This edition first published 2019
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Library of Congress Cataloging‐in‐Publication Data
Names: Kim, Kwang‐Yong, 1956‐ author.
Title: Design optimization of fluid machinery : applying computational fluid
dynamics and numerical optimization / Kwang‐Yong Kim, Professor, Inha
University, Incheon, Abdus Samad, Associate Professor, Indian Institute of
Technology Madras, Chennai, India, Ernesto Benini, Professor, University
of Padova, Italy.
Description: Hoboken, NJ : Wiley, 2019. | Includes bibliographical references
and index. |
Identifiers: LCCN 2018044844 (print) | LCCN 2018045697 (ebook) | ISBN
9781119188322 (Adobe PDF) | ISBN 9781119188308 (ePub) | ISBN 9781119188292
(hardcover)
Subjects: LCSH: Computational fluid dynamics.
Classification: LCC TA357.5.D37 (ebook) | LCC TA357.5.D37 K56 2019 (print) |
DDC 620.1/064–dc23
LC record available at https://lccn.loc.gov/2018044844
Cover Design by Wiley
Cover Image: Courtesy of Kwang‐Yong Kim
Chihee, Minji, and Soonwook
– Kim
My wife Husnahara, son Sohail and daughter Arshi
– Samad
My beloved family
– Benini
This book introduces methods for design optimization and their applications to design of fluid machinery, such as pumps, compressors, turbines, fans, and so on. Although flow analysis in a complex flow passage is difficult and takes a lot of computing time unlike structural analysis, design optimization based on three‐dimensional flow analysis has become popular even in the fluid machinery area in the last couple of decades with recent developments in computing power. Design technology of fluid machinery has developed with the development of fluid mechanics over a long time. Thus, before computational fluid dynamics (CFD) became practical, there were various design methods using empirical formulas and approximate analysis. Now, fluid machinery design has been further improved with the application of design optimization based on CFD as an additional design procedure.
Inverse design methods, where the optimum geometry of a fluid machine is deduced from prescribed objectives, require low computational cost but it is difficult to specify the target flow field. Thus, design optimization, where optimum objectives are found by changing the design variables, has recently become popular in fluid machinery design. This book is concerned with the design optimization method. The design optimization methods can be classified into gradient‐based and statistical methods. Because the computing time depends on the number of design variables, gradient‐based methods are not suitable for design problems that have a large number of design variables, except for the adjoint method. As a statistical approach, surrogate‐based optimization methods are widely used in the design optimization of turbomachinery due to their easy implementation and affordable computing time. Surrogate modeling of objective function(s) largely reduces the number of objective function evaluations required for optimization, and thus is suitable for fluid machinery design where CFD analysis takes a long computing time. This book introduces general methods of surrogate‐based optimization and their applications to fluid machinery.
Design objectives, such as efficiency, pressure ratio, weight, and so on, and geometrical/operational design variables are set depending on the characteristics of the fluid machinery to be optimized. From the huge number of examples of design optimizations for different kinds of fluid machinery presented in this book, fluid machinery designers are expected to have some idea as to how the optimization methods, design objective(s), and variables are selected in order to achieve their design goals.
This book aims to provide engineers and graduate students in universities with a general understanding of surrogate‐based design optimization of fluid machinery using two‐ or three‐dimensional numerical analysis of fluid flow, and also to introduce applications of various design optimization techniques to different types of fluid machinery.
The authors are grateful to the following graduate students for their assistance in completing this book: Tapas, Karthikeyan, Ezhil, Madhan, Hamid, Murshid, and Paresh at IIT Madras, and Hyeon‐Seok Shim, Sang‐Bum Ma, Jun‐Hee Kim, and Han‐Sol Jeong at Inha University.
Kwang‐Yong Kim
Abdus Samad
Ernesto Benini