In-service welding is a type of hot work repair process using pipeline sleeves. It is hazardous and necessitates thorough planning and procedures. The inside pipe surface and fluid temperature are all unknown and unpredictable. Therefore, the risk resulting from burn-through where the welding arc causes pipe wall breaching, hydrogen cracking, and the likelihood of occupational health risks are high. Hence, this work aimed to predict the pipe’s inner surface temperature and fluid temperature in contact with the pipe’s inner surface during the buttering layers welding of the pipeline sleeve to determine if it is safe to carry out welding of the buttering layers in a no-flow condition. This analysis was achieved through 2-dimensional Steady-State Thermal Analysis in Ansys APDL (Ansys Parametric Design Language). The Ansys simulation results showed that the fluid temperature was high, almost getting to the fluid autoignition temperature, and in some instances, even higher than the fluid autoignition temperature. The implication is that, in-service buttering layers welding of pipeline sleeves must not be performed in a no-flow condition during pipeline repair. Also, hot work repair welding for pipeline sleeves by the in-service method could be safely done following all necessary precautions and preventive measures where in-service welding for pipeline sleeves during the buttering layers might cause a severe hazard and dangerous incidents such as explosion. The temperature prediction helps to assure safety in in-service welding for pipeline sleeves to avoid pipeline explosion due to extremely high temperature or decrease in the toughness of the Heat-affected-zone (HAZ) in the welded joint because of the high cooling rate of the weldment, which reduces the pipe mechanical strength.

Heavy oil represents the greatest portion of the remaining oil and with the increasing demand for energy sources, the interest and efforts are directed toward producing from reservoirs with such kind of oil. The common method to produce the heavy oil is the thermal recovery method, but recently a new technology applied for enhancing the heavy oil recovery which is using Nano additives due to their ability to alter certain factors in the formation and in oil properties. In this study the catalytic effect of Nano-Fe2O3 in the viscosity of Sudanese heavy oil was studied and the result indicate that the Fe2O3 decreases the viscosity considerably at certain concentration and temperature, for this study 0.25% wt additive gave the maximum reduction in the viscosity. Also, this work investigated experimentally the effect of Nano-Fe2O3 in the oil recovery by steam injection and the result showed that when injecting a mixture of steam and Nano-Fe2O3 there is an increase in the oil recovery factor due to cracking reactions which convert the heavy component to lighter components, in this experiment there was an increase of 8% in the recovery factor.

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with significant applications in logistics, transportation, and network design. Efficiently solving this problem requires a careful balance between exploration and exploitation while addressing challenges such as premature convergence and stagnation in local optima. To tackle these issues, numerous algorithms from different perspective have been designed and developed. Among them, Simulated Annealing (SA) is a widely used meta-heuristic approach for solving TSP due to its ability to escape local optima and explore a broad solution space. However, conventional SA can still become trapped in local minima, leading to suboptimal solutions. In this paper, we propose an enhanced SA algorithm that incorporates self-escape mechanism to improve the solution quality of the TSP instances. The self-escape mechanism dynamically identifies trapped routes and facilitate better exploration and diversification. Specifically, the self-escape mechanism introduces a local search refinement process, allowing solutions to effectively escape local optima. Simulation results on benchmark TSP instances demonstrate that the proposed algorithm outperforms conventional SA in terms of solution accuracy. The findings suggest that self-escape mechanism can significantly enhance the effectiveness of SA by preventing premature convergence in complex optimization problems.

This research presents a comprehensive thermal and structural evaluation of a newly developed aluminium matrix composite reinforced with selected agricultural by-products—palm kernel shell, bamboo fibre, rice husk, and groundnut shell—using Finite Element Analysis (FEA) within the ANSYS 2025 environment. Aluminium scrap served as the matrix material in the composite fabrication. The experimental design followed a D-Optimal mixture approach, yielding twenty-five specimen combinations, each tested thrice, with mean values recorded. Specimen fabrication employed the stir casting technique. Optimization of process parameters and response outcomes was performed using Design Expert software. The composite model was developed using SOLIDWORKS for subsequent simulation analysis. Results from the thermal and structural simulations indicate a fatigue life of 1 × 10⁶ cycles. The computed maximum and minimum total heat fluxes were 1.8122 × 10⁶ W/m² and 1.515 × 10⁶ W/m², respectively, while the fatigue damage factor reached 1000. The safety factor varied between 4.836 and 15. Temperature values ranged from 23.685°C to 170.000°C. The composite exhibited equivalent elastic strain values between 1.054 × 10⁻⁶ and 2.9051 × 10⁻⁵. Directional deformation along the x-axis ranged from –2.5905 × 10⁻⁷ m to 2.5889 × 10⁻⁷ m. Equivalent (Von-Mises) stress was recorded between 1.3224 × 10⁵ Pa and 5.8104 × 10⁷ Pa, while total deformation ranged from 0.0000 m to 2.5912 × 10⁻⁷ m. These findings underscore the mechanical and thermal reliability of the developed composite material for engineering applications under variable thermal and mechanical loads.