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.
Author(s): Prashant Maheshwary, Chandrahas C. Handa, Neetu Gyanchandani, Pramod Belkhode
Publisher: CRC Press
Year: 2023
Language: English
Pages: 148
City: Boca Raton
Cover
Half Title
Title
Copyright
Contents
Preface
Author Bios
1 Nanofluids
1.1 Nanotechnology
1.2 Nanomaterials
1.3 Application 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
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
3 Design of Experimentation
3.1 Introduction
3.2 Design of Experimentation – Methodical Approach
3.3 Experimental Setup and Procedure
3.4 Two-Wire Method: Experimental Procedure
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 Phenomena
3.9 Mathematical Relationship for Heat Transfer Phenomena
3.10 Formation of Pi Terms for Dependent and Independent
3.10.1 For Two-Wire Method
3.10.2 For Radiator as a Heat Exchanger
3.11 Reduction of Variables by Dimensional Analysis
3.12 Plan for Experimentation
3.12.1 Determination of Test Envelope
3.12.2 Determination of Test Points
3.12.3 Determination of Test Sequence
3.12.4 Determination of Test Plan for Experiment
3.13 Experimental Observations
3.13.1 For Two-Wire Method
3.13.2 For Radiator as a Heat Exchanger
3.14 Sample Selection
3.14.1 Sampling
3.14.2 Sample Design
3.14.3 Types of Sample Designs
3.14.4 Sample Size for Experiments
4 Mathematical Models
4.1 Introduction
4.2 Model Classification
4.3 Formulation of Experimental Data-Based Models (Two-Wire Method)
4.3.1 Model of Dependent Pi Term for πD1 (Kϕ) (Concentration)
4.3.2 Model of Dependent Pi Term for πD2 (Size, Kt)
4.3.3 Model of Dependent Pi Term for πD3 (Ks ) (Shape)
4.3.4 Model of Dependent Pi Term for πD1 (ΔT)
4.3.5 Model of Dependent Pi Term for πD2 (Q)
4.3.6 Model of Dependent Pi Term for πD3 (Heat Transfer Coefficient, h)
4.4 Sample Calculations of Pi Terms
4.4.1 For Two-Wire Method
4.4.2 For Experimental Model (Radiator as a Heat Exchanger)
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 Based on 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)
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 Conductivity Ks (Shape)
6.3.4 ANN using MATLAB Program for πD1 (Temperature Difference, ΔT)
6.3.5 Comparison of Various Model Values
7 Analysis from the Indices of the 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 (∆T)
7.6 Analysis of the Model for Dependent Pi Term πD2 (Q)
7.7 Analysis of the Model for Dependent Pi Term πD3 (h)
8 Optimization and Sensitivity Analysis
8.1 Introduction
8.2 Optimization of the Models
8.2.1 For Two-Wire Method
8.2.2 For Experimental Model (Radiator as a Heat Exchanger)
8.3 Sensitivity Analysis for Two-Wire Method
8.3.1 Effect of Introduced Change on the Dependent Pi Term πD1 (KΦ)
8.3.2 Effect of Introduced Change on the Dependent Pi Term πD2 (Kt )
8.3.3 Effect of Introduced Change on the Dependent Pi Term πD3 (Ks )
8.3.4 Effect of Introduced Change on the Dependent Pi Term πD1 (ΔT)
8.3.5 Effect of Introduced Change on the Dependent Pi Term πD2 (Q)
8.3.6 Effect of Introduced Change on the Dependent Pi Term πD3 (h)
8.4 Estimation of Limiting Values of Response Variables
8.4.1 For Two-Wire Method
8.4.2 For Radiator as a Heat Exchanger Experimental Setup
8.5 Performance of the Models
8.6 Reliability of Models
8.6.1 Life Distribution
8.6.2 Reliability Analysis
8.6.3 Error Frequency Distribution
8.7 Coefficient of Determinant R2 for Two-Wire Method
8.7.1 For Two-Wire Method
8.7.2 For Radiator as a Heat Exchanger Experimental Setup
8.7.3 Coefficient of Determinant Results
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
References
Index
