Study of the Thermal Conductivity of Ice Based on Higher-Order Fitting Functions

Thermal conductivity is one of the most important parameters reflecting the thermal conductivity of a substance. Studying how different temperatures and impurities affect the thermal conductivity of ice is of great significance for studying the heat transfer performance of ice, heat transfer efficiency of ice-containing buildings and so on. And higher order fits allow for more accurate fitting of the data and the exploration of more complex patterns in the data. When the data has a highly nonlinear structure, the relationship between the variables can be better investigated by adding higher order terms to the independent variables. In order to investigate the effect of different temperatures and impurity concentrations on the thermal conductivity of ice, this paper 3D modelled the device by analysing the experimental situation and area corrected the thermal conductivity expression to reduce the measurement error. A curve of the thermal conductivity of ice as a function of temperature at different temperature intervals was fitted by a higher order function fit. By observing the fitted curves, it was found that the thermal conductivity of pure ice gradually increases as the temperature decreases within a certain range, and that the temperature affects the thermal conductivity of ice to different degrees in different intervals. In the study of the effect of different impurity concentrations on the thermal conductivity of ice, also by fitting a higher-order function, the curve of the thermal conductivity of ice as a function of impurity concentration was fitted, and it was found that impurities such as NaCl and white sugar all reduce the thermal conductivity of ice, and the higher the content at a constant control temperature, the smaller the thermal conductivity of impurity ice.