On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis

Gadalla, Mahmoud; Cianferra, Marta; Tezzele, Marco; Stabile, Giovanni; Mola, Andrea; Rozza, Gianluigi

doi:10.1016/j.compfluid.2020.104819

“…We refer the readers interested in parametric hull shape variations using ROMs to [9], while we mention [10,11] for design-space dimensionality reduction in shape optimization with POD. Moving from hulls to propellers, data-driven POD has also been successfully incorporated in the study of marine propellers efficiency [12,13] as well as hydroacoustics performance [14].…”

Section: Introductionmentioning

confidence: 99%

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

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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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“…We refer the readers interested in parametric hull shape variations using ROMs to [51], while we mention [10,41] for design-space dimensionality reduction in shape optimization with POD. Moving from hulls to propellers, data-driven POD has also been successfully incorporated in the study of marine propellers efficiency [30,14] as well as hydroacoustics performance [13].…”

Section: Introductionmentioning

confidence: 99%

Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing

Demo,

Tezzele,

Mola

et al. 2021

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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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“…With the growing complexity of chemical kinetic mechanisms, the cost of solving the chemistry problem often exceeds the cost of fluid flow solution by up to two orders of magnitude [2]. There have been considerable efforts in the literature to improve the computational performance of CFD simulations involving turbulent flows [3][4][5] and chemical reactions [6][7][8] through model reduction and machine learning strategies. Nevertheless, accurate and high-fidelity reactive simulations can be still feasible through efficient usage of the provided computational resources, in addition to cell-based optimization of the chemistry solution.…”

Section: Introductionmentioning

confidence: 99%

Fast reactive flow simulations using analytical Jacobian and dynamic load balancing in OpenFOAM

Morev,

Tekgül,

Gadalla

et al. 2021

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Detailed chemistry-based computational fluid dynamics (CFD) simulations are computationally expensive due to the solution of the underlying chemical kinetics system of ordinary differential equations (ODEs). Here, we introduce a novel open-source library aiming at speeding up such reactive flow simulations using OpenFOAM, an open-source C++ software for CFD. First, our dynamic load balancing model DLBFoam (Tekgül et al., 2021) is utilized to mitigate the computational imbalance due to chemistry solution in multiprocessor reactive flow simulations. Then, the individual (cell-based) chemistry solutions are optimized by implementing an analytical Jacobian formulation using the open-source library pyJac, and by increasing the efficiency of the ODE solvers by utilizing the linear algebra package LAPACK. We demonstrate the speed-up capabilities of this new library on various combustion problems.These test problems include a 2D turbulent reacting shear layer and 3D stratified combustion to highlight the favorable scaling aspects of the library on ignition/flame front initiation setups for dual-fuel combustion. Furthermore,

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On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis

Cited by 22 publications

References 78 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

Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing

Fast reactive flow simulations using analytical Jacobian and dynamic load balancing in OpenFOAM

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