Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Mandar Tabib"'
Publikováno v:
Frontiers in Energy Research, Vol 11 (2023)
Externí odkaz:
https://doaj.org/article/02533d39203a4e0f9ca9efbbbb120cca
Publikováno v:
Engineering Proceedings, Vol 39, Iss 1, p 98 (2023)
A computationally efficient predictive digital twin (DT) of a small-scale greenhouse needs an accurate and faster modelling of key variables such as the temperature field and flow field within the greenhouse. This involves: (a) optimally placing sens
Externí odkaz:
https://doaj.org/article/6689ce59bd38412fbeaa28a7b02f3413
Publikováno v:
International Journal of Chemical Engineering, Vol 2013 (2013)
Externí odkaz:
https://doaj.org/article/1f805986e3e94f05ba15b496ae390d4e
Autor:
Philippe Nivlet, Knut Steinar Bjorkevoll, Mandar Tabib, Jan Ole Skogestad, Bjornar Lund, Roar Nybo, Adil Rasheed
Publikováno v:
Day 2 Mon, February 20, 2023.
Monitoring of Equivalent Circulating Density (ECD) may improve assessment of potential bad hole cleaning conditions if calculated and measured sufficiently accurately. Machine learning (ML) models can be used for predicting ECD integrating both along
Publikováno v:
Digital. 1:111-129
Digital twins are meant to bridge the gap between real-world physical systems and virtual representations. Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations of objects in the digita
Publikováno v:
2021 International Conference on Applied Artificial Intelligence (ICAPAI).
A methodology involving machine learning with subsequent physics-based method and a visualization map has been developed for enhancing environment-friendly transport planning. This methodology is applied for enabling predictions of traffic counts of
Publikováno v:
Renewable Energy
The key to the better design of an industrial scale wind turbine is to understand the influence of blade geometry and its dynamics on the complicated flow-structures. An industrial-scale wind turbine can be numerically represented using various appro
Publikováno v:
Journal of Physics: Conference Series (JPCS)
Good understanding of micro-scale urban-wind phenomena is needed for optimizing power generation capabilities of building-integrated wind turbines and for safety of futuristic urban transport involving drones. The current work involves development of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db3dad72fbf76a47eb46c0e7d1dc95e8
https://hdl.handle.net/11250/2989800
https://hdl.handle.net/11250/2989800
Publikováno v:
Journal of Physics: Conference Series (JPCS)
In this study, we present a parametric non-intrusive reduced order modeling framework as a potential digital twin enabler for fluid flow related applications. The case study considered here involves building-induced flows and turbulence with inlet tu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a84ab8d5a9f6a8d56f952ff8fd9e0e3f
https://hdl.handle.net/11250/3010377
https://hdl.handle.net/11250/3010377
Publikováno v:
arXiv
We put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements. Toward emerging applications of digital twins in aviation, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::634f4cf8f536b2e1d8eea3c393b7113e
https://hdl.handle.net/11250/2728800
https://hdl.handle.net/11250/2728800