A highly accurate strategy for data-driven turbulence modeling

Computational and Applied Mathematics, Год журнала: 2024, Номер 43(1)

Опубликована: Янв. 19, 2024

Язык: Английский

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Constrained re-calibration of two-equation Reynolds-averaged Navier–Stokes models

Theoretical and Applied Mechanics Letters, Год журнала: 2024, Номер 14(2), С. 100503 - 100503

Опубликована: Фев. 7, 2024

Machine-learned augmentations to turbulence models can be advantageous for flows within the training dataset but often cause harm outside. This lack of generalizability arises because constants (as well as functions) in a Reynolds-averaged Navier–Stokes (RANS) model are coupled, and un-constrained re-calibration these (and disrupt calibrations baseline model, preservation which is critical model's generalizability. To safeguard behaviors beyond dataset, machine learning must constrained such that basic like law wall kept intact. letter aims identify constraints two-equation RANS so future work performed without violating constraints. We demonstrate identified not limiting. Furthermore, they help preserve model.

Язык: Английский

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A data‐driven turbulence modeling for the Reynolds stress tensor transport equation

International Journal for Numerical Methods in Fluids, Год журнала: 2024, Номер 96(7), С. 1194 - 1214

Опубликована: Март 26, 2024

Abstract The long lasting demand for better turbulence models and the still prohibitively computational cost of high‐fidelity fluid dynamics simulations, like direct numerical simulations large eddy have led to a rising interest in coupling available datasets popular, yet limited, Reynolds averaged Navier–Stokes through machine learning (ML) techniques. Many recent advances used stress tensor or, less frequently, force vector as target these corrections. In present work, we considered an unexplored strategy, namely use modeled terms transport equation ML predictions, employing neural network approach. After that, solve coupled set governing equations obtain mean velocity field. We apply this strategy flow square duct. obtained results consistently recover secondary flow, which is not baseline that model. were compared with other approaches literature, showing path can be useful seek more universal turbulence.

Язык: Английский

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Progressive augmentation of Reynolds stress tensor models for secondary flow prediction by computational fluid dynamics driven surrogate optimisation

International Journal of Heat and Fluid Flow, Год журнала: 2023, Номер 104, С. 109242 - 109242

Опубликована: Ноя. 7, 2023

Generalisability and the consistency of a posteriori results are most critical points view regarding data-driven turbulence models. This study presents progressive improvement models using simulation-driven Bayesian optimisation with Kriging surrogates where is achieved by multi-objective approach based on duct flow quantities. We aim for augmentation secondary-flow prediction capability in linear eddy-viscosity model k−ω SST without violating its original performance canonical cases e.g. channel flow. Progressively data-augmented explicit algebraic Reynolds stress (PDA-EARSMs) obtained enabling secondary flows that standard fails to predict. The new tested guaranteeing they preserve successful model. Subsequently, numerical verification performed various test cases. Regarding generalisability models, unseen demonstrate significant streamwise velocity. These highlight potential enhance fluid simulation while preserving robustness stability solver.

Язык: Английский

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Mitigating ill-conditioning of Reynolds-averaged Navier–Stokes equations for experimental data-driven turbulence closures

Physics of Fluids, Год журнала: 2025, Номер 37(4)

Опубликована: Апрель 1, 2025

Experiments are a fundamental source of high-fidelity data in turbulence, providing reliable targets for modeling. As Direct Numerical Simulations (DNS), they can capture flow dynamics without an underlying model but less restrictive than DNS regarding geometry and Reynolds number limits. In this work, we conducted experimental campaign to generate quality on the turbulent quantities square duct flows. The recent literature has revealed ill-conditioned nature Reynolds-averaged Navier–Stokes (RANS) equations. These studies suggest that other target besides stress tensor (RST) compose closures lead more accurate solutions. We investigated statistical derived from these experiments feed RANS equations, testing various methods comparing different closure strategies. anticipated, using measured RST directly equations led significant error propagation predicted average velocity fields due equations' nature. Conversely, approaches successfully yielded propagated mean fields. Notably, implicit treatment linear components force vector provided precise results. By extending range tested numbers beyond those achievable by DNS, study underscores critical need careful selection data-driven turbulence highlights importance exploring new closing

Язык: Английский

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Progressive augmentation of turbulence models for flow separation by multi-case computational fluid dynamics driven surrogate optimization

Physics of Fluids, Год журнала: 2023, Номер 35(12)

Опубликована: Дек. 1, 2023

In the field of data-driven turbulence modeling, consistency a posteriori results and generalizability are most critical aspects new models. this study, we combine multi-case surrogate optimization technique with progressive augmentation approach to enhance performance popular k−ω shear stress transport (SST) model in prediction flow separation. We introduce separation factor into equation turbulent specific dissipation rate (ω) correct underestimation viscosity by SST case for two-dimensional cases. The is optimized based on their training cases including periodic hills curved backward-facing step flow. Simulation channel likewise included process guarantee that original preserved absence verified multiple unseen different Reynolds numbers geometries. Results show significant improvement recirculation zone, velocity components, distribution friction coefficient both testing cases, where expected. models test no shows they preserve successful when not

Язык: Английский

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Exploring the Potential and the Practical Usability of a Machine Learning Approach for Improving Wall Friction Predictions of RANS Wall Functions in Non-equilibrium Turbulent Flows

Flow Turbulence and Combustion, Год журнала: 2024, Номер 112(4), С. 975 - 1000

Опубликована: Март 28, 2024

Язык: Английский

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Implementation and validation of a generalized wall stress function

Physics of Fluids, Год журнала: 2024, Номер 36(7)

Опубликована: Июль 1, 2024

The generalized wall function by Shih et al. [Report No. M-1999-209398 (1999)], which accounts for non-equilibrium effects the presence of favorable and adverse pressure gradients in turbulent flows, is addressed with aim performing high Reynolds number large-eddy simulations wall-bounded flow. model uses a corrected law gradient contribution to approximate stress applies entire viscous layer, buffer inertial region. A fully developed channel flow first tested validate solver implementation, then assessed over periodic hill. Wall-resolved are good agreement reference results. priori investigation own experimental results corroborates mathematical form suggests using different coefficients. wall-modeled show that implemented able improve shear predictions compared standard equilibrium model. It corrects underestimation stresses models region overestimation positions separation reattachment points also Furthermore, prediction maximum zone at windward side hill quite robust against coarsening wall-normal grid spacing.

Язык: Английский

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Using delayed detached Eddy simulation to create datasets for data-driven turbulence modeling: A periodic hills with parameterized geometry case

Computers & Fluids, Год журнала: 2024, Номер unknown, С. 106506 - 106506

Опубликована: Дек. 1, 2024

Язык: Английский

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