Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Sandeep Reddy Bukka"'
In this paper, an end-to-end nonlinear model reduction methodology is presented based on the convolutional recurrent autoencoder networks. The methodology is developed in the context of the overall data-driven reduced-order model framework proposed i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68a4d7734b0f343189afd8784b82343f
https://doi.org/10.1115/1.0000880v
https://doi.org/10.1115/1.0000880v
Publikováno v:
Day 2 Tue, November 03, 2020.
A flotel is often used to house personnel and equipment for on-site maintenance of ageing FPSOs. Although tankers are designed to be dry-docked, operating FPSOs (which may be reconverted tankers) must be maintained in the field. The advantage to indu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae9642573596f08dfb15a564a16e2c70
https://doi.org/10.4043/30080-ms
https://doi.org/10.4043/30080-ms
Publikováno v:
Journal of Fluid Mechanics. 886
In this paper, we present a stability analysis of passive suppression devices for the vortex-induced vibration (VIV) in the laminar flow condition. A data-driven model reduction approach based on the eigensystem realization algorithm is used to const
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a80b9af8c821d5876a075d4478cf69b
https://doi.org/10.1017/jfm.2019.1026
https://doi.org/10.1017/jfm.2019.1026
Publikováno v:
Physics of Fluids
In this paper, we introduce a reduced order model (ROM) for the propagation of nonlinear multi-directional ocean wave-fields. The ROM relies on Galerkin projection of Zakharov equations embedded in the high-order spectral (HOS) method, which describe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ee19a9d7e68662560717d44430e862e
https://doi.org/10.1063/5.0070246
https://doi.org/10.1063/5.0070246
Publikováno v:
Physics of Fluids. 33:013601
In this paper, we present two deep learning-based hybrid data-driven reduced-order models for prediction of unsteady fluid flows. These hybrid models rely on recurrent neural networks (RNNs) to evolve low-dimensional states of unsteady fluid flow. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9f23458b2216b916eb018e7839f89b43
https://doi.org/10.1063/5.0030137
https://doi.org/10.1063/5.0030137