Version 1
: Received: 21 September 2023 / Approved: 21 September 2023 / Online: 22 September 2023 (11:14:24 CEST)
How to cite:
Luo, Y.; Chen, Y.; Zhang, Z. CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics. Preprints2023, 2023091550. https://doi.org/10.20944/preprints202309.1550.v1
Luo, Y.; Chen, Y.; Zhang, Z. CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics. Preprints 2023, 2023091550. https://doi.org/10.20944/preprints202309.1550.v1
Luo, Y.; Chen, Y.; Zhang, Z. CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics. Preprints2023, 2023091550. https://doi.org/10.20944/preprints202309.1550.v1
APA Style
Luo, Y., Chen, Y., & Zhang, Z. (2023). CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics. Preprints. https://doi.org/10.20944/preprints202309.1550.v1
Chicago/Turabian Style
Luo, Y., Yingfa Chen and Zhen Zhang. 2023 "CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics" Preprints. https://doi.org/10.20944/preprints202309.1550.v1
Abstract
In recent years, applying deep learning to solve physics problems has attracted much attention. Data-driven deep learning methods produce operators that can learn solutions to the whole system of partial differential equations. However, the existing methods are only evaluated on simple flow equations (e.g., Burger's equation), and only consider the generalization ability on different initial conditions. In this paper, we construct CFDBench, a benchmark with four classic problems in computational fluid dynamics (CFD): lid-driven cavity flow, laminar boundary layer flow in circular tubes, dam flows through the steps, and periodic Karman vortex street. Each flow problem includes data with various boundary conditions, fluid physical properties, and domain geometries. Compared to existing datasets, the advantages of CFDBench are (1) comprehensive. It contains common physical parameters such as velocity, pressure, and cavity fraction. (2) realistic. It is very suitable for deep learning solutions of fluid mechanics equations. (3) challenging. It has a certain learning difficulty, prompting to find models with strong learning ability. (4) standardized. CFDBench facilitates a comprehensive and fair comparison of different deep learning methods for CFD. We make appropriate modifications to popular deep neural networks to apply them to CFDBench and enable the accommodation of more changing inputs. The evaluation on CFDBench reveals some new shortcomings of existing works and we propose possible directions for solving such problems.
Keywords
Computational fluid dynamics; Deep learning; Partial differential equations; Neural operators
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
