To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

A salinity gradient solar pond (SGSP) is a large and deep artificial basin of layered brine, that collects and stores simultaneous solar energy for use in various applications. Experimental and theoretical studies have been launched to understand the thermal behavior of SGSPs, under different operating conditions. This article then traces the history of SGSPs, from their natural discovery to their current artificial applications and the progress of studies and research, according to their chronological sequence, in terms of determining their physical and dynamic aspects, their operation, management, and maintenance. It has extensively covered the theoretical and experimental studies, as well as the direct and laboratory applications of this technology, especially the most famous and influential in this field, classified according to the aspect covered by the study, with a comparison between the different results obtained. In addition, it highlighted the latest methods to improve the performance of an SGSP and facilitate its operation, such as the use of a magnetic field and the adoption of remote data acquisition, with the aim of expanding research and enhancing the benefit of this technology.

The current study provides a comprehensive analysis of MHD hybrid nanofluids and stagnation point flow toward a porous stretched cylinder in the presence of thermal radiation. Here, alumina (Al₂O₃) and copper (Cu) are considered the hybrid nanoparticles, while water (H₂O) is the base fluid. To begin, the required similarity transformations are applied to transform the nonlinear coupled PDEs into nonlinear coupled ODEs. The obtained highly nonlinear sets of ODEs are then solved analytically by using the HAM procedure. The calculations of the thermal radiation term in the energy equation are done based on the Roseland approximation. The result of various embedded variables on temperature and velocity profiles is drawn and explained briefly. Aside from that, the numerical solution of well-known physical quantities, like skin friction and the Nusselt number, is computed by means of tables for the modification of the relevant parameter. The analysis shows that the magnetic field has opposite behavior on θ(η) and f'(η) profiles. It is seen that more magnetic factors M decline f'(η) and upsurge θ(η). Moreover, the behavior of skin friction and the Nusselt number are the same for the magnetic parameter M. Meanwhile, a higher Reynolds number R_e declines temperature profile and skin friction while upsurging the local Nusselt number. There are many applications of this study that are not limited to engineering and manufacturing, such as polymer industry, crystal growth, tumor therapy, plasma, fusing metal in electric heaters, nuclear reactors, asthma treatment, gastric medication, cooling of atomic systems, electrolytic biomedicine, helical coil heat exchangers, axial fan design, polymer industry, plane counter jets, and solar collectors.

Modelling and simulation have now become standard methods that serve to cut the economic costs of R&D for novel advanced systems. This paper introduces the study of modelling and simulation of the infrared thermography process to detect defects in the hydroelectric penstock. A 3-D penstock model was built in ANSYS version 19.2.0. Flat bottom holes of different sizes and depths were created on the inner surface of the model as an optimal scenario to represent the subsurface defect in the penstock. The FEM was applied to mimic the heat transfer in the proposed model. The model’s outer surface was excited at multiple excitation frequencies by a sinusoidal heat flux, and the thermal response of the model was presented in the form of thermal images to show the temperature contrast due to the presence of defects. The harmonic approximation method was applied to calculate the phase angle, and its relationship with respect to defect depth and defect size was also studied. The results confirmed that the FEM model has led to a better understanding of lock-in infrared thermography and can be used to detect subsurface defects in the hydroelectric penstock.

The work is devoted to the numerical solution of the initial boundary value problem for the heat equation with a fractional Riesz derivative. Explicit and implicit difference schemes are constructed that approximate the boundary value problem for the heat equation with a fractional Riesz derivative with respect to the coordinate. In the case of an explicit difference scheme, a condition is obtained for the time step at which the difference scheme converges. For an implicit difference scheme, a theorem on unconditional convergence is proved. An example of a numerical calculation using an implicit difference scheme is given. It has been established that when passing to a fractional derivative, the process of heat propagation slows down.