Abstract
This book is a graduate-level review of the theory and application of methods for the analysis of large datasets generated in experiments and simulations of reactive turbulent flows. The book originates from the“Combustion-DNS Strategy & Data Analysis Workshop”organized by the editors in Sorrento, Italy, in May 2018. The book consists of a collection of chapters describing traditional and innovative methods to extract global and local features from massive datasets and to inform the development of reduced models for the simulation of turbulent combustion.A number of different methodologies are presented, including analysis based onflame topology, dissipation elements, explosive modes (CEMA), computational singular perturbation (CSP), high-order tensors, and dynamic mode decomposition (DMD). In addition, techniques based on machine learning, which are steadily gaining popularity in the fluid and combustion communities, are presented in a series of chapters on evolutionary algorithms, data assimilation, principal component analysis (PCA), and artificial (ANN) and convolutional (CNN) neural networks.This book is primarily intended for graduate-level engineering students and researchers interested in the analysis of large-scale data of reactive flows, but it can be also useful in other fields, including general fluid mechanics, applied mathematics, and physics.
Original language | English |
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Title of host publication | Data Analysis for Direct Numerical Simulations of Turbulent Combustion |
Subtitle of host publication | From Equation-Based Analysis to Machine Learning |
Publisher | Springer |
Pages | i-ix |
ISBN (Electronic) | 9783030447182 |
ISBN (Print) | 9783030447175 |
DOIs | |
Publication status | Published - 1 Jan 2020 |