Literatura académica sobre el tema "Spectral Proper Orthogonal Decomposition"
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Artículos de revistas sobre el tema "Spectral Proper Orthogonal Decomposition"
Sieber, Moritz, C. Oliver Paschereit y Kilian Oberleithner. "Spectral proper orthogonal decomposition". Journal of Fluid Mechanics 792 (4 de marzo de 2016): 798–828. http://dx.doi.org/10.1017/jfm.2016.103.
Texto completoSchmidt, Oliver T. y Tim Colonius. "Guide to Spectral Proper Orthogonal Decomposition". AIAA Journal 58, n.º 3 (marzo de 2020): 1023–33. http://dx.doi.org/10.2514/1.j058809.
Texto completoHe, Xiao, Zhou Fang, Georgios Rigas y Mehdi Vahdati. "Spectral proper orthogonal decomposition of compressor tip leakage flow". Physics of Fluids 33, n.º 10 (octubre de 2021): 105105. http://dx.doi.org/10.1063/5.0065929.
Texto completoBaars, Woutijn J. y Charles E. Tinney. "Proper orthogonal decomposition-based spectral higher-order stochastic estimation". Physics of Fluids 26, n.º 5 (mayo de 2014): 055112. http://dx.doi.org/10.1063/1.4879255.
Texto completoSchmidt, Oliver T. y Aaron Towne. "An efficient streaming algorithm for spectral proper orthogonal decomposition". Computer Physics Communications 237 (abril de 2019): 98–109. http://dx.doi.org/10.1016/j.cpc.2018.11.009.
Texto completoMendez, M. A., M. Balabane y J. M. Buchlin. "Multi-scale proper orthogonal decomposition of complex fluid flows". Journal of Fluid Mechanics 870 (15 de mayo de 2019): 988–1036. http://dx.doi.org/10.1017/jfm.2019.212.
Texto completoMengaldo, Gianmarco y Romit Maulik. "PySPOD: A Python package for Spectral Proper Orthogonal Decomposition (SPOD)". Journal of Open Source Software 6, n.º 60 (16 de abril de 2021): 2862. http://dx.doi.org/10.21105/joss.02862.
Texto completoGambassi, Henrique, Paul Ziadé y Chris Morton. "Sparse sensor-based flow estimation with spectral proper orthogonal decomposition". AIP Advances 12, n.º 8 (1 de agosto de 2022): 085208. http://dx.doi.org/10.1063/5.0094874.
Texto completoCho, Woon, Samir Sahyoun, Seddik M. Djouadi, Andreas Koschan y Mongi A. Abidi. "Reduced-order spectral data modeling based on local proper orthogonal decomposition". Journal of the Optical Society of America A 32, n.º 5 (9 de abril de 2015): 733. http://dx.doi.org/10.1364/josaa.32.000733.
Texto completoTowne, Aaron, Oliver T. Schmidt y Tim Colonius. "Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis". Journal of Fluid Mechanics 847 (29 de mayo de 2018): 821–67. http://dx.doi.org/10.1017/jfm.2018.283.
Texto completoTesis sobre el tema "Spectral Proper Orthogonal Decomposition"
Le, Thai Hoa. "UNSTEADY BUFFETING FORCES AND GUST RESPONSE OF BRIDGES WITH PROPER ORTHOGONAL DECOMPOSITION APPLICATIONS". 京都大学 (Kyoto University), 2007. http://hdl.handle.net/2433/49126.
Texto completoThe unsteady buffeting forces and the gust response prediction of bridges in the atmospheric turbulent flows is recently attracted more attention due to uncertainties in both experiment and analytical theory. The correction functions such as the aerodynamic admittance function and the spatial coherence function have been supplemented to cope with limitations of the quasi-steady theory and strip one so far. Concretely, so-called single-variate quasi-steady aerodynamic admittance functions as the transfer functions between the wind turbulence and induced buffeting forces, as well as coherence of wind turbulence has been widely applied for the gust response prediction. Recent literatures, however, pointed out that the coherence of force exhibits higher than that of turbulence. These correction functions, in the other words, contain their uncertainties which are required to be more understanding. Proper orthogonal decomposition (POD), known as the Karhunen-Loeve decomposition has been applied popularly in many engineering fields. Main advantage of the POD is that the multi-variate correlated random fields/processes can be decomposed and described in such simplified way as a combination of limited number of orthogonally low-order dominant eigenvectors (or turbulent modes) which is convenient and applicable for order-reduced representation, simulation of the random fields/processes such as the turbulent fields, turbulent-induced force fields and stochastic response prediction as well. The POD and its proper transformations based on either zero-time-lag covariance matrix or cross spectral one of random fields/processes have been branched by either the covariance proper transformation (CPT) in the time domain or the spectral proper transformation (SPT) in the frequency domain. So far, the covariance matrix-based POD and its covariance proper transformation in the time domain has been used almost in the wind engineering topics due to its simplification in computation and interpretation. In this research, the unsteady buffeting forces and the gust response prediction of bridges with emphasis on the POD applications have been discussed. Investigations on the admittance function of turbulent-induced buffeting forces and the coherence one of the surface pressure as well as the spatial distribution and correlation of the unsteady pressure fields around some typically rectangular cylinders in the different unsteady flows have been carried out thanks to physical measurements in the wind tunnel. This research indicated effect of the bluff body flow and the wind-structure interaction on the higher coherence of buffeting forces than the coherence of turbulence, thus this effect should be accounted and undated for recent empirical formulae of the coherence function of the unsteady buffeting forces. Especially, the multi-variate nonlinear aerodynamic admittance function has been proposed in this research, as well as the temporo-spectral structure of the coherence functions of the wind turbulence and the buffeting forces has been firstly here using the wavelet transform-based coherence in order to detect intermittent characteristics and temporal correspondence of these coherence functions. In POD applications, three potential topics in the wind engineering field have been discussed in the research: (i) analysis and identification, modeling of unsteady pressure fields around model sections; (ii) representation and simulation of multi-variate correlated turbulent fields and (iii) stochastic response prediction of structures and bridges. Especially, both POD branches and their proper transformations in the time domain and the frequency one have been used in these applications. It found from these studies that only few low-order orthogonal dominant modes are enough accuracy for representing, modeling, simulating the correlated random fields (turbulence and unsteady surface pressure, unsteady buffeting forces), as well as predicting stochastic response of bridges in the time and frequency domains. The gust response prediction of bridges has been formulated in the time domain at the first time in this research using the covariance matrix-based POD and its covariance proper transformation which is very promising to solve the problems of the nonlinear and unsteady aerodynamics. Furthermore, the physical linkage between these low-order modes and physical causes occurring on physical models has been interpreted in some investigated cases.
Kyoto University (京都大学)
0048
新制・課程博士
博士(工学)
甲第13372号
工博第2843号
新制||工||1418(附属図書館)
25528
UT51-2007-Q773
京都大学大学院工学研究科社会基盤工学専攻
(主査)教授 松本 勝, 教授 河井 宏允, 准教授 白土 博通
学位規則第4条第1項該当
Malm, Johan. "Spectral-element simulations of turbulent wall-bounded flows including transition and separation". Doctoral thesis, KTH, Stabilitet, Transition, Kontroll, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-50294.
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Spitz, Nicolas. "Prediction of Trailing Edge Noise from Two-Point Velocity Correlations". Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/32637.
Texto completoMaster of Science
Di, Donfrancesco Fabrizio. "Reduced Order Models for the Navier-Stokes equations for aeroelasticity". Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS603.
Texto completoThe numerical prediction of aeroelastic systems responses becomes unaffordable when parametric analyses with high-fidelity CFD are required. Reduced order modeling (ROM) methods have therefore been developed in view of reducing the costs of the numerical simulations while preserving a high level of accuracy. The present thesis focuses on the family of projection based methods for the compressible Navier-Stokes equations involving deforming meshes in the case of aeroelastic applications. A vector basis obtained by Proper Orthogonal Decomposition (POD) combined to a Galerkin projection of the system equations is used in order to build a ROM for fluid mechanics. Masked projection approaches are therefore implemented and assessed for different test cases with fixed boundaries in order to provide a fully nonlinear formulation for the projection-based ROMs. Then, the ROM is adapted in the case of deforming boundaries and aeroelastic applications in a parametric context. Finally, a Reduced Order Time Spectral Method (ROTSM) is formulated in order to address the stability issues which involve the projection-based ROMs for fluid mechanics applications
Allison, Timothy Charles. "System Identification via the Proper Orthogonal Decomposition". Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/29424.
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Omar, Ahmed F. "Calibrating pressure sensitive paints using proper orthogonal decomposition". [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0013431.
Texto completoToal, David J. J. "Proper orthogonal decomposition & kriging strategies for design". Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/72023/.
Texto completoDOLCI, VALENTINA. "Proper Orthogonal Decomposition for Surrogate Models in Aerodynamics". Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2678186.
Texto completoAkkari, Nissrine. "Etude mathématique de la sensibilité POD (Proper orthogonal decomposition)". Phd thesis, Université de La Rochelle, 2012. http://tel.archives-ouvertes.fr/tel-01066073.
Texto completoBehzad, Fariduddin. "Proper Orthogonal Decomposition Based Reduced Order Modeling for Fluid Flow". Thesis, Clarkson University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3682451.
Texto completoProper orthogonal decomposition-based reduced order modeling is a technique that can be used to develop low dimensional models of fluid flow. In this technique, the Navier-Stokes equations are projected onto a finite number of POD basis functions resulting in a system of ODEs that model the system. The overarching goal of this work is to determine the best methods of applying this technique to generate reliable models of fluid flow. The first chapter investigates some basic characteristics of the proper orthogonal decomposition using the Burgers equation as a surrogate model problem. In applying the POD to this problem, we found that the eigenvalue spectrum is affected by machine precision and this leads to non-phsical negative eigenvalues in the POD. To avoid this, we introduced a new method called deflation that gives positive eigenvalues, but has the disadvantage that the orthogonality of the POD modes is more affected by numerical precision errors. To reduce the size of eigenproblem of POD process, the well-known snapshot method was tested. It was found that the number of snapshots required to obtain an accurate eigenvalue spectrum was determined by the smallest time scale of the phenomenon. After resolving this time scale, the errors in the eigenvalues and modes drop rapidly then converge with second-order accuracy. After obtaing POD modes, the ROM error was assessed using two errors, the error of projection of the problem onto the POD modes (the out-plane error) and the error of the ROM in the space spanned by POD modes (the in-plane error). The numerical results showed not only is the in-plane error bounded by the out-plane error (in agreement with theory) but it actually converges faster than the out-of-plane error. The second chapter is dedicated to building a robust POD-ROM for long term simulation of Navier-Stokes equation. The ability of the POD method to decompose the simulation and the capability of POD-ROM to simulate a low and high Reynolds flow over a NACA0015 airfoil was studied. We observed that POD can be applied for low Reynolds flows successfully if a proper stabilization method is used. For the high Reynolds case, the convergence of the eigenvalues spectrum with respect to duration of time window from we observed that the number of modes needed to simulate a certain time window increases almost linearly with the length of the time window. So, generating a POD-ROM for high Reynolds flow that reproduced the correct long-term limit cycle behavior needs many more modes than has been usually used in the literature. In the last chapter, we address the problem that the standard method of generating POD modes may be inaccurate when used "off-design" (at parameter values not used to generate the POD). We tested some of the popular methods developed to remedy that problem. The accuracy of these methods was in direct relation with the amount of data provided for those methods. So, in order to generate appropriate POD modes, very large POD problems must be solved. To avoid this, a new multi-level method, called recursive POD, for enriching the POD modes is introduced that mathematically provides optimal POD modes while reducing the computational size of problem to a manageable degrees. A low Reynolds flow over NACA 0015, actuated with constant suction/blowing of a fluidic jet located on top surface of airfoil is used as benchmark to test the technique. The flow is shifted from one periodic state to another periodic state due to fluidic jet effect. It was found that the modes extracted with the recursive POD method are as accurate as the modes of the best known method, global POD, while the computational effort is lower.
Libros sobre el tema "Spectral Proper Orthogonal Decomposition"
Center, Langley Research, ed. Proper orthogonal decomposition in optimal control of fluids. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1999.
Buscar texto completoArian, Eyal. Trust-region proper orthogonal decomposition for flow control. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 2000.
Buscar texto completoInverse analyses with model reduction: Proper orthogonal decomposition in structural mechanics. Berlin: Springer, 2012.
Buscar texto completoProper Orthogonal Decomposition Methods for Partial Differential Equations. Elsevier, 2019. http://dx.doi.org/10.1016/c2017-0-04826-7.
Texto completoProper Orthogonal Decomposition Methods for Partial Differential Equations. Academic Press, 2018.
Buscar texto completoNational Aeronautics and Space Administration (NASA) Staff. Proper Orthogonal Decomposition in Optimal Control of Fluids. Independently Published, 2018.
Buscar texto completoLuo, Zhendong y Goong Chen. Proper Orthogonal Decomposition Methods for Partial Differential Equations. Elsevier Science & Technology Books, 2018.
Buscar texto completoBuljak, Vladimir. Inverse Analyses with Model Reduction: Proper Orthogonal Decomposition in Structural Mechanics. Springer, 2014.
Buscar texto completoBuljak, Vladimir. Inverse Analyses with Model Reduction: Proper Orthogonal Decomposition in Structural Mechanics. Springer, 2011.
Buscar texto completoAzam, Saeed Eftekhar. Online Damage Detection in Structural Systems: Applications of Proper Orthogonal Decomposition, and Kalman and Particle Filters. Springer London, Limited, 2014.
Buscar texto completoCapítulos de libros sobre el tema "Spectral Proper Orthogonal Decomposition"
Gatski, T. B. y M. N. Glauser. "Proper Orthogonal Decomposition Based Turbulence Modeling". En Instability, Transition, and Turbulence, 498–510. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2956-8_48.
Texto completoSilva, José P., E. Jan W. ter Maten, Michael Günther y Matthias Ehrhardt. "Proper Orthogonal Decomposition in Option Pricing". En Novel Methods in Computational Finance, 441–52. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61282-9_24.
Texto completoBerkooz, Gal. "Observations on the Proper Orthogonal Decomposition". En Studies in Turbulence, 229–47. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2792-2_16.
Texto completoPinnau, René. "Model Reduction via Proper Orthogonal Decomposition". En Mathematics in Industry, 95–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78841-6_5.
Texto completoAlfonsi, Giancarlo, Leonardo Primavera, Giuseppe Passoni y Carlo Restano. "Proper Orthogonal Decomposition of Turbulent Channel Flow". En Computational Fluid Dynamics 2000, 473–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56535-9_71.
Texto completoKeuzer, E. y O. Kust. "Controlling Torsional Vibrations Through Proper Orthogonal Decomposition". En Solid Mechanics and Its Applications, 207–14. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5778-0_26.
Texto completoCueto, Elías, Francisco Chinesta y Antonio Huerta. "Model Order Reduction based on Proper Orthogonal Decomposition". En Separated Representations and PGD-Based Model Reduction, 1–26. Vienna: Springer Vienna, 2014. http://dx.doi.org/10.1007/978-3-7091-1794-1_1.
Texto completoAwrejcewicz, Jan, Vadim A. Krys’ko y Alexander F. Vakakis. "Order Reduction by Proper Orthogonal Decomposition (POD) Analysis". En Nonlinear Dynamics of Continuous Elastic Systems, 177–238. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08992-7_3.
Texto completoDu, Qiang y Max D. Gunzburger. "Centroidal Voronoi Tessellation Based Proper Orthogonal Decomposition Analysis". En Control and Estimation of Distributed Parameter Systems, 137–50. Basel: Birkhäuser Basel, 2003. http://dx.doi.org/10.1007/978-3-0348-8001-5_9.
Texto completoPham, Toan y Damien Tromeur-Dervout. "Proper Orthogonal Decomposition In Decoupling Large Dynamical Systems". En Lecture Notes in Computational Science and Engineering, 193–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14438-7_20.
Texto completoActas de conferencias sobre el tema "Spectral Proper Orthogonal Decomposition"
He, Xiao, Fang Zhou, Georgios Rigas y Mehdi Vahdati. "Spectral Proper Orthogonal Decomposition of Compressor Tip Leakage Flow". En European Conference on Turbomachinery Fluid Dynamics and Thermodynamics. European Turbomachinery Society, 2021. http://dx.doi.org/10.29008/etc2021-491.
Texto completoSieber, Moritz, Alexander Kuhn, Hans-Christian Hege, C. Oliver Paschereit y Kilian Oberleithner. "Poster: A graphical representation of the spectral proper orthogonal decomposition". En 68th Annual Meeting of the APS Division of Fluid Dynamics. American Physical Society, 2015. http://dx.doi.org/10.1103/aps.dfd.2015.gfm.p0007.
Texto completoCottier, Stephanie, Christopher S. Combs y Leon Vanstone. "Spectral Proper Orthogonal Decomposition Analysis of Shock-Wave/Boundary-Layer Interactions". En AIAA Aviation 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-3331.
Texto completoAcharya, Adit, Todd Lowe y Wing Ng. "Spectral Proper Orthogonal Decomposition Downstream of a Vortex Tube Separator Array". En AIAA SCITECH 2023 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2023. http://dx.doi.org/10.2514/6.2023-2354.
Texto completoTowne, Aaron. "Space-time Galerkin projection via spectral proper orthogonal decomposition and resolvent modes". En AIAA Scitech 2021 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2021. http://dx.doi.org/10.2514/6.2021-1676.
Texto completoEppink, Jenna L. "Three-Dimensional Instantaneous Flow-field Reconstruction Using Planar Spectral Proper Orthogonal Decomposition". En AIAA SCITECH 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-2430.
Texto completoHeidt, Liam, Tim Colonius, Akhil Nekkanti, Oliver Schmdit, Igor Maia y Peter Jordan. "Analysis of forced subsonic jets using spectral proper orthogonal decomposition and resolvent analysis". En AIAA AVIATION 2021 FORUM. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2021. http://dx.doi.org/10.2514/6.2021-2108.
Texto completoGeorgiou, Ioannis T. y Christos I. Papadopoulos. "A Novel Vibration Analysis of Stiff-Soft Structural Systems by the Method of Spectral Proper Orthogonal Decomposition". En ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85296.
Texto completoWitte, Matthias, Benjamin Torner y Frank-Hendrik Wurm. "Analysis of Unsteady Flow Structures in a Radial Turbomachine by Using Proper Orthogonal Decomposition". En ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76596.
Texto completoRosafio, Nicola, Giove De Cosmo, Simone Salvadori, Mauro Carnevale y Daniela Anna Misul. "Identification of Fluctuation Modes for a Cylindrical Film Cooling Hole Using the Spectral Proper Orthogonal Decomposition Method". En ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-79528.
Texto completoInformes sobre el tema "Spectral Proper Orthogonal Decomposition"
Oxberry, Geoffrey M., Tanya Kostova-Vassilevska, Bill Arrighi y Kyle Chand. Limited-memory adaptive snapshot selection for proper orthogonal decomposition. Office of Scientific and Technical Information (OSTI), abril de 2015. http://dx.doi.org/10.2172/1224940.
Texto completoLy, Hung V. y Hien T. Tran. Modeling and Control of Physical Processes Using Proper Orthogonal Decomposition. Fort Belvoir, VA: Defense Technical Information Center, febrero de 1999. http://dx.doi.org/10.21236/ada454477.
Texto completoDel Rosario, R. C., H. T. Tran y H. T. Banks. Proper Orthogonal Decomposition Based Control of Transverse Beam Vibrations: Experimental Implementation. Fort Belvoir, VA: Defense Technical Information Center, enero de 1999. http://dx.doi.org/10.21236/ada454479.
Texto completoNarayanan, Vinod y Benn Eilers. Identification of Coherent Structure Dynamics in Wall-Bounded Sprays using Proper Orthogonal Decomposition. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2010. http://dx.doi.org/10.21236/ada532067.
Texto completoLy, Hung V. y Hien T. Tran. Proper Orthogonal Decomposition for Flow Calculations and Optimal Control in a Horizontal CVD Reactor. Fort Belvoir, VA: Defense Technical Information Center, marzo de 1998. http://dx.doi.org/10.21236/ada451227.
Texto completoMcDaniel, Dwayne, George Dulikravich y Paul Cizmas. Development of a Reduced-Order Model for Reacting Gas-Solids Flow using Proper Orthogonal Decomposition. Office of Scientific and Technical Information (OSTI), noviembre de 2017. http://dx.doi.org/10.2172/1411716.
Texto completoViggiano, Bianca. Reduced Order Description of Experimental Two-Phase Pipe Flows: Characterization of Flow Structures and Dynamics via Proper Orthogonal Decomposition. Portland State University Library, enero de 2000. http://dx.doi.org/10.15760/etd.5723.
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