Ten Years of Parameter Estimation Applied to Dynamic Thermophysical Property Measurements

The parameter estimation theory is considered the best way to estimate thermophysical properties from dynamic experiments. This approach deals with measurement and model errors in a statistical context and provides useful information to optimize the experiment. The experience gained in ten years of implementation of inverse algorithms based on the parameter estimation theory (OLS, MAP, and Kalman filtering) is summarized and presented. Several examples of estimation of thermophysical properties using transient and pulse techniques are reported and discussed. The thermal conductivity, specific heat capacity, and total hemispherical emissivity of different materials (light insulators, Pyrex, and niobium) are presented and compared with data obtained with consolidated techniques and with literature data.