Abstract
We develop a Takagi-Sugeno (TS) fuzzy model of a concentric-tubes heat exchanger. The model is structured on fuzzy logic reasoning with sets of linguistic rules describing the dynamic characteristics of the thermal system. Using a system identification technique based on adaptive neural networks and subtractive clustering, the fuzzy rules are derived from experimental data of flow rates and fluid temperatures that were previously collected in a heat exchanger test facility. The accuracy of the resulting model is assessed by comparing predictions, versus experimental measurements, of the time-dependent response of the outlet hot- and cold-water temperatures under a step-change in the mass flow rate of the cold fluid. The results indicate that the TS fuzzy model is able to estimate the behavior of the physical system with predicting errors of the order of the experimental uncertainties.
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