Data-Driven Prediction of Unsteady Vortex Phenomena in a Conical Diffuser

The application of machine learning to solve engineering problems is in extremely high demand. This article proposes a tool that employs machine learning algorithms for predicting the frequency response of an unsteady vortex phenomenon, the precessing vortex core (PVC), occurring in a conical diffus...

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書誌詳細

出版年:	Energies
主要な著者:	Sergey Skripkin, Daniil Suslov, Ivan Plokhikh, Mikhail Tsoy, Evgeny Gorelikov, Ivan Litvinov
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2023-02-01
主題:	swirling flow precessing vortex core (PVC) prediction of vortex machine learning
オンライン･アクセス:	https://www.mdpi.com/1996-1073/16/5/2108