation results to reduce the safety risks of the vehicle.
[0090] S6. Determine the flow path of the liquid in the vehicle to be simulated based on the distribution of the liquid in the grid model.
[0091] In some embodiments, as Figure 8 As shown in FIG, the distribution of the liquid in the grid model includes: the number of liquid particles in each grid cell corresponding to each time step within the solution time period of the target solver and the residual change curve of the target solver; based on the distribution of the liquid in the grid model, the method of determining the flow path of the liquid in the vehicle to be simulated may include the following steps:
[0092] S61. When it is determined that the solution result of the target solver reaches a stable state according to the residual variation curve, the number of liquid particles in each grid unit in the grid model is obtained.
[0093] First, according to the residual variation curve, determine whether the solution result of the target solver reaches a stable state.
[0094] In some embodiments, the method of determining whether the solution result of the target solver reaches a stable state based on the residual change curve can be that when the fluctuation amount of the residual change curve is less than a preset threshold within a time step of the solution time period, it is determined that the solution result of the target solver reaches a stable state.
[0095] Among them, the solution time period and the preset threshold are both preset, for example, a value set by the user according to actual conditions, or a default value, for example, the solution time period is 10s and the preset threshold is 0.1.
[0096] Afterwards, when it is determined based on the residual change curve that the solution result of the target solver has not reached a stable state and is within the solution time period, the simulation data of the liquid in the water storage container model in the grid model when the simulated vehicle to be simulated moves in the simulation scene is continued to be solved using the target solver until the solution result reaches a stable state, or the solution time is reached, and the simulation data is obtained.
[0097] Finally, when it is determined that the solution result of the target solver reaches a stable state according to the residual variation curve, the number of liquid particles in each grid unit in the grid model is obtained.
[0098] S62. Determine a flow path of the liquid in the preset part model according to the number of liquid particles in each grid unit in the grid model.
[0099] In some embodiments, after step S62 , the method further includes outputting the flow path.
[0100] In some embodiments, the method of outputting the flow path can be to first discretize the calculation space of the simulation data based on the Lagrangian method, then perform continuous space rendering on the simulation data, and finally output a schematic diagram of the scalar and vector physical field surface distribution. After the fluid dynamics (CFD) simulation calculation reaches a stable state, the output parameter is the flow path of the liquid overflow water flow.
[0101] In the above scheme, when the target solver's solution reaches a stable state based on the residual variation curve, the number of liquid particles in each grid cell in the grid model is obtained; based on the number of liquid particles in each grid cell in the grid model, the flow path of the liquid in the preset part model is determined; and the flow path is output. The simulation data solved by the target solver can be analyzed to determine the flow path of the liquid. That is, before the vehicle is produced, the water exposure risk of the preset part structure of the simulated vehicle can be analyzed in advance, and problems with the preset part structure of the actual vehicle corresponding to the simulated vehicle in the vehicle wading simulation process can be discovered in the wading scenario corresponding to the simulation scenario, thereby discovering safety hazards in advance.
[0102] In some embodiments, after step S62 , the method further includes adjusting the vehicle to be simulated according to the flow path.
[0103] Specifically, the method of adjusting the simulated vehicle according to the flow path may be to adjust the position of the electronic plug-in if the flow path includes the electronic plug-in, or to add a waterproof device to the electronic plug-in to avoid potential safety hazards caused by liquid flowing through it in actual application.
[0104] In the above scheme, a preset part model, a water storage container model and grid parameters of the vehicle to be simulated are obtained; the preset part model is gridded according to the grid parameters to obtain a grid model; physical field parameters and motion parameters are obtained, and the physical field parameters at least include the physical field parameters of the grid model, the physical field parameters of the water storage container model, and the physical field parameters of the liquid in the water storage container model; a simulation scene of the vehicle to be simulated is constructed according to the physical field parameters, and the vehicle to be simulated is controlled to perform simulated motion in the simulation scene according to the motion parameters; a target solver is used to solve the simulation data of the liquid in the water storage container model in the grid model; the water storage container model is placed at the bracket position of the preset part model, and the bracket is set as a support device for the water storage container in the vehicle to be simulated; the simulation data includes the distribution of the liquid in the grid model; and the flow path of the liquid in the vehicle to be simulated is determined according to the distribution of the liquid in the grid model. In this way, when the simulated vehicle is performing simulated movement in the simulation scene, the state of the liquid in the water storage container model in the grid model can be simulated, that is, the risk of water contact of the preset part structure of the simulated vehicle can be evaluated in advance before the vehicle is produced, and problems existing in the preset part structure of the actual vehicle corresponding to the vehicle to be simulated in the vehicle wading simulation process in the water wading scene corresponding to the simulation scene can be discovered, thereby saving test costs and shortening the development cycle.
[0105] The embodiment of the present application can divide the functional modules of the vehicle wading simulation analysis device according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.
[0106] like Figure 9 As shown, it is a structural diagram of the vehicle wading simulation analysis device provided in an embodiment of the present application. The vehicle wading simulation analysis device includes an acquisition module 91, a processing module 92, a setting module 93, a determination module 94, a simulation module 95, and an output module 96.
[0107] An acquisition module 91 is used to acquire a preset part model, a water storage container model, and grid parameters of the vehicle to be simulated; a processing module 92 is used to grid the preset part model according to the grid parameters to obtain a grid model; a setting module 93 is used to acquire physical field parameters and motion parameters, wherein the physical field parameters at least include the physical field parameters of the grid model, the physical field parameters of the water storage container model, and the physical field parameters of the liquid in the water storage container model; a determination module 94 is used to construct a simulation scene of the vehicle to be simulated according to the physical field parameters, and control the simulated motion of the vehicle to be simulated in the simulation scene according to the motion parameters; a simulation module 95 is used to use a target solver to solve the simulation data of the liquid in the water storage container model in the grid model; the water storage container model is placed at the bracket position of the preset part model, and the bracket is set as a support device for the water storage container in the vehicle to be simulated; the simulation data includes the distribution of the liquid in the grid model; an output module 96 is used to determine the flow path of the liquid in the vehicle to be simulated according to the distribution of the liquid in the grid model.
[0108] In some embodiments, the determination module 94 is specifically used to: determine the gap size of the preset part of the vehicle to be simulated, the adhesion coefficient of the preset part, the roughness of the preset part, and the friction coefficient of the preset part according to the physical field parameters of the grid model; determine the capacity of the water storage container model, the positional relationship between the water storage container model and the vehicle to be simulated, the adhesion coefficient of the water storage container model, the roughness of the water storage container model, and the friction coefficient of the water storage container model according to the physical field parameters of the water storage container model; determine the liquid particle size, the cohesion coefficient of the liquid, the adhesion coefficient of the liquid, the shear viscosity of the liquid, and the volume viscosity of the liquid according to the physical field parameters of the liquid in the water storage container model; and determine that the surfaces of the preset part of the vehicle to be simulated and the water storage container are solid walls according to the physical field parameters of the grid model and the physical field parameters of the water storage container model.
[0109] In some embodiments, the motion parameters include an association between speed and time; the determination module 94 is specifically used to control the simulated vehicle to perform simulated motion at a preset speed at a preset time based on the association; the preset time is any time in the association, and the preset speed is the speed in the association corresponding to the preset time.
[0110] In some embodiments, the simulation module 95 is specifically used to: determine the size of a preset part of the vehicle to be simulated; construct a solution restriction domain based on the part size; and use a target solver to solve the simulation data of the liquid in the water storage container model within the solution restriction domain.
[0111] In some embodiments, the grid model includes multiple grid units; the simulation module 95 is specifically used to determine, through the target solver, the simulation data of the interaction between the liquid in the water storage container model and the grid model during the simulated movement of the simulated vehicle in the simulation scene, with each grid unit as a unit.
[0112] In some embodiments, the distribution of liquid in the grid model includes: the number of liquid particles in each grid unit corresponding to each time step within the solution time period of the target solver and the residual change curve of the target solver; the output module 96 is specifically used to: obtain the number of liquid particles in each grid unit in the grid model when it is determined that the solution result of the target solver reaches a stable state according to the residual change curve; determine the flow path of the liquid in the preset part model according to the number of liquid particles in each grid unit in the grid model.
[0113] In some embodiments, the output module 96 is specifically configured to determine that the solution result of the target solver reaches a stable state when the fluctuation amount of the residual variation curve is less than a preset threshold within a time step of the solution time period.
[0114] The vehicle wading simulation analysis device provided in this embodiment can execute the vehicle wading simulation analysis method provided in the above method embodiment. Its implementation principle and technical effects are similar to those of the above method and will not be repeated here.
[0115] Figure 10 An electronic device according to an exemplary embodiment may include a processor 1002 configured to execute application code to implement the vehicle wading simulation analysis method of the present application.
[0116] The processor 1002 may be a central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application.
[0117] like Figure 10 As shown, the electronic device may further include a memory 1003. The memory 1003 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 1002.
[0118] The memory 1003 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 1003 may exist independently and be connected to the processor 1002 via the bus 1004. The memory 1003 may also be integrated with the processor 1002.
[0119] like Figure 10 As shown, the electronic device may further include a communication interface 1001, wherein the communication interface 1001, the processor 1002, and the memory 1003 may be coupled to each other, for example, via a bus 1004. The communication interface 1001 is used to exchange information with other devices, for example, to support information exchange between the electronic device and other devices.
[0120] It should be pointed out that Figure 10 The device structure shown in the figure does not constitute a limitation on the electronic device, except Figure 10 In addition to the components shown, the electronic device may include more or fewer components than shown, or combine certain components, or arrange the components differently. Furthermore, the electronic device provided in this embodiment can execute the vehicle wading simulation analysis method provided in the above method embodiment. Its implementation principles and technical effects are similar to those of the above method and will not be further described here.
[0121] An embodiment of the present application provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the various processes of the vehicle wading simulation analysis method in the above-mentioned method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
[0122] The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
[0123] An embodiment of the present application provides a computer program product, which stores a computer program. When the computer program is executed by a processor, it implements the various processes of the vehicle wading simulation analysis method in the above method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
[0124] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.
[0125] In this application, the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.
[0126] In this application, memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
[0127] In this application, computer-readable media includes permanent and non-permanent, removable and non-removable storage media. Storage media can be implemented by any method or technology to store information, and the information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media 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, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data and carrier waves.
[0128] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.
[0129] The foregoing description is intended only to provide specific embodiments of the present application, which will enable those skilled in the art to understand and implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments described herein, but is intended to be construed in the broadest manner consistent with the principles and novel features disclosed herein.
Claims
1. A vehicle wading simulation analysis method, characterized in that: include: Obtaining a preset part model, a water storage container model, and grid parameters of the vehicle to be simulated; Performing grid processing on the preset part model according to the grid parameters to obtain a grid model; Acquiring physical field parameters and motion parameters, wherein the physical field parameters include at least physical field parameters of the grid model, physical field parameters of the water storage container model, and physical field parameters of the liquid in the water storage container model; Constructing a simulation scene of the vehicle to be simulated according to the physical field parameters, and controlling the vehicle to be simulated to perform simulated motion in the simulation scene according to the motion parameters; Using a target solver, solving simulation data of the liquid in the water storage container model in the grid model; the water storage container model is placed at a bracket position of the preset part model, and the bracket is configured as a support device for the water storage container in the vehicle to be simulated; the simulation data includes the distribution of the liquid in the grid model; A flow path of the liquid in the vehicle to be simulated is determined according to the distribution of the liquid in the grid model.
2. The vehicle wading simulation analysis method according to claim 1, characterized in that: The constructing of a simulation scene of the vehicle to be simulated according to the physical field parameters includes: Determining, based on the physical field parameters of the grid model, a gap size of a preset portion of the vehicle to be simulated, an adhesion coefficient of the preset portion, a roughness of the preset portion, and a friction coefficient of the preset portion; Determining, based on the physical field parameters of the water storage container model, the capacity of the water storage container model, the positional relationship between the water storage container model and the vehicle to be simulated, the adhesion coefficient of the water storage container model, the roughness of the water storage container model, and the friction coefficient of the water storage container model; Determining the liquid particle size, the cohesion coefficient, the adhesion coefficient, the shear viscosity, and the volume viscosity of the liquid according to the physical field parameters of the liquid in the water storage container model; According to the physical field parameters of the grid model and the physical field parameters of the water storage container model, it is determined that the preset part of the vehicle to be simulated and the surface of the water storage container are solid walls.
3. The vehicle wading simulation analysis method according to claim 1, characterized in that: The motion parameters include a correlation between speed and time; and controlling the simulated vehicle to perform simulated motion in the simulation scene according to the motion parameters includes: According to the association relationship, the simulated vehicle is controlled to perform simulation movement at a preset speed at a preset time; the preset time is any time in the association relationship, and the preset speed is the speed in the association relationship corresponding to the preset time.
4. The vehicle wading simulation analysis method according to claim 1, characterized in that: The method of using a target solver to solve the simulation data of the liquid in the water storage container model in the grid model includes: Determining the size of a preset part of the vehicle to be simulated; Constructing a solution restriction domain according to the size of the part; The target solver is used to solve the simulation data of the liquid in the water storage container model within the solution restriction domain.
5. The vehicle wading simulation analysis method according to any one of claims 1 to 4, characterized in that: The grid model includes a plurality of grid cells; and using a target solver to solve simulation data of liquid in the water storage container model in the grid model includes: The target solver is used to determine the simulation data of the interaction between the liquid in the water storage container model and the grid model during the process in which the vehicle to be simulated performs the simulated movement in the simulation scene, with each grid unit as a unit.
6. The vehicle wading simulation analysis method according to claim 5, characterized in that: The distribution of the liquid in the grid model includes: the number of liquid particles in each grid unit corresponding to each time step within the solution time period of the target solver and the residual change curve of the target solver; and determining the flow path of the liquid in the vehicle to be simulated based on the distribution of the liquid in the grid model includes: When it is determined according to the residual variation curve that the solution result of the target solver reaches a stable state, the number of liquid particles in each grid unit in the grid model is obtained; The flow path of the liquid in the preset part model is determined according to the number of liquid particles in each grid unit in the grid model.
7. The vehicle wading simulation analysis method according to claim 6, characterized in that: Determining, based on the residual variation curve, that the solution result of the target solver reaches a stable state includes: When the fluctuation amount of the residual variation curve is less than a preset threshold value within a time step of the solution time period, it is determined that the solution result of the target solver reaches a stable state.
8. A vehicle wading simulation analysis device, characterized in that: include: An acquisition module is used to obtain a preset part model of the vehicle to be simulated, a water storage container model, and grid parameters; A processing module, configured to perform grid processing on the preset part model according to the grid parameters to obtain a grid model; A setting module is used to obtain physical field parameters and motion parameters, wherein the physical field parameters at least include physical field parameters of the grid model, physical field parameters of the water storage container model, and physical field parameters of the liquid in the water storage container model; a determination module, configured to construct a simulation scene of the vehicle to be simulated according to the physical field parameters, and control the vehicle to be simulated to perform simulated motion in the simulation scene according to the motion parameters; a simulation module configured to use a target solver to solve simulation data of the liquid in the water storage container model in the grid model; the water storage container model is placed at a bracket position of the preset part model, and the bracket is configured as a support device for the water storage container in the vehicle to be simulated; the simulation data includes the distribution of the liquid in the grid model; The output module is used to determine the flow path of the liquid in the vehicle to be simulated according to the distribution of the liquid in the grid model.
9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the vehicle wading simulation analysis method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that include: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle wading simulation analysis method according to any one of claims 1 to 7 is implemented.