Robust data-driven turbulence closures for improved heat transfer prediction in complex geometries

Hammond, James; Pietropaoli, Marco; Montomoli, Francesco

doi:10.1016/j.ijheatfluidflow.2022.109072

International Journal of Heat and Fluid Flow

2022

DOI: 10.1016/j.ijheatfluidflow.2022.109072

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Robust data-driven turbulence closures for improved heat transfer prediction in complex geometries

James Hammond

Marco Pietropaoli²,

Francesco Montomoli³

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2024

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Cited by 2 publications

References 23 publications

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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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Generalization Limits of Data-Driven Turbulence Models

Mandler,

Weigand

2024

Flow Turbulence Combust

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Generalized field inversion strategies for data-driven turbulence closure modeling

Mandler,

Weigand

2024

Physics of Fluids

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Most data-driven turbulence closures are based on the general structure of nonlinear eddy viscosity models. Although this structure can be embedded into the machine learning algorithm and the Reynolds stress tensor itself can be fit as a function of scalar- and tensor-valued inputs, there exists an alternative two-step approach. First, the spatial distributions of the optimal closure coefficients are computed by solving an inverse problem. Subsequently, these are expressed as functions of solely scalar-valued invariants of the flow field by virtue of an arbitrary regression algorithm. In this paper, we present two general inversion strategies that overcome the limitation of being applicable only when all closure tensors are linearly independent. We propose to either cast the inversion into a constrained and regularized optimization problem or project the anisotropy tensor onto a set of previously orthogonalized closure tensors. Using the two-step approach together with either of these strategies then enables us to quantify the model-form error associated with the closure structure independent of a particular regression algorithm. Eventually, this allows for the selection of the a priori optimal set of closure tensors for a given, arbitrary complex test case.

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Robust data-driven turbulence closures for improved heat transfer prediction in complex geometries

Cited by 2 publications

References 23 publications

Generalization Limits of Data-Driven Turbulence Models

Generalization Limits of Data-Driven Turbulence Models

Generalized field inversion strategies for data-driven turbulence closure modeling

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