In this paper, the main goal is to study the impact of nanopowder volume concentration and ultrasonication treatment time on the stability and thermophysical properties of MgO-DW nanofluid at room temperature. The co-precipitation method was utilized to prepare pure MgO nanoparticles with an average particle size of 33 nm. The prepared MgO nanopowder was characterized by using XRD, SEM, and EDX analyses. Then, MgO-DW nanofluid was obtained with different volume concentrations (i.e., 0.05, 0.1, 0.15, 0.2, and 0.25 vol.%) and different ultrasonication time periods (i.e., 45, 90, 135, and 180 min) by using a novel two-step technique. With volume concentration and ultrasonication time of 0.15 vol.% and 180 min, respectively, good stability was achieved, according to the zeta potential analysis. With increasing volume concentration and ultrasonication time period of the nanofluid samples, the thermal conductivity measurements showed significant increases. As a result, the maximum enhancement was found to be 25.08% at a concentration ratio of 0.25 vol.% and agitation time of 180 min. Dynamic viscosity measurements revealed two contrasting trends with volume concentration and ultrasonication time. The lowest value of relative viscosity was gained by 0.05 vol.% MgO-DW nanofluid. The chemical and physical interactions between MgO nanoparticles and DW molecules play an important function in determining the thermal conductivity and dynamic viscosity of MgO-DW nanofluid. These findings exhibit that MgO-DW nanofluid has the potential to be used as an advanced heat transfer fluid in cooling systems and heat exchangers.

Power transformers are one of the most important elements of electrical power systems. The fast diagnosis of transformer faults improves the efficiency of power systems. One of the most favored methodologies for transformer fault diagnostics is based on dissolved gas analysis (DGA) techniques, including the Duval triangle method (DTM), the Doernenburg ratio method (DRM), and the Rogers ratio method (RRM), which are suitable for on-line diagnosis of transformers. The imbalanced, insufficient, and overlapping state of gas-decomposed DGA datasets, however, remains a limitation to the deployment of a powerful and accurate diagnosis approach. This study presents a new approach for transformer fault diagnosis based on DGA, one which aims to improve the performance evaluation criteria to predict current faults and to lower the associated costs. We used six optimized machine learning methods (MLMs) for dataset transformation to organize the dataset. The MLMs used for transformer fault diagnosis were random forest (RF), backpropagation neural network (BPNN), K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and Naive Bayes (NB). The MLMs were implemented by using 628 dataset samples, which were obtained from laboratories, other studies, and electricity stations in Iraq. Accordingly, 502 dataset samples constituted the training set while the remaining 126 dataset samples served as the testing set. The results were examined according to six important measurements (accuracy ratio, precision, recall, specificity, F1 score, and Matthews correlation coefficient (MCC)). The best results were found for case A with RF (95.2%). In cases B and C, the best results were found for RF and DT (100% and 99.2%, respectively). With respect to the advanced machine learning method, the transformer fault diagnosis based on the MLMs was exceedingly more accurate in its predictions than the conventional and artificial intelligence-based methods.

Features of the tubular type of heat exchanger were examined experimentally in the current study. A rig is fitted with a novel insert as a negative heat transfer increase technique. The core fluid used is air under steady heat flux and a turbulent discharge state (6000 ≤ Re ≤ 19,500) conditions. Two heat transfer augmentation inserts are employed; one is the basket turbulators utilized as a turbulator and placed inside the heat exchanger with a constant pitch ratio (PR = 150 mm), and the other is the basket turbulators together with twisted tape that are installed at the core of the basket turbulators. The measurements illustrated that the Nusselt number (Nu) was found to be higher by about 131.8%, 169.5%, 187.7%, and 206.5% in comparison with the plain heat exchanger for basket turbulators and the combined basket–twisted tape inserts with y/w = 6, 3, and 2, respectively. The highest thermal efficiency factor of the increased tubular heat exchanger is 1.63 times more elevated than that of the simple heat exchanger on average, due to a binary basket-quirky strip for a twisting percentage y/w equal to 2 under steady pumping energy. Further, practical correlations for the Nusselt number, as well as friction characteristics, were established and presented.

In the present experimental work, the energy and exergy for single slope passive solar still with different basin water depths are experimentally investigated under the Baghdad climate condition. The analysis is performed using the governing equations formulated according to the first and second laws of thermodynamics. Compared to solar still with 1 cm water depth, the obtained results indicated that raising the water depth to 2 and 3 cm caused an appreciable drop in water basin temperature, and high levels of water basin reduction were about 4% and 9%, respectively, from 8:00 a.m. to 14:00 p.m., which significantly affects heat and mass transfer and ultimately hinders further water productivity. The maximum evaporation and convection heat transfer coefficients are found (32 W/m²·k) and (2.62 W/m²·k), respectively, while the maximum productivity of solar still is found to be 1468.84 mL/m² with 1 cm water depth. Conversely, stills with 2 and 3 cm water depth, exhibit an increment of the daily exergy efficiency after 14:00 p.m., this increment was the most for the still with 3 cm water depth. Therefore, we have concluded that the still with 1 cm of water depth attained the highest water productivity, while the still with 3 cm of water depth attained the best exergy efficiency with no additional costs.

Dynamic simulation provides an efficient approach for improving the efficiency of parabolic trough power plants and control circuits. In the dynamic simulation, the possibilities and operating conditions of the plant are evaluated regarding materials, processes, emissions, or economics. Several studies related to the dynamic simulation of the parabolic trough technology are summarised and discussed in this work. This study is the first research that presents a thorough description of the advanced control circuits used in the solar field and thermal storage system of a parabolic trough power plant. This power plant was implemented using advanced process simulation software (APROS). The dynamic model was built based on the real specifications of the power plant.