Zobrazeno 1 - 10
of 2 546
pro vyhledávání: '"Reduced-Order Modeling"'
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, 2024, Vol. 34, Issue 8, pp. 3253-3277.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/HFF-10-2023-0659
Autor:
Hyeonah Shin, Seungin Oh, Yongbeom Cho, Jinyoung Kil, Byunyoung Chung, Jinwon Shin, Jin-Gyun Kim
Publikováno v:
Nuclear Engineering and Technology, Vol 56, Iss 9, Pp 3491-3500 (2024)
In this work, the hybrid vibro-acoustic model reduction technique that is a physical-modal combined formulation is proposed to accelerate the finite element model updating process of the vibro-acoustic pipeline system. Particularly, the new formulati
Externí odkaz:
https://doaj.org/article/a3c9eb1413734bddb0bdf9a5625c02dc
Autor:
Snyder, William David
High-fidelity computer simulations of childbirth are time consuming, making them impractical for guiding decision-making during obstetric emergencies. The complex geometry, micro-structure, and large finite deformations undergone by the vagina during
Externí odkaz:
https://hdl.handle.net/10919/119425
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 16, Iss 7, Pp n/a-n/a (2024)
Abstract Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While
Externí odkaz:
https://doaj.org/article/bc518d30d50d4bbfbc6d326491f47123
Publikováno v:
Digital Chemical Engineering, Vol 11, Iss , Pp 100145- (2024)
Autoencoder-based reduced-order machine learning models have been developed for modeling and predictive control of nonlinear chemical processes with high dimensionality such as discretization of reaction–diffusion processes. However, in the presenc
Externí odkaz:
https://doaj.org/article/cc86c30fa69f42c8af3ae13031c49e26
Autor:
Mohammad Adnan K. Magableh, Amr Ahmed A. Radwan, Yasser Abdel-Rady I. Mohamed, Ehab Fahmy El-Saadany
Publikováno v:
IEEE Open Journal of Power Electronics, Vol 5, Pp 1459-1483 (2024)
This paper presents a novel reduced-order modeling approach for efficient modeling and dynamic stability analysis of a utility-scale hybrid grid-tied system comprising a photovoltaic (PV) array, wind turbine (WT), battery energy storage system (BESS)
Externí odkaz:
https://doaj.org/article/56bab1e217b145babad59ebc24195acf
Publikováno v:
Wind Energy, Vol 27, Iss 1, Pp 75-100 (2024)
Abstract Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, ob
Externí odkaz:
https://doaj.org/article/b95c69b87a904641b57594ebeed0d4d9
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 10, Iss 1, Pp 1-35 (2023)
Abstract Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The process of obtaining the solution from such PDEs may be computationally expensive for large-scal
Externí odkaz:
https://doaj.org/article/a86315a609b84c8e8b68d16335a483ec
Publikováno v:
Mathematics in Engineering, Vol 5, Iss 6, Pp 1-36 (2023)
Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional ROMs–built, e.g., through proper orthogonal decomposition (POD)–when applied to nonlinear time-dependent parametr
Externí odkaz:
https://doaj.org/article/4965f7368f754dce9236c9f8af808f4a
Publikováno v:
Fluids, Vol 9, Iss 8, p 178 (2024)
Reduced-order models (ROMs) have achieved a lot of success in reducing the computational cost of traditional numerical methods across many disciplines. In fluid dynamics, ROMs have been successful in providing efficient and relatively accurate soluti
Externí odkaz:
https://doaj.org/article/27a508832b484eaca3809a8e55dc922c