Data Analysis for Direct Numerical Simulations of Turbulent Combustion - From Equation-Based Analysis to Machine Learning (Paperback, 1st ed. 2020)

This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. It describes the development of models that can be used to analyse large eddy simulations, and highlights both the most common techniques and newly emerging ones. The chapters, written by internationally respected experts, invite readers to consider DNS of turbulence and combustion from a formal, data-driven standpoint, rather than one led by experience and intuition. This perspective allows readers to recognise the shortcomings of existing models, with the ultimate goal of quantifying and reducing model-based uncertainty. In addition, recent advances in machine learning and statistical inferences offer new insights on the interpretation of DNS data. The book will especially benefit graduate-level students and researchers in mechanical and aerospace engineering, e.g. those with an interest in general fluid mechanics, applied mathematics, and the environmental and atmospheric sciences.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	May 2021
First published:	2020
Editors:	Heinz Pitsch • Antonio Attili
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	292
Edition:	1st ed. 2020
ISBN-13:	978-3-03-044720-5
Categories:	Books > Science & Mathematics > Mathematics > Numerical analysis Books > Earth & environment > The environment > General Books > Computing & IT > General theory of computing > General Books > Computing & IT > Applications of computing > General Books > Science & Mathematics > Mathematics > Applied mathematics > General Books > Science & Mathematics > Physics > Thermodynamics & statistical physics > Thermodynamics Books > Professional & Technical > Mechanical engineering & materials > Materials science > Mechanics of fluids > General Promotions
LSN:	3-03-044720-0
Barcode:	9783030447205

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