Gas-solid fluidization technology has been commercialized in many industrial applications
since its implementation in the fluid catalytic cracking process in the early 1940s, however …

This paper reviews the problems and successes of computing turbulent flow. Most of the flow
phenomena that are important to modern technology involve turbulence. The review is …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Abstract Machine learning models are gaining increasing popularity in the domain of fluid
dynamics for their potential to accelerate the production of high-fidelity computational fluid …

This work presents a review and perspectives on recent developments in the use of machine
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …

Incompressible flow problems appear in many models of physical processes and
applications. Their numerical simulation requires in particular a spatial discretization. Finite …

A fully connected neural network (NN) is used to develop a subgrid-scale (SGS) model
mapping the relation between the SGS stresses and filtered flow variables in a turbulent …

Combustion instability remains a critical issue limiting the development of low-emission,
lean-premixed (LPM) gas turbine combustion systems. The present work provides a …

First concise textbook on Large-Eddy Simulation, a very important method in scientific
computing and engineering From the foreword to the third edition written by Charles …

For compressible flow simulations involving both turbulence and shockwaves, the
competing requirements render it challenging to develop high-order numerical methods …