Data-driven POD-Galerkin reduced order model for turbulent flows

Hijazi, Saddam; Stabile, Giovanni; Mola, Andrea; Rozza, Gianluigi

doi:10.1016/j.jcp.2020.109513

Cited by 162 publications

(121 citation statements)

References 76 publications

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“…In this community, proper orthogonal decomposition (POD) is a widespread technique [4,38] since its capability to provide orthogonal basis that have an energetically hierarchy. While a possible approach for turbulent flows involving projection-based ROM is available in [18], we prefer the data-driven approach for the higher integrability in many industrial workflows. POD needs as input a matrix containing samples of the solution manifold.…”

Section: Reduced Order Model Exploiting Proper Orthogonal Decompositionmentioning

confidence: 99%

An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques

Demo

Ortali

Gustin

et al. 2020

Boll Unione Mat Ital

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This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. We adopt reduced order models in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation. The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive—especially dealing with complex industrial geometries—we propose also a dynamic mode decomposition enhancement to reduce the computational cost of a single numerical simulation. The real-time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.

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Section: Reduced Order Model Exploiting Proper Orthogonal Decompositionmentioning

confidence: 99%

An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques

Demo

Ortali

Gustin

et al. 2020

Boll Unione Mat Ital

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“…For parametric time-dependent problems, a proper orthogonal decomposition approach can be applied to reduce the dimensionality of the system, as in [19,25]. In this work we propose a novel data-driven approach for parametric dynamical systems, combining dynamic mode decomposition (DMD) with active subspaces (AS) property.…”

Section: Introductionmentioning

confidence: 99%

Enhancing CFD predictions in shape design problems by model and parameter space reduction

Tezzele

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Stabile

et al. 2020

Adv. Model. and Simul. in Eng. Sci.

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In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD.

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“…As future perspectives, it will be certainly interesting also to use the developed methodology to perform shape optimization in computational fluid dynamic problems at higher Reynolds number and to couple the present approach to what developed in References and for turbulent flows.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Efficient geometrical parametrization for finite‐volume‐based reduced order methods

Stabile

Zancanaro

Rozza

2020

Numerical Meth Engineering

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In this work, we present an approach for the efficient treatment of parametrized geometries in the context of proper orthogonal decomposition (POD)-Galerkin reduced order methods based on finite-volume full order approximations. On the contrary to what is normally done in the framework of finite-element reduced order methods, different geometries are not mapped to a common reference domain: the method relies on basis functions defined on an average deformed configuration and makes use of the discrete empirical interpolation method to handle together nonaffinity of the parametrization and nonlinearities. In the first numerical example, different mesh motion strategies, based on a Laplacian smoothing technique and on a radial basis function approach, are analyzed and compared on a heat transfer problem. Particular attention is devoted to the role of the nonorthogonal correction. In the second numerical example, the methodology is tested on a geometrically parametrized incompressible Navier-Stokes problem. In this case, the reduced order model is constructed following the same segregated approach used at the full order level. K E Y W O R D Sfinite-volume method, geometrical parametrization, incompressible flows, proper orthogonal decomposition-Galerkin, reduced order methods Int J Numer Methods Eng. 2020;121:2655-2682.wileyonlinelibrary.com/journal/nme

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Data-driven POD-Galerkin reduced order model for turbulent flows

Cited by 162 publications

References 76 publications

An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques

An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques

Enhancing CFD predictions in shape design problems by model and parameter space reduction

Efficient geometrical parametrization for finite‐volume‐based reduced order methods

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