Results 11 to 20 of about 6,618 (260)
Comparative Study on Modal Decomposition Methods of Unsteady Separated Flow in Compressor Cascade
Xibei Gongye Daxue Xuebao, 2020 The present work investigated the vortex structure and fluctuation frequency characteristics generated by boundary layer separation of a high-load compressor cascade using modal decomposition methods.
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Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
Applied Sciences, 2023 This study focuses on developing and examining the effectiveness of Transfer Learning (TL) for structural health monitoring (SHM) systems that transfer knowledge about damage states from one structure (i.e., the source domain) to another structure (i.e.,
Samira Ardani +2 more
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Sparse sensor-based flow estimation with spectral proper orthogonal decomposition
AIP Advances, 2022 The application of Artificial Neural Networks (ANNs) in developing sensor-based estimators for unsteady flows has become an active area of research over the last decade.
Henrique Gambassi +2 more
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An analysis of galerkin proper orthogonal decomposition for subdiffusion [PDF]
ESAIM: Mathematical Modelling and Numerical Analysis, 2016 25 pp, 5 ...
Jin, B, Zhou, Z
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Proper orthogonal decomposition method for Schrödinger equation
Lietuvos Matematikos Rinkinys, 2013 In this paper we consider the proper orthogonal decomposition (POD) method for one-dimensional Schrödinger equation. We begin of the review of basic ideas of POD. Later this method is applied to study the linear Schrödinger equation.
Raimondas Čiegis +2 more
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Proper orthogonal decomposition method for some parabolic type equations
Lietuvos Matematikos Rinkinys, 2012 In this paper proper orthogonal decomposition method for 1D parabolic type equations is described. First basic ideas of method are presented and application scheme for compiutations is derived.
Gerda Jankevičiūtė +2 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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Spectral proper orthogonal decomposition [PDF]
Journal of Fluid Mechanics, 2016 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 the dominant flow structures.
Moritz Sieber +2 more
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Uncertainty Analysis of Neutron Diffusion Eigenvalue Problem Based on Reduced-order Model
Yuanzineng kexue jishu, 2023 In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model ...
In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model based on POD-Galerkin method in core physical uncertainty analysis. The two-dimensional two group TWIGL benchmark question was taken as the research object, the key variation characteristics of the core flux distribution were extracted under the finite perturbation of the group constants of each material region, and the full-order neutron diffusion problem was projected on the variation characteristics to establish a reduced-order neutron diffusion model. The reduced-order model was used to replace the full-order model to carry out the uncertainty analysis of the group constants of the material region. The results show that the bias of the mathematical expectation of keff calculated by reduced-order and full-order models is close to 1 pcm. In addition, compared with the calculation time required for uncertainty analysis of full-order model, the analysis time of reduced-order model (including the calculation time of the full-order model required for the construction of reduced-order model) is only 11.48%, which greatly improves the efficiency of uncertainty analysis. The biases of mathematical expectation of keff calculated by reduced-order and full-order models based on Latin hypercube sampling and simple random sampling are less than 8 pcm, and under the same sample size, the bias from the Latin hypercube sampling result is smaller. From the TWIGL benchmark test results, under the same sample size, Latin hypercube sampling method is more recommended for POD-Galerkin reduced-order model.
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The performance of proper orthogonal decomposition in discontinuous flows
Theoretical and Applied Mechanics Letters, 2016 In this paper, flow reconstruction accuracy and flow prediction capability of discontinuous transonic flow field by means of proper orthogonal decomposition (POD) method is studied. Although linear superposition of “high frequency waves” in different POD
Jing Li, Weiwei Zhang
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