A hybrid reduced order method for modelling turbulent heat transfer problems

Georgaka, Sokratia; Stabile, Giovanni; Star, Kelbij; Rozza, Gianluigi; Bluck, Michael J.

doi:10.1016/j.compfluid.2020.104615

Cited by 37 publications

(34 citation statements)

References 47 publications

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“…Such basis can be exploited in a Galerkin projection framework [29][30][31] , in an hybrid framework combining data-driven methods with projection [32,33], or used to project onto the reduced space the initial snapshots. Thus we can approximate the snapshots x j as a linear combination of the modes as…”

Section: The Reduced Order Model: Pod-gprmentioning

confidence: 99%

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

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Tezzele

Mola

et al. 2021

JMSE

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In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a data-driven framework involving multiple reduction techniques is proposed to reduce such computational burden. Proper orthogonal decomposition (POD) and active subspace genetic algorithm (ASGA) are applied for a dimensional reduction of the original (high fidelity) model and for an efficient genetic optimization based on active subspace property. The parameterization of the shape is applied directly to the computational mesh, propagating the generic deformation map applied to the surface (of the object to optimize) to the mesh nodes using a radial basis function (RBF) interpolation. Thus, topology and quality of the original mesh are preserved, enabling application of POD-based reduced order modeling techniques, and avoiding the necessity of additional meshing steps. Model order reduction is performed coupling POD and Gaussian process regression (GPR) in a data-driven fashion. The framework is validated on a benchmark ship.

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Section: The Reduced Order Model: Pod-gprmentioning

confidence: 99%

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Demo

Tezzele

Mola

et al. 2021

JMSE

Self Cite

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“…As illustrated in this paper, our stochastic closure naturally prevents truncation-induced instabilities, and we expect a similar behavior at higher Reynolds. If the proposed in-house stabilization is not sufficient, existing stabilisation and/or data-driven methods[33,37,77] could be added. Furthermore, CFD outputs may also be at a resolution different from the resolution of the measurements.…”

mentioning

confidence: 99%

Quantifying Truncation-Related Uncertainties in Unsteady Fluid Dynamics Reduced Order Models

Resseguier¹,

Picard²,

Mémin³

et al. 2021

SIAM/ASA J. Uncertainty Quantification

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In this paper, we present a new method to quantify the uncertainty introduced by the drastic dimensionality reduction commonly practiced in the field of computational fluid dynamics, the ultimate goal being to simulate accurate priors for real-time data assimilation. Our key ingredient is a stochastic Navier-Stokes closure mechanism that arises by assuming random unresolved flow components. This decomposition is carried out through Galerkin projection with a Proper Orthogonal Decomposition (POD-Galerkin) basis. The residual velocity fields, model structure and evolution of coefficients of the reduced order's solutions are used to compute the resulting multiplicative and additive noise's correlations. The low computational cost of these consistent correlation estimators makes them applicable to the study of complex fluid flows. This stochastic POD-ROM is applied to 2D and 3D DNS wake flows at Reynolds 100 and 300, respectively, with Uncertainty Quantification (UQ) and forecasting outside the learning interval being the main focus. The proposed stochastic POD-ROM approach is shown to stabilize the unstable temporal coefficients and to maintain their variability under control, while exhibiting an impressively accurate predictive capability.

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“…Different possibilities are available for the closure of turbulent problems (see [34]); to make the ROM independent from the chosen turbulence model in the full order model (FOM), different approaches are eligible (see, e.g., [15,35]). In this case, a datadriven approach is employed for the eddy viscosity ν t .…”

Section: Neural Network Eddy Viscosity Evaluationmentioning

confidence: 99%

“…These considerations have been used to propose a reduced order model that could be applied to any eddy viscosity turbulence model and that exploits a projection-based technique for mass and momentum conservation and a data-driven approach for the reconstruction of the eddy viscosity field. The model is constructed extending the work done in [14,15] to geometrically parametrized problems [16] with a modification of the approach to reconstruct the eddy viscosity mapping.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters

Zancanaro

Mrosek

Stabile

et al. 2021

Fluids

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Geometrically parametrized partial differential equations are currently widely used in many different fields, such as shape optimization processes or patient-specific surgery studies. The focus of this work is some advances on this topic, capable of increasing the accuracy with respect to previous approaches while relying on a high cost–benefit ratio performance. The main scope of this paper is the introduction of a new technique combining a classical Galerkin-projection approach together with a data-driven method to obtain a versatile and accurate algorithm for the resolution of geometrically parametrized incompressible turbulent Navier–Stokes problems. The effectiveness of this procedure is demonstrated on two different test cases: a classical academic back step problem and a shape deformation Ahmed body application. The results provide insight into details about the properties of the architecture we developed while exposing possible future perspectives for this work.

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A hybrid reduced order method for modelling turbulent heat transfer problems

Cited by 37 publications

References 47 publications

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Quantifying Truncation-Related Uncertainties in Unsteady Fluid Dynamics Reduced Order Models

Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters

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