THE pH AND ELECTRICAL CONDUCTIVITY OF NANOFLUIDS

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BACKGROUND

Energy sustainability demands the efficient management of resources, which includes energy and thermal processes. The global objective is to achieve sustainable and efficient energy development in line with sustainable development goals. Therefore, technological advancement is growing at a geometric rate and, among other things, it is tailored towards the achievement of sustainable energy and energy management processes. Recently, devices such as lab-on-a-chip (LOC), micro-electro-mechanical systems (MEMS), nano- electro-mechanical systems (NEMS), and micro- and nano-processors are required to accomplish high-end precision tasks. For instance, MEMS are currently used for surgical procedures that require high-level precision, especially in human surgery. These devices, from microelectronics to industrial machines, are being made smaller and lighter, but more sophisticated in their intended functions. Consequently, they generate more heat per unit area (high heat flux), which needs to be removed properly through a heat exchange medium, and if not, they will lead to overheating, hot spot, performance reduction and equipment damage.
There are several ways of resolving the thermal management challenges posed by the new and technologically advanced devices. Some of these methods are the use of functionally graded materials (FGM), the geometric modification of heat exchanger microchannels to find optimum configurations, increasing the effective heat transfer surface area, and increasing the convective heat transfer coefficient. The use of FGM raise production costs, which makes affordability a serious concern. On the other hand, microchannel size constraint requires little working fluid. Therefore, heat removal is not efficient. Especially with the conventional heat transfer fluid, increasing the effective heat transfer surface area results in increased weight and size, and increasing the convective heat transfer coefficient incurs greater running cost in terms of the pumping power requirement.
Functionally modified heat transfer fluid is a new class of heat transfer fluid called nanofluid. This new class of fluid is produced from the dispersion of ultrafine particles (nanoparticles) of metal, metal oxide, non-metals and non-metal oxides in the conventional heat transfer fluid (base fluid), such as water, ethylene glycol (EG), glycerol, propylene glycol (PG) and engine oil. The dispersion was thought desirable in order to modify the transport properties of the conventional heat transfer fluids, since they are characteristically poor in thermal transport properties such as thermal conductivity, heat transfer coefficient and electrical conductivity. The idea of particle dispersion in base fluid can be traced to Maxwell in 1873 [1], when the conductivity of heat transfer fluid was first modified with micrometric particles. The challenges with Maxwell’s types of fluid were numerous, ranging from the rapid settling of particles (poor stability), abrasion of flow equipment and significant pressure drops. Recently, researchers have shown that different nanofluids show excellent thermal transport properties, better than conventional heat transfer fluids, good stability and reduced pressure drop in heat exchangers compared to the Maxwell’s fluid types.
Therefore, nanofluids will provide valuable benefits in industrial processes and systems that require liquid as a working fluid, especially as heat transfer and lubricating fluids. Numerous applications of nanoparticles and nanofluids cut across the sciences, biomedical sciences, pharmaceuticals and engineering fields. Specifically, in the context of sustainable energy development and thermal management, nanofluids are becoming more essential as the need for efficient thermal management is becoming more important. Nanofluids have capabilities that make them the right candidates for the proper and efficient cooling of MEMS, NEMS, LOCs, fuel cells or larger devices that are found in industrial processes, such as nuclear power plant processes, chemical processes, automobile cooling and large-scale microelectronic cooling systems. These capabilities are centred on their improved thermophysical properties, such as thermal conductivity, electrical conductivity, pH, density, Nusselt number (Nu) and heat capacity. Much research has been carried out to investigate the effects of different parametric inputs, mainly on the thermal conductivity of nanofluids and Nu in different flow configurations.
The viscosity of nanofluids is as important as its thermal conductivity because both the Reynolds (Re) and Prandtl (Pr) numbers are highly influenced by viscosity. It can also be argued that the Nu depends on viscosity, but this thermophysical property is sparingly investigated, especially considering different parameters such as particle size, temperature, volume fraction, different base fluids, different particle types and energy of ultrasonication used during nanofluid preparation. Other poorly investigated properties of nanofluids that are critical to the attainment of their full potential are pH and electrical conductivity, which are important because they are related to the nanofluids’ stability. Moreover, the study of electrical conductivity is important for LOCs, fuel cells, electrically conducting adhesives and electrospray technology. Understanding the behaviour of the pH of nanofluids is essential for material selection and corrosion monitoring.

AIM OF THIS RESEARCH

The aim of this research is to prepare uniformly homogenised and stable Al2O3-glycerol, MgO-EG, SiO2-glycerol and SiO2-EG nanofluids using an ultrasonication assist mechanism, performing experimental investigations to measure the viscosity, electrical conductivity and pH of the stably prepared nanofluids, as well as proposing accurate models through non-dimensional analysis and artificial intelligence (AI) methods for the prediction of nanofluids’ viscosity.

TABLE OF CONTENTS :

• DECLARATION
• DEDICATION
• ACKNOWLEDGEMENTS
• ABSTRACT
• TABLE OF CONTENTS
• LIST OF FIGURES
• LIST OF TABLES
• NOMENCLATURE
• PUBLICATIONS IN JOURNALS AND CONFERENCE PROCEEDINGS
• CHAPTER 1: INTRODUCTION
  • 1.1 BACKGROUND
  • 1.2 AIM OF THIS RESEARCH
  • 1.3 RESEARCH OBJECTIVES
  • 1.4 SCOPE OF THE STUDY
  • 1.5 ORGANISATION OF THE THESIS
• CHAPTER 2: LITERATURE REVIEW
  • 2.1 INTRODUCTION
  • 2.2 THEORETICAL BACKGROUND OF SUSPENSION RHEOLOGY
    • 2.2.1 Classical theoretical viscosity models
    • 2.2.2 New theoretical models
    • 2.2.3 Empirical models
  • 2.3 EXPERIMENTAL STUDIES
    • 2.3.1 Methods of preparation of nanoparticles and nanofluids
    • 2.3.2 Nanofluid stability
    • 2.3.3 Experimental set-ups
    • 2.3.4 Parameters involved in the effective viscosity of nanofluids
  • 2.4 MODELLING NANOFLUID PROPERTIES WITH ARTIFICIAL INTELLIGENCE
  • 2.5 THE pH OF NANOFLUIDS
  • 2.6 ELECTRICAL CONDUCTIVITY
  • 2.7 CONCLUSION
• CHAPTER 3: METHODOLOGY
  • 3.1 INTRODUCTION
  • 3.2 MATERIALS AND EQUIPMENT
    • 3.2.1 Materials
    • 3.2.2 Equipment
  • 3.3 NANOPARTICLES’ CHARACTERISATION AND NANOFLUIDS’ PREPARATION
  • 3.4 VISCOSITY MEASUREMENT
  • 3.5 THE pH AND ELECTRICAL CONDUCTIVITY MEASUREMENT
    • 3.5.1 The pH measurement
    • 3.5.2 Electrical conductivity measurement
  • 3.6 UNCERTAINTY ANALYSIS
    • 3.6.1 Uncertainty in viscosity measurement
    • 3.6.2 Uncertainty in pH and electrical conductivity measurement
  • 3.7 MODELLING
    • 3.7.1 Dimensional analysis
    • 3.7.2 Artificial intelligence
  • 3.8 CONCLUSION
• CHAPTER 4: VISCOSITY OF NANOFLUIDS
  • 4.1 INTRODUCTION
  • 4.2 CHARACTERISATION AND VISCOSITY OF Al2O3-GLYCEROL NANOFLUIDS
    • 4.2.1 The characterisation of Al2O3 nanoparticles and nanofluids
    • 4.2.2 Influence of ultrasonication energy density
    • 4.2.3 Influence of temperature
    • 4.2.4 Influence of Al2O3 concentration and size on the dispersion viscosity
  • 4.3 CHARACTERISATION AND VISCOSITY OF MgO-EG NANOFLUIDS
    • 4.3.1 MgO nanoparticles and nanofluids characterisation
    • 4.3.2 The influence of ultrasonication energy density
    • 4.3.3 The influence of temperature
    • 4.3.4 Influence of volume fraction and particle size of MgO
  • 4.4 CHARACTERISATION AND VISCOSITY OF SiO2-GLYCEROL AND SiO EG NANOFLUIDS
    • 4.4.1 SiO2 nanoparticle and nanofluid characterisation
    • 4.4.2 Influence of temperature
    • 4.4.3 Influence of volume fraction
  • 4.5 THE EFFECT OF DIFFERENT BASE FLUIDS ON VISCOSITY ENHANCEMENT
  • 4.6 THE EFFECT OF DIFFERENT NANOPARTICLES ON VISCOSITY
  • ENHANCEMENT
  • 4.7 CONCLUSION AND RECOMMENDATIONS
• CHAPTER 5: THE pH AND ELECTRICAL CONDUCTIVITY OF NANOFLUIDS
• • 5.1 INTRODUCTION
  • 5.2 THE pH AND ELECTRICAL CONDUCTIVITY OF MgO-EG NANOFLUIDS
    • 5.2.1 The influence of temperature on the pH and electrical conductivity of MgO-EG nanofluids
    • 5.2.2 The effect of volume fraction and particle size on the pH and electrical conductivity of MgO-EG nanofluid
  • 5.3 THE pH AND ELECTRICAL CONDUCTIVITY OF SiO2-EG AND SiO GLYCEROL NANOFLUIDS
    • 5.3.1 The influence of temperature on the pH and electrical conductivity of SiO2-EG
    • and SiO2-glycerol nanofluids
    • 5.3.2 The influence of volume fraction on the pH and electrical conductivity of SiO
    • EG and SiO2-glycerol nanofluids
  • 5.4 THE INFLUENCE OF DIFFERENT BASE FLUIDS ON THE PH AND ELECTRICAL CONDUCTIVITY OF NANOFLUIDS
  • 5.5 CONCLUSION AND RECOMMENDATIONS
• CHAPTER 6: MODEL DEVELOPMENT FOR NANOFLUID VISCOSITY
  • 6.1 INTRODUCTION
  • 6.2 MODELLING THE VISCOSITY OF MgO-EG NANOFLUIDS
    • 6.2.1 Modelling the viscosity of MgO-EG nanofluids using non-dimensional analysis

• • 6.2.2 Modelling the viscosity of MgO-EG nanofluid using FCM-ANFIS and GA-PNN modelling techniques
• 6.3 MODELLING THE VISCOSITY OF Al2O3-GLYCEROL NANOFLUIDS
  • 6.3.1 Modelling the viscosity of Al2O3-glycerol nanofluids using non-dimensional analysis
  • 6.3.2 Modelling the viscosity of Al2O3-glycerol nanofluids using the GMDH-NN
  • modelling technique
• 6.4 MODELLING THE VISCOSITY OF SiO2-EG AND SiO2-GLYCEROL
• NANOFLUIDS
  • 6.4.1 Modelling the viscosity of SiO2-EG and SiO2-glycerol nanofluids with non dimensional analysis
  • 6.4.2 Modelling the viscosity of SiO2-EG and SiO2-glycerol nanofluids using the GMDH-NN modelling technique
• 6.5 CONCLUSION AND RECOMMENDATION
• CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS
  • 7.1 SUMMARY
  • 7.2 CONCLUSIONS
  • 7.3 RECOMMENDATIONS
  • REFERENCES
  • APPENDIX A: Al2O3 NANOPARTICLES EDS ANALYSIS
  • APPENDIX A.1 Al2O3 EDS RESULTS
  • APPENDIX B: ARTIFICIAL INTELLIGENCE GRAND MODELS
  • APPENDIX B.1 HYBRID GA-PNN GRAND MODEL FOR MgO-EG NANOFLUID
• • APPENDIX B2 HYBRID GMDH-NN GRAND MODEL FOR Al2O3-GLYCEROL NANOFLUID
  • APPENDIX B3 HYBRID GMDH-NN GRAND MODEL FOR SiO2-EG NANOFLUID
• • APPENDIX B4 HYBRID GMDH-NN GRAND MODEL FOR SiO2-GLYCEROL NANOFLUID

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