Scale-resolving simulations of turbulent flows with coherent structures: Toward cut-off dependent data-driven closure modeling DOI
Salar Taghizadeh, Freddie Witherden, Sharath S. Girimaji

и другие.

Physics of Fluids, Год журнала: 2024, Номер 36(6)

Опубликована: Июнь 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

Язык: Английский

Turbulence closure modeling with machine learning: a foundational physics perspective DOI Creative Commons
Sharath S. Girimaji

New Journal of Physics, Год журнала: 2024, Номер 26(7), С. 071201 - 071201

Опубликована: Июль 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.

Язык: Английский

Процитировано

7
Active learning of data-assimilation closures using graph neural networks DOI

Michele Quattromini,

Michele Alessandro Bucci, Stefania Cherubini

и другие.

Theoretical and Computational Fluid Dynamics, Год журнала: 2025, Номер 39(1)

Опубликована: Янв. 14, 2025

Язык: Английский

Процитировано

0
Data-driven turbulent heat flux modeling with inputs of multiple fidelity DOI
Matilde Fiore, Enrico Saccaggi, Lilla Koloszár

и другие.

Physical Review Fluids, Год журнала: 2025, Номер 10(3)

Опубликована: Март 17, 2025

The widespread application of data-driven turbulence models is currently limited by challenges in generalization and robustness to inconsistencies between input data varying fidelity levels. This especially true for thermal turbulent closures, which inherently depend on momentum statistics provided low or high-fidelity models. work investigates the impact modeling a closure trained with dataset multiple fidelities (DNS RANS).

Язык: Английский

Процитировано

0
Scale-resolving simulations of turbulent flows with coherent structures: Toward cut-off dependent data-driven closure modeling DOI
Salar Taghizadeh, Freddie Witherden, Sharath S. Girimaji

и другие.

Physics of Fluids, Год журнала: 2024, Номер 36(6)

Опубликована: Июнь 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

Язык: Английский

Процитировано

2