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Self-adaptive Cartesian grid generation method for three-dimensional streaming problem of any shape

A Cartesian grid and self-adaptive technology, applied in special data processing applications, constraint-based CAD, instruments, etc., can solve problems such as sudden increase in storage capacity, occupation of computing resources, and limited accuracy of computer floating-point numbers

Active Publication Date: 2021-10-15
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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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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  • Self-adaptive Cartesian grid generation method for three-dimensional streaming problem of any shape
  • Self-adaptive Cartesian grid generation method for three-dimensional streaming problem of any shape
  • Self-adaptive Cartesian grid generation method for three-dimensional streaming problem of any shape

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Embodiment 1

[0144] 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. like 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

[0145] 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

[0146] 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. like 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 self-adaptive Cartesian grid generation method for a three-dimensional streaming problem of any shape, and the method comprises the steps: generating an isotropic self-adaptive Cartesian grid suitable for an immersed boundary method based on geometric information in the three-dimensional streaming problem, carrying out the calculation of a flow field, and encrypting an area containing key flow features according to the calculation result of the flow field. In order to solve the problem of computational fluid mechanics numerical simulation with complex three-dimensional streaming, a surface set composed of triangles is adopted as input, a grid intersection judgment method based on a separation axis theory and a grid inside and outside judgment method based on an improved ray algorithm are adopted for grid classification, a grid subdivision method based on a unit is adopted for encrypting and coarsening grid units, and a self-adaptive Cartesian grid meeting the requirements of an immersed boundary method and flow field calculation resolution can be efficiently and robustly generated; and a region containing a feature structure is selectively encrypted according to flow field parameters obtained subsequently, and the flow field feature structure in the current flow field area is displayed 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 Applications(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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