Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Frédéric Gibou"'
Publikováno v:
International Journal of Differential Equations, Vol 2018 (2018)
We analyze the accuracy of two numerical methods for the variable coefficient Poisson equation with discontinuities at an irregular interface. Solving the Poisson equation with discontinuities at an irregular interface is an essential part of solving
Externí odkaz:
https://doaj.org/article/a32c6515826a49059503d47b60e059ed
Autor:
Samuel D. Tomlinson, Frédéric Gibou, Paolo Luzzatto-Fegiz, Fernando Temprano-Coleto, Oliver E. Jensen, Julien R. Landel
Publikováno v:
Tomlinson, S D, Gibou, F, Luzzatto-Fegiz, P, Temprano-Coleto, F, Jensen, O E & Landel, J R 2023, ' Laminar drag reduction in surfactantcontaminated superhydrophobic channels ', Journal of Fluid Mechanics, vol. 963, no. A10 . https://doi.org/10.1017/jfm.2023.264
Although superhydrophobic surfaces (SHSs) show promise for drag reduction applications, their performance can be compromised by traces of surfactant, which generate Marangoni stresses that increase drag. This question is addressed for soluble surfact
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::611d3fd1cf8e5eb35f960a6509ed91ec
https://doi.org/10.1017/jfm.2023.264
https://doi.org/10.1017/jfm.2023.264
Autor:
Fernando Temprano-Coleto, Scott M. Smith, François J. Peaudecerf, Julien R. Landel, Frédéric Gibou, Paolo Luzzatto-Fegiz
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 120 (3)
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America, 2023, 120 (3), pp.e2211092120. ⟨10.1073/pnas.2211092120⟩
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America, 2023, 120 (3), pp.e2211092120. ⟨10.1073/pnas.2211092120⟩
Recent experimental and computational investigations have shown that trace amounts of surfactants, unavoidable in practice, can critically impair the drag reduction of superhydrophobic surfaces (SHSs), by inducing Marangoni stresses at the air-liquid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39c3da8d939d1a9a966a1c68cdde960f
Machine learning algorithms for three-dimensional mean-curvature computation in the level-set method
Publikováno v:
Journal of Computational Physics. 478:111995
We propose a data-driven mean-curvature solver for the level-set method. This work is the natural extension to $\mathbb{R}^3$ of our two-dimensional strategy in [DOI: 10.1007/s10915-022-01952-2][1] and the hybrid inference system of [DOI: 10.1016/j.j
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ab0c2fafa3c14c78950e396f6e2ce82
https://doi.org/10.1016/j.jcp.2023.111995
https://doi.org/10.1016/j.jcp.2023.111995
We present an error-neural-modeling-based strategy for approximating two-dimensional curvature in the level-set method. Our main contribution is a redesigned hybrid solver [Larios-C\'ardenas and Gibou, J. Comput. Phys. (May 2022), 10.1016/j.jcp.2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca67cad44831955bc3868db1ca6c929e
We present a machine learning framework that blends image super-resolution technologies with passive, scalar transport in the level-set method. Here, we investigate whether we can compute on-the-fly, data-driven corrections to minimize numerical visc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28822dc1e2772cae8f675de464e89a47
We present a novel hybrid strategy based on machine learning to improve curvature estimation in the level-set method. The proposed inference system couples enhanced neural networks with standard numerical schemes to compute curvature more accurately.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a55fe99d6e3e49f1c983572b00d33191
Publikováno v:
PLoS ONE, Vol 11, Iss 3, p e0150889 (2016)
We introduce a fast error-free tracking method applicable to sequences of two and three dimensional images. The core idea is to use Quadtree (resp. Octree) data structures for representing the spatial discretization of an image in two (resp. three) s
Externí odkaz:
https://doaj.org/article/47fe9fb5a19d4ef48eeb4e291a0abaac