On the Application of Proper Orthogonal Decomposition (POD) for In-Cylinder Flow Analysis
Energies, 2018 Proper orthogonal decomposition (POD) is a coherent structure identification technique based on either measured or computed data sets. Recently, POD has been adopted for the analysis of the in-cylinder flows inside internal combustion engines.
Mohammed El-Adawy +8 more
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Energies, 2022 A comprehensive study was performed to analyze turbine wake characteristics by using a Proper-Orthogonal-Decomposition (POD) method to identify the dominant flow features from a comprehensive experimental database.
Pavithra Premaratne, Wei Tian, Hui Hu
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Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics. [PDF]
J Biomech, 2023 Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental hemodynamic models. In this work, we explore the use of Reduced Order Models (ROMs) to capture the main flow features in a ...
Chatpattanasiri C +4 more
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Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition [PDF]
Advances in Computational Mathematics, 2023 POD-DL-ROMs have been recently proposed as an extremely versatile strategy to build accurate and reliable reduced order models (ROMs) for nonlinear parametrized partial differential equations, combining (i) a preliminary dimensionality reduction obtained
Simone Brivio +3 more
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Unsteady effects at the interface between impeller-vaned diffuser in a low pressure centrifugal compressor [PDF]
INCAS Bulletin, 2013 In this paper, Proper Orthogonal Decomposition (POD) is applied to the analysis of the unsteady rotor-stator interaction in a low-pressure centrifugal compressor.
Mihai Leonida NICULESCU, Sterian DANAILA
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The Transient POD Method Based on Minimum Error of Bifurcation Parameter
Mathematics, 2021 An invariable order reduction model cannot be obtained by the adaptive proper orthogonal decomposition (POD) method in parametric domain, there exists uniqueness of the model with different conditions. In this paper, the transient POD method based on the
Kuan Lu +6 more
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MODULO: A software for Multiscale Proper Orthogonal Decomposition of data
SoftwareX, 2020 In the era of the Big Data revolution, methods for the automatic discovery of regularities in large datasets are becoming essential tools in applied sciences. This article presents an open software package, named MODULO (MODal mULtiscale pOd), to perform
Davide Ninni, Miguel A. Mendez
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IEEE Access, 2022 In this work, we propose a data-driven reduced-order model (ROM) for high dimensional flow fields by combining flow modal decomposition and multiple regression.
Li Xu +4 more
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Some practical remarks in solving partial differential equations using reduced order schemes obtained through the POD method [PDF]
INCAS Bulletin, 2022 In this paper we address the subject of mathematical modelling, more precisely the optimization of algorithms for numerically solving partial differential equations.
Alexandru SOLOMON +2 more
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Phase proper orthogonal decomposition of non-stationary turbulent flow [PDF]
The Physics of Fluids, 2023 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 Phase POD method
Yisheng Zhang +4 more
semanticscholar +1 more source