A Database for Reduced-Complexity Modeling of Fluid Flows

Aaron Towne; Scott T. M. Dawson; Guillaume A. Brès; Adrián Lozano-Durán; Theresa Saxton-Fox; Aadhy Parthasarathy; Anya R. Jones; Hulya Biler; Chi-An Yeh; Het D. Patel; Kunihiko Taira

arXiv · 2206.11801

A Database for Reduced-Complexity Modeling of Fluid Flows

Aaron Towne·Scott T. M. Dawson·Guillaume A. Brès·Adrián Lozano-Durán·Theresa Saxton-Fox·Aadhy Parthasarathy·Anya R. Jones·Hulya Biler·Chi-An Yeh·Het D. Patel·Kunihiko Taira

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Abstract

We present a publicly accessible database designed to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. Availability of high-quality flow data is essential for all of these aspects of model development for both data-driven and physics-based methods. The database contains time-resolved data for six distinct datasets: a large eddy simulation of a turbulent jet, direct numerical simulations of a zero-pressure-gradient turbulent boundary layer, particle-image-velocimetry measurements for the same boundary layer at several Reynolds numbers, direct numerical simulations of laminar stationary and pitching flat-plate airfoils, particle-image-velocimetry and force measurements of an airfoil encountering a gust, and a large eddy simulation of the separated, turbulent flow over an airfoil. These six cases span several key flow categories: laminar and turbulent, statistically stationary and transient, tonal and broadband spectral content, canonical and application-oriented, wall-bounded and free-shear flow, and simulation and experimental measurements. For each dataset, we describe the flow setup and computational/experimental methods, catalog the data available in the database, and provide examples of how these data can be used for reduced-complexity modeling. All data can be downloaded using a browser interface or Globus. Our vision is that the common testbed provided by this database will aid the fluid mechanics community in clarifying the distinct capabilities of new and existing methods.

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Aaron Towne, Scott T. M. Dawson, Guillaume A. Brès, Adrián Lozano-Durán, Theresa Saxton-Fox, Aadhy Parthasarathy, Anya R. Jones, Hulya Biler, Chi-An Yeh, Het D. Patel, Kunihiko Taira. 2022-06-23. A Database for Reduced-Complexity Modeling of Fluid Flows. https://arxiv.org/abs/2206.11801

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