This study seeks to understand the thermal and energetic behavior of a domestic refrigerator more widely by experimentally evaluating the main effects of the thermal load (food) and the variation of the ambient temperature. To carry out the experiments, the thermal load was classified based on the results of a survey conducted on different consumers in the state of Guanajuato, Mexico. The thermal behavior of both compartments of the refrigerator, the total energy consumption, the power of the compressor in its first on-state, and the coefficient of performance, according to the classification of the thermal loads and the room temperature, were evaluated. Finally, it is verified that the thermal load and the room temperature have a significant influence on the energy performance of the refrigerator.

This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab^® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches.

This work presents the main behaviors shown in the habits of consumers of domestic refrigerators, which influences the energy consumption of this appliance. This study is based on a series of surveys answered by 200 consumers from four cities in the State of Guanajuato, Mexico. The questions were arranged with the aim of evaluating the general characteristics and usage habits such as refrigerator age, door opening frequency, damper position, load of food supplies, external and internal cleaning habits, and nearby heat sources, among other things. The randomly interviewed consumers were individuals between 20 and 60 years of age, who were interviewed using handmade surveys by experts in the field of refrigeration. In some cases, photographic evidence was gathered from the consumers’ refrigerators to represent the typical usage habits. In general, the results show that better usage habits are necessary from an energy point of view. Most consumers agree with adopting best practices for using their refrigerator.

This work provides a structural analysis of small-scale 3D-printed wind turbine ribs subjected to compression. The ribs were manufactured according to NACA 23015 and NACA 633618 geometries, with polylactic acid (PLA) and polylactic acid with carbon fiber additives (CF-PLA). In addition, holes were manufactured into the sample bodies by either 3D printing or drilling for being compared with solid samples. The compression testing was performed by following the ASTM 695D standard, whereas the beginning and propagation of delamination were assessed with the ASTM 5528 standard. Experimental results revealed that 3D-printed built-in holes provided higher compression strength, hence higher structural efficiency, than the drilled samples. Significant improvement by adding carbon fiber additives into the PLA resin system in comparison to raw PLA was detected for at least one of the studied airfoil profiles. NACA geometries also represented a key parameter for avoiding stress concentration areas, as the FEM modeling supported. However, in damaged areas, fracture mechanisms were observed such as bead-bridging, which is a key parameter in reinforcing and consolidating the specimen bodies. Working in better interphase bonding and different additives between beads and layers is highly suggested for future studies.

Multiple fault identification in induction motors is essential in industrial processes due to the high costs that unexpected failures can cause. In real cases, the motor could present multiple faults, influencing systems that classify isolated failures. This paper presents a novel methodology for detecting multiple motor faults based on quaternion signal analysis (QSA). This method couples the measured signals from the motor current and the triaxial accelerometer mounted on the induction motor chassis to the quaternion coefficients. The QSA calculates the quaternion rotation and applies statistics such as mean, variance, kurtosis, skewness, standard deviation, root mean square, and shape factor to obtain their features. After that, four classification algorithms are applied to predict motor states. The results of the QSA method are validated for ten classes: four single classes (healthy condition, unbalanced pulley, bearing fault, and half-broken bar) and six combined classes. The proposed method achieves high accuracy and performance compared to similar works in the state of the art.