© 2020

Data Analysis for Direct Numerical Simulations of Turbulent Combustion

From Equation-Based Analysis to Machine Learning

  • Heinz Pitsch
  • Antonio Attili


  • 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


Table of contents

  1. Front Matter
    Pages i-ix
  2. Shrey Trivedi, Girish V. Nivarti, R. Stewart Cant
    Pages 1-17
  3. Dominik Denker, Antonio Attili, Heinz Pitsch
    Pages 19-41
  4. Chun Sang Yoo, Tianfeng Lu, Jacqueline H. Chen
    Pages 89-108
  5. Hemanth Kolla, Konduri Aditya, Jacqueline H. Chen
    Pages 109-134
  6. Nicholas Arnold-Medabalimi, Cheng Huang, Karthik Duraisamy
    Pages 135-155
  7. Jan Schilliger, Nils Keller, Severin Hänggi, Thivaharan Albin, Christopher Onder
    Pages 197-213
  8. Giuseppe D’Alessio, Antonio Attili, Alberto Cuoci, Heinz Pitsch, Alessandro Parente
    Pages 233-251
  9. M. Schöpplein, J. Weatheritt, M. Talei, M. Klein, R. D. Sandberg
    Pages 253-271
  10. Shashank Yellapantula, Marc T. Henry de Frahan, Ryan King, Marc Day, Ray Grout
    Pages 273-292

About this book


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.


Turbulent Combustion Direct Numerical Simulation Big Data Analysis Turbulent Reactive Flows Combustion Modeling Large Eddy Simulation Turbulence Modeling

Editors and affiliations

  • Heinz Pitsch
    • 1
  • Antonio Attili
    • 2
  1. 1.Institute for Combustion TechnologyRWTH Aachen UniversityAachenGermany
  2. 2.Institute for Combustion TechnologyRWTH Aachen UniversityAachenGermany

About the editors

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.

Bibliographic information

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