Data Analysis for Direct Numerical Simulations of Turbulent Combustion: From Equation-Based Analysis to Machine Learning
Editat de Heinz Pitsch, Antonio Attilien Limba Engleză Paperback – 29 mai 2021
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 1193.61 lei 43-57 zile | |
| Springer International Publishing – 29 mai 2021 | 1193.61 lei 43-57 zile | |
| Hardback (1) | 1199.64 lei 43-57 zile | |
| Springer International Publishing – 29 mai 2020 | 1199.64 lei 43-57 zile |
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Specificații
ISBN-13: 9783030447205
ISBN-10: 3030447200
Ilustrații: IX, 292 p. 167 illus., 136 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.43 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030447200
Ilustrații: IX, 292 p. 167 illus., 136 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.43 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
Partial 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
Notă biografică
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.
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.
Textul de pe ultima copertă
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.
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.
Caracteristici
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 analyse large data sets produced by DNS