Machine Learning And Its Application To Reacting Flows
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Machine Learning and Its Application to Reacting Flows
Author: Nedunchezhian Swaminathan
language: en
Publisher: Springer Nature
Release Date: 2023-01-01
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.
Computational Fluid Dynamics - Analysis, Simulations, and Applications
Author: Mahboub Baccouch
language: en
Publisher: BoD – Books on Demand
Release Date: 2025-02-26
This book comprehensively explores numerical methods and their applications across diverse fields, strongly focusing on computational fluid dynamics (CFD) and advanced modeling techniques. Starting with numerical approaches for solving the viscid and inviscid Burgers equations establishes a foundation for understanding complex fluid dynamics. Subsequent chapters delve into cutting-edge topics, including Large Eddy Simulations (LES) for turbulence modeling, heat transfer analysis, and the influence of working fluids on vortex dynamics in industrial pipelines. The book also explores emerging areas such as nanoscale simulations, plasmonic excitations, and biomedical applications like hemodynamics in atrial fibrillation. Real-world case studies and practical examples demonstrate the versatility of CFD in addressing challenges in engineering, biology, and energy systems. This book combines theoretical rigour with practical insights and is designed for advanced undergraduate and graduate students, researchers, and professionals. It bridges the gap between numerical theory and real-world applications, providing readers with the tools to solve complex problems across various scientific and engineering domains. Whether you’re looking to deepen your understanding of numerical methods, enhance your CFD expertise, or explore innovative applications, this book is a valuable resource for gaining actionable insights and fostering innovation in computational modeling.
Innovative Machine Learning Applications in the Aerospace Industry
Author: Ponnada, Venkata Tulasiramu
language: en
Publisher: IGI Global
Release Date: 2025-06-17
The aerospace industry evolves with the integration of machine learning (ML) applications. From optimizing flight operations and predictive maintenance to advancing autonomous navigation and air traffic management, ML enables efficiency, safety, and performance. As aerospace systems grow more complex, ML offers the ability to analyze data in real-time, uncover hidden patterns, and support intelligent decision-making. This emerging collaboration between aerospace engineering and AI reshapes traditional practices while opening new frontiers in exploration and innovation. Innovative Machine Learning Applications in the Aerospace Industry explores the potential of machine learning applications, examining its impact on various sectors. It investigates the diverse realms of machine learning applications and their profound implications for the future. This book covers topics such as drone navigation, aerial images, and computer vision, and is a useful resource for business owners, engineers, academicians, researchers, and computer scientists.