Zobrazeno 1 - 10
of 77
pro vyhledávání: '"and, Romit Maulik"'
Autor:
Coleman Moss, Matteo Puccioni, Romit Maulik, Clément Jacquet, Dale Apgar, Giacomo Valerio Iungo
Publikováno v:
Wind Energy, Vol 27, Iss 11, Pp 1268-1285 (2024)
Abstract Flow modifications induced by wind turbine rotors on the incoming atmospheric boundary layer (ABL), such as blockage and speedups, can be important factors affecting the power performance and annual energy production (AEP) of a wind farm. Fu
Externí odkaz:
https://doaj.org/article/be0b2294d28240a3a310127d360729f1
Publikováno v:
Wind Energy, Vol 27, Iss 11, Pp 1245-1267 (2024)
Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites invo
Externí odkaz:
https://doaj.org/article/8c53a004e8e840ddb9465257d4d45659
Publikováno v:
Theoretical and Applied Mechanics Letters, Vol 14, Iss 1, Pp 100488- (2024)
With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations, machine learning (ML) models are poised to advance our understanding of the physics underpinning the interaction between the at
Externí odkaz:
https://doaj.org/article/ffd963e1bb984140869aac59a37758d4
Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then b
Externí odkaz:
http://arxiv.org/abs/2008.06731
Autor:
René Steijl, Romit Maulik
Publikováno v:
Frontiers in Mechanical Engineering, Vol 9 (2023)
Externí odkaz:
https://doaj.org/article/7bee8f410de8404b9027b45ced16d26a
Autor:
Varun Shankar, Vedant Puri, Ramesh Balakrishnan, Romit Maulik, Venkatasubramanian Viswanathan
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 1, p 015017 (2023)
Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences. We discuss an approach using the differentiable physics paradigm that combines known physics with machine learni
Externí odkaz:
https://doaj.org/article/3278eece3e17409c93974b13e0777508
Autor:
Romit Maulik, Omer San
Publikováno v:
Journal of Ocean Engineering and Science, Vol 1, Iss 4, Pp 300-324 (2016)
This paper puts forth a simplified dynamic modeling strategy for the eddy viscosity coefficient parameterized in space and time. The eddy viscosity coefficient is dynamically adjusted to the local structure of the flow using two different nonlinear e
Externí odkaz:
https://doaj.org/article/eeda2684dd6b48e78a1d31cf014fb05b
Publikováno v:
Neural Computing and Applications. 34:6171-6186
Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions, and the interaction between wakes. Physics-based models that capture the wake flow-field with high-fidelity are computationally v
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c746c27959bb786f231c299dc9ba3aac
https://doi.org/10.1007/s00521-021-06799-6
https://doi.org/10.1007/s00521-021-06799-6
Publikováno v:
Review of Policy Research.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50ed8a97ac17e90d9a85144897bb208d
https://doi.org/10.1111/ropr.12523
https://doi.org/10.1111/ropr.12523
Autor:
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, Rao Kotamarthi
Data assimilation (DA) in geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction and is a crucial building block that has allowed dramatic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f54a52b9534d4dca24711b4b6e45c899
https://gmd.copernicus.org/articles/15/3433/2022/
https://gmd.copernicus.org/articles/15/3433/2022/