Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Trond Kvamsdal"'
Publikováno v:
Energy Informatics, Vol 7, Iss 1, Pp 1-36 (2024)
Abstract In recent years, there has been growing interest in digital twin technology in both industry and academia. This versatile technology has found applications across various industries. Wind energy systems are particularly suitable for digital
Externí odkaz:
https://doaj.org/article/0c7a095941544dbe99c474101de9865c
Autor:
Florian Stadtmann, Adil Rasheed, Trond Kvamsdal, Kjetil Andre Johannessen, Omer San, Konstanze Kolle, John Olav Tande, Idar Barstad, Alexis Benhamou, Thomas Brathaug, Tore Christiansen, Anouk-Letizia Firle, Alexander Fjeldly, Lars Froyd, Alexander Gleim, Alexander Hoiberget, Catherine Meissner, Guttorm Nygard, Jorgen Olsen, Havard Paulshus, Tore Rasmussen, Elling Rishoff, Francesco Scibilia, John Olav Skogas
Publikováno v:
IEEE Access, Vol 11, Pp 110762-110795 (2023)
This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels
Externí odkaz:
https://doaj.org/article/3db25e99bdfa4a6385b8d19f703f6eb1
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and
Externí odkaz:
https://doaj.org/article/dc7beb6894c94891922172b414818dd9
Publikováno v:
IEEE Access, Vol 8, Pp 21980-22012 (2020)
Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines,
Externí odkaz:
https://doaj.org/article/cd433b3244ed4802937b1490051950df
Publikováno v:
Applied Sciences, Vol 12, Iss 6, p 2915 (2022)
Pressure oscillations at small time steps have been known to be an issue in poroelasticity simulations. A review of proposed approaches to overcome this problem is presented. Critical time steps are specified to alleviate this in finite element analy
Externí odkaz:
https://doaj.org/article/157a8e5359634fd8a50c8e00fe5ffd25
Publikováno v:
Energies, Vol 12, Iss 7, p 1271 (2019)
We present a nonintrusive approach for combining high-fidelity simulations using Finite-Volume (FV) methods with Proper Orthogonal Decomposition (POD) and Galerkin Reduced-Order Modeling (ROM) methodology. By nonintrusive we here imply an approach th
Externí odkaz:
https://doaj.org/article/58e83daca8d6486ca299fd499b961bdf
Publikováno v:
Neural Networks
In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA) – a novel approach to Hybrid Analysis and Modeling (HAM). The objective of HAM is to combine physics-based modeling (PBM) and data-driven modeling (DDM)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d912c56bd690b8901cd164b56a5749b
https://doi.org/10.1016/j.neunet.2021.11.021
https://doi.org/10.1016/j.neunet.2021.11.021
Publikováno v:
Applied Soft Computing
Upcoming technologies like digital twins, autonomous, and artificial intelligent systems involving safety–critical applications require accurate, interpretable, computationally efficient, and generalizable models. Unfortunately, the two most common
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3ad1251d667976fd9359656b55e8b58
https://hdl.handle.net/11250/3018976
https://hdl.handle.net/11250/3018976
