Machine Learning and Its Application to Reacting Flows

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

Machine Learning and Its Application to Reacting Flows

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

Turbulence models with adaptive meshing for industrial CFD

Computational fluid dynamics (CFD) and affordable computing power have advanced considerably in recent years, bringing full 3D simulation of complex high Reynolds number flows within reach of industry. However, providing accurate and trustworthy results in diverse flows with constraints on computational resources is still a considerable challenge. Owing to the complexity of commonly-encountered turbulent flows, robust turbulence models are required which do not have to be manually tuned to specific flow conditions to ensure their accuracy. In this regard, a highly effective approach is unstructured mesh adaptivity which automatically refines or coarsens the mesh locally in order to achieve a desired accuracy with minimum computational effort. However, the use of such adaptive meshes with turbulence models raises questions about the origins and interactions of various errors. This thesis describes the development, verification and validation of robust turbulence models suited to high Reynolds number single-phase turbulent flow using unstructured adaptive meshes. The main focus of this thesis is a new tensorial dynamic large eddy simulation (LES) model. The novel combination of the dynamic LES method with a tensorial definition of filter width is ideal for capturing the anisotropy and inhomogeneity of turbulence. This model is designed for use with unstructured mesh adaptivity, which enables the simulation of turbulent flow with high efficiency in terms of mesh resolution. Furthermore, the model is robust since both the resolution and the sub-filter-scale (SFS) stresses adapt to local flow conditions so that little a priori knowledge of the flow is required. Verification tests of the filtering method and validation of the new LES model in the 3D backward-facing step are presented. To provide context for the research, the contribution made by CFD simulations to the analysis of nuclear reactor safety and performance is discussed. The practicalities of performing simulations on high performance computing (HPC) facilities are also discussed. Background theory necessary to understand the research is presented, including a mathematical description of turbulent flow and the classes of CFD methods used to approximate it. A review of turbulence models, discretisation methods, boundary conditions and adaptive meshing methods is included. The construction and testing of a Reynolds-averaged Navier-Stokes (RANS) k - ε turbulence model and a scale-adaptive very large eddy simulation (VLES) model in the open-source CFD code Fluidity are also described. The development of a law-of-the-wall boundary condition for turbulent flow in variational (weak) form is also presented. Verification tests are performed to establish that the k - ε model has been coded correctly. Validation of the RANS model and the wall function using fixed and adaptive meshes is carried out in the 2D backward-facing step. Finally, results of simulations of a vortex diode device using various turbulence models are presented and compared to results from the commercial CFD code CFX and experimental results. This study was carried out during the industrial component of the Engineering Doctorate, which was intended to further the development and understanding of CFD at Rolls-Royce Nuclear. The device presents a challenging test case for CFD but some useful conclusions are reached about how to model it. The thesis concludes with a summary of findings and proposals for further research

Bibliography of Lewis Research Center technical publications announced in 1989

This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1989. All the publications were announced in the 1989 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses

Heat Transfer

Over the past few decades there has been a prolific increase in research and development in area of heat transfer, heat exchangers and their associated technologies. This book is a collection of current research in the above mentioned areas and describes modelling, numerical methods, simulation and information technology with modern ideas and methods to analyse and enhance heat transfer for single and multiphase systems. The topics considered include various basic concepts of heat transfer, the fundamental modes of heat transfer (namely conduction, convection and radiation), thermophysical properties, computational methodologies, control, stabilization and optimization problems, condensation, boiling and freezing, with many real-world problems and important modern applications. The book is divided in four sections : "Inverse, Stabilization and Optimization Problems", "Numerical Methods and Calculations", "Heat Transfer in Mini/Micro Systems", "Energy Transfer and Solid Materials", and each section discusses various issues, methods and applications in accordance with the subjects. The combination of fundamental approach with many important practical applications of current interest will make this book of interest to researchers, scientists, engineers and graduate students in many disciplines, who make use of mathematical modelling, inverse problems, implementation of recently developed numerical methods in this multidisciplinary field as well as to experimental and theoretical researchers in the field of heat and mass transfer