This study presents numerical analyses of the thermal, metallurgical, and mechanical processes involved in welding. The temperature fields were computed by solving the transient heat transfer equation using the ABAQUS/Standard 2024 finite element solver. Two types of moving heat sources were applied: a surface Gaussian distribution and a volumetric model, both implemented via DFLUX subroutines to simulate welding on butt-jointed plates. The simulation accounted for key welding parameters, including current, voltage, welding speed, and plate dimensions. The thermophysical properties of the INC 738 LC nickel superalloy were used in the model. Solidification characteristics, such as dendritic arm spacing, were estimated based on cooling rates around the weld pool. The model also calculated transverse residual stresses and applied a hot cracking criterion to identify regions vulnerable to cracking. The peak transverse stress, recorded in the heat-affected zone (HAZ), reached 1.1 GPa under Goldak’s heat input model. Additionally, distortions in the welded plates were evaluated for both heat source configurations.

This study presents a quantitative comparison between the Jensen and Ishihara wake models to assess their impact on power output in wind farms. Both models were applied in a MATLAB-based simulation environment using pseudo-random number generation to optimize the configuration of 30 ENERCON E2 turbines on a 100-cell grid. Wind speeds between 7 m/s and 12 m/s were simulated. The Ishihara model predicted 57.96 MW at 12 m/s, corresponding to a 2.09% reduction in power, while the Jensen model predicted 55.71 MW, corresponding to a 5.89% reduction. For the Jensen model, the wake radius at x/D=10 was 4.53; for the Ishihara model, it was 2.05. The results show that the Ishihara model provides more accurate predictions of wake behavior and energy yield. At wind speeds close to the rated value (13 m/s), where wake effects become insignificant, both models agree. These results highlight the need to select an appropriate wake model in the design of wind farms to achieve optimal turbine placement and accurate energy predictions.