Global POD-Galerkin ROMs for Fluid Flows with Moving Solid Structures

Xu, Bolun; Hu, Gao; Wei, Mingjun; Hrynuk, John

doi:10.2514/1.j060795

Cited by 5 publications

References 51 publications

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A Multi‐Fidelity Model for Wave Energy Converters

Battisti,

Bracco,

Bergmann

2024

Numerical Methods in Fluids

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The objective of this study is to develop a three‐dimensional numerical model for a floating point absorber wave energy converter in the presence of sea waves, considering its interaction with a bi‐fluid flow (comprising air and water). The primary aim is to create an efficient computational tool that achieves two key objectives: firstly, reducing the computational time typically associated with high‐fidelity Computational Fluid Dynamics (CFD) models, and secondly, curing the lack of accuracy of low‐fidelity asymptotic or projection‐based reduced‐order models in regions subjected to viscous and highly nonlinear effects. To address these objectives, we propose a multi‐fidelity model based on domain decomposition. This approach combines a high‐fidelity CFD solver, which accurately captures the behavior in viscous and nonlinear regions, with a Reduced Order Model (ROM) based on Proper Orthogonal Decomposition (POD), tailored for weakly nonlinear regions. By integrating these components spatially, we simulate the dynamics of the floating body within a unified framework. This methodology ensures precise predictions of the body's motion for both in‐sample (reproduction) and out‐of‐sample (prediction) configurations.

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A Multi‐Fidelity Model for Wave Energy Converters

Battisti,

Bracco,

Bergmann

2024

Numerical Methods in Fluids

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Reduced-order model and attractor identification for large eddy simulation of squirrel cage fan

Xiao,

Jiang,

Yang

et al. 2023

Physics of Fluids

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A large eddy simulation (LES) of a squirrel cage fan (SCF) provides a precise representation of turbulent flows with different degrees of complexity. This study comprehensively analyzes the coherent structures of turbulent flows in an SCF using an LES, proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and multi-resolution dynamic mode decomposition (mrDMD). An intelligent reduced-order model is established by integrating hierarchical deep learning and the sparse identification of nonlinear dynamics. The result shows that the evolution of the global DMD modes is attenuated due to the spatial distribution variations of localized high-frequency mrDMD modes, along with the fragmented and non-steady development of modal patterns. Unlike POD, DMD quantifies the quality of the impeller inlet environment and captures the antisymmetric low-dimensional flows associated with the shedding of rotating vortex structures. The interaction strength between stationary and dynamic rotating areas is accurately represented by attractors characterized by petal-like structures. The trajectory of the attractors faithfully maps the antisymmetric structural attributes, quasi-periodic behavior, and gradual attenuation characteristics exhibited by DMD modes. The number of petal-like systems and their temporal oscillations are in good agreement with the number of fan blades and their rotational cycles. This study provides new insight into fan engineering to advance flow control strategies and improve the understanding of the underlying flow mechanisms.

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