In this study, the effect of through-thickness shear (TTS) stress has been examined on the prediction of forming limit diagrams (FLDs). Determination of the FLD is based on the Marciniak–Kuczynski (M–K) model with some modifications on the stress states for consideration of the TTS stress effects. For solving the equations, the Newton–Raphson method has been used. Furthermore, the Nakazima test has been simulated to investigate the stress state which occurs in the sheet during the test. Results showed that the formability of sheet metal could be better as the through-thickness stress increased. Also, implementation of TTS stress in the present model, the corresponding FLD has better agreement than the conventional FLD with the experimental test result.

In this research, a combined cycle using a solid oxide fuel cell system, a single-stage H₂O-NH₃ absorption chiller and a residential hot water HX, is developed for the electricity production, hot water and cooling all at the same time, and it is studied from an exergy, energy, and exergoeconomic standpoint. Performance of system under the design condition is analyzed and the mathematical model is simulated. After analyzing the results in the initial input mode, changing the fuel cell current density effect and fuel utilization factor on the system efficiency is evaluated. The result indicates that total energy is 4.418 kW, the total exergy efficiency is 37.8%. And the overall irreversibility is 1.650 kW. On the other hand, the air HX, fuel cell and water HX are designed as elements that must be given more attention than others from the exergoeconomic perspective, because they have nearly the most amount of price compared to other parts.

Improper location of gas stations leads to waste of resources, time, and user dissatisfaction. On the other hand, the optimal location of these facilities will have a significant impact not only on the quality of traffic in the network but also on their economic success. The aim of this research is the spatial-physical organization of inner-city structures with an emphasis on the location of gas stations using the weighted linear integrated model method on the GIS platform using the descriptive-analytical method. First, the location of the existing stations and the areas that need gas stations were determined using the weighted linear integrated model (WLC) and ArcGIS. A scoring-based method was used to convert the maps into a standard scale ranging from 0 to 1 and 0 to 255. The Analytical Hierarchy Process (AHP) method and the Expert Choice app were used to determine the criteria weights. Then, the GIS and WLC capability to provide a suitable model for locating stations was tested. The result states that for the construction of gas stations, the Bahmanyar region will be the priority. North Khani Abad region is the second priority, and South Khani Abad and Esfandiari regions are the following priorities. Finally, with the local investigation of the prioritized areas by WLC, it was found that these areas are suitable for constructing gas stations. This method can be used for finding a suitable location for gas station construction in all other cases.

Nowadays, due to stricter pollution standards, more attention has been focused on pollutants emitted from cars. As a very dangerous pollutant, NO_x has always triggered the sensitivity of the related organizations. In the process of developing and designing the engine, estimating the amount of this pollutant is of great importance to reduce future expenses. Calculating the amount of this pollutant has usually been complicated and prone to error. In the present paper, neural networks have been used to find the coefficients of correcting NO_x calculation. The Zeldovich method calculated the value of NO_x with 20% error. By applying the progressive neural network and correcting the equation coefficient, this value decreased. The related model has been validated with other fuel equivalence ratios. The neural network model has fitted the experimental points with a convergence ratio of 0.99 and a squared error of 0.0019. Finally, the value of NO_x anticipated by the neural network has been calculated and validated according to empirical data by applying maximum genetic algorithm. The maximum point for the fuel composed of 20% hydrogen and 80% methane occurred in the equivalence ratio of 0.9; and the maximum point for the fuel composed of 40% hydrogen occurred in equivalence ratio of 0.92. The consistency of the model findings with the empirical data shows the potential of the neural network in anticipating the amount of NO_x.

The use of different energy sources and the worry of running out of some of them in the modern world have made factors such as environmental pollution and even energy sustainability vital. Vital resources for humanity include water, environment, food, and energy. As a result, building strong trust in these resources is crucial because of their interconnected nature. Sustainability in security of energy, water and food, generally decreases costs and improves durability. This study introduces and describes the components of a system named “Desktop Energetic Dark Greenhouse” in the context of the quadruple nexus of water, environment, food, and energy in urban life. This solution can concurrently serve to strengthen the sustainable security of water, environment, food, and energy. For home productivity, a small-scale version of this project was completed. The costs and revenues for this system have been determined after conducting an economic study from the viewpoints of the investor and the average household. The findings indicate that the capital return period is around five years from the investor’s perspective. The capital return on investment for this system is less than 4 years from the standpoint of the households. According to the estimates, this system annually supplies about 20 kg of vegetables or herbs, which means about one third of the annual needs of a family.