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Dimensionality Reduction of Embedded...

Youkilis, Noah Ben.

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Dimensionality Reduction of Embedded Boundary Models for Nonlinear Fluid-Structure Interaction.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dimensionality Reduction of Embedded Boundary Models for Nonlinear Fluid-Structure Interaction./
作者:	Youkilis, Noah Ben.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	172 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Aircraft. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30742192
ISBN:	9798381026627

Dimensionality Reduction of Embedded Boundary Models for Nonlinear Fluid-Structure Interaction.
Youkilis, Noah Ben.

Dimensionality Reduction of Embedded Boundary Models for Nonlinear Fluid-Structure Interaction. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 172 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

This item must not be sold to any third party vendors.

Embedded (sometimes immersed) boundary methods (EBMs) for CFD and fluidstructure interaction (FSI) - also known as immersed boundary methods, Cartesian methods, or fictitious domain methods - are the most robust solution methods for flow problems past obstacles that undergo large motions, deformations, shape changes, and/or surface topology changes. They can also introduce a high degree of automation in the task of mesh generation and significant flexibility in the meshing of complex geometries. However, despite massive advancements in compute power, the application of CFD- or FSI-based EBMs to the simulation of many realistic engineering systems can still take days or weeks on many cores of a supercomputer, rendering the use of such models in many-query applications challenging, impractical or simply infeasible.The application of projection-based model order reduction (PMOR) to nonlinear CFD and FSI models has been shown in recent years to produce low-dimensional projection-based reduced order models (PROMs) which can be queried in real-time, without sacrificing the accuracy of the underlying high-fidelity, high-dimensional computational model. However, the application of PMOR to EBMs for CFD and FSI remains a challenge, primarily because EBMs dynamically partition the computational fluid domain into real and fictitious subdomains. Consequently, despite their myriad benefits, EBMs hamper the acceleration of the solution of computationally intensive problems by these types of physics-based surrogate models.This dissertation addresses several issues stemming from the real/fictitious partitioning of the computational fluid domain to pave the way for the PMOR of EBMs.Specifically, it motivates, presents, and illustrates a computational framework for constructing EBM-PROMs that incorporates state-of-the-art methodologies for mitigating computational bottlenecks associated with strong nonlinearities in the underlying high-dimensional model. This framework includes a weighted low-rank matrix factorization approach for computing suitable reduced-order bases; and adaptations of the energy-conserving sampling and weighting hyperreduction procedure as well as the piecewise-affine approximation method that account for the real/fictitious dynamic partitioning of the nodes of the embedding CFD mesh. It also employs an adaptive sampling approach for choosing an optimal set of parameter points on which to train a parametric EBM-PROM, based on a novel error indicator. Finally, the performance of the proposed computational framework for the PMOR of embedded boundary models is investigated for two representative, unsteady, nonlinear, turbulent flow problems that necessitate the use of an EBM; as well as two prototypical steady-state, shape-parametric studies of relevance to MDAO.

ISBN: 9798381026627Subjects--Topical Terms:

832698
Aircraft.

Dimensionality Reduction of Embedded Boundary Models for Nonlinear Fluid-Structure Interaction.
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520 $a Embedded (sometimes immersed) boundary methods (EBMs) for CFD and fluidstructure interaction (FSI) - also known as immersed boundary methods, Cartesian methods, or fictitious domain methods - are the most robust solution methods for flow problems past obstacles that undergo large motions, deformations, shape changes, and/or surface topology changes. They can also introduce a high degree of automation in the task of mesh generation and significant flexibility in the meshing of complex geometries. However, despite massive advancements in compute power, the application of CFD- or FSI-based EBMs to the simulation of many realistic engineering systems can still take days or weeks on many cores of a supercomputer, rendering the use of such models in many-query applications challenging, impractical or simply infeasible.The application of projection-based model order reduction (PMOR) to nonlinear CFD and FSI models has been shown in recent years to produce low-dimensional projection-based reduced order models (PROMs) which can be queried in real-time, without sacrificing the accuracy of the underlying high-fidelity, high-dimensional computational model. However, the application of PMOR to EBMs for CFD and FSI remains a challenge, primarily because EBMs dynamically partition the computational fluid domain into real and fictitious subdomains. Consequently, despite their myriad benefits, EBMs hamper the acceleration of the solution of computationally intensive problems by these types of physics-based surrogate models.This dissertation addresses several issues stemming from the real/fictitious partitioning of the computational fluid domain to pave the way for the PMOR of EBMs.Specifically, it motivates, presents, and illustrates a computational framework for constructing EBM-PROMs that incorporates state-of-the-art methodologies for mitigating computational bottlenecks associated with strong nonlinearities in the underlying high-dimensional model. This framework includes a weighted low-rank matrix factorization approach for computing suitable reduced-order bases; and adaptations of the energy-conserving sampling and weighting hyperreduction procedure as well as the piecewise-affine approximation method that account for the real/fictitious dynamic partitioning of the nodes of the embedding CFD mesh. It also employs an adaptive sampling approach for choosing an optimal set of parameter points on which to train a parametric EBM-PROM, based on a novel error indicator. Finally, the performance of the proposed computational framework for the PMOR of embedded boundary models is investigated for two representative, unsteady, nonlinear, turbulent flow problems that necessitate the use of an EBM; as well as two prototypical steady-state, shape-parametric studies of relevance to MDAO.
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