Machine Learning Based Fluid-Transportation Monitoring and Controlling

نویسندگان

چکیده

The discipline of fluid mechanics is developing quickly, propelled by previously unheard-of data volumes from experiments, field measurements, and expansive simulations at various spatiotemporal scales. machine learning (ML) provides a plethora methods for gleaning insights that can be used to inform our understanding the dynamics play. As an added bonus, ML algorithms automate duties associated with flow control optimization, while also enhancing domain expertise. This article review background, current state, potential future applications in mechanics. We provide introduction most fundamental approaches describe their study, modelling, management flows. From standpoint scientific inquiry, which treats as integral aspect simulations, benefits drawbacks these are discussed. Since robust information-processing framework, it supplement potentially revolutionize conventional study industrial applications.

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ژورنال

عنوان ژورنال: International Journal on Recent and Innovation Trends in Computing and Communication

سال: 2023

ISSN: ['2321-8169']

DOI: https://doi.org/10.17762/ijritcc.v11i9s.7455