Computational Methods in Fluid Dynamics: Advances and Prospects

Dear Colleagues,

Mathematical models used for describing the complex nature of fluid behavior provide scientists and engineers with powerful tools to analyze and simulate a wide range of flows and to design state-of-the-art technology in fields as diverse as oceanography, biomedical applications, and aerospace engineering. Each of these applications poses its own mathematical challenges, and most of them require sophisticated algorithms to overcome them, as well as powerful computational resources for their implementation.

High-performance computer (HPC) architectures and sophisticated numerical methods developed over the recent decades have gradually expanded our ability to study and simulate ever more descriptive and complex mathematical models over larger solution domains with increasingly complicated geometries. And, in more recent years, machine learning (ML) techniques, such as neural networks, are increasingly being integrated with physical models to create surrogate models capable of predicting fluid flow behavior more quickly.

However, this increased computational power alone is not sufficient to solve the complex problems arising in computational fluid dynamics (CFDs). To fully leverage the capabilities of HPC systems and ML, more sophisticated algorithms are necessary: processors must communicate efficiently in multi-processor architectures, domain decomposition algorithms must be scalable and accurate, and the large amount of data generated should be managed efficiently to prevent slowing down the computations, and surrogate models require sophisticated algorithms for their training and validation.

The primary aim of this Special Issue is to bring together original research discussing innovative efforts on computational methods in fluid dynamics, and the integration of ML and AI in analyzing and simulating fluid flows.

Dr. Jorge Balbas
Guest Editor

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• high-performance computing in CFD
• advanced numerical methods
• surrogate modeling and reduced order models
• data-driven turbulence modeling
• multiphysics and multiscale modeling
• uncertainty quantification and reliability
• biomedical fluid dynamics
• geophysical flows
• optimization and control
• advanced visualization techniques