This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.

This book presents the selected peer-reviewed proceedings of the International Conference on Thermal Engineering and Management Advances (ICTEMA 2020). The contents discuss latest research in the areas of thermal engineering, manufacturing engineering, and production management. Some of the topics covered include multiphase fluid flow, turbulent flows, reactive flows, atmospheric flows, combustion and propulsion, computational methods for thermo-fluid arena, micro and nanofluidics, renewable energy and environment sustainability, non-conventional energy resources, energy principles and management, machine dynamics and manufacturing, casting and forming, green manufacturing, production planning and management, quality control and management, and traditional and non-traditional manufacturing. The contents of this book will be useful for students, researchers as well as professionals working in the area of mechanical engineering and allied fields.