Estimating convective heat transfer coefficients and uncertainty thereof using the general uncertainty management (GUM) framework

Abstract

The rate of convective heat transfer is often required to design and optimize food engineering processes. The temperature sensor technique is the most widely used method to measure convective heat transfer coefficients (h-values). Being able to reliably determine h-values is becoming increasingly important with the advances in sophisticated food engineering modelling using it as an empirical input. This contribution uses the general uncertainty management (GUM) framework to analyze the standard uncertainty resulting in the h-value obtained with sensor experiments. It is demonstrated (using a pilot scale air blast freezer example) that the reliability in estimating h-values is often overestimated when using the methodology suggested in literature. Moreover, the uncertainty can be reduced substantially by an uncertainty-minimizing data analysis method (supplied with the study). This uncertainty analysis also provides a method for a priori determining under which conditions the temperature sensor technique will result in reliable estimations for a given food engineering application.