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Superfluid turbulent wakes behind a square prism are studied theoretically and numerically by proper orthogonal decomposition (POD). POD is a data science approach that can efficiently extract the principal vibration modes of a physical…

Quantum Gases · Physics 2025-06-16 Sota Yoneda , Hiromitsu Takeuchi

The present study investigates unsteady wake dynamics in flow past oscillating cylinder. Two-dimensional computational study is carried out for flow past sinusoidally oscillating cylinder. Operating parameters like Reynolds number, forcing…

Fluid Dynamics · Physics 2022-01-25 Anubhav Sinha , Parasuram IVLN

We investigate the near-wake flow of an Ahmed body which is characterized by switches between two asymmetric states that are mirrors of each other in the spanwise direction. The work focuses on the relationship between the base pressure…

The turbulent flow past a wall-mounted square cylinder with an aspect ratio of four was investigated with the aid of Spalart-Allmaras improved delayed detached-eddy simulation (S-A IDDES) and proper orthogonal decomposition (POD). The…

Fluid Dynamics · Physics 2020-12-22 Mustafa Zhuhair Gheni Yousif , HeeChang Lim

The proper orthogonal decomposition (POD) is a powerful classical tool in fluid mechanics used, for instance, for model reduction and extraction of coherent flow features. However, its applicability to high-resolution data, as produced by…

Fluid Dynamics · Physics 2020-11-11 Philipp Krah , Thomas Engels , Kai Schneider , Julius Reiss

The fluid flow around a bluff body is complex and time dependent, which also contains a wide range of time and length scales. The first few eigenmodes of the proper orthogonal decomposition (POD) of such a flow provide significant insight…

Fluid Dynamics · Physics 2021-11-10 Jahrul Alam , Asokan Variyath

Proper orthogonal decomposition (POD) is often employed in developing reduced-order models (ROM) in fluid flows for design, control, and optimization. Contrary to the usual practice where velocity field is the focus, we apply the POD…

Computational Engineering, Finance, and Science · Computer Science 2020-10-27 Muhammad Sufyan , Hamayun Farooq , Imran Akhtar , Zafar Bangash

We present a formulation of proper orthogonal decomposition (POD) producing a velocity-temperature basis optimized with respect to an $H^1$ dissipation norm. This decomposition is applied, along with a conventional POD optimized with…

We study reduced-order models of three-dimensional perturbations in linearized channel flow using balanced proper orthogonal decomposition (BPOD). The models are obtained from three-dimensional simulations in physical space as opposed to…

Optimization and Control · Mathematics 2009-11-13 Miloš Ilak , Clarence W. Rowley

Turbulent flows, despite their apparent randomness, exhibit coherent structures that underpin their dynamics. Proper orthogonal decomposition (POD) has been widely used to extract these structures from experimental data. While periodic…

Fluid Dynamics · Physics 2026-03-02 Shaun Davey , Callum Atkinson , Julio Soria

We apply the proper orthogonal decomposition (POD) to large eddy simulation data of a wind turbine wake in a turbulent atmospheric boundary layer. The turbine is modeled as an actuator disk. Our analyis mainly focuses on the question…

Fluid Dynamics · Physics 2015-03-11 David Bastine , Björn Witha , Matthias Wächter , Joachim Peinke

The identification of coherent structures from experimental or numerical data is an essential task when conducting research in fluid dynamics. This typically involves the construction of an empirical mode base that appropriately captures…

Fluid Dynamics · Physics 2016-04-20 Moritz Sieber , Kilian Oberleithner , Christian Oliver Paschereit

A phase proper orthogonal decomposition (Phase POD) method is demonstrated, utilizing phase averaging for the decomposition of spatio-temporal behaviour of statistically non-stationary turbulent flows in an optimized manner. The proposed…

Fluid Dynamics · Physics 2024-03-01 Yisheng Zhang , Azur Hodzic , Fabien Evrard , Berend Van Wachem , Clara M. Velte

Two data-driven modal analysis approaches, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), are applied to analyze the unsteady flow obtained by solving the Reynolds-averaged Navier-Stokes (RANS) equations in a…

Fluid Dynamics · Physics 2026-03-27 Yalu Zhu , Feng Liu

Data-driven decompositions are becoming essential tools in fluid dynamics, allowing for tracking the evolution of coherent patterns in large datasets, and for constructing low order models of complex phenomena. In this work, we analyze the…

Fluid Dynamics · Physics 2020-04-15 M. A. Mendez , M. Balabane , J. -M. Buchlin

Data-driven decompositions of Particle Image Velocimetry (PIV) measurements are widely used for a variety of purposes, including the detection of coherent features (e.g., vortical structures), filtering operations (e.g., outlier removal or…

Fluid Dynamics · Physics 2020-08-26 Miguel A Mendez , David Hess , Bo B Watz , Jean-Marie Buchlin

The coherent structures in the turbulent wake of a disk at a moderately high Reynolds number ($\Rey$) of $50,000$ are examined using spectral proper orthogonal decomposition (SPOD) which considers all three velocity components in a…

Fluid Dynamics · Physics 2021-01-04 Sheel Nidhan , Karu Chongsiripinyo , Oliver T. Schmidt , Sutanu Sarkar

Direct numerical simulations, performed with a high-order spectral-element method, are used to study coherent structures in turbulent pipe flow at friction Reynolds numbers $Re_{\tau} = 180$ and $550$. The database was analysed using…

Fluid Dynamics · Physics 2023-07-19 Leandra Abreu , André Cavalieri , Philipp Schlatter , Ricardo Vinuesa , Dan Henningson

We consider the frequency domain form of proper orthogonal decomposition (POD) called spectral proper orthogonal decomposition (SPOD). Spectral POD is derived from a space-time POD problem for statistically stationary flows and leads to…

Fluid Dynamics · Physics 2018-06-05 Aaron Towne , Oliver T. Schmidt , Tim Colonius

Temporal or spatial structures are readily extracted from complex data by modal decompositions like Proper Orthogonal Decomposition (POD) or Dynamic Mode Decomposition (DMD). Subspaces of such decompositions serve as reduced order models…

Fluid Dynamics · Physics 2019-02-25 Jörn Sesterhenn , Amir Shahirpour
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