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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Machine Learning and Its Application to Reacting Flows - ML and Combustion (Paperback, 1st ed. 2023)
Loot Price: R1,415
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Machine Learning and Its Application to Reacting Flows - ML and Combustion (Paperback, 1st ed. 2023)
Series: Lecture Notes in Energy, 44
Expected to ship within 10 - 15 working days
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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.
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