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Flows Over Periodic Hills of Parameterized Geometries: A Dataset for Data-Driven Turbulence Modeling From Direct Simulations

Xiao, Heng and Wu, Jin-Long and Laizet, Sylvain and Duan, Lian (2020) Flows Over Periodic Hills of Parameterized Geometries: A Dataset for Data-Driven Turbulence Modeling From Direct Simulations. Computers & Fluids, 200 . Art. No. 104431. ISSN 0045-7930. https://resolver.caltech.edu/CaltechAUTHORS:20200109-111609613

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Abstract

Computational fluid dynamics models based on Reynolds-averaged Navier–Stokes equations with turbulence closures still play important roles in engineering design and analysis. However, the development of turbulence models has been stagnant for decades. With recent advances in machine learning, data-driven turbulence models have become attractive alternatives worth further explorations. However, a major obstacle in the development of data-driven turbulence models is the lack of training data. In this work, we survey currently available public turbulent flow databases and conclude that they are inadequate for developing and validating data-driven models. Rather, we need more benchmark data from systematically and continuously varied flow conditions (e.g., Reynolds number and geometry) with maximum coverage in the parameter space for this purpose. To this end, we perform direct numerical simulations of flows over periodic hills with varying slopes, resulting in a family of flows over periodic hills which ranges from incipient to mild and massive separations. We further demonstrate the use of such a dataset by training a machine learning model that predicts Reynolds stress anisotropy based on a set of mean flow features. We expect the generated dataset, along with its design methodology and the example application presented herein, will facilitate development and comparison of future data-driven turbulence models.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.compfluid.2020.104431DOIArticle
ORCID:
AuthorORCID
Xiao, Heng0000-0002-3323-4028
Additional Information:© 2020 Elsevier Ltd. Received 2 October 2019, Revised 22 December 2019, Accepted 7 January 2020, Available online 9 January 2020.
Subject Keywords:physics-informed machine learning; Turbulence modeling; Separated flows
Record Number:CaltechAUTHORS:20200109-111609613
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200109-111609613
Official Citation:Heng Xiao, Jin-Long Wu, Sylvain Laizet, Lian Duan, Flows over periodic hills of parameterized geometries: A dataset for data-driven turbulence modeling from direct simulations, Computers & Fluids, Volume 200, 2020, 104431, ISSN 0045-7930, https://doi.org/10.1016/j.compfluid.2020.104431. (http://www.sciencedirect.com/science/article/pii/S0045793020300074)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100599
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:09 Jan 2020 19:32
Last Modified:19 Feb 2020 17:58

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