Viscosity of nanofluids based on an artificial intelligence model

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Nanolayering of the liquid at the liquid/particle interface

Due to primary inter-atomic bonding at the solid particle interface, some of the base fluid molecules attach themselves on the nanoparticle and form a layer which has been shown schematically in Figure 2.2.
This layer is called a nanolayer and has the properties of the solid phase of the base fluid. This molecular thin film at the solid/liquid interface plays a crucial role in the thermal conductivity of nanofluids enhancement.

Electric charge on the surface of nanoparticles

Wamkam et al experimentally studied the influence of surface charge on the stability of ZrO2-water and TiO2-water nanofluids and observed a significant enhancement on thermal conductivity of nanofluids near the iso-electric point. The iso-electric point is the pH value that particle surface carries no net electrical charge and is sometimes abbreviated to IEP in literature.
Furthermore, it has been observed that at the IEP, the repulsive forces among the nanoparticles are zero and nanoparticles stuck together and became rigid. Based on DLVO (Derjaguin, Landau, Verwey and Overbeek) theory, nanoparticles tend to aggregate to each other and form a cluster when the pH of the dilution is equal or close to the IEP value. Consequently, the bigger clusters trap more water molecules and therefore volume fraction of nanoparticles will increase due to well-packed water molecules inside the clusters. Furthermore, the shape of the clusters with trapped water is like chains which result in higher thermal transport due to a longer link and finally enhance the thermal conductivity of nanofluids. Lee et al [26] studied the influence of pH on the potential, surface charge and stability of CuO-Water and SiO2-water nanofluids. In their experimental investigations, it has been observed that as the pH moves away from point of zero charge (PZC), the surface charge increases due to more frequent attacks on the surface hydroxyl groups. Furthermore, it has been shown that the pH of the colloidal liquid strongly affects the thermal conductivity of the nanofluids. They observed that the colloidal particles get more stable when the pH of the solution moves far from the IEP of particles and eventually alter the thermal conductivity.

Preparation and surfactants

Nasiri et al studied the effect of dispersion method on thermal conductivity of different CNT nanofluids in which functionalisation, SDS/ultrasonic probe and SDS/ultrasonic bath were chosen as preparation methods. It has been concluded that the preparation method has a significant effect on thermal conductivity of CNT nanofluids. Furthermore, they observed that the functionalised CNT structures have the best dispersion and least tendency for agglomeration due to having the smallest mean diameter of particles which had increased from functionalisation method to SDS/ultrasonic bath method. It has also been shown that thermal conductivity of all CNT nanofluids decrease over time, but the rate varies based on the dispersion methods. The functionalised nanofluids had soon begun to level off but the other two types of nanofluids had been continuing their downward trend. Hwang et al measured the thermal conductivity of nanofluids by using a transient hot-wire system. Furthermore, the stability of nanofluids with sedimentation time has been estimated with UV-vis spectrum analysis. The effect of addition of a surfactant has been studied by adding SDS (sodium dodecyl sulphate) to the nanofluid and it has been indicated that it can improve the stability of nanoparticles in aqueous suspensions.
Furthermore, it has been concluded that morphology, the chemical structure of the nanoparticle and base fluid and the addition of a surfactant can strongly affect the stability of nanofluids and consequently the thermo physical properties of nanofluids such as the thermal conductivity.
Ghadimi et al [33] studied the effect of preparation methods as well as adding surfactant on stability and thermal conductivity of nanofluids. The single and two-step preparation methods have been studied and it has been shown that nanofluid preparation methods affect the stability of nanofluids since the two-step method needs a higher nanoparticle concentration to achieve the same heat transfer enhancement by the single-step method. Consequently, a higher concentration caused more sedimentation, although in the most experimental works to date the two-step method is applied for nanofluid preparation since the single-step method is not yet industrialised. Another factor is the higher cost of this method in comparison with the two-step method.
Adding a surfactant as one of the general methods to avoid sedimentation has been studied and it has been shown that addition of surfactant can improve the stability of nanofluids. However, care should be taken to choose the right surfactant as well as applying enough surfactant since choosing the wrong surfactant or applying inadequate surfactant will not encourage the stability of nanofluids.

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CHAPTER 1: INTRODUCTION  
1.1. BACKGROUND
1.2. AIM OF THE PRESENT RESEARCH
1.3. OBJECTIVE OF THE PRESENT RESEARCH
1.4. SCOPE OF THE STUDY
1.5. ORGANISATION OF THE THESIS
CHAPTER 2: THERMAL CONDUCTIVITY OF NANOFLUIDS  
2.1. INTRODUCTION
2.2. POSSIBLE MECHANISMS OF THERMAL CONDUCTION ENHANCEMENT IN NANOFLUIDS
2.3. THEORETICAL MODELS FOR THERMAL CONDUCTIVITY OF NANOFLUIDS
2.4. EXPERIMENTAL DATA OF THERMAL CONDUCTIVITY OF NANOFLUIDS IN LITERATURE
2.5. SUMMARY
CHAPTER 3: THERMAL CONDUCTIVITY OF NANOFLUIDS BASED ON ARTIFICIAL INTELLIGENCE TECHNIQUES  
3.1. INTRODUCTION
3.2. ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)
3.3. POLYNOMIAL NEURAL NETWORKS
3.4. APPLICATION OF THE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) AND THE GENETIC ALGORITHM-POLYNOMIAL NEURAL NETWORK (GA-PNN) METHODS FOR MODELLING THE THERMAL CONDUCTIVITY OF AL2O3-WATER NANOFLUID
3.5. RESULTS AND DISCUSSION
3.6. CONCLUSION AND RECOMMENDATIONS
CHAPTER 4: VISCOSITY OF NANOFLUIDS BASED ON AN ARTIFICIAL INTELLIGENCE MODEL 
4.1. INTRODUCTION
4.2. APPLICATION OF THE FUZZY C-MEANS CLUSTERING NEUROFUZZY INFERENCE SYSTEM (FCM-ANFIS) FOR MODELLING THE VISCOSITY OF NANOFLUIDS
4.3. EXPERIMENTAL DATA USED FOR THE TRAINING AND TESTING PROCEDURE
4.4. PREDICTION MODELS
4.5. RESULT AND DISCUSSION
4.6. CONCLUSION AND RECOMMENDATIONS
CHAPTER 5: MULTI-OBJECTIVE OPTIMISATION OF THE CONVECTIVE HEAT TRANSFER CHARACTERISTICS AND PRESSURE DROP OF NANOFLUIDS  
5.1. INTRODUCTION
5.2. GENETIC ALGORITHM-POLYNOMIAL NEURAL NETWORK HYBRID SYSTEM
5.3. CONVECTIVE HEAT TRANSFER OF TIO2-WATER NANOFLUID
5.4. PRESSURE DROP OF TIO2-WATER NANOFLUIDS
5.5. PREDICTIVE ABILITY OF THE PROPOSED MODELS
5.6. MULTI-OBJECTIVE OPTIMISATION BY USING NSGA-II
5.8 CONCLUSION AND RECOMMENDATIONS
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS  
6.1. SUMMARY
6.2. CONCLUSIONS
6.3. RECOMMENDATIONS
REFERENCES

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