Specifications
| book-author |
Kwang-Yong Kim, Abdus Samad, Emesto Benini
|
| publisher |
Wiley; 1st edition
|
| file-type |
PDF
|
| pages |
304 pages
|
| language |
English
|
| asin |
B07MTTP5LQ
|
| isbn10 |
1119188296
|
| isbn13 |
9781119188292
|
Book Description
Utilizing Computational Fluid Dynamics and Numerical Optimization for the Purpose of Design Optimization of Fluid Machinery (PDF)
This timely ebook brings together numerical optimization methods for fluid machinery and its key industrial applications. It does so by drawing on extensive experience as well as extensive research. It presents the fundamentals of fluid mechanics, including fluid machines and their component parts, and thereby lays out in a rational manner the context that is necessary to comprehend computational fluid dynamics. After that, students are given an introduction to various methods of optimization, including single and multi-objective optimization, surrogate models, automated optimization, and evolutionary algorithms. In conclusion, clear explanations have been provided for design strategies and applications in the fields of pumps, compressors, turbines, and other fluid machinery systems, with an emphasis placed specifically on renewable energy systems.
- Authored by a group consisting of leading experts from all over the world in their respective fields
- Contains applications that are crucial to the industry, along with key sections on various renewable energy systems
- Consolidates in a single, user-friendly reference all of the optimization techniques for fluid machinery that make use of computational fluid dynamics.
The Design Optimization of Fluid Machinery ebook, which is available in PDF format, is an indispensable resource for researchers, graduate students, and engineers who are interested in fluid machinery and its optimization methods. For advanced students studying mechanical engineering and the related fields of fluid dynamics and aerospace engineering, this comprehensive reference text is an excellent resource.
PLEASE BE AWARE That this PDF eBook does not come with any access codes.
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
