Introduction

Sal Rodriguez
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Sal Rodriguez: Sandia National Laboratories

Chapter Chapter 1 in Applied Computational Fluid Dynamics and Turbulence Modeling, 2019, pp 1-9 from Springer

Abstract: Abstract The motivation for learning and mastering CFD and turbulence modeling is based on their ever-increasing potential to solve problems, especially those that cannot be resolved experimentally due to physical limitations, economic cost, safety, time constraints, or environmental regulatory procedures. Current and future employment trends, computational capacity growth, and experimentation costs favor CFD. The integration of CFD, theory, advanced manufacturing, and experiments will lead towards even more economical and faster development of prototypes and systems for more streamlined concept-to-market development. As Moore’s law eventually ceases to apply, quantum and nano-computing devices, as well as quantum algorithms, will continue the computational growth trend for many decades to come, if not centuries. The near future holds the potential for simulations that are thousands of times faster than is currently feasible. This fascinating subject is elaborated further in Sect. 5.2.

Keywords: CFD vs. experiments; Future technologies; Employment trends; Quantum algorithms; computing (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/978-3-030-28691-0_1

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