The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep (DL) has opened opportunities for fluid dynamics its applications science, engineering medicine. Developing AI encompass different challenges than with massive data, such as the Internet Things. For many scientific, biomedical problems, data are not massive, which poses limitations algorithmic chall...

Environmental fluid mechanics underlies a wealth of natural, industrial, and, by extension, societal challenges. As we strive toward more sustainable planet, there is wide range problems to be tackled, from fundamental advances in understanding and modeling stratified turbulence consequent mixing applied studies pollution transport the ocean, atmosphere, urban environments. The discussions outc...

Abstract This paper provides a short overview of how to use machine learning build data-driven models in fluid mechanics. The process is broken down into five stages: (1) formulating problem model, (2) collecting and curating training data inform the (3) choosing an architecture with which represent (4) designing loss function assess performance (5) selecting implementing optimization algorithm...

A multi-scale framework is proposed for more realistic molecular dynamics simulations in continuum solvent models by coupling a molecular mechanics treatment of solute with a fluid mechanics treatment of solvent. This article reports our initial efforts to formulate the physical concepts necessary for coupling the two mechanics and develop a 3D numerical algorithm to simulate the solvent fluid ...

179 The last few lectures of an undergraduate class in fluid mechanics offer instructors an opportunity to teach students some advanced topics that go beyond the traditional course material.[1] Fluid-particle flows, where both the fluid and particle are in motion, are prevalent in many industries including the chemical, materials, and energy industries.[2] In the pharmaceutical and biotechnolog...