LEADER 05280nam 22007095 450 001 996418278703316 005 20200703144957.0 010 $a3-030-44718-9 024 7 $a10.1007/978-3-030-44718-2 035 $a(CKB)4100000011273843 035 $a(MiAaPQ)EBC6212469 035 $a(DE-He213)978-3-030-44718-2 035 $a(PPN)248397826 035 $a(EXLCZ)994100000011273843 100 $a20200528d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Analysis for Direct Numerical Simulations of Turbulent Combustion$b[electronic resource] $eFrom Equation-Based Analysis to Machine Learning /$fedited by Heinz Pitsch, Antonio Attili 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (294 pages) 311 $a3-030-44717-0 320 $aIncludes bibliographical references. 327 $aPartial A-Posteriori LES of DNS Data of Turbulent Combustion -- Application of the Optimal Estimator Analysis to Turbulent Combustion Modeling -- Reduced Order Modeling of Rocket Combustion Flows -- Dynamic Mode Decompositions: A Tool to Extract Structure Hidden in Massive Dataset -- Analysis of Combustion-Modes Through Structural and Dynamic Technique -- Analysis of the Impact of Combustion On Turbulence: Triadic Analysis, Wavelets, Structure Functions, Spectra -- Analysis of Flame Topology and Burning Rates -- Dissipation Element Analysis of Turbulent Combustion -- Higher Order Tensors for DNS Data Analysis and Compression -- Covariant Lyapunov Vector Analysis of Turbulent Reacting Flows -- CEMA Analysis Applied to DNS Data -- Combined Computational Singular Perturbation-Tangential Stretching Rate Diagnostics of Large -- Scale Simulations of Reactive Turbulent Flows: Feature Tracking, Time Scale Characterization, and Cause/Effect Identification -- Genetic Algorithms Applied to LES Model Development -- Sub-grid Scale Signal Reconstruction: From Discrete and Iterative Deconvolution Operators to Convolutional Neural Networks -- Machine Learning for Combustion Rate Shaping -- Machine Learning of Combustion LES Models from DNS -- Developing Artificial Neural Networks Based Models for Complex Turbulent Flow by Utilizing DNS Database. 330 $aThis 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. 606 $aComputer mathematics 606 $aFluid mechanics 606 $aMathematical physics 606 $aThermodynamics 606 $aComputer science 606 $aEnvironment 606 $aComputational Mathematics and Numerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M1400X 606 $aEngineering Fluid Dynamics$3https://scigraph.springernature.com/ontologies/product-market-codes/T15044 606 $aTheoretical, Mathematical and Computational Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/P19005 606 $aThermodynamics$3https://scigraph.springernature.com/ontologies/product-market-codes/P21050 606 $aComputer Science, general$3https://scigraph.springernature.com/ontologies/product-market-codes/I00001 606 $aEnvironment, general$3https://scigraph.springernature.com/ontologies/product-market-codes/U00009 615 0$aComputer mathematics. 615 0$aFluid mechanics. 615 0$aMathematical physics. 615 0$aThermodynamics. 615 0$aComputer science. 615 0$aEnvironment. 615 14$aComputational Mathematics and Numerical Analysis. 615 24$aEngineering Fluid Dynamics. 615 24$aTheoretical, Mathematical and Computational Physics. 615 24$aThermodynamics. 615 24$aComputer Science, general. 615 24$aEnvironment, general. 676 $a532.0527015118 702 $aPitsch$b Heinz$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAttili$b Antonio$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418278703316 996 $aData Analysis for Direct Numerical Simulations of Turbulent Combustion$91936234 997 $aUNISA