Mathematical Modelling of Heat Transfer Performance of Heat Exchanger using Nanofluids (Paperback, 1)

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 K? (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 K?
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 (K?)
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