Dieses aktuelle Referenzwerk behandelt numerische Optimierungsmethoden für Strömungsmaschinen und die wichtigsten industriellen Anwendungen. Grundlagen sind umfangreiche Forschung und Erfahrung der Autoren. Die logischen Zusammenhänge, um den Bereich der numerischen Strömungssimulation (CFD) zu verstehen, werden anhand der Grundlagen der Strömungsmechanik, von Strömungsmaschinen und ihrer Komponenten erläutert. Im Anschluss folgt eine Einführung in Methoden der Ein- und Mehrzieloptimierung, die automatische Optimierung, in Ersatzmodelle und Entwicklungsalgorithmen. Das Fachbuch schließt mit der ausführlichen Erklärung von Designansätzen und Anwendungen für Pumpen, Turbinen, Kompressoren und weiteren Systemen von Strömungsmaschinen. Der Nachdruck liegt hier bei Systemen für erneuerbare Energien. - Die Autoren sind führende Experten des Fachgebiets. - Ein handliches Fachbuch zu Optimierungsmethoden mittels numerischer Strömungssimulation bei Strömungsmaschinen. - Beschreibt wichtige Anwendungsbereiche in der Industrie und enthält Kapitel zu Systemen für erneuerbaren Energien. Design Optimization of Fluid Machinery ist ein wichtiger Leitfaden für Graduierte, Forscher und Ingenieure aus den Bereichen Strömungsmaschinen und zugehörige Optimierungsmethoden. Als Fachbuch mit allem Wissenswerten zu dem Thema richtet es sich an Studenten höherer Semester der Fachrichtungen Maschinenbau und verwandter Bereiche der Strömungssimulation und Luft-/Raumfahrttechnik.

Multidisciplinary Design Optimization supported by Knowledge Based Engineering supports engineers confronting this daunting and new design paradigm. It describes methodology for conducting a system design in a systematic and rigorous manner that supports human creativity to optimize the design objective(s) subject to constraints and uncertainties. The material presented builds on decades of experience in Multidisciplinary Design Optimization (MDO) methods, progress in concurrent computing, and Knowledge Based Engineering (KBE) tools. Key features: Comprehensively covers MDO and is the only book to directly link this with KBE methods Provides a pathway through basic optimization methods to MDO methods Directly links design optimization methods to the massively concurrent computing technology Emphasizes real world engineering design practice in the application of optimization methods Multidisciplinary Design Optimization supported by Knowledge Based Engineering is a one-stop-shop guide to the state-of-the-art tools in the MDO and KBE disciplines for systems design engineers and managers. Graduate or post-graduate students can use it to support their design courses, and researchers or developers of computer-aided design methods will find it useful as a wide-ranging reference.

Optimization is a mathematical tool developed in the early 1960's used to find the most efficient and feasible solutions to an engineering problem. It can be used to find ideal shapes and physical configurations, ideal structural designs, maximum energy efficiency, and many other desired goals of engineering. This book is intended for use in a first course on engineering design and optimization. Material for the text has evolved over a period of several years and is based on classroom presentations for an undergraduate core course on the principles of design. Virtually any problem for which certain parameters need to be determined to satisfy constraints can be formulated as a design optimization problem. The concepts and methods described in the text are quite general and applicable to all such formulations. Inasmuch, the range of application of the optimum design methodology is almost limitless, constrained only by the imagination and ingenuity of the user. The book describes the basic concepts and techniques with only a few simple applications. Once they are clearly understood, they can be applied to many other advanced applications that are discussed in the text. Allows engineers involved in the design process to adapt optimum design concepts in their work using the material in the text Basic concepts of optimality conditions and numerical methods are described with simple examples, making the material high teachable and learnable Classroom-tested for many years to attain optimum pedagogical effectiveness

The scientific field of design optimization has evolved tremendously both in terms of theory and of the software available to support it.

Calculus has been used in solving many scientific and engineering problems. For optimization problems, however, the differential calculus technique sometimes has a drawback when the objective function is step-wise, discontinuous, or multi-modal, or when decision variables are discrete rather than continuous. Thus, researchers have recently turned their interests into metaheuristic algorithms that have been inspired by natural phenomena such as evolution, animal behavior, or metallic annealing. This book especially focuses on a music-inspired metaheuristic algorithm, harmony search. Interestingly, there exists an analogy between music and optimization: each musical instrument corresponds to each decision variable; musical note corresponds to variable value; and harmony corresponds to solution vector. Just like musicians in Jazz improvisation play notes randomly or based on experiences in order to find fantastic harmony, variables in the harmony search algorithm have random values or previously-memorized good values in order to find optimal solution.