Specifications
| book-author | Kwang-Yong Kim, Abdus Samad, Emesto Benini |
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| publisher | Wiley; 1st edition |
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| file-type | PDF |
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| pages | 304 pages |
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| language | English |
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| asin | B07MTTP5LQ |
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| isbn10 | 1119188296 |
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| isbn13 | 9781119188292 |
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Book Description
Design Optimization of Fluid Machinery: Applying Computational Fluid Dynamics and Numerical Optimization (PDF)
Drawing on extensive experience and research; this timely ebook 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. Students are then introduced to single and multi-objective optimization methods; surrogate models; automated optimization; and evolutionary algorithms. Finally; design approaches and applications in the areas of pumps; compressors; turbines; 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
- Features industrially important applications; with key sections on renewable energy systems
- Brings together optimization methods using computational fluid dynamics for fluid machinery in one handy reference
The ebook Design Optimization of Fluid Machinery (PDF) is an essential guide for researchers; graduate students; 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.
NOTE: No access codes included with this PDF eBook
Table of contents
Table of contents :
Content: Cover
Title Page
Copyright
Contents
Preface
Chapter 1 Introduction
1.1 Introduction
1.2 Fluid Machinery: Classification and Characteristics
1.3 Analysis of Fluid Machinery
1.4 Design of Fluid Machinery
1.4.1 Design Requirements
1.4.2 Determination of Meanline Parameters
1.4.3 Meanline Analysis
1.4.4 3D Blade Design
1.4.5 Quasi 3D Through‐Flow Analysis
1.4.6 Full 3D Flow Analysis
1.4.7 Design Optimization
1.5 Design Optimization of Turbomachinery
References
Chapter 2 Fluid Mechanics and Computational Fluid Dynamics
2.1 Basic Fluid Mechanics
2.1.1 Introduction 2.1.2 Classification of Fluid Flow2.1.2.1 Based on Viscosity
2.1.2.2 Based on Compressibility
2.1.2.3 Based on Flow Speed (Mach Number)
2.1.2.4 Based on Flow Regime
2.1.2.5 Based on Number of Phases
2.1.3 One‐, Two‐, and Three‐Dimensional Flows
2.1.3.1 One‐Dimensional Flow
2.1.3.2 Two‐ and Three‐Dimensional Flow
2.1.4 External Fluid Flow
2.1.5 The Boundary Layer
2.1.5.1 Transition from Laminar to Turbulent Flow
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 Basic Steps Involved in CFD Analysis2.2.2.1 Problem Statement
2.2.2.2 Mathematical Model
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 Modeling
2.2.4.1 What is Turbulence?
2.2.4.2 Need for Turbulence Modeling
2.2.4.3 Reynolds‐Averaged Navier-Stokes Equations
2.2.4.4 Turbulence Closure Models
2.2.4.5 Large Eddy Simulation (LES)
2.2.4.6 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 Conditions2.2.5.4 Symmetry Boundary Conditions
2.2.6 Moving Reference Frame (MRF)
2.2.7 Verification and Validation
2.2.8 Commercial CFD Software
2.2.9 Open Source Codes
2.2.9.1 OpenFOAM
References
Chapter 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 Optimization3.3.1 Constrained Optimization
3.3.2 Unconstrained Optimization
3.3.3 Discrete Optimization
3.4 Surrogate Modeling
3.4.1 Overview
3.4.2 Optimization Procedure
3.4.3 Surrogate Modeling 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 (KRG) Model
3.4.3.4 PRESS‐Based‐Averaging (PBA) Model
3.4.3.5 Simple Average (SA) Model
3.5 Error Estimation
3.5.1 General Errors When Simulating and Optimizing a Turbomachinery System
