Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 304 pp. Englisch.

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T�tulo: Data Analysis for Direct Numerical Simulations of Turbulent Combustion
Editor: Springer International Publishing, Springer International Publishing Mai 2020
Fecha de publicaci�n: 2020
Idioma: Ingl�s
ISBN 10: 3030447170
ISBN 13: 9783030447175
Encuadernaci�n: Buch
Estado: Neu
Dimensiones: 241�mil�metros de ancho por 160 de alto por 22 de largo
Peso: 623 gramos

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Sinopsis

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.

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Professor Heinz Pitsch received his PhD from the RWTH Aachen University in 1998, where he is now a Full Professor and director of the Institute for Combustion Technology. He has received numerous honours and awards, including an ERC Advanced Grant, and Fellow Awards of the American Physical Society and the International Combustion Institute. He has served on the board of directors of the International Combustion Institute since 2014 and has been the chair of the German section of the Institute since 2017. Professor Pitsch has over 200 ISI-listed, peer-reviewed journal publications to his credit.

Dr. Antonio Attili received his PhD from Sapienza University of Rome in 2009 and he is now Lecturer in Computational Reactive Flows in the School of Engineering at the University of Edinburgh, United Kingdom. Before, he was a Research Scientist at the Institute for Combustion Technology, RWTH Aachen University, and at KAUST, Saudi Arabia. He co-chaired and organized several workshops, including the Combustion-DNS Strategy & Data Analysis Workshop. Dr Attili has received several fellowship, including a European Space Agency and AVIO Groups Graduate Research Fellowship in 2007. He has authored and co-authored over 50 research papers published in journals and conference proceedings.

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