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Full Description
The book presents a detailed discussion of nanomaterials, nanofluids and application of nanofluids as a coolant to reduce heat transfer. It presents a detailed approach to the formulation of mathematical modelling applicable to any type of case study with a validation approach and sensitivity and optimization.
Covers the aspects of formulation of mathematical modelling with optimization and sensitivity analysis
Presents a case study based on heat transfer improvement and performs operations using nanofluids
Examines the analysis of experimental data by the formulation of a mathematical model and correlation between input data and output data
Illustrates heat transfer improvement of heat exchangers using nanofluids through the mathematical modelling approach
Discusses applications of nanofluids in cooling systems
This book discusses the aspect of formulation of mathematical modelling with optimization and sensitivity analysis. It further presents a case study based on the heat transfer improvement and performing operations using nanofluids. The text covers sensitivity analysis and analysis from the indices of the model. It also discusses important concepts such as nanomaterials, applications of nanomaterials, and nanofluids. It will serve as an ideal reference text for senior undergraduate, and graduate students in fields including mechanical engineering, chemical engineering, aerospace engineering, industrial engineering, and manufacturing engineering.
Contents
Chapter 1
Nanofluids
1.1 Nanotechnology
1.2 Nanomaterials
1.3 Applications of Nanomaterials
1.4 Nanofluids
1.5 Compact Heat Exchangers
1.6 Heat Transfer Enhancement through Nanofluids
1.7 Improvement in Heat Exchanger Performance
1.8 Application of Nanofluid in Cooling Systems
1.9 Mathematical Modelling
Chapter 2
Concept of Experimental Data-Based Modelling
2.1 Introduction
2.2 Nanofluid for Heat Transfer
2.3 Brief Methodology of Theory of Experimentation
2.4 Methods of Experimentation
Chapter 3
Design of Experimentation
3.1 Introduction
3.2 Design of Experiment - Methodical Approach
3.3 Experimental Setup and Procedure
3.4 Two-Wire Method
3.5 Radiator as a Heat Exchanger: Experimental Procedure
3.6 Design of Instrumentation for Experimental Setup
3.7 Components of Instrumentation Systems
3.8 Identification of Variables in Phenomenon
3.9 Mathematical Relationship for Heat Transfer Phenomena
3.10 Formation of Pi Terms for Dependent & Independent
3.11 Reduction of Variables by Dimensional Analysis
3.12 Plan for Experimentation
3.13 Experimental Observations
3.14 Sample Selection
Chapter 4
Mathematical Models
4.1 Introduction
4.2 Model Classification
4.3 Formulation of Experimental Data-Based Models (Two-Wire Method)
4.4 Sample Calculations of Pi Terms
Chapter 5
Analysis using SPSS Statistical Packages Software
5.1 Introduction
5.2 Developing the SPSS Model for Individual Pi Terms
5.3 SPSS Output for Thermal Conductivity Kϕ (Concentration)
5.4 SPSS Output for Thermal Conductivity Kt (Size)
5.5 SPSS Output for Thermal Conductivity Ks (Shape)
5.6 SPSS Output for πD1 (Temperature Difference, ΔT)
5.7 SPSS Output for πD2 (Heat Flow, Q)
5.8 SPSS Output for πD3 (Heat Transfer Coefficient, h)
Chapter 6
Analysis of Model using Artificial Neural Network Programming
6.1 Introduction
6.2 Procedure for Artificial Neural Network Phenomenon
6.3 Performance of Models by ANN
6.3.1 ANN using SPSS o/p for Thermal Conductivity Kϕ
6.3.2 ANN using SPSS o/p for Thermal Conductivity Kt (Size)
6.3.3 ANN using SPSS o/p for Thermal Conduct. Ks (Shape)
6.3.4 ANN using MATLAB Program for πD1 (Temp. Diffe., ΔT)
6.3.5 Comparison of Various Model Values
Chapter 7
Analysis of the Indices of Model
7.1 Introduction
7.2 Analysis of the Model for Dependent Pi Term πD1 (Kϕ)
7.3 Analysis of the Model for Dependent Pi Term πD2 (Kt)
7.4 Analysis of the Model for Dependent Pi Term πD3 (Ks)
7.5 Analysis of the Model for Dependent Pi Term πD1 (ΔT)
7.6 Analysis of the Model for Dependent Pi Term πD2 (Q)
7.7 Analysis of the Model for Dependent Pi Term πD3 (h)
Chapter 8
Optimization and Sensitivity Analysis
8.1 Introduction
8.2 Optimization of the Models
8.3 Sensitivity Analysis for Two-Wire Method
8.4 Estimation of Limiting Values of Response Variables
8.5 Performance of the Models
8.6 Reliability of Models
8.7 Coefficient of Determinants R2 for Two-Wire Method
Chapter 9
Interpretation of the Simulation
9.1 Interpretation of Independent Variables vs. Response Variables after Optimization
9.2 Interpretation of Temperature Difference against the Mass Flow Rate
9.3 Interpretation of Reliability and Coefficient of Determinant
9.4 Interpretation of Mean Error of Models Corresponding to Response Variables
