Intrinsic phase-based proper orthogonal decomposition (IPhaB POD): a method for physically interpretable modes in near-periodic systems

Fluid dynamics systems driven by dominant, near-periodic dynamics are common across wakes, jets, rotating machinery and high-speed flows. Traditional modal decomposition techniques have been used to gain insight into these flows, but can require many modes to represent key physical processes. With the aim of generating modes that intuitively convey the underlying physical mechanisms, we propose an intrinsic phase-based proper orthogonal decomposition (IPhaB POD) method, which creates energetically ranked modes that evolve along a characteristic cycle of the dominant near-periodic dynamics. Our proposed formulation is set in the time domain, which is particularly useful in cases where the cyclical content is imperfectly periodic. We formally derive IPhaB POD within a POD framework that therefore inherits the energetically ranked decomposition and optimal low-rank representation inherent to POD. As part of this derivation, a dynamical systems representation is utilized, facilitating a definition of phase within the system's near-periodic cycle in the time domain. An expectation operator and inner product are also constructed relative to this definition of phase in a manner that allows for the various cycles within the data to demonstrate imperfect periodicity. The formulation is tested on two sample problems: a simple, low Reynolds number aerofoil wake, and a complex, high-speed pulsating shock wave problem. The method is compared to space-only POD, spectral POD (SPOD) and cyclostationary SPOD. The method is shown to better isolate the dominant, near-periodic global dynamics in a time-varying IPhaB mean, and isolate the tethered, local-in-phase dynamics in a series of time-varying modes.

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Publication Details

Journal
Journal of Fluid Mechanics
Published
2025-01-09
DOI
https://doi.org/10.1017/jfm.2024.1061
Citations
3
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
3.97

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article

Intrinsic phase-based proper orthogonal decomposition (IPhaB POD): a method for physically interpretable modes in near-periodic systems

Andres Goza, Akhileshwar Borra, Theresa Saxton-Fox, Zoey Flynn
3 citations
Journal of Fluid Mechanics
Model Reduction and Neural Networks
3.97
article

Intrinsic phase-based proper orthogonal decomposition (IPhaB POD): a method for physically interpretable modes in near-periodic systems

Andres Goza, Akhileshwar Borra, Theresa Saxton-Fox, Zoey Flynn
article en
3 citations

Abstract

Fluid dynamics systems driven by dominant, near-periodic dynamics are common across wakes, jets, rotating machinery and high-speed flows. Traditional modal decomposition techniques have been used to gain insight into these flows, but can require many modes to represent key physical processes. With the aim of generating modes that intuitively convey the underlying physical mechanisms, we propose an intrinsic phase-based proper orthogonal decomposition (IPhaB POD) method, which creates energetically ranked modes that evolve along a characteristic cycle of the dominant near-periodic dynamics. Our proposed formulation is set in the time domain, which is particularly useful in cases where the cyclical content is imperfectly periodic. We formally derive IPhaB POD within a POD framework that therefore inherits the energetically ranked decomposition and optimal low-rank representation inherent to POD. As part of this derivation, a dynamical systems representation is utilized, facilitating a definition of phase within the system's near-periodic cycle in the time domain. An expectation operator and inner product are also constructed relative to this definition of phase in a manner that allows for the various cycles within the data to demonstrate imperfect periodicity. The formulation is tested on two sample problems: a simple, low Reynolds number aerofoil wake, and a complex, high-speed pulsating shock wave problem. The method is compared to space-only POD, spectral POD (SPOD) and cyclostationary SPOD. The method is shown to better isolate the dominant, near-periodic global dynamics in a time-varying IPhaB mean, and isolate the tethered, local-in-phase dynamics in a series of time-varying modes.

Journal of Fluid MechanicsVol. 1002
University of Illinois Urbana-Champaign (US)
National Science Foundation, Indian Institute of Science
Openalex Percentile: Top 8%
Model Reduction and Neural Networks
3.97
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