A multifidelity approach coupling parameter space reduction and nonintrusive POD with application to structural optimization of passenger ship hulls

Self Cite

In this article, we propose a shape optimization pipeline for propeller blades, applied to naval applications. The geometrical features of a blade are exploited to parametrize it, allowing to obtain deformed blades by perturbating their parameters. The optimization is performed using a genetic algorithm that exploits the computational speed‐up of reduced order models to maximize the efficiency of a given propeller. A standard offline–online procedure is exploited to construct the reduced‐order model. In an expensive offline phase, the full order model, which reproduces an open water test, is set up in the open‐source software OpenFOAM and the same full order setting is used to run the CFD simulations for all the deformed propellers. The collected high‐fidelity snapshots and the deformed parameters are used in the online stage to build the nonintrusive reduced‐order model. This article provides a proof of concept of the pipeline proposed, where the optimized propeller improves the efficiency of the original propeller.

Section: Introductionsupporting

confidence: 60%

A shape optimization pipeline for marine propellers by means of reduced order modeling techniques

Ivagnes,

Demo,

Rozza

2024

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“…This multi-fidelity framework has also the potential to be integrated with other reduced order modeling techniques [54][55][56] to further increase the accuracy in the resolution of parametric problems, especially for high-dimensional surrogate-based optimization. 57 Mandatory for real applications is a model management strategy providing theoretical guarantees and establishing accuracy and/or convergence of outer-loop applications. Some attempts toward multi-source Bayesian optimization/Experimental design are being studied.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

“…Future research lines should investigate the use of different active subspaces‐based methods, such as kernel AS, 31 or local AS, 32 which exploit kernel‐based and localization techniques, respectively. This multi‐fidelity framework has also the potential to be integrated with other reduced order modeling techniques 54‐56 to further increase the accuracy in the resolution of parametric problems, especially for high‐dimensional surrogate‐based optimization 57 …”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Multi‐fidelity data fusion through parameter space reduction with applications to automotive engineering

Romor,

Tezzele,

Mrosek

et al. 2023

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Multi‐fidelity models are of great importance due to their capability of fusing information coming from different numerical simulations, surrogates, and sensors. We focus on the approximation of high‐dimensional scalar functions with low intrinsic dimensionality. By introducing a low dimensional bias we can fight the curse of dimensionality affecting these quantities of interest, especially for many‐query applications. We seek a gradient‐based reduction of the parameter space through linear active subspaces or a nonlinear transformation of the input space. Then we build a low‐fidelity response surface based on such reduction, thus enabling nonlinear autoregressive multi‐fidelity Gaussian process regression without the need of running new simulations with simplified physical models. This has a great potential in the data scarcity regime affecting many engineering applications. In this work we present a new multi‐fidelity approach that involves active subspaces and the nonlinear level‐set learning method, starting from the preliminary analysis previously conducted (Romor F, Tezzele M, Rozza G. Proceedings in Applied Mathematics & Mechanics. Wiley Online Library; 2021). The proposed framework is tested on two high‐dimensional benchmark functions, and on a more complex car aerodynamics problem. We show how a low intrinsic dimensionality bias can increase the accuracy of Gaussian process response surfaces.

“… 13 Reduction in parameter space has been coupled with model order reduction techniques 14 , 15 , 16 to enable more complex numerical studies without increasing the computational load. We mention the use of AS in cardiovascular applications with POD‐Galerkin, 17 in nonlinear structural analysis, 18 in nautical and naval engineering, 19 , 20 , 21 , 22 coupled with POD with interpolation for structural and computational fluid dynamics (CFD) analysis, 23 , 24 and with dynamic mode decomposition in Reference 25 . Applications in automotive engineering within a multi‐fidelity setting can be found in Reference 26 , for turbomachinery, see Reference 27 , while for results in chemistry, see References 28 and 29 .…”

Section: Introductionmentioning

confidence: 99%

Kernel‐based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method

Romor

Tezzele

Lario

et al. 2022

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Nonlinear extensions to the active subspaces method have brought remarkable results for dimension reduction in the parameter space and response surface design. We further develop a kernel‐based nonlinear method. In particular, we introduce it in a broader mathematical framework that contemplates also the reduction in parameter space of multivariate objective functions. The implementation is thoroughly discussed and tested on more challenging benchmarks than the ones already present in the literature, for which dimension reduction with active subspaces produces already good results. Finally, we show a whole pipeline for the design of response surfaces with the new methodology in the context of a parametric computational fluid dynamics application solved with the discontinuous Galerkin method.