We consider the problem of accuracy in the algorithmic classification of thermodynamic properties of fluids from a fuzzy C-means (FCM) clustering methodology. The analysis emphasizes on the way the algorithm is affected by factors such as the natural scale of the data, and the following strategies chosen for the classification: (1) data normalization, (2) transformation, (3) sample size of furnished data, and (4) the value of the fuzzy parameter. Experimental data corresponding to pressure, volume, and temperature of water are taken from the literature and used to show that the natural scaling of the data, the normalization and transformation strategies, as well as the values chosen for the fuzzy parameter are all important factors in the classification. Also, a decrease in the number of data used during the process degrades the quality of the solution. A complete consideration of the issues examined here are undoubtedly beneficial every time a FCM classification is tried on a new problem.

In the present study we carry out three-dimensional fluid flow and heat transfer simulations on the external side of a compact heat exchanger to analyze the interaction between the fluid and its geometry. The overall objective is to use the resulting information for the design of more compact devices. The type of heat exchanger considered here is the common plain-fin and tube, with air flowing over the tubes and water as the inner-tube fluid. Two heat exchanger configurations, in which the tube arrangement is either in-line or staggered, conform the basic geometries. The size of the heat exchanger –regardless of the type of arrangement– which serves as the baseline for the parametric analysis, is defined by fixing its length; i.e., the number of rows in the flow direction. For the two heat exchanger configurations examined here, the dimensional form of the governing equations, along with the corresponding boundary conditions, are solved under specific flow and temperature values using a finite element method to compute the velocity, pressure and temperature fields. From these, the heat transfer rate and pressure drop are then calculated. The computations are performed for a range in the values of the Reynolds number within the laminar regime. For all cases considered, results from this investigation indicate that the geometrical arrangement plays a major role in the amount of heat being exchanged and that, for a given device, the length needed to exchange 99% of the corresponding amount of energy that may be transferred by the baseline model, is confined to less than 30% of the size of the original device.

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.

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.