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Titlebook: Data Analysis for Direct Numerical Simulations of Turbulent Combustion; From Equation-Based Heinz Pitsch,Antonio Attili Book 2020 Springer

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書目名稱Data Analysis for Direct Numerical Simulations of Turbulent Combustion
副標(biāo)題From Equation-Based
編輯Heinz Pitsch,Antonio Attili
視頻videohttp://file.papertrans.cn/263/262658/262658.mp4
概述Gathers contributions from authoritative figures in model development, Big Data, and DNS.Broadens readers’ understanding of DNS of turbulence and combustion.Identifies the main approaches used to anal
圖書封面Titlebook: Data Analysis for Direct Numerical Simulations of Turbulent Combustion; From Equation-Based  Heinz Pitsch,Antonio Attili Book 2020 Springer
描述.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..
出版日期Book 2020
關(guān)鍵詞Turbulent Combustion; Direct Numerical Simulation; Big Data Analysis; Turbulent Reactive Flows; Combusti
版次1
doihttps://doi.org/10.1007/978-3-030-44718-2
isbn_softcover978-3-030-44720-5
isbn_ebook978-3-030-44718-2
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Higher Order Tensors for DNS Data Analysis and Compression,bulent combustion DNS data, being inherently multiscale and multivariate, pose many challenges and higher order tensors are a natural abstraction to organise, probe and analyse them. The chapter gives a high-level overview of prominent tensor decomposition methods, their interpretation, algorithmic
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From Discrete and Iterative Deconvolution Operators to Machine Learning for Premixed Turbulent Combng strategies appear which are based on a direct treatment of the now well resolved, but still not fully resolved scalar signals. Along this line, deconvolution or inverse filtering, either based on discrete or iterative operators, is first discussed. Recent results obtained from a direct numerical
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