Abstract
The production of numerous high fidelity simulations has been a key aspect of research for many-query problems in fluid dynamics. The computational resources and time required to generate these simulations can be so large and impractical. With several successes of generative models, we explore the performance and powerful generative capabilities of both generative adversarial network (GAN) and adversarial autoencoder (AAE) to predict the evolution in time of a highly nonlinear fluid flow. These generative models are incorporated within a reduced-order model framework. The test case comprises two-dimensional Gaussian vortices governed by the time-dependent Navier-Stokes equation. We show that both the GAN and AAE are able to predict the evolution of the positions of the vortices forward in time, generating new samples that have never before been seen by the neural networks.
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Acknowledgements
The authors would like to acknowledge the following EPSRC grants: RELIANT, Risk EvaLuatIon fAst iNtelligent Tool for COVID19 (EP/V036777/1); MAGIC, Managing Air for Green Inner Cities (EP/N010221/1); MUFFINS, MUltiphase Flow-induced Fluid-flexible structure InteractioN in Subsea applications (EP/P033180/1); the PREMIERE programme grant (EP/T00 0414/1); and INHALE, Health assessment across biological length scales (EP/T00 3189/1). Most sincere appreciation goes out to the Department of Earth Science and Engineering at Imperial College London for support and resources provided over the period of this project. We would also like to extend gratitude to everyone who was engaged in discussions during the project. Thank you for giving your time and sharing your ideas.
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Jolaade, M., Silva, V.L.S., Heaney, C.E., Pain, C.C. (2022). Generative Networks Applied to Model Fluid Flows. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_61
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