Date of Award
Master of Science
Attempts to identify a fundamental mechanism that increases the thermal conductivity of liquids with nanoparticle suspension (nanofluids) have not been entirely successful. Models based on the thermal conductivity of the component materials, volume fraction, and temperature have proven to predict the thermal conductivity well for larger nanoparticles. However, when the nanoparticle size becomes smaller, a large increase in thermal conductivity becomes more prominent at low volume fractions. Indicating an unknown mechanism is contributing to the thermal conductivity enhancement. This research provides a new direction for analyzing nanofluid thermal conductivity enhancements by investigating mechanisms related to the distance between suspended nanoparticles. From this study, an initial model that represents an ideal nanofluid has been developed. The ideal model can then be evolved to match Maxwell’s conductivity model by estimating particle migration with Einstein’s displacement theory coupled with thermal penetration analysis. The analytical model was also compared with a conjugate heat transfer computational fluid dynamics (CFD) simulation which provided additional insight into this phenomenon by confirming that local temperature gradients exist between sufficiently close nanoparticles. The results from the analytical model and CFD simulation indicate that particle displacement will cause significant local fluctuations in thermal conductivity for a colloidal fluid.
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