Thermal Conductivity Measurement and Prediction of Particulate Foods

Thermal Conductivity of Particulate Nanocomposites

The modeling of the thermal conductivity of composites made up of metallic and non-metallic micro/nanoparticles embedded in a solid matrix is discussed in detail, at both the dilute and non-dilute limits of particle concentrations. By modifying both the thermal conductivity of the matrix and particles, to take into account the strong scattering of the energy carriers with the surface of the nan...

Effective Thermal Conductivity Estimates Of Particulate Mixtures

Measurement Thermal Conductivity and Thermal Diffusivity of Chromium Nanofluids

In this study, nanofluids of Chromium (Cr) in water, Ethylene Glycol and Ethanol have been prepared using single step method. The thermal conductivity and diffusivity of these nanofluids were measured via hot wire-photothermal deflection technique. Based on finite difference method (FDM) temperature distribution and photothermal deflection caused by the hot wire inside nanofluids was obtained. ...

Prediction of thermal conductivity of steel

A model of thermal conductivity as a function of temperature and steel composition has been produced using a neural network technique based upon a Bayesian statistics framework. The model allows the estimation of conductivity for heat transfer problems, along with the appropriate uncertainty. The performance of the model is demonstrated by making predictions of previous experimental results whi...

Thermal conductivity of Water-based nanofluids: Prediction and comparison of models using machine learning

Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learni...