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020 ▼a 9780438098732
035 ▼a (MiAaPQ)AAI10901935
035 ▼a (MiAaPQ)OhioLINK:osu1512058039822368
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 629.1
1001 ▼a Mohan, Arvind Thanam.
24510 ▼a Data-Driven Analysis Methodologies for Unsteady Aerodynamics from High Fidelity Simulations.
260 ▼a [S.l.] : ▼b The Ohio State University., ▼c 2017
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2017
300 ▼a 275 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Datta Gaitonde.
5021 ▼a Thesis (Ph.D.)--The Ohio State University, 2017.
520 ▼a Numerical methods and computational approaches for studying fluid flow have become increasingly popular and more mature in their capabilities since the 1960s, when the prohibitive experimental costs associated with flight vehicle development pro
520 ▼a First, model reduction as an effective means to study complex, realistic flows is investigated. The recently popularized model reduction technique, Dynamic Mode Decomposition (DMD) has been given special emphasis in addition to the well known Pr
520 ▼a Apart from model reduction for physics extraction, a major emphasis of this work has been on analysis of non-stationary signals arising from turbulent flows. Despite Fast Fourier Transform (FFT) and DMD being effective tools to study local and g
520 ▼a Although EMD proves to be a useful solution for feature extraction from non-stationary signals, it has some significant practical limitations. A crucial impediment is that EMD in its basic form can be applied only for univariate/single-channel d
590 ▼a School code: 0168.
650 4 ▼a Aerospace engineering.
690 ▼a 0538
71020 ▼a The Ohio State University. ▼b Aero/Astro Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0168
791 ▼a Ph.D.
792 ▼a 2017
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000333 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자