Applied Data Analysis and Modeling for Energy Engineers and Scientists
Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate co
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T. Agami Reddy
Applied Data Analysis and Modeling for Energy Engineers and Scientists
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T. Agami Reddy The Design School and School of Sustainability Arizona State University PO Box 871605, Tempe, AZ 85287-1605 USA [email protected]
ISBN 978-1-4419-9612-1 e-ISBN 978-1-4419-9613-8 DOI 10.1007/978-1-4419-9613-8 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011931864 © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Thou must bear the sorrow that thou claimst to heal; The day-bringer must walk in darkest night. He who would save the world must share its pain. If he knows not grief, how shall he find grief’s cure? Savitri—Sri Aurobindo
In loving memory of my father and grandmother
Preface
A Third Need in Engineering Education At its inception, engineering education was predominantly process oriented, while engineering practice tended to be predominantly system oriented1. While it was invaluable to have a strong fundamental knowledge of the processes, educators realized the need to have courses where this knowledge translated into an ability to design systems; therefore, most universities, starting in the 1970s, mandated that seniors take at least one design/capstone course. However, a third aspect is acquiring increasing importance: the need to analyze, interpret and model data. Such a skill set is proving to be crucial in all scientific activities, none so as much as in engineering and the physical sciences. How can data collected from a piece of equipment be used to assess the claims of the manufacturers? How can performance data either from a natural system or a man-made system be respectively used to maintain it more sustainably or to operate it more efficiently? Such needs are driven by the fact that system performance data is easily available in our present-day digital age where sensor and data acquisition systems have become reliable, cheap and part of the system design itself. This applies both to experimental data (gathered from experiments performed according to some predetermined strategy) and to observational data (where one can neither intrude on system functioning nor have the ability to control the experiment, such as in astronomy). Techniques f
