Geometry-Based Hyperbolic Grid Generation for Computational Fluid Dynamics

Lal, S. Anil; Prasad, B. V. S. S. S.; Sitaram, N.

doi:10.2514/2.1491

AIAA Journal

2001

DOI: 10.2514/2.1491

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Geometry-Based Hyperbolic Grid Generation for Computational Fluid Dynamics

S. Anil Lal

B. V. S. S. S. Prasad

N. Sitaram

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Wind Energy literature survey no. 3

2001

Wind Energy

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In order to help keep readers up‐to‐date in the field, Wind Energy contains a list of recently published articles drawn from issues of the following periodicals: IEEE Transactions on Energy Conversion, IEEE Transactions on Power Electronics, IEEE Transactions on Control Systems Technology, International Journal of Energy Research, Journal of Wind Engineering and Industrial Aerodynamics, American Institute of Aeronautics and Astronautics Journal, Transactions of the ASME: Journal of Dynamic Systems Measurement and Control, and Renewable Energy. This survey additionally contains articles from a special wind energy issue of Transactions of the ASME: Journal of Solar Energy Engineering. The list is arranged alphabetically by author. Compiled by Martin Hird, UMIST, PO Box 88, Manchester, M60 1QD, UK. Please e‐mail any suggestions to martin.hird@stud.umist.ac.uk. Copyright © 2001 John Wiley & Sons, Ltd.

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Wind Energy literature survey no. 3

2001

Wind Energy

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Three-Dimensional Hyperbolic Mesh Generation Method Based on the Neural Network

Yue,

Li,

et al. 2024

Applied Sciences

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Aiming at the limitations of the traditional hyperbolic mesh generation method, specifically the limited types of boundary control strategy along the advancing direction and the inability to control the outer boundary, this paper employs physics-informed neural networks with output range constraints to approximate the solutions of the governing equations that are used to generate the hyperbolic mesh. After transforming the form of the governing equations, the solution was fitted using the neural network driven solely by boundary data. By incorporating the governing equations and the boundary conditions into the loss function, the neural network method can directly control the boundaries along the advancing direction. For the outer boundary, a novel variance constraint strategy was proposed. Based on the proposed method, meshes were generated for three-dimensional surfaces and three-dimensional solids derived from the terrain surface. The quality of these meshes was compared with the traditional method. The results demonstrate that this method can effectively achieve boundary control during the hyperbolic mesh generation process and consistently produces high-quality hyperbolic meshes. Therefore, neural network-based hyperbolic mesh generation is an effective approach to achieving boundary control, which can further enhance the applicability of hyperbolic mesh generation methods.

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Geometry-Based Hyperbolic Grid Generation for Computational Fluid Dynamics

Cited by 2 publications

References 4 publications

Wind Energy literature survey no. 3

Wind Energy literature survey no. 3

Three-Dimensional Hyperbolic Mesh Generation Method Based on the Neural Network

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