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Laboratory Evaluation of Dynamic Viscosity and Heat Conductivity of Functionalized Carbon Nanotube Nano Fluids in the Engine Oil and Modeling with the Neural Network

Emami, Ali | 2016

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 48337 (58)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Moosavi, Ali; Akbari, Mohammad
  7. Abstract:
  8. In recent years, Rheological behavior and heat transfer of nanofluid studies have been increasing considerably and results show significant progress in this area. In this study we analyze the laboratory examination of the influence of parameters of volume fraction and temperature on thermal conductivity coefficient, dynamic viscosity of new and useful nanofluid carbon nanotube in engine oil. Most of fluid comparing to solid has lower thermal conductivity coefficient therefore solid particles increase thermal conductivity coefficient. On the other hand by adding particles, the dynamic viscosity of nanofluid also increases. Since nanoparticles have high volume ratio to surface (SSA) they have different characteristics comparing with their usual form and have different thermal conductivity coefficient. In this study, nanoparticles of carbon nanotube in Cylindrical (nanotube) shape with an inner diameter of 2-6 nm and outer diameter of 5-20 nm. An ultrasound vibrator also was used to break up clusters. The nanofluid with 0.05, 0.1, 0.15, 0.2, 0.3, 0.6, 1.2 volume percentage to analyze the effect of volume fraction on thermal conductivity coefficient and dynamic viscosity was prepared. To analyze the effect of temperature, we restrict it to 25, 30, 35, 40, 45, 50 degree of centigrade. For measuring thermal conductivity coefficient, the KD2-Pro machine and Probe KS1was used. For measuring the dynamic viscosity the rotational viscosity meter of Brookfield model. At the end we compare the findings with presented analytical methods and we specify that the models are unable to describe the behavior of the nanofluid and do not have sufficient approximation, So we used artificial neural network based on feed forward back propagation algorithm to simulation of the thermal conductivity and viscosity of nanofluid
  9. Keywords:
  10. Nanofluid ; Cvoid Fraction ; Themperature ; Thermal Conductivity ; Neural Network ; Dynamic Viscosity

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