Obálka

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network

Uloženo v:
Vydáno v:arXiv.org (Dec 23, 2024), p. n/a
Hlavní autor: Rostamijavanani, Abdolvahhab
Další autoři: Li, Shanwu, Yang, Yongchao
Vydáno:
Cornell University Library, arXiv.org
Témata:
Accuracy
Parameter identification
Parameterization
Generative adversarial networks
Fluid flow
Parameter modification
Two dimensional flow
System effectiveness
Nonlinear systems
Dynamical systems
Reynolds number
Fluid dynamics
Nonlinear dynamics
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Abstrakt:This work presents a data-driven solution to accurately predict parameterized nonlinear fluid dynamical systems using a dynamics-generator conditional GAN (Dyn-cGAN) as a surrogate model. The Dyn-cGAN includes a dynamics block within a modified conditional GAN, enabling the simultaneous identification of temporal dynamics and their dependence on system parameters. The learned Dyn-cGAN model takes into account the system parameters to predict the flow fields of the system accurately. We evaluate the effectiveness and limitations of the developed Dyn-cGAN through numerical studies of various parameterized nonlinear fluid dynamical systems, including flow over a cylinder and a 2-D cavity problem, with different Reynolds numbers. Furthermore, we examine how Reynolds number affects the accuracy of the predictions for both case studies. Additionally, we investigate the impact of the number of time steps involved in the process of dynamics block training on the accuracy of predictions, and we find that an optimal value exists based on errors and mutual information relative to the ground truth.
ISSN:2331-8422
Zdroj:Engineering Database