Turbulence closure modeling with machine learning: a foundational physics perspective

New Journal of Physics, Journal Year: 2024, Volume and Issue: 26(7), P. 071201 - 071201

Published: July 1, 2024

Abstract Turbulence closure modeling using machine learning (ML) is at an early crossroads. The extraordinary success of ML in a variety challenging fields had given rise to expectation similar transformative advances the area turbulence modeling. However, by most accounts, current rate progress toward accurate and predictive ML-RANS (Reynolds Averaged Navier–Stokes) models has been very slow. Upon retrospection, absence rapid can be attributed two factors: underestimation intricacies overestimation ML’s ability capture all features without employing targeted strategies. To pave way for more meaningful closures tailored address nuances turbulence, this article seeks review foundational flow physics assess challenges context data-driven approaches. Revisiting analogies with statistical mechanics stochastic systems, key physical complexities mathematical limitations are explicated. It noted that approaches do not systematically inherent approach or inadequacies forms expressions. study underscores drawbacks supervised learning-based stresses importance discerning framework. As methods evolve (which happening pace) our understanding phenomenon improves, inferences expressed here should suitably modified.

Language: Английский

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Scale-resolving simulations of turbulent flows with coherent structures: Toward cut-off dependent data-driven closure modeling

Physics of Fluids, Journal Year: 2024, Volume and Issue: 36(6)

Published: June 1, 2024

Complex turbulent flows with large-scale instabilities and coherent structures pose challenges to both traditional data-driven Reynolds-averaged Navier–Stokes methods. The difficulty arises due the strong flow-dependence (the non-universality) of unsteady structures, which translates poor generalizability models. It is well-accepted that dynamically active reside in larger scales, while smaller scales turbulence exhibit more “universal” (generalizable) characteristics. In such flows, it prudent separate treatment flow-dependent aspects from universal features field. Scale resolving simulations (SRS), as partially averaged (PANS) method, seek resolve motion model only stochastic features. Such an approach requires development scale-sensitive closures not allow for but also appropriate dependence on cut-off length scale. objectives this work are (i) establish physical characteristics dependent turbulence; (ii) develop a procedure subfilter stress neural network at different cut-offs using high-fidelity data; (iii) examine optimal incorporation consistent posteriori use. scale-dependent closure physics analysis performed context PANS approach, technique can be extended other SRS benchmark “flow past periodic hills” case considered proof concept. self-similarity parameters incorporating identified. study demonstrates when data suitably normalized, machine learning based indeed insensitive

Language: Английский

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Modeling the Unsteady Wake of an Impulsively Started Circular Cylinder Using Refined Potential Flow Theory

Physica Scripta, Journal Year: 2024, Volume and Issue: 99(8), P. 085207 - 085207

Published: May 13, 2024

Abstract Cylindrical structures find usage in many engineering applications including tethered oil drums and engine canisters slung beneath helicopters flight. The motion of air around such circular cylindrical the helicopter presents interesting phenomena flow separation, wakes turbulence. physics these are enshrined continuity equation Navier–Stokes equations. Therefore, their studies not only important mathematics physics, but they also required for efficient operations. In practice, reduced-order models operations that take aerodynamics loads utilized stability analysis, flight certification pilot training because prohibitive cost experimentation computational analyses configurations. However, there is a dearth realistic analytical finite cylinder flows problem. Classical potential theory provides an avenue to develop models, extant gaps its predictions significantly preclude applications. Attempting bridge gaps, this article introduces refined which governing equations boundary conditions satisfied. Viscous effects, fluctuations mean three-dimensional effects incorporated. For characterization, employed on incompressible over impulsively started Reynolds numbers non-dimensional times range 30 < Re 10 4 0.2 ≤ T 77, 047 respectively. There excellent prediction 0.209 Strouhal number at = 3 , 900 . At transitional number, harmonics frequency captured, characteristic irregular sub-Strouhal frequencies discernible velocity spectra. As becomes more turbulent, become pronounced 9 500 when predicted within 10% experimental result. fully developed stage, spectra wake components some downstream locations display Kolmogorov's Five-Thirds law homogeneous isotropic present model can thus aid development feature loads.

Language: Английский

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Field Inversion and Machine Learning Based on the Rubber-Band Spalart-Allmaras Model

Theoretical and Applied Mechanics Letters, Journal Year: 2024, Volume and Issue: unknown, P. 100564 - 100564

Published: Dec. 1, 2024

Language: Английский

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