Space-dependent turbulence model aggregation using machine learning

de Zordo-Banliat, M.; Dergham, G.; Merle, X.; Cinnella, P.

doi:10.1016/j.jcp.2023.112628

Journal of Computational Physics

2024

DOI: 10.1016/j.jcp.2023.112628

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Space-dependent turbulence model aggregation using machine learning

M. de Zordo-Banliat,

G. Dergham,

X. Merle

et al.

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2024

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Cited by 5 publications

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Enhancing non-intrusive reduced-order models with space-dependent aggregation methods

Ivagnes,

Tonicello,

Cinnella

et al. 2024

Acta Mech

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In this manuscript, we combine non-intrusive reduced-order models (ROMs) with space-dependent aggregation techniques to build a mixed-ROM, able to accurately capture the flow dynamics in different physical settings. The flow prediction obtained using the mixed formulation is derived from a convex combination of the predictions of several previously trained reduced-order models (ROMs), with each model assigned a space-dependent weight. The ROMs incorporated in the mixed model utilize different reduction methods, such as proper orthogonal decomposition and autoencoders, and various approximation techniques, including radial basis function interpolation (RBF), Gaussian process regression, and feed-forward artificial neural networks. Each model’s contribution is given higher weights in regions where it performs best and lower weights where its accuracy is lower compared to the other models. Additionally, a random forest regression technique is used to determine the weights for previously unseen conditions. The performance of the aggregated model is assessed through two test cases: the 2D flow past a NACA 4412 airfoil at a 5-degree angle of attack, with the Reynolds number ranging between $$1 \times 10^{5}$$ 1 × 10 5 and $$1 \times 10^{6}$$ 1 × 10 6 , and a transonic flow over a NACA 0012 airfoil, with the angle of attack as the varying parameter. In both scenarios, the mixed-ROM demonstrated improved accuracy compared to each individual ROM technique, while providing an estimate for the predictive uncertainty.

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Enhancing non-intrusive reduced-order models with space-dependent aggregation methods

Ivagnes,

Tonicello,

Cinnella

et al. 2024

Acta Mech

View full text Add to dashboard Cite

show abstract

Generalization Limits of Data-Driven Turbulence Models

Mandler,

Weigand

2024

Flow Turbulence Combust

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Many industrial applications require turbulent closure models that yield accurate predictions across a wide spectrum of flow regimes. In this study, we investigate how data-driven augmentations of popular eddy viscosity models affect their generalization properties. We perform a systematic generalization study with a particular closure model that was trained for a single flow regime. We systematically increase the complexity of the test cases up to an industrial application governed by a multitude of flow patterns and thereby demonstrate that tailoring a model to a specific flow phenomenon decreases its generalization capability. In fact, the accuracy gain in regions that the model was explicitly calibrated for is smaller than the loss elsewhere. We furthermore show that extrapolation or, generally, a lack of training samples with a similar feature vector is not the main reason for generalization errors. There is actually only a weak correlation. Accordingly, generalization errors are probably due to a data-mismatch, i.e., a systematic difference in the mappings from the model inputs to the required responses. More diverse training sets unlikely provide a remedy due to the strict stability requirements emerging from the ill-conditioned RANS equations. The universality of data-driven eddy viscosity models with variable coefficients is, therefore, inherently limited.

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Data-Driven Algebraic Models Tuned with a Vast Experimental Database of Separated Flows

Carlucci,

Petronio,

Dellacasagrande

et al. 2024

Flow Turbulence Combust

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Space-dependent turbulence model aggregation using machine learning

Cited by 5 publications

References 40 publications

Enhancing non-intrusive reduced-order models with space-dependent aggregation methods

Enhancing non-intrusive reduced-order models with space-dependent aggregation methods

Generalization Limits of Data-Driven Turbulence Models

Data-Driven Algebraic Models Tuned with a Vast Experimental Database of Separated Flows

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