This open access book introduces and explains machine learning (ML) algorithms and techniques
developed for statistical inferences on a complex process or system and their applications to
simulations of chemically reacting turbulent flows. These two fields ML and turbulent
combustion have large body of work and knowledge on their own and this book brings them
together and explain the complexities and challenges involved in applying ML techniques to
simulate and study reacting flows. This is important as to the world's total primary energy
supply (TPES) since more than 90% of this supply is through combustion technologies and the
non-negligible effects of combustion on environment. Although alternative technologies based on
renewable energies are coming up their shares for the TPES is are less than 5% currently and
one needs a complete paradigm shift to replace combustion sources. Whether this is practical or
not is entirely a different question and an answer to this question depends on the respondent.
However a pragmatic analysis suggests that the combustion share to TPES is likely to be more
than 70% even by 2070. Hence it will be prudent to take advantage of ML techniques to improve
combustion sciences and technologies so that efficient and greener combustion systems that are
friendlier to the environment can be designed. The book covers the current state of the art in
these two topics and outlines the challenges involved merits and drawbacks of using ML for
turbulent combustion simulations including avenues which can be explored to overcome the
challenges. The required mathematical equations and backgrounds are discussed with ample
references for readers to find further detail if they wish. This book is unique since there is
not any book with similar coverage of topics ranging from big data analysis and machine
learning algorithm to their applications for combustion science and system design for energy
generation.
