This paper aims to evaluate the performance of synchronous reluctance motors assisted by a permanent magnet (PMa-SynRM) focused on efficiency and torque pulsations. PMa-SynRM shows high efficiency and power factor, compared to induction motors (IM), although they have a greater cost. These machines develop relatively high torque ripple, cogging torque, and torque imbalances. Consequently, the electromagnetic torque is reduced, the motor temperature is increased, and mechanical vibrations are induced. The optimal design of the machine structures such as flow barriers, permanent magnets, and stator slots, among others, allow reducing torque pulsations. A comparison is made between different designs of the PMa-SynRM reported in the scientific literature, and the effects on efficiency, torque pulsation, and operating costs are evaluated. A case study on the motor driving the air conditioner blower in a hotel room was made, to determine the best economic variant between IM or PMa-SynRM. A sensitive analysis was made to evaluate several uncertainties. The advantages of using one of the PMa-SynRM analyzed were demonstrated. Also, it was proved that the investment is feasible economically, although NPV and payback are not the best, due to low load factor in inverter-controlled motors in air conditioners.

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Experimental data obtained from tests relating to two different single-row heat exchangers are analyzed by means of an artificial neural network. The experiments are carried out by using hot water and room-temperature air as the rn-tube and over-tube fluids, respectively. The two heat exchangers are similar in the air side but with different water-side circuiting. The data obtained consisted of values for the flow rates, the inlet temperatures, and outlet temperatures for both fluids. Data analysis was through a neural network program written using the generalized-delta back-propagation algorithm. Through a random selection process, 75% of the experimental runs are chosen for training and 25% for testing. Flow rates and the inlet temperatures for both water and air are used as the input data for the neural network, and the heat transfer rates are the output. Once the neural network was trained, the values of the heat transfer rates for the test data are predicted. The predictions are compared with the actual measurements, and with the results of a linear interpolation procedure.