Results 221 to 230 of about 326,257 (262)

Physics-Constrained Hybrid Solvers for Computational Fluid Dynamics: Integrating Deep Learning into the Numerical Core

Nicola Spieser
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Local structure and dynamics in colloidal fluids, clusters and gels(Poster session 2, New Frontiers in Colloidal Physics : A Bridge between Micro- and Macroscopic Concepts in Soft Matter)

, 2007
Takehiro Ohtsuka, Paddy Royall, Hajime Tanaka   +2 more
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Ablowitz–Kaup–Newell–Segur system, conservation laws and Bäcklund transformation of a variable-coefficient Korteweg–de Vries equation in plasma physics, fluid dynamics or atmospheric science

, 2020
For a variable-coefficient Korteweg–de Vries equation in a lake/sea, two-layer liquid, atmospheric flow, cylindrical plasma or interactionless plasma, in this paper, we derive the bilinear Backlund...
Yu-Qi Chen, B. Tian, Qi-Xing Qu, He Li, Xue-Hui Zhao, He-Yuan Tian, Meng Wang   +6 more
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Quantum-inspired framework for computational fluid dynamics

Communications Physics, 2023
Computational fluid dynamics is both a thriving research field and a key tool for advanced industry applications. However, the simulation of turbulent flows in complex geometries is a compute-power intensive task due to the vast vector dimensions ...
Raghavendra D Peddinti, Stefano Pisoni, Alessandro Marini, Philippe Lott, Henrique Argentieri, Egor Tiunov, Leandro Aolita   +6 more
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Solitons and periodic waves for a generalized (3+1)-dimensional Kadomtsev–Petviashvili equation in fluid dynamics and plasma physics

Communications in Theoretical Physics, 2020
Under investigation in this paper is a generalized (3+1)-dimensional Kadomtsev–Petviashvili equation in fluid dynamics and plasma physics. Soliton and one-periodic-wave solutions are obtained via the Hirota bilinear method and Hirota–Riemann method ...
Dong Wang, Yi-Tian Gao, Cui-Cui Ding, Cai-Yin Zhang   +3 more
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Fluid Dynamics of Axial Turbomachinery: Blade- and Stage-Level Simulations and Models

Annual Review of Fluid Mechanics, 2021
The current generation of axial turbomachines are the culmination of decades of experience, and detailed understanding of the underlying flow physics has been a key factor for achieving high efficiency and reliability.
R. Sandberg, V. Michelassi
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Machine Learning Computational Fluid Dynamics

Annual Workshop of the Swedish Artificial Intelligence Society, 2021
Numerical simulation of fluid flow is a significant research concern during the design process of a machine component that experiences fluid-structure interaction (FSI).
A. Usman, M. Rafiq, Muhammad Saeed, Alissa Nauman, A. Almqvist, M. Liwicki   +5 more
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A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics

The Physics of Fluids
Physics-informed neural networks (PINNs) represent an emerging computational paradigm that incorporates observed data patterns and the fundamental physical laws of a given problem domain.
Chi Zhao, Feifei Zhang, Wenqiang Lou, Xi Wang, Jianyong Yang   +4 more
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A Unified Computational Fluid Dynamics Framework from Rarefied to Continuum Regimes

, 2021
This Element presents a unified computational fluid dynamics framework from rarefied to continuum regimes. The framework is based on the direct modelling of flow physics in a discretized space.
K. Xu
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Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey

arXiv.org
This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques. We begin by introducing fundamental concepts, traditional methods, and benchmark datasets, then examine the ...
Haixin Wang, Yadi Cao, Zijie Huang, Yuxuan Liu, Peiyan Hu, Xiao Luo, Zezheng Song, Wanjia Zhao, Jilin Liu, Jinan Sun, Shikun Zhang, Long Wei, Yue Wang, Tailin Wu, Zhi-Ming Ma, Yizhou Sun   +15 more
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