Turbulence Modeling: Roadblocks, and the Potential for CFD-Driven Machine Learning
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 335
Special Issue Editor
Interests: CFD; turbulence; environment; fluid mechanics; fuel cells; wind engineering; heat exchangers; environmental engineering; industrial safety; risk assessment; environmental and energy processes
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Turbulence, with all its manifestations in industrial processes, e.g., combustion and energy production, in environmental pollution, in climate change, and in biological flows, is a vital field of science and engineering because of its multiple applications in society. Most of the modeling to date is based on the following principle:
“All turbulence modelers must follow the Kolmogorov pattern, viz. by solving equations for statistical averages such as k, ε, vorticity fluctuations, Reynolds stresses, etc.”
This is an untruth, although most modelers do believe it and modish variants such as Large Eddy Simulation (of which there are many) may create the illusion of novelty. Such models perform badly when body forces act differently on, say, hotter and colder elements in the turbulent mixture, as, e.g., in forest fires. Fluid population theory is probably the best way forward. The purpose of this book is to investigate the above research matters but most importantly to illustrate the application of methods of Artificial Intelligence (for example, Artificial Neural Networks) to turbulence big data.
Prof. Dr. N.C. Markatos
Guest Editor
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Keywords
- turbulence
- modeling
- artificial intelligence in turbulence big data
- artificial neural networks in turbulence big data
- computational fluid dynamics (CFD)
- fluid dynamics
- wind engineering
- heat exchangers
- environmental engineering
- environmental and energy processes
- machine learning
