Kwang-Yong Kim, Abdus Samad, Ernesto Benini

Design Optimization of Fluid Machinery (eBook, PDF)

Applying Computational Fluid Dynamics and Numerical Optimization

• Format: PDF

Produktbeschreibung

Design Optimization of Fluid Machinery: Applying Computational Fluid Dynamics and Numerical Optimization Drawing on extensive research and experience, this timely reference brings together numerical optimization methods for fluid machinery and its key industrial applications. It logically lays out the context required to understand computational fluid dynamics by introducing the basics of fluid mechanics, fluid machines and their components. Readers are then introduced to single and multi-objective optimization methods, automated optimization, surrogate models, and evolutionary algorithms. Finally, design approaches and applications in the areas of pumps, turbines, compressors, and other fluid machinery systems are clearly explained, with special emphasis on renewable energy systems. * Written by an international team of leading experts in the field * Brings together optimization methods using computational fluid dynamics for fluid machinery in one handy reference * Features industrially important applications, with key sections on renewable energy systems Design Optimization of Fluid Machinery is an essential guide for graduate students, researchers, engineers working in fluid machinery and its optimization methods. It is a comprehensive reference text for advanced students in mechanical engineering and related fields of fluid dynamics and aerospace engineering.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 312
• Erscheinungstermin: 11. Januar 2019
• Englisch
• ISBN-13: 9781119188322
• Artikelnr.: 55005745

Autorenporträt

Kwang-Yong Kim, Inha University, Republic of Korea Abdus Samad, Indian Institute of Technology Madras, India Ernesto Benini, University of Padova, Italy

Inhaltsangabe

Preface xiii

1 Introduction 1

1.1 Introduction 1

1.2 Fluid Machinery: Classification and Characteristics 2

1.3 Analysis of Fluid Machinery 4

1.4 Design of Fluid Machinery 7

1.4.1 Design Requirements 7

1.4.2 Determination of Meanline Parameters 7

1.4.3 Meanline Analysis 8

1.4.4 3D Blade Design 8

1.4.5 Quasi 3D Through-Flow Analysis 8

1.4.6 Full 3D Flow Analysis 8

1.4.7 Design Optimization 8

1.5 Design Optimization of Turbomachinery 9

References 10

2 Fluid Mechanics and Computational Fluid Dynamics 11

2.1 Basic Fluid Mechanics 11

2.1.1 Introduction 11

2.1.2 Classification of Fluid Flow 11

2.1.2.1 Based on Viscosity 12

2.1.2.2 Based on Compressibility 12

2.1.2.3 Based on Flow Speed (Mach Number) 12

2.1.2.4 Based on Flow Regime 13

2.1.2.5 Based on Number of Phases 14

2.1.3 One-, Two-, and Three-Dimensional Flows 14

2.1.3.1 One-Dimensional Flow 15

2.1.3.2 Two- and Three-Dimensional Flow 15

2.1.4 External Fluid Flow 15

2.1.5 The Boundary Layer 15

2.1.5.1 Transition from Laminar to Turbulent Flow 16

2.2 Computational Fluid Dynamics (CFD) 16

2.2.1 CFD and its Application in Turbomachinery 17

2.2.1.1 Advantages of Using CFD 18

2.2.1.2 Limitations of CFD in Turbomachinery 18

2.2.2 Basic Steps Involved in CFD Analysis 19

2.2.2.1 Problem Statement 19

2.2.2.2 Mathematical Model 19

2.2.3 Governing Equations 19

2.2.3.1 Mass Conservation 20

2.2.3.2 Momentum Conservation 20

2.2.3.3 Energy Conservation 21

2.2.4 Turbulence Modeling 21

2.2.4.1 What is Turbulence? 22

2.2.4.2 Need for Turbulence Modeling 22

2.2.4.3 Reynolds-Averaged Navier-Stokes Equations 22

2.2.4.4 Turbulence Closure Models 23

2.2.4.5 Large Eddy Simulation (LES) 27

2.2.4.6 Direct Numerical Simulation (DNS) 27

2.2.5 Boundary Conditions 27

2.2.5.1 Inlet/Outlet Boundary Conditions 28

2.2.5.2 Wall Boundary Conditions 28

2.2.5.3 Periodic/Cyclic Boundary Conditions 28

2.2.5.4 Symmetry Boundary Conditions 29

2.2.6 Moving Reference Frame (MRF) 29

2.2.7 Verification and Validation 30

2.2.8 Commercial CFD Software 30

2.2.9 Open Source Codes 31

2.2.9.1 OpenFOAM 31

References 32

3 Optimization Methodology 35

3.1 Introduction 35

3.1.1 Engineering Optimization Definition 36

3.1.2 Design Space 36

3.1.3 Design Variables and Objectives 37

3.1.4 Optimization Procedure 40

3.1.5 Search Algorithm 40

3.2 Multi-Objective Optimization (MOO) 41

3.2.1 Weighted Sum Approach 42

3.2.2 Pareto-Optimal Front 42

3.3 Constrained, Unconstrained, and Discrete Optimization 43

3.3.1 Constrained Optimization 43

3.3.2 Unconstrained Optimization 44

3.3.3 Discrete Optimization 44

3.4 Surrogate Modeling 44

3.4.1 Overview 44

3.4.2 Optimization Procedure 44

3.4.3 Surrogate Modeling Approach 44

3.4.3.1 Response Surface Approximation (RSA) Model 45

3.4.3.2 Artificial Neural Network (ANN) Model 46

3.4.3.3 Kriging Model (KRG) Model 47

3.4.3.4 PRESS-Based-Averaging (PBA) Model 47

3.4.3.5 Simple Average (SA) Model 48

3.5 Error Estimation 49

3.5.1 General Errors When Simul