Adaptive mesh encryption strategy and method based on vehicle exterior shape and flow field characteristics

Abstract

The present invention relates to the technical field of automobile aerodynamic CFD analysis, and discloses an adaptive mesh encryption strategy and method based on automobile exterior shape and flow field characteristics, comprising the following steps: step 1: extracting the exterior shape geometric model of the automobile; step 2: expanding the extracted exterior shape geometric model with a preset point as a reference point, and encrypting it through the expansion area; step 3: adaptively encrypting the flow field based on the size distribution of the total pressure flow field function in the flow field domain. The present invention can effectively solve the discrete errors existing in the existing mesh encryption strategy without changing the flow field parameters and other boundary conditions, and can achieve higher mesh encryption accuracy and numerical calculation accuracy.

Description

Technical Field

[0001] The invention relates to the technical field of automobile aerodynamics CFD analysis, and in particular to an adaptive grid encryption strategy and method based on automobile exterior modeling and flow field characteristics. Background Art

[0002] The external flow field characteristics of a car have a very important impact on its power, economy, comfort and handling stability. In order to achieve better comprehensive performance of the car, it is very important to accurately analyze and improve the external flow field characteristics of the car.

[0003] At present, the main research methods for automobile external flow field are: numerical simulation method (CFD) and wind tunnel test method. Compared with the expensive wind tunnel test, numerical simulation method has great advantages in development cycle, cost and scope of application.

[0004] In CFD, the factors that affect the accuracy of numerical simulation of the external flow field of automobiles are mainly discrete error, turbulence model error, geometric model error and residual error. With the development of technology, the numerical simulation calculation software has been polished for many years and can better control the turbulence model error; the use of CNC machining to prepare the wind tunnel test model can effectively reduce the geometric error between the two models; the current convergence standard of numerical simulation calculation is generally 10 -4 , some reach 10 -8 , so the residual error is also very small. Therefore, the current numerical simulation error of the external flow field is mainly the discrete error.

[0005] In addition, in order to achieve high-precision CFD predictions, the grid strategy and method used play a vital role in improving the accuracy of numerical simulation predictions. When constructing a fluid grid, it is often required that the grid units in places with large physical quantity gradients are small enough, that the density of the grid changes smoothly and evenly, that the grid units do not appear seriously distorted, that the boundary layer grid has good orthogonality, and that the underlying grid thickness is reasonable. At present, the industry can better solve the problem of small enough grid units by adopting the strategy of local grid encryption (such as the attached Figure 1 and Figure 2 However, this method ignores the problem of smooth and uniform changes in the density of the grid, which can easily lead to discrete errors in numerical calculations and greatly affect the accuracy of numerical simulations.

[0006] Therefore, there is an urgent need for a grid strategy and method that can effectively deal with CFD discretization errors. Summary of the invention

[0007] The present invention aims to provide an adaptive mesh encryption strategy and method based on the exterior shape of the automobile and the flow field characteristics, which can effectively solve the discrete errors existing in the existing mesh encryption strategies without changing the flow field parameters and other boundary conditions, and can achieve higher mesh encryption accuracy and numerical calculation accuracy.

[0008] To achieve the above object, the present invention provides the following basic scheme:

[0009] Solution 1

[0010] The adaptive mesh encryption method based on the vehicle exterior shape and flow field characteristics includes the following steps:

[0011] Step 1: Extract the exterior geometric model of the car;

[0012] Step 2: The extracted external geometric model is expanded with the preset points as reference points, and encrypted through the expansion area;

[0013] Step 3: Based on the size distribution of the total pressure flow field function in the flow field, perform adaptive encryption of the flow field.

[0014] Solution 2

[0015] An adaptive mesh encryption strategy based on the exterior shape and flow field characteristics of the automobile is applied to the adaptive mesh encryption method based on the exterior shape and flow field characteristics of the automobile as described in Scheme 1; it includes a geometric adaptive strategy and a flow field adaptive strategy; the geometric adaptive strategy includes expanding the extracted exterior shape geometric model with a preset point as a reference point, and encrypting the expanded area; the flow field adaptive strategy includes adaptively encrypting the flow field based on the size distribution of the total pressure flow field function in the flow field domain.

[0016] The working principle and advantages of the present invention are: the extracted external shape geometric model is expanded with a preset point as a reference point, and encrypted through the expansion area. In addition, the flow field is adaptively encrypted with the total pressure flow field function in the flow field size distribution. The former is encrypted through the expansion area, and with a specific preset point as the expansion reference point, it can effectively amplify the external shape information of the car, and the encryption of areas with large gradients of physical quantity changes such as the rear turning curve and the back streamline can also be completed more carefully, ensuring that the density grid changes smoothly and evenly, thereby effectively eliminating the discrete errors that may be generated during numerical calculations. The latter is based on the size distribution of the total pressure flow field function in the flow field, and the flow field is adaptively encrypted. According to the actual flow field distribution of the vehicle, targeted personalized encryption can be performed, which can further ensure that the density grid changes in various areas of the vehicle remain uniform, the encryption is more detailed, the discrete error is smaller, and the numerical simulation accuracy is higher.

[0017] This solution can address the shortcomings of the existing CFD mesh encryption strategy. Without changing the flow field parameters and other boundary conditions, the adaptive mesh encryption area can be constructed through the vehicle exterior shape (corresponding to step 2) and flow field characteristics (corresponding to step 3). The adaptive encryption area can smoothly connect the vehicle area with a large gradient of physical quantity changes, ensure that the density grid changes evenly, and fit the actual flow field distribution of the vehicle, and can effectively solve the discrete error caused by the mesh encryption strategy during numerical simulation prediction. Moreover, under the premise of meeting the overall requirements of numerical calculation discretization, since the mesh encryption area is adaptive and specifically matches the actual exterior shape and flow field distribution of the vehicle, it can further reduce the invalid body mesh that often exists in the conventional encryption strategy, obtain more accurate and fully developed flow field information and effectively save computing power, thereby improving the prediction accuracy of the wind resistance performance of the whole vehicle (the error is controlled within 2%) and the prediction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is the first schematic diagram of the encryption effect of the traditional grid encryption scheme;

[0019] Figure 2 The second schematic diagram is the encryption effect of the traditional grid encryption scheme;

[0020] Figure 3 A schematic diagram of a method flow of an embodiment of a strategy and method for adaptively encrypting a grid based on the exterior shape of a car and flow field characteristics according to the present invention;

[0021] Figure 4 It is a side view of the adaptive mesh encryption effect of the embodiment of the adaptive mesh encryption strategy and method based on the exterior shape of the automobile and the flow field characteristics of the present invention;

[0022] Figure 5 A top view of the adaptive mesh encryption effect of an embodiment of the adaptive mesh encryption strategy and method based on the exterior shape of the automobile and flow field characteristics of the present invention;

[0023] Figure 6 It is a comparison diagram of the adaptive mesh encryption results of the embodiment of the adaptive mesh encryption strategy and method based on the exterior shape and flow field characteristics of the automobile in the present invention and the encryption results of the traditional local encryption scheme and the kernel used. DETAILED DESCRIPTION

[0024] The following is a further detailed description through specific implementation methods:

[0025] The embodiment is basically as shown in the attached Figure 3 As shown: The adaptive mesh encryption method based on the exterior shape of the car and the flow field characteristics includes the following steps:

[0026] Specifically, the following steps 1 to 4 are all performed based on Siemens StarCCM+ secondary development.

[0027] Among them, Siemens StarCCM+ is a new generation of general CFD simulation software launched by Siemens Industrial Software. It adopts continuous medium mechanics numerical technology and can not only perform fluid analysis, but also perform other physical fields such as structural stress, aerodynamics and acoustics, or multiple physical fields coupled simulation analysis.

[0028] In this embodiment, the secondary development operation includes: adding a secondary development plug-in "Geometry Adaptive" to Siemens StarCCM+, which is a geometry adaptive method plug-in, and has a geometry adaptive strategy for automatically completing geometry adaptive encryption based on the exterior shape of the car and completing the grid setting of the encrypted area (corresponding to the subsequent step 2).

[0029] A secondary development plug-in "CCM_Cal_Adaptive" is added to Siemens StarCCM+. This plug-in is a flow field adaptive method plug-in, which has a flow field adaptive strategy and is used to automatically complete the flow field adaptive encryption based on the size distribution of the total pressure flow field function in the flow field domain (corresponding to the subsequent step 3).

[0030] Step 1: Based on Siemens StarCCM+ secondary development, extract the exterior geometric model of the car.

[0031] The car geometry model imported into Siemens StarCCM+ contains PID and boundary naming words include words such as "1_Body_Wall" and "Glass_Front". When extracting the exterior geometry model, automatic recognition and extraction are performed based on the boundary naming words. Specifically, automatic recognition and extraction are performed based on the words containing "1_Body_Wall" in the boundary naming.

[0032] Step 2: The extracted external geometric model is expanded with the preset points as reference points, and the expanded area is encrypted.

[0033] The preset point is: the point of the minimum value of the X direction of the front windshield of the car is projected to the corresponding point on the balance surface. Selecting this point as the preset point (reference point) can make the encrypted area of ​​the rear of the car longer and the flow field develop more smoothly during subsequent encryption. And compared with setting the reference point at the center of the car, etc., using the center of the car as the reference point will make the length of the encrypted area of ​​the front and rear of the car obtained later the same, which does not conform to the actual situation of the vehicle, the encrypted area is not real, and the numerical simulation accuracy is not high; and the encrypted area with a longer length at the rear of the car obtained by this solution is more in line with the actual situation of the vehicle, and the flow field development is more real and smooth, which helps to achieve higher numerical simulation accuracy.

[0034] When extracting the external geometry model and selecting preset points, automatic recognition and extraction are performed based on the boundary naming vocabulary. Specifically, automatic recognition and extraction are performed based on the vocabulary containing "Glass_Front" or "WindShield_Front" in the boundary naming. Point extraction based on boundary naming is accurate and efficient.

[0035] Before expansion with the preset point as the reference point, the external modeling geometry model is automatically closed. When expansion is performed with the preset point as the reference point, the closed external modeling geometry model is expanded based on the reference point at expansion ratios of 1.4 and 1.6 times, respectively. And when the expansion ratio is 1.6 times, the mesh size of the encrypted area is set to 32mm; when the expansion ratio is 1.4 times, the mesh size of the encrypted area is set to 16mm. Here, under the expansion ratios of 1.4 and 1.6 times, the effect of encryption through the expansion area is optimal, and it can complete natural and smooth encryption of areas with large gradients of physical quantity changes based on the exterior shape of the car, and the mesh density changes evenly.

[0036] Step 3: Based on the size distribution of the total pressure flow field function in the flow field, perform adaptive encryption of the flow field.

[0037] Based on the size distribution of the total pressure flow field function in the flow field, the execution mechanism of the flow field adaptive encryption is: when the flow field is relatively stable after 3000 iterations of flow field calculation, the flow field adaptive encryption is performed. At this time, the flow field distribution is stable, and the adaptive encryption results are more stable and reliable.

[0038] When performing adaptive flow field encryption, the flow field envelope with a total pressure less than or equal to zero is encrypted, and the mesh size of the encrypted area is set to 8mm; the mesh of the flow field body with a total pressure greater than or equal to zero is not changed. Here, the mesh of the encrypted area is further accurately encrypted, and the resulting mesh unit is small enough and closely fits the vehicle flow field distribution.

[0039] After the adaptive mesh encryption is completed by this method, the encrypted mesh diagram is shown in the attached figure. Figure 4 and Figure 5 As shown in the figure, the number 2 is the external shape adaptive level 1 encryption area, the number 3 is the external shape adaptive level 2 encryption area, and the number 4 is the flow field adaptive encryption area; compared with the encrypted grid diagram obtained by the traditional local grid encryption method (i.e., the attached Figure 1 and Figure 2 , the boxed areas corresponding to number 1 in the figure are all local encrypted areas), it can be seen intuitively that the density of the grid units in this scheme changes more smoothly and evenly, the grid arrangement is smoother, coherent and more in line with the streamline of the vehicle body; the grid units do not have serious distortion, the grid units are small enough, the orthogonality of the boundary layer grid is good and has a reasonable bottom grid thickness.

[0040] This embodiment also provides an adaptive mesh encryption strategy based on the exterior shape and flow field characteristics of the automobile, which is applied to an adaptive mesh encryption method based on the exterior shape and flow field characteristics of the automobile as described above; it includes a geometric adaptive strategy and a flow field adaptive strategy; the geometric adaptive strategy includes expanding the extracted exterior shape geometric model with a preset point as a reference point, and encrypting it through the expanded area; the flow field adaptive strategy includes adaptively encrypting the flow field based on the size distribution of the total pressure flow field function in the flow field domain.

[0041] The present embodiment provides a grid encryption strategy and method based on the exterior shape and flow field characteristics of the automobile. Through geometric adaptation (corresponding to step 2) and flow field adaptation (corresponding to step 3), the former is encrypted by expanding the area, and a specific preset point is used as the expansion reference point, which can effectively amplify the exterior shape information of the automobile. The encryption of the area with a large gradient of physical quantity change, such as the turning curve of the rear end of the car and the streamline of the back of the car, can also be completed more carefully, ensuring that the density grid changes smoothly and evenly, thereby effectively eliminating the discrete error that may be generated during numerical calculation; the point of the selected minimum value of the front windshield in the X direction can obtain a longer rear end encryption area, and the flow field development is more sufficient and real, which helps to achieve a higher numerical simulation accuracy. The latter is based on the size distribution of the total pressure flow field function in the flow field domain, and the flow field is adaptively encrypted. According to the actual flow field distribution of the vehicle, targeted personalized encryption can be performed, which can further ensure that the density grid changes in various areas of the vehicle remain uniform, the encryption is more detailed, the discrete error is smaller, and the numerical simulation accuracy is higher.

[0042] The overall solution can further reduce the invalid body mesh while meeting the overall requirements of numerical calculation discretization, obtain more accurate and fully developed flow field information, and improve the prediction accuracy of the vehicle's wind resistance performance (the error can be controlled within 2%). Compared with the conventional local encryption solution, this solution fully reduces the invalid body mesh, which can save 60% of computing power and effectively reduce the length of wind tunnel tests in wind resistance performance development, which is conducive to solving the problem of shortage of wind tunnel resources and helping the industry reduce the cost of wind resistance performance development. In addition, this solution does not need to change the flow field parameters or other boundary conditions during operation, has low improvement costs, simple application methods, and can be adapted to different types of vehicle analysis scenarios, with strong versatility.

[0043] In addition, taking the DrivAer Sendan car model as an example, the size information of this model is as follows Figure 4 As shown in the attached Figure 6The figure shows a comparison of the encryption results and the kernel time of this scheme and the traditional local encryption scheme. It can be seen from the figure that compared with the traditional local encryption scheme, this scheme has significant improvements in many aspects, including: in terms of computing power, this scheme saves 70.8%; the numerical simulation calculation results of this scheme have an error of only 0.4% compared with the wind tunnel test results, while the error between the traditional local encryption method and the wind tunnel test results is 1.5%. The numerical simulation accuracy of this scheme has been significantly improved.

[0044] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field are aware of all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement the scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for the ordinary technicians in the relevant field to implement this application. It should be pointed out that for the technicians in this field, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. An adaptive mesh encryption method based on the exterior shape of the car and flow field characteristics, characterized in that: The following steps are involved: Step 1: Extract the exterior geometric model of the car; Step 2: The extracted exterior geometric model is expanded with a preset point as a reference point, and encrypted through the expansion area; the preset point is: the point of the minimum value of the front windshield of the car in the X direction is projected to the corresponding point on the balance surface; Step 3: Based on the size distribution of the total pressure flow field function in the flow field, perform adaptive encryption of the flow field; When performing adaptive flow field encryption, the flow field envelope with a total pressure less than or equal to zero is encrypted, and the mesh size of the encrypted area is set to 8 mm; For flow field volume grids with total pressure greater than or equal to zero, no changes are made.

2. The method for adaptive mesh encryption based on automobile exterior shape and flow field characteristics according to claim 1 is characterized in that: Based on Siemens StarCCM+ secondary development, perform steps 1 to 3.

3. The method for adaptive mesh encryption based on automobile exterior shape and flow field characteristics according to claim 2 is characterized in that: When extracting the external geometric model and selecting preset points, automatic identification and extraction are performed based on boundary naming vocabulary.

4. The method for adaptive mesh encryption based on automobile exterior shape and flow field characteristics according to claim 1 is characterized in that: In step 2, the external geometric model is automatically closed before expansion using the preset point as the reference point.

5. The method for adaptive mesh encryption based on automobile exterior shape and flow field characteristics according to claim 4 is characterized in that: In step 2, when the expansion is performed with the preset point as the reference point, the closed outer shape geometric model is expanded based on the reference point at expansion ratios of 1.4 and 1.6 respectively.

6. The method for adaptive mesh encryption based on automobile exterior shape and flow field characteristics according to claim 5 is characterized in that: When the expansion ratio is 1.6 times, the mesh size of the encrypted area is set to 32 mm; when the expansion ratio is 1.4 times, the mesh size of the encrypted area is set to 16 mm.

7. The method for adaptive mesh encryption based on automobile exterior shape and flow field characteristics according to claim 1 is characterized in that: Based on the size distribution of the total pressure flow field function in the flow field, the execution mechanism of the flow field adaptive encryption is: when the flow field is relatively stable after 3000 iterations of flow field calculation, the flow field adaptive encryption is performed.

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