This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R²), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R² obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R² is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.

Natural fiber-reinforced polymer composite is a rapidly growing topic of research due to the simplicity of obtaining composites that is biodegradable and environmentally friendly. The resulting composites have mechanical properties comparable to synthetic fiber-reinforced composites. In this regard, the present work is formulated with the objectives related to the development, characterization, and optimization of the wt of reinforcements and the process parameters. The novelty of this work is related to the identification and standardization of the appropriate wt of reinforcements and parameters for the processing of the areca nut leaf sheath fiber-based polymer composites for enhanced performance attributes. With this basic purview and scope, the composites are synthesized using the hand layup process, and the composite samples of various fiber compositions (20, 30, 40, and 50) are fabricated. The mechanical characteristics of biodegradable polymer composites reinforced with areca nut leaf sheath fibers are investigated in the present work, with a focus on the effect of fiber composition (tensile properties, flexural strength, and impact strength). The properties of composites are enhanced by combining the areca nut leaf sheath fiber and epoxy resin, with a fiber content of 50 being the optimal wt. The Scanning electron microscopy (SEM) investigations also ascertain this by depicting the good interfacial adhesion between the areca nut leaf sheath fiber and the epoxy resin. The tensile strength of the composite specimen reinforced with 50 areca nut fiber increases to 44.6MPa, while the youngs modulus increases to 1900MPa, flexural strength increases to 64.8MPa, the flexural modulus increases to 37.9GPa, and impact strength increases to 34.1kJ/m². As a result, the combination of areca nut leaf sheath fiber reinforced epoxy resin shows considerable potential as a renewable and biodegradable polymer composite. Furthermore, areca nut leaf sheath fiber-reinforced epoxy resin composites are likely to replace petroleum-based polymers in the future. The ecosustainability and biodegradability of the composite specimen alongside the improved mechanical characteristics serve as the major highlight of the present work, and can help the polymer composite industry to further augment the synthetic matrix and fiber-based composites with the natural fiber-reinforced composites.

The current study examines the usage of biodiesel as a diesel substitute that is produced through the transesterification of pumpkin and maize with the addition of a diethyl ether (DEE) additive. Pumpkin-maize (PM) biodiesel and addition of diethyl ether (DEE), as well as their blends of 10, 20, 30, 40, and 50 with diesel, were used in performance, combustion, and emission examinations under various load conditions. According to the experimental findings, adding 5ml of the DEE boosts BTE by 31.91 percent (B20 blend) compared to diesel. While the diesel equivalent of BSFC decreases by 9.519. NO emission dropped by 34.91 percent at peak loads, HC emissions were augmented by 32.43, and smoke opacity improved by 27.24. To enhance the engine performance, combustion, and emission features of the substitute biodiesel diesel, the study emphasises the precise mix proportions of PM biodiesel with DEE addition. Using ANN, BTE, and NO were predicted with R² values of 0.93 and 0.95, respectively. As can be observed, the R² value for the ANN model was almost one, suggesting that the ANN models had better predictive power than other machine learning (ML) models.

Unmanned aerial vehicles (UAVs) are gaining in popularity and sophistication in today’s modern world. UAVs are now available in a wide range of configurations. A UAV’s many applications include aerial photography and videography and target tracking. The upward-pointing propellers of some modern fixed-wing UAVs make it possible for them to take off and land vertically. Surveillance and intruder inspections are two areas where the blended wing body (BWB) configuration shines. This is because its weight is spread uniformly throughout the body, its radar signal is weaker than that of alternative configurations, and there is a relatively small amount of interference with its movement. With common design factors in mind, like vertical takeoff and landing, aerodynamic drag, and fundamental wing stability, the optimal BWB plan form for surveillance is designed. CATIA is used to finish the conceptual design of the BWB-based UAV. A fluid-structure interaction (FSI) study is carried out after the model has been examined in ANSYS Fluent. The UAV’s responsiveness is improved through simulation in the MATLAB environment after a proportional-integral-derivative-type altitude controller was developed. The results demonstrate that providing the UAV with an altitude instruction enhances its performance. Given the flexibility of the suggested BWB UAV’s design, we have decided to limit its maximum forward speed to 75 m/s and its maximum rate of vertical ascension to 50 m/s. Rapid BWB UAVs like the one seen here are quite helpful in dangerous situations.

The traditional curing processes necessitate a large amount of water. This is especially difficult in locations where there is a lack of water and for construction of high-rise structures. In this research article, we provide a remedy by inventing concrete that will not require additional water for curing. In the present work, polyethylene glycol was utilized as an internal curing agent in varying percentages. The internally cured concrete with polyethylene glycol was cured at ambient conditions, whereas the conventional concrete without polyethylene glycol was kept in water for curing. Fresh and hardened characteristics of concrete with and without polyethylene glycol are compared. The results revealed that 1.5% polyethylene glycol is an optimum percentage where maximum strength and durable properties are achieved. The microstructure of internal curing concrete indicates that the pore sizes are small compared to conventional concrete. The crystallite size is smaller in internal curing compared to conventional concrete mixtures, resulting in an acceleration of the hydration process in concrete with smaller crystallites.Further experimental results are compared with ANN. Predicted results are very close to experimental values.