Zobrazeno 1 - 10
of 435
pro vyhledávání: '"Guy, Y"'
Autor:
Hou, Chang, Marra, Luigi, Maceda, Guy Y. Cornejo, Jiang, Peng, Chen, Jingguo, Liu, Yutong, Hu, Gang, Chen, Jialong, Ianiro, Andrea, Discetti, Stefano, Meilán-Vila, Andrea, Noack, Bernd R.
We propose a physics-informed data-driven framework for urban wind estimation. This framework validates and incorporates the Reynolds number independence for turbulent flows, thus allowing the extrapolation for wind conditions far beyond the training
Externí odkaz:
http://arxiv.org/abs/2410.02427
Autor:
Marra, Luigi, Maceda, Guy Y. Cornejo, Meilán-Vila, Andrea, Guerrero, Vanesa, Rashwan, Salma, Noack, Bernd R., Discetti, Stefano, Ianiro, Andrea
We propose a data-driven methodology to learn a low-dimensional actuation manifold of controlled flows. The starting point is resolving snapshot flow data for a representative ensemble of actuations. Key enablers for the actuation manifold are isomet
Externí odkaz:
http://arxiv.org/abs/2403.03653
Autor:
Li, Yiqing, Noack, Bernd R., Wang, Tianyu, Maceda, Guy Y. Cornejo, Pickering, Ethan, Shaqarin, Tamir, Tyliszczak, Artur
We optimize the jet mixing using large eddy simulations (LES) at a Reynolds number of $3000$. Key methodological enablers consist of Bayesian optimization, a surrogate model enhanced by deep learning, and persistent data topology for physical interpr
Externí odkaz:
http://arxiv.org/abs/2311.02330
Autor:
Tamir Shaqarin, Zhutao Jiang, Tianyu Wang, Chang Hou, Guy Y. Cornejo Maceda, Nan Deng, Nan Gao, Bernd R. Noack
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Jet mixing is a critical factor in various engineering applications, influencing pollutant dispersion, chemical processes, medical treatments, and combustion enhancement. Hitherto, jet mixing has typically been optimized by either passive or
Externí odkaz:
https://doaj.org/article/4857218df4d64905a33aae6665251c99
Autor:
Wang, Tianyu, Yang, Yannian, Chen, Xuanwu, Li, Pengyu, Iollo, Angelo, Maceda, Guy Y. Cornejo, Noack, Bernd R.
We develop and apply a novel shape optimization exemplified for a two-blade rotor with respect to the figure of merit ($FM$). This topologically assisted optimization (TAO) contains two steps. First a global evolutionary optimization is performed for
Externí odkaz:
http://arxiv.org/abs/2302.08728
xMLC is the second book of this `Machine Learning Tools in Fluid Mechanics' Series and focuses on Machine Learning Control (MLC). The objectives of this book are two-fold: First, provide an introduction to MLC for students, researchers, and newcomers
Externí odkaz:
http://arxiv.org/abs/2208.13172
We stabilize an open cavity flow experiment to 1% of its original fluctuation level. For the first time, a multi-modal feedback control is automatically learned for this configuration. The key enabler is automatic in-situ optimization of control laws
Externí odkaz:
http://arxiv.org/abs/2202.01686
We stabilize the flow past a cluster of three rotating cylinders, the fluidic pinball, with automated gradient-enriched machine learning algorithms. The control laws command the rotation speed of each cylinder in an open- and closed-loop manner. Thes
Externí odkaz:
http://arxiv.org/abs/2011.06661
We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich data base of machine learning control (MLC) optimizing a feedback law for a cost fu
Externí odkaz:
http://arxiv.org/abs/2008.12924
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