Thermal losses and emissions are considered as major contributing factors in decreasing the efficiency of engines and life span of engines. To prevent heat losses and emissions, thermal ceramic coatings are considered as potential contenders. Moreover, ceramic coatings also prevent engine parts from damage due to thermal stresses. In this computational study, the effect of Zirconium Carbide coating on the thermal behavior of V12 engine is explored. Geometry of V12 engine’s piston and cylinder is generated in SolidWorks 2016. After that, piston’s surface and cylinder’s inner walls are coated with Zirconium Carbide ceramic coating and thermal behavior is analyzed using ANSYS Workbench 19.2. Two cases are discussed in this work. In the first case, piston is made of Al-3003 alloy and cylinder walls are made of gray cast iron whereas in the second case, whole assembly is made of Al-3003 alloy. Temperature and heat flux profiles of both the cases are compared with coating and without coating. Maximum temperature is recorded to be at the top of piston head surface. The results show 44.9% increase in maximum temperature for the first case whereas 83.5% increase in the second case. Heat flux results after show reduction after coating. Zirconium Carbide ceramic coatings can act as thermal barrier and prevent engine parts from thermal stresses. Moreover, it provides insulation to cylinder which prevents heat transfer and retains more temperature inside the cylinder which in turn enhances the efficiency.

This research focuses on design, simulation and development of a high precision Computer Numerically Controlled (CNC) spindle for vertical machining center. High speed spindle is designed and analyzed under the varying load conditions. SolidWorks 2020 is used for designing of CNC spindle (based on ISO BT40 standards) and ANSYS Workbench 2020 is used for analysis. Moreover, analytical calculations for deformation are also done. Three different materials i.e. AISI 4340 steel, Grey cast iron, and Ti6Al4V alloy are chosen for the analysis. Effect of varying load on deformation and stress on CNC spindle is discussed. AISI 4340 steel showed better mechanical characteristics as compared to other materials. Moreover, the results showed that steel gives lesser values of deformation as compared to other materials, when applied under different load conditions. Although Grey cast iron can be economical and weight of the spindle can also be reduced but Grey Cast iron has greater value of deformation when load is applied. On the other hand, Ti6Al4V alloy specimen exhibited less stress as compared to other materials but mechanical properties in terms of modulus of elasticity are not that good as AISI 4340 steel. So, AISI 4340 steel could be a good option for spindle materials that require desired properties such as strong corrosion resistance, tensile strength, yield strength, and propagation. This will boost the accuracy and efficiency of spindle. Based on these results, high-precision CNC spindle was developed.

Aluminum alloy 2024 (AA2024) is widely used in the aerospace sector, where a fine, uniform, and equiaxed grain structure is crucial for achieving enhanced mechanical properties. This study examines the effect of dynamic solidification, assisted by mechanical vibrations and conformal cooling, on the microstructural evolution and mechanical properties of permanent mold-cast AA2024. Mechanical vibrations were applied during solidification in the frequency range of 15–45 Hz and acceleration of 0.5–1.5 g. Process parameters, including pouring temperature, die temperature, vibration frequency, and acceleration, were optimized using an L9 orthogonal array based on the Taguchi method. Analysis of variance (ANOVA) was performed to determine the significance of the aforementioned process parameters. In addition, the alloy’s microstructure was observed through a microscope, which revealed a transition from dendritic to non-dendritic microstructure due to dynamic solidification. The average grain size of the alloy was significantly reduced by 40.9%. Moreover, the values of hardness and Ultimate Tensile Strength (UTS) of the alloy were improved by 13.5% and 10.6%, respectively. Optimal results were obtained at a pouring temperature of 750 °C, die temperature of 150 °C, frequency of 45 Hz, and acceleration of 1.0 g. Moreover, uncertainty analysis for all three responses was also performed.

Accurate solar irradiance (SI) forecasting is essential for photovoltaic operation, grid stability, and sustainable energy planning, yet its nonlinear dependence on temporal, meteorological, and radiation‐related variables remains difficult to model across diverse climatic regions. Existing forecasting studies often emphasize single‐site accuracy, with limited attention to dataset‐specific feature relevance, interpretability, ablation‐based architectural validation, and cross‐regional robustness. This study proposes a reproducible tree‐boost–guided convolutional neural network (CNN)–bidirectional long short‐term memory (BiLSTM)–transformer framework for SI forecasting. A scikit‐learn Gradient Boosting Regressor is first used for dataset‐specific feature selection, followed by 24‐h sequence modeling through CNN‐based local temporal extraction, BiLSTM‐based bidirectional dependency learning, and Transformer‐based attention refinement. The framework is evaluated on 7 hourly NASA POWER sites and one utility‐scale QASP ground dataset. The model achieved strong cross‐site performance, with best‐site MAE = 0.129, RMSE = 0.224, and R ² = 0.947, while on QASP‐GDATA it obtained MAPE = 0.551, MAE = 0.139, RMSE = 0.246, and R ² = 0.942. Compared with Prophet, Seasonal ARIMA (SARIMA), artificial neural network (ANN), gated recurrent unit (GRU), CNN–LSTM, temporal convolutional network (TCN), and Informer, the proposed model showed robust and competitive performance across different forecasting families. SHAP analysis confirmed physically meaningful predictors, and ablation results verified the contribution of the complete hybrid architecture. Overall, the framework offers an interpretable and scalable forecasting tool for solar planning and grid‐support applications.