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An Adaptive Cartesian Mesh Generation Method for 3D Flow Around Arbitrary Shapes

A Cartesian grid, self-adaptive technology, applied in the field of flow field numerical simulation and grid generation, can solve problems such as limited precision of computer floating point numbers, multi-size object surface structure errors, and storage capacity steep rise.

Active Publication Date: 2021-12-14
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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  • Description
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AI Technical Summary

Problems solved by technology

[0005] It should be noted that the current traditional 3D non-body-fitted Cartesian grid is mostly generated by the ray method, and the algorithm in the judgment process mostly involves multiplication and division operations. Due to the limited accuracy of computer floating point numbers, errors are prone to occur when involving multi-dimensional object surface structures
In addition, when generating non-equidistant isotropic Cartesian grids, block-based subdivision techniques are currently used, that is, to encrypt all flow field grids in a given range. This method requires the sharing of topological structure information of the entire grid. With the increase of the number of grid adaptations, the storage capacity increases sharply, greatly occupying computing resources
Based on the above problems, there is currently no standard automatic generation method for adaptive Cartesian grids for three-dimensional flow problems that take into account both robustness and grid generation efficiency.

Method used

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  • An Adaptive Cartesian Mesh Generation Method for 3D Flow Around Arbitrary Shapes
  • An Adaptive Cartesian Mesh Generation Method for 3D Flow Around Arbitrary Shapes
  • An Adaptive Cartesian Mesh Generation Method for 3D Flow Around Arbitrary Shapes

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Embodiment 1

[0146] Embodiment 1, ONERA-M6 three-dimensional non-body-fit adaptive Cartesian grid generation. The ONERA-M6 airfoil is a classic example to test the stability of the computational fluid dynamics numerical method and the flow field solver. Its numerical simulation results and experimental results are very complete. At the same time, its model is relatively simple, which is very suitable as an initial method verification example. . The current ONERA-M6 model surface set is composed of 8132 triangles, and the triangles are densely distributed in the wingtips and other parts. A total of 7 mesh adaptive operations have been performed. The buffer factor α Take 3, the number of grids is 386044, and 32 cores are used in parallel, which takes 32s. Such as Figure 5 Shown is a multi-section schematic diagram of an adaptive Cartesian grid based on the shape of the ONERA-M6 wing.

Embodiment 2

[0147] Embodiment 2: The wing-body assembly model DLR-F6 with the engine nacelle and the pylon is generated with a three-dimensional non-body-fitting adaptive Cartesian grid. DLR-F6 is a twin-engine wide-body airliner. The DLR-F6 wing-body assembly model without the engine is the drag prediction model selected by AIAA DPW III, a series of drag prediction seminars organized by AIAA. This example is to verify the robustness of the algorithm , considering complex shapes such as hollow shells and concave surfaces, using the DLR-F6 model of the engine shell as the input object to generate an adaptive Cartesian grid. The surface of the current DLR-F6 model is composed of 35532 triangles, which are densely distributed at the leading edge of the fuselage, wingtips and other places with large geometric changes. A total of 9 geometric adaptive operations have been performed, and the buffer factor α Take 5, the number of grids is 17483250, and 96 cores are used in parallel, which takes 9...

Embodiment 3

[0148] Embodiment 3. Generation of three-dimensional non-body-fitted Cartesian grids of the COVID-19 virus model. In order to fully verify the robustness of the current invention, a 3D Cartesian grid is generated with the input shape of COVID-19. The COVID-19 virus model is different from the streamlined shape of the wing. The surface contains a total of 54 tentacles, which are composed of 188,280 discrete triangles. It includes multiple concave surfaces, convex tentacles and other special complex shape structures. A total of 6 geometric adaptive operations have been performed. buffer factor α Take 3, the number of grids is 2032927, and 96 cores are used in parallel, which takes 596s. Such as Figure 7 Shown is a multi-section diagram of an adaptive Cartesian grid constructed based on the shape of COVID-19.

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Abstract

The invention discloses a method for generating an adaptive Cartesian grid for a three-dimensional surrounding flow problem with arbitrary shapes. Based on the geometric information existing in the three-dimensional surrounding flow problem, an isotropic adaptive Cartesian grid suitable for the immersion boundary method is generated, and the Perform flow field calculations, and encrypt areas containing key flow characteristics based on the flow field calculation results. Aiming at the computational fluid dynamics numerical simulation problem with complex three-dimensional flow around, the present invention adopts the surface set composed of triangles as input, and adopts the grid intersection determination method based on the separation axis theory and the grid internal and external determination method based on the improved ray algorithm to carry out grid meshing. Classification, using the element-based meshing method to refine and coarsen the grid cells, can efficiently and robustly generate an adaptive Cartesian grid that meets the requirements of the immersion boundary method and the resolution of the flow field calculation, and obtains according to the subsequent The flow field parameters of the device selectively encrypt the area containing the characteristic structure, and display the flow field characteristic structure in the current flow field area in real time.

Description

technical field [0001] The invention belongs to the technical field of flow field numerical simulation and grid generation, and in particular relates to an adaptive Cartesian grid generation method for a three-dimensional flow around an arbitrary shape. Background technique [0002] In recent decades, efficient and high-quality mesh generation technology has been a key research content as a prerequisite for computational fluid dynamics numerical simulation. NASA's CFD Vision 2030 report pointed out that up to now, the grid generation task still occupies 60%-70% of the entire CFD computing task cycle, which is one of the decisive factors affecting the quality, stability and resource consumption of numerical simulation. [0003] With the increase in the complexity of object structures in practical applications, structured grids are difficult to adapt to engineering needs due to their strict topology requirements, and unstructured grids have vortexes due to their large memory o...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/15G06F30/28G06F111/04G06F113/08G06F119/14
CPCG06F30/15G06F30/28G06F2111/04G06F2113/08G06F2119/14
Inventor 杨宇辰赵宁齐昕宇
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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