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arXiv · 2407.14132

Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics

Rik Hoekstra·Daan Crommelin·Wouter Edeling
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Abstract

We introduce a simple, stochastic, a-posteriori, turbulence closure model based on a reduced subgrid scale term. This subgrid scale term is tailor-made to capture the statistics of a small set of spatially-integrate quantities of interest (QoIs), with only one unresolved scalar time series per QoI. In contrast to other data-driven surrogates the dimension of the "learning problem" is reduced from an evolving field to one scalar time series per QoI. We use an a-posteriori, nudging approach to find the distribution of the scalar series over time. This approach has the advantage of taking the interaction between the solver and the surrogate into account. A stochastic surrogate parametrization is obtained by random sampling from the found distribution for the scalar time series. Compared to an a-priori trained convolutional neural network, evaluating the new method is computationally much cheaper and gives similar long-term statistics.

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Rik Hoekstra, Daan Crommelin, Wouter Edeling. 2024-07-19. Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics. https://doi.org/10.1016/j.compfluid.2024.106469

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