Design Optimization of Fluid Machinery

Applying Computational Fluid Dynamics and Numerical Optimization

Inbunden, Engelska, 2019

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Beskrivning

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 fieldBrings together optimization methods using computational fluid dynamics for fluid machinery in one handy referenceFeatures industrially important applications, with key sections on renewable energy systemsDesign 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.

Produktinformation

Utgivningsdatum:2019-04-12
Mått:170 x 241 x 20 mm
Vikt:612 g
Format:Inbunden
Språk:Engelska
Antal sidor:304
Förlag:John Wiley & Sons Inc
ISBN:9781119188292

Utforska kategorier

Klassisk mekanik inom Naturvetenskap och teknik

Mer om författaren

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

Innehållsförteckning

Preface xiii1 Introduction 11.1 Introduction 11.2 Fluid Machinery: Classification and Characteristics 21.3 Analysis of Fluid Machinery 41.4 Design of Fluid Machinery 71.4.1 Design Requirements 71.4.2 Determination of Meanline Parameters 71.4.3 Meanline Analysis 81.4.4 3D Blade Design 81.4.5 Quasi 3D Through-Flow Analysis 81.4.6 Full 3D Flow Analysis 81.4.7 Design Optimization 81.5 Design Optimization of Turbomachinery 9References 102 Fluid Mechanics and Computational Fluid Dynamics 112.1 Basic Fluid Mechanics 112.1.1 Introduction 112.1.2 Classification of Fluid Flow 112.1.2.1 Based on Viscosity 122.1.2.2 Based on Compressibility 122.1.2.3 Based on Flow Speed (Mach Number) 122.1.2.4 Based on Flow Regime 132.1.2.5 Based on Number of Phases 142.1.3 One-, Two-, and Three-Dimensional Flows 142.1.3.1 One-Dimensional Flow 152.1.3.2 Two- and Three-Dimensional Flow 152.1.4 External Fluid Flow 152.1.5 The Boundary Layer 152.1.5.1 Transition from Laminar to Turbulent Flow 162.2 Computational Fluid Dynamics (CFD) 162.2.1 CFD and its Application in Turbomachinery 172.2.1.1 Advantages of Using CFD 182.2.1.2 Limitations of CFD in Turbomachinery 182.2.2 Basic Steps Involved in CFD Analysis 192.2.2.1 Problem Statement 192.2.2.2 Mathematical Model 192.2.3 Governing Equations 192.2.3.1 Mass Conservation 202.2.3.2 Momentum Conservation 202.2.3.3 Energy Conservation 212.2.4 Turbulence Modeling 212.2.4.1 What is Turbulence? 222.2.4.2 Need for Turbulence Modeling 222.2.4.3 Reynolds-Averaged Navier–Stokes Equations 222.2.4.4 Turbulence Closure Models 232.2.4.5 Large Eddy Simulation (LES) 272.2.4.6 Direct Numerical Simulation (DNS) 272.2.5 Boundary Conditions 272.2.5.1 Inlet/Outlet Boundary Conditions 282.2.5.2 Wall Boundary Conditions 282.2.5.3 Periodic/Cyclic Boundary Conditions 282.2.5.4 Symmetry Boundary Conditions 292.2.6 Moving Reference Frame (MRF) 292.2.7 Verification and Validation 302.2.8 Commercial CFD Software 302.2.9 Open Source Codes 312.2.9.1 OpenFOAM 31References 323 Optimization Methodology 353.1 Introduction 353.1.1 Engineering Optimization Definition 363.1.2 Design Space 363.1.3 Design Variables and Objectives 373.1.4 Optimization Procedure 403.1.5 Search Algorithm 403.2 Multi-Objective Optimization (MOO) 413.2.1 Weighted Sum Approach 423.2.2 Pareto-Optimal Front 423.3 Constrained, Unconstrained, and Discrete Optimization 433.3.1 Constrained Optimization 433.3.2 Unconstrained Optimization 443.3.3 Discrete Optimization 443.4 Surrogate Modeling 443.4.1 Overview 443.4.2 Optimization Procedure 443.4.3 Surrogate Modeling Approach 443.4.3.1 Response Surface Approximation (RSA) Model 453.4.3.2 Artificial Neural Network (ANN) Model 463.4.3.3 Kriging Model (KRG) Model 473.4.3.4 PRESS-Based-Averaging (PBA) Model 473.4.3.5 Simple Average (SA) Model 483.5 Error Estimation 493.5.1 General Errors When Simulating and Optimizing a Turbomachinery System 493.5.2 Error Estimation in Surrogate Modeling 523.5.3 Sensitivity Analysis 553.5.3.1 Number of Variables and Performance Improvement 553.5.3.2 Example of Sensitivity Analysis 563.6 Sampling Technique 573.6.1 Sampling 573.6.2 Sample Size 573.6.3 Design Space 573.6.4 Dimensionality Curse 573.6.5 Design of Experiments (DOE) 573.6.6 Full Factorial Design 583.6.7 Latin Hypercube Sampling (LHS) 583.7 Optimizers 593.8 Multidisciplinary Design Optimization 593.8.1 What is Multidisciplinary Optimization? 593.8.2 Gradient-Based Methods 603.8.3 Non-Gradient-Based Methods 603.8.4 Recent MDO Methods 603.9 Inverse Design 603.9.1 Inverse Design versus Direct Design 603.9.2 Direct Design Optimization with CFD 613.9.3 Inverse Design Optimization with CFD 613.10 Automated Optimization 613.10.1 Coupling Method with Adjoint CFD 633.10.2 Case Studies 633.10.2.1 CFD-Based Design Automated Design Optimization for Hydro Turbines 633.10.2.2 AO with OPAL++ 653.10.2.3 PADRAM: Parametric Design and Rapid Meshing System for Turbomachinery Optimization 653.10.2.4 Problems of AO 663.11 Conclusions 68References 684 Optimization of Industrial Fluid Machinery 714.1 Pumps 714.1.1 Centrifugal, Mixed-Flow, and Axial-Flow Pumps 714.1.1.1 Centrifugal (or Radial) Pumps 714.1.1.2 Mixed-Flow and Axial-Flow Pumps 724.1.2 Parametric Shape Models and Flow Solvers for Pump Optimization 734.1.2.1 1D Models 734.1.2.2 2D Models 824.1.2.3 3D Models 884.2 Compressors and Turbines 984.2.1 Axial, Radial, Multistage Compressors 984.2.2 Parametric Shape Models and Flow Solvers for Axial Compressor Optimization 994.2.2.1 1D Models 994.2.2.2 2D Models 1004.2.2.3 Advanced Throughflow Design Techniques (2D) 1014.2.2.4 Streamline Curvature Methods 1024.2.2.5 Advanced Cascade Design Techniques (2D-Quasi-3D) 1054.2.2.6 Geometry Definition and Parameterization 1074.2.2.7 Flow Solvers 1114.2.2.8 3D Methods 1144.2.3 Radial Compressor Optimization 1174.2.3.1 3D Models 1184.2.3.2 CFD Analysis 1214.2.3.3 Multi-Objective Optimization Problem and Results 1224.2.4 Turbines 1244.2.4.1 Axial-Flow Turbines 1264.2.4.2 Outflow and Inflow Turbines 1264.2.4.3 Axial 1D 1274.2.4.4 Case Study: Multi-Point Optimization of an Axial Turbine Stage 1314.2.4.5 Axial 2D 1354.2.4.6 CFD Models: Implementation and Validation 1354.2.4.7 Case Study: Description, Geometry Parametrization, and Meshing 1384.2.4.8 Results 1404.2.4.9 RSM 1424.2.4.10 SQP 1424.3 Fans 1464.3.1 Centrifugal, Axial-Flow, Mixed-Flow, and Cross-Flow Fans 1464.3.1.1 Axial-Flow Fans 1464.3.1.2 Centrifugal Fans 1474.3.1.3 Mixed-Flow Fans 1484.3.1.4 Cross-Flow Fans 1494.3.2 Fan Pressure, Efficiency, and Laws 1494.3.3 Aerodynamic Analysis of Fans 1514.3.3.1 Axial-Flow Fans 1514.3.3.2 Centrifugal Fans 1604.3.4 Optimization Problems and Algorithms Used for Fan Optimization 1714.3.4.1 Axial-Flow Fans 1714.3.4.2 Axial-Flow Fans 1754.3.4.3 Centrifugal Fans 1844.4 Hydraulic Turbines 1924.4.1 Introduction 1924.4.2 Cavitation in Hydraulic Turbines 1954.4.3 Analysis of Hydraulic Turbines 2004.4.3.1 Francis Turbines 2004.4.3.2 Kaplan Turbines 2074.4.3.3 Pump-Turbines 2104.4.4 Optimization of Hydraulic Turbines 2134.4.4.1 Kaplan Turbines 2134.4.4.2 Francis Turbines 2164.4.4.3 Draft Tubes and Others 2234.4.4.4 Pump-Turbines 2244.5 Others 2264.5.1 Regenerative Blowers 2264.5.2 Others 232References 2405 Optimization of Fluid Machinery for Renewable Energy Systems 2575.1 Wind Energy 2575.1.1 Optimization of Horizontal-Axis Wind Turbines 2595.1.2 Blade Element Methods 2605.1.3 Turbine Parameterization 2615.1.4 Strategies for Rotor Optimization 2645.2 Ocean Energy 2645.2.1 Temperature Gradients 2665.2.2 Tides and Tidal Currents 2665.2.3 Salinity Gradients 2665.2.4 Waves 2665.3 Energy Extraction from Ocean Waves 2665.4 Oscillating Water Column (OWC) 2675.4.1 Fixed-Structure OWC 2695.4.2 Floating-Structure OWC 2695.5 Classification of Turbines 2695.5.1 Wells Turbine 2695.5.2 Impulse Turbine 2725.6 Optimization of Air Turbines 272References 276Nomenclature 279Index 287