Classification of Condensing Heat Exchangers Performance Data by Gaussian Mixtures

Abstract

We introduce a methodology to extract the regimes of operation from condensing heat exchanger data. The methodology uses a Gaussian mixture clustering algorithm to determine the number of groups from the data, and a maximum likelihood decision rule to classify the data into these clusters. In order to assess the accuracy of clustering technique, experimental data from the literature visually classified as dry-surface, dropwise condensation, and film condensation, are used in the analysis. Though there is a discrepancy between the clustering classification and the visual one, an independent evaluation using artificial neural networks (ANNs) shows that the clustering methodology is able to both find the different regimes of operation and classify the data corresponding to each regime.