Mathematical Modelling of Heat Transfer Performance of Heat Exchanger using Nanofluids
portes grátis
Mathematical Modelling of Heat Transfer Performance of Heat Exchanger using Nanofluids
Gyanchandani, Neetu; C. Handa, Chandrahas; Maheshwary, Prashant; Belkhode, Pramod
Taylor & Francis Ltd
01/2025
134
Mole
9781032557656
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
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 ?D1 (Temperature Difference, ?T)
5.7 SPSS Output for ?D2 (Heat Flow, Q)
5.8 SPSS Output for ?D3 (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 ?D1 (Temp. Diffe., ?T)
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 ?D1 (K?)
7.3 Analysis of the Model for Dependent Pi Term ?D2 (Kt)
7.4 Analysis of the Model for Dependent Pi Term ?D3 (Ks)
7.5 Analysis of the Model for Dependent Pi Term ?D1 (?T)
7.6 Analysis of the Model for Dependent Pi Term ?D2 (Q)
7.7 Analysis of the Model for Dependent Pi Term ?D3 (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
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 ?D1 (Temperature Difference, ?T)
5.7 SPSS Output for ?D2 (Heat Flow, Q)
5.8 SPSS Output for ?D3 (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 ?D1 (Temp. Diffe., ?T)
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 ?D1 (K?)
7.3 Analysis of the Model for Dependent Pi Term ?D2 (Kt)
7.4 Analysis of the Model for Dependent Pi Term ?D3 (Ks)
7.5 Analysis of the Model for Dependent Pi Term ?D1 (?T)
7.6 Analysis of the Model for Dependent Pi Term ?D2 (Q)
7.7 Analysis of the Model for Dependent Pi Term ?D3 (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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Heat Exchangers;Artificial Neural Network Simulation;Sensitivity Analysis;Heat Transfer Phenomena;Dimensional Analysis;Nanomaterials;Nano Fluid;Heat Transfer Coefficient;Heat Transfer Performance;Pi Terms;Nano Fluid Particles;Heat Exchanger;Thermo Physical Properties;Water Nanofluid;Heat Transfer Fluids;Test Envelope;Plate Heat Exchangers;Ann Model;Mass Flow Rate;Regression Standardized Residual;SPSS Model;SPSS Output;Double Pipe Heat Exchangers;Traditional Heat Transfer Fluids;Thermal Energy Storage;Convective Heat Transfer Coefficient Increases;Absolute Index;CHEs;Thermal Energy Storage Applications;Maximum Heat Transfer Enhancements
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 ?D1 (Temperature Difference, ?T)
5.7 SPSS Output for ?D2 (Heat Flow, Q)
5.8 SPSS Output for ?D3 (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 ?D1 (Temp. Diffe., ?T)
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 ?D1 (K?)
7.3 Analysis of the Model for Dependent Pi Term ?D2 (Kt)
7.4 Analysis of the Model for Dependent Pi Term ?D3 (Ks)
7.5 Analysis of the Model for Dependent Pi Term ?D1 (?T)
7.6 Analysis of the Model for Dependent Pi Term ?D2 (Q)
7.7 Analysis of the Model for Dependent Pi Term ?D3 (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
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 ?D1 (Temperature Difference, ?T)
5.7 SPSS Output for ?D2 (Heat Flow, Q)
5.8 SPSS Output for ?D3 (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 ?D1 (Temp. Diffe., ?T)
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 ?D1 (K?)
7.3 Analysis of the Model for Dependent Pi Term ?D2 (Kt)
7.4 Analysis of the Model for Dependent Pi Term ?D3 (Ks)
7.5 Analysis of the Model for Dependent Pi Term ?D1 (?T)
7.6 Analysis of the Model for Dependent Pi Term ?D2 (Q)
7.7 Analysis of the Model for Dependent Pi Term ?D3 (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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Heat Exchangers;Artificial Neural Network Simulation;Sensitivity Analysis;Heat Transfer Phenomena;Dimensional Analysis;Nanomaterials;Nano Fluid;Heat Transfer Coefficient;Heat Transfer Performance;Pi Terms;Nano Fluid Particles;Heat Exchanger;Thermo Physical Properties;Water Nanofluid;Heat Transfer Fluids;Test Envelope;Plate Heat Exchangers;Ann Model;Mass Flow Rate;Regression Standardized Residual;SPSS Model;SPSS Output;Double Pipe Heat Exchangers;Traditional Heat Transfer Fluids;Thermal Energy Storage;Convective Heat Transfer Coefficient Increases;Absolute Index;CHEs;Thermal Energy Storage Applications;Maximum Heat Transfer Enhancements
