Hybrid Nanofluids: Preparation, Characterization and Applications presents the history of hybrid nanofluids, preparation techniques, thermoelectrical properties, rheological behaviors, optical properties, theoretical modeling and correlations, and the effect of all these factors on potential applications, such as solar energy, electronics cooling, heat exchangers, machining, and refrigeration. Future challenges and future work scope have also been included. The information from this book enables readers to discover novel techniques, resolve existing research limitations, and create novel hybrid nanofluids which can be implemented for heat transfer applications.
Author(s): Zafar Said
Series: Micro and Nano Technologies
Publisher: Elsevier
Year: 2022
Language: English
Pages: 278
City: Amsterdam
Front Cover
Hybrid Nanofluids: Preparation, Characterization and Applications
Copyright
Contents
Contributors
Preface
Acknowledgments
Chapter 1: Introduction to hybrid nanofluids
1.1. Introduction
1.1.1. Development of nanomaterials and nanofluids
1.1.2. Drawbacks of mono nanofluids
1.1.3. Development of hybrid nanofluids
1.2. Preparation of hybrid nanofluids
1.3. Properties of hybrid nanofluids
1.3.1. Thermal conductivity
1.3.2. Viscosity
1.3.3. Density
1.3.4. Specific heat capacity
1.3.5. Thermal diffusivity
1.3.6. Electrical, magnetic, dielectric
1.4. Applications of hybrid nanofluids
1.4.1. Electronic cooling
1.4.2. Solar collectors
1.4.3. Heat exchangers
1.4.4. Nuclear PWR
1.4.5. Engine cooling
1.4.6. Refrigeration
1.4.7. Machining
1.4.8. Desalination
1.5. Challenges and outlook
1.6. Conclusion
References
Chapter 2: Preparation and stability of hybrid nanofluids
2.1. Introduction
2.1.1. One-step method
2.1.2. Two-step method
2.1.3. Comparison of one-step and two-step methods
2.2. Stability of nanofluids
2.2.1. Stability evaluation methods
Sedimentation method
Centrifugation method
Zeta potential method
Spectral absorbance analysis
Thermal conductivity measurement
Electron microscopy
2.2.2. Stability enhancement methods
Ultrasonication
Addition of surfactants
Surface modification of nanoparticles
pH change
2.3. Challenges and outlook
2.4. Summary
References
Chapter 3: Thermophysical, electrical, magnetic, and dielectric properties of hybrid nanofluids
3.1. Thermophysical properties
3.1.1. Thermal conductivity
3.1.2. Viscosity of hybrid nanofluids
3.1.3. Specific heat and density of hybrid nanofluids
3.1.4. Magnetic property
3.1.5. Dielectric property
3.2. Conclusion
Acknowledgments
References
Chapter 4: Hydrothermal properties of hybrid nanofluids
4.1. Introduction
4.2. Surface tension
4.3. Friction factor
4.4. Pressure drop
4.5. Pumping power
4.6. Fouling factor of nanofluid
4.7. Conclusions and challenges
Acknowledgments
References
Chapter 5: Rheological behavior of hybrid nanofluids
5.1. Introduction
5.2. Experimental and numerical studies on rheology
5.3. Effects of various parameters on the rheology of hybrid nanofluids
5.3.1. Temperature
5.3.2. Particle size and shape
5.3.3. Volume concentration
5.3.4. Other factors
5.4. Conclusion and future outlook
References
Chapter 6: Radiative transport of hybrid nanofluid
Subscript
6.1. Introduction
6.2. Optical properties
6.2.1. Rayleigh scattering approximation
6.2.2. Maxwell-Garnett approximation
6.2.3. Mie scattering approximation
6.3. Radiative transfer
6.4. Effect of different parameters on optical properties
6.4.1. Effect of particle size
6.4.2. Effect of volume fraction
6.5. Challenges and outlook
6.6. Summary
References
Chapter 7: Theoretical analysis and correlations for predicting properties of hybrid nanofluids
7.1. Introduction
7.2. Different theoretical models
7.3. Different correlations to predict the properties of hybrid nanofluid
7.3.1. Thermal conductivity
7.3.2. Specific heat capacity
7.3.3. Density
7.3.4. Viscosity
7.4. Challenges and summary
References
Chapter 8: Brief overview of the applications of hybrid nanofluids
8.1. Introduction
8.2. Electronics cooling
8.3. Solar collectors
8.4. Heat exchangers
8.5. Engine cooling
8.6. Refrigeration
8.7. Machining
8.8. Desalination
8.9. Challenges and outlook
8.10. Summary
References
Chapter 9: Recent advances in the prediction of thermophysical properties of nanofluids using artificial intelligence
9.1. Introduction
9.2. Modeling structure using AI methods
Data collecting
9.2.1. Data preprocessing
Statistical assessment of datasets
Feature selection
Mutual information
Best subset regression (BSR)
9.2.2. Introduction to artificial intelligence methods
Artificial neural network
Feedforward network of neurons (FFNN)
Multilayer perceptron (MLP) neural network
Radial basis function neural network (RBF-NN)
Cascaded forward neural network (CFNN)
Generalized regression neural network (GRNN)
Extreme learning machine (ELM)
Adaptive neuro-fuzzy inferences system (ANFIS)
Group method of data handling (GMDH)
Ensemble machine learning methods
M5 tree (M5Tree) models
Random forest (RF)
Support vector machine regression (SVR)
Evolutionary machine learning approaches
Gene expression programming
Genetic programming
Gaussian process regression (GPR)
9.3. Sensitivity analysis
9.4. Summary
References
Chapter 10: Challenges and difficulties in developing hybrid nanofluids and way forward
10.1. Introduction
10.2. Foam formation
10.3. Stability
10.4. Safety and environmental concerns
10.5. High cost
10.6. Degradation of original properties
10.7. Increased friction factor, pumping power, and pressure drop
10.8. Selecting suitable hybrid nanofluids
10.9. Predicting models for thermophysical properties
10.10. Challenges and outlook
10.11. Conclusion
References
Index
Back Cover
