Design Optimization of Fluid Machinery: Applying Computational Fluid Dynamics and Numerical Optimization (Hardcover)

1 INTRODUCTION 1.1 Introduction 1.2 Fluid Machinery: Classification and Characteristics 1.3 Analysis of Fluid Machinery 1.4 Design of Fluid Machinery 1.5 Design Optimization of Turbomachinery 2 FLUID MECHANICS AND COMPUTATIONAL FLUID DYNAMICS 2.1 Basic Fluid Mechanics 2.1.1 Introduction 2.1.2 Classification of Fluid Flow 2.1.3 1, 2 And 3 Dimensional Flows 2.1.4 External Fluid Flow 2.1.5 The Boundary Layer 2.2 Computational Fluid Dynamics (CFD) 2.2.1 CFD and Its Application in Turbomachinery 2.2.1.1 Advantages of Using CFD 2.2.1.2 Limitations of CFD in Turbomachinery 2.2.2 Basics Steps Involved in CFD Analysis 2.2.3 Governing Equations 2.2.3.1 Mass Conservation 2.2.3.2 Momentum Conservation 2.2.3.3 Energy Conservation 2.2.4 Turbulence Modelling 2.2.4.1 Reynolds-Averaged Navier-Stokes Equations 2.2.4.2 Large Eddy Simulation (LES) 2.2.4.3 Direct Numerical Simulation (DNS) 2.2.5 Boundary Conditions 2.2.5.1 Inlet/Outlet Boundary Conditions 2.2.5.2 Wall Boundary Conditions 2.2.5.3 Periodic/Cyclic Boundary Conditions 2.2.5.4 Symmetry Boundary Conditions 2.2.6 Moving Reference Frame 2.2.7 Verification and Validation 2.2.8 Commercial CFD Softwares 2.2.9 Open Source Codes 3 OPTIMIZATION METHODOLOGY 3.1 Introduction 3.1.1 Engineering Optimization Definition 3.1.2 Design Space 3.1.3 Design Variables and Objectives 3.1.4 Optimization Procedure 3.1.5 Search Algorithm 3.2 Multi-Objective Optimization (MOO) 3.2.1 Weighted Sum Approach 3.2.2 Pareto Optimal Front 3.3 Constrained, Unconstrained and Discrete Optimization 3.3.1 Constrained Optimization 3.3.2 Unconstrained Optimization 3.3.3 Discrete Optimization 3.4 Surrogate Modelling 3.4.1 Overview 3.4.2 Optimization Procedure 3.4.3 Surrogate Modelling Approach 3.4.3.1 Response Surface Approximation (RSA) Model 3.4.3.2 Artificial Neural Network (ANN) Model 3.4.3.3 Kriging Model 3.4.3.4 PBA Model 3.4.3.5 Simple Average Model 3.5 Error Estimation 3.5.1 General Error When Simulating and Optimizing a Turbomachinery System 3.5.2 Error Estimation in Surrogate Modeling 3.5.3 Sensitivity Analysis 3.5.3.1 Number of variables and performance improvement 3.5.3.2 Example of Sensitivity Analysis 3.6 Sampling Technique 3.6.1 Sampling 3.6.2 Sample Size 3.6.3 Design Space 3.6.4 Dimensionality Curse 3.6.5 Design of Experiments 3.6.6 Full Factorial Design 3.6.7 Latin Hypercube Sampling 3.7 Optimizers 3.8 Multidisciplinary Design Optimization 3.8.1 What is Multidisciplinary Optimization? 3.8.2 Gradient-Based Methods 3.8.3 Non-Gradient-Based Methods 3.8.4 Recent MDO Methods 3.9 Inverse Design 3.9.1 Inverse Design versus Direct Design 3.9.2 Direct Design Optimization with CFD 3.9.3 Inverse Design Optimization with CFD 3.10 Automated Optimization 3.10.1 Case Studies 3.11 Conclusions 4 OPTIMIZATION OF INDUSTRIAL FLUID MACHINERY 4.1 Pumps 4.1.1 Centrifugal, Mixed-Flow and Axial-Flow Pumps 4.1.2 Parametric Shape Models and Flow Solvers for Pump Optimization 4.1.2.1 1-D models 4.1.2.2 Considerations about 1-D Models 4.1.2.3 2-D models 4.2 Compressors and Turbines 4.2.1 Axial, Radial, Multistage Compressors 4.2.2 Parametric Shape Models and Flow Solvers for Axial Compressor Optimization 4.2.3 Radial Compressor Optimization 4.2.4 Turbines 4.3 Fans 4.3.1 Centrifugal, Axial-Flow, Mixed-Flow and Cross-Flow Fans 4.3.2 Fan Pressure, Efficiency and Laws 4.3.3 Aerodynamic Analysis of Fans 4.3.4 Optimization problems and algorithms used for fan optimization 4.4 Hydraulic Turbines 4.4.1 Introduction 4.4.2 Cavitation in Hydraulic Turbines 4.4.3 Analysis of Hydraulic Turbines 4.4.4 Optimization of Hydraulic Turbine 4.5 Others 4.5.1 Regenerative Blowers 4.5.2 Others 5 OPTIMIZATION OF FLUID MACHINERY FOR RENEWAB