Results 11 to 20 of about 4,689,237 (339)
The Fluid Mechanics of Deep-Sea Mining
Annual Review of Fluid Mechanics, 2022 Fluid mechanics lies at the heart of many of the physical processes associated with the nascent deep-sea mining industry. The evolution and fate of sediment plumes that would be produced by seabed mining activities, which are central to the assessment of
T. Peacock, R. Ouillon
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A Review of Physics-Informed Machine Learning in Fluid Mechanics
Energies, 2023 Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—
Bassem Akoush +2 more
exaly +2 more sources
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations [PDF]
Science, 2020 Machine-learning fluid flow Quantifying fluid flow is relevant to disciplines ranging from geophysics to medicine. Flow can be experimentally visualized using, for example, smoke or contrast agents, but extracting velocity and pressure fields from this ...
M. Raissi +2 more
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Machine Learning for Fluid Mechanics [PDF]
Annual Review of Fluid Mechanics, 2019 The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract
S. Brunton, B. R. Noack, P. Koumoutsakos
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The Fluid Mechanics of Ureteroscope Irrigation [PDF]
Journal of Endourology, 2019 Purpose: To develop a physical understanding of ureterorenoscopy irrigation, we derive mathematical models from basic physical principles and compare these predictions with the results of benchtop experiments.
Williams, J +5 more
openaire +4 more sources
Physics-informed neural networks (PINNs) for fluid mechanics: a review [PDF]
Acta Mechanica Sinica, 2021 Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is ...
Shengze Cai +4 more
semanticscholar +1 more source
Physics-informed deep-learning applications to experimental fluid mechanics [PDF]
Measurement science and technology, 2022 High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy ...
Hamidreza Eivazi +2 more
semanticscholar +1 more source
Culinary fluid mechanics and other currents in food science [PDF]
Reviews of Modern Physics, 2022 Innovations in fluid mechanics are leading to better food since ancient history, while creativity in cooking inspires applied and fundamental science. Here, we review how recent advances in hydrodynamics are changing food science, and we highlight how ...
A. Mathijssen +3 more
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Applying machine learning to study fluid mechanics [PDF]
Acta Mechanica Sinica, 2021 This paper provides a short overview of how to use machine learning to build data-driven models in fluid mechanics. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training ...
S. Brunton
semanticscholar +1 more source
Thermosuperrepellency of a hot substrate caused by vapour percolation
Communications Physics, 2021 Droplet impact on surfaces has wide applications regardless of the discipline area and several hypotheses have been put forward to explain the mechanism of film boiling.
J. Benedikt Schmidt +5 more
doaj +1 more source