Algorithmic performance-data classification of condensing heat exchangers

Abstract

We describe a methodology able to extract the regimes of operation from condensing heat exchanger data. The methodology applies a Gaussian mixtures clustering algorithm to determine the number of groups directly from the data, and a maximum likelihood decision rule to classify such data into these clusters. Published measurements visually classified as dry-surface, dropwise condensation, and film condensation, are used to illustrate the applicability of the classification technique. Since some results from the algorithmic and visual classifications differ, the second part presents an independent evaluation of the allocation process by a procedure based on artificial neural networks (ANNs) and a variant of cross-validation. Results from the ANN approach to the same data show remarkable agreement with the clustering technique confirming that an algorithmic classification of complex physical phenomena is not only possible but also accurate, and represents an excellent alternative to typical visual-based procedures for the analysis of thermal and other systems.