The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number (Nu_avg) data using four activations functions. The battery Nu_avg is highly nonlinear as reported in the literature, which depends mainly on flow velocity, coolant type, heat generation, thermal conductivity, battery length to width ratio, and space between the parallel battery packs. Nu_avg is modeled at first using only one hidden layer in the network (NN₁). The neurons in NN₁ are experimented from 1 to 10 with activation functions: Sigmoidal, Gaussian, Tanh, and Linear functions to get the optimized NN₁. Similarly, deep NN (NN_D) was also analyzed with neurons and activations functions to find an optimized number of hidden layers to predict the Nu_avg. RSME (root mean square error) and R-Squared (R²) is accessed to conclude the optimized NN model. From this computational experiment, it is found that NN₁ and NN_D both accurately predict the battery data. Six neurons in the hidden layer for NN₁ give the best predictions. Sigmoidal and Gaussian functions have provided the best results for the NN₁ model. In NN_D, the optimized model is obtained at different hidden layers and neurons for each activation function. The Sigmoidal and Gaussian functions outperformed the Tanh and Linear functions in an NN₁ model. The linear function, on the other hand, was unable to forecast the battery data adequately. The Gaussian and Linear functions outperformed the other two NN-operated functions in the NN_D model. Overall, the deep NN (NN_D) model predicted better than the single-layered NN (NN₁) model for each activation function.

Friction stir welding (FSW) is increasingly utilized in aerospace for welding dissimilar Al–Mg alloys without melting, overcoming fusion welding challenges. This summary highlights FSW’s key aspects for dissimilar Al–Mg alloys and its aerospace relevance. These alloys are widely used in aerospace due to their beneficial properties, but fusion welding faces issues like brittle intermetallic compounds (IMC) and decreased mechanical properties. FSW addresses these challenges by using a rotating tool to generate frictional heat, plasticizing the material for solid-state joining without melting. This reduces IMC formation, enhancing joint strength and mechanical properties. Critical parameters like rotational speed, traverse speed, tool design, and process variables are emphasized for optimal FSW of dissimilar Al–Mg alloys. Joining these alloys is crucial in aerospace for applications such as aircraft structures, engine components, and fuel tanks. FSW offers advantages like weight reduction, improved fuel efficiency, and structural integrity enhancement. It allows welding dissimilar Al–Mg alloys with varying compositions for tailored material combinations meeting specific needs. In conclusion, FSW of dissimilar aluminum alloys is promising for aerospace, creating defect-free joints with improved mechanical properties. However, further research is needed to optimize parameters, explore tool designs, and validate long-term performance in aerospace environments.

In the past few years, significant research efforts have been directed toward improving the electrochemical capabilities of supercapacitors by advancing electrode materials. The present work signifies the development of poly(lactic acid)/alloysite nano-clay as an electrode material for supercapacitors. Physico-chemical characterizations were analyzed by Fourier transform infrared spectroscopy, wide-angle X-ray diffraction, and a universal testing machine. Cyclic voltammetry, electrochemical impedance spectroscopy, and galvanostatic charge–discharge techniques were employed to evaluate electrochemical characteristics. The optimized poly(lactic acid)/halloysite nano-clay film revealed the highest specific capacitance of 205.5 F g⁻¹ at 0.05 A g⁻¹ current density and showed 14.6 Wh kg⁻¹ energy density at 72 W kg⁻¹ power density. Capacitance retention of 98.48% was achieved after 1000 cycles. The microsupercapacitor device presented a specific capacitance of 197.7 mF g⁻¹ at a current density of 0.45 mA g⁻¹ with 10.8 mWh kg⁻¹ energy density at 549 mW kg⁻¹ power density.

Nowadays, power quality under the excessive implementation of power electronics devices is quite challenging issue. The compensation of non-sinusoidal; reactive and harmonic; components is the main role for power quality devices which highly depend on the robustness of the control system. Some common control systems are implemented using Synchronous Stationary Frame (DQ) theory. This paper proposes a new version of DQ control technique to control dynamic voltage restorer under severe transient voltage conditions. The power system network with the new DQ control technique is studied and analyzed under different scenarios to compensate for severe balanced and unbalanced voltage sags and swells. This new scheme is based on extraction of positive sequence components to implement the control algorithm. A mathematical model of the dynamic voltage restorer (DVR), hysteresis voltage control, converter controller model, new DQ scheme with complete system equations are carried out and verified using Simulink / MATLAB. The proposed system is validated experimentally using DSpace 1104 based laboratory system. The obtained results of the proposed compensation algorithm are compared with the results obtained from the traditional DQ method. Simulation and experimental results are correlated and show effectiveness of the proposed DQ control scheme.

The present study aims at understanding the effects of fuel preheating on engine characteristics of waste animal fat-oil (WAF-O) biodiesel in a single-cylinder CI engine, with the preheating technique proposed as an effective means for enhancing the fuel properties. To understand the effects of the preheated fuel, the WAF-O biodiesel was preheated at 60, 80, 100 and 120 °C and tested along with neat diesel and unheated WAF-O biodiesel. For this purpose, biodiesel was produced from different animal wastes by means of KOH-assisted ethanol-based transesterification, reporting its maximum yield as 96.37 ± 1.8%, with significant distribution of unsaturated oleic acid, saturated palmitic acid and stearic acid. Upon evaluating its fuel characteristics as per ASTM D6751 standards, a rise in preheating temperature by 1 °C reduced the density and kinematic viscosity of WAF-O biodiesel by 0.383 kg/m³ and 0.025 mm²/s, respectively, and was explained by the weakening of intermolecular forces between its fatty acid ester molecules. Preheated samples reported superior combustion characteristics by exhibiting increased in-cylinder pressure (2.24%, on average) and heat release rates in addition to their shortened ignition delay (1–4 °CA). Furthermore, preheating of WAF-O biodiesel reduced its specific fuel consumption and increased its brake thermal efficiency by 7.86% (on average) and 9.23% (on average), respectively. However, higher preheating temperatures (>120 °C) resulted in increased fuel consumption owing to its varied flow characteristics. In addition to the changes in combustion characteristics, preheating WAF-O bio-diesel also resulted in reduced carbon monoxide, nitrous oxide and hydrocarbon emission by 13.88%, 7.21% and 26.94%, respectively, and increased carbon dioxide emission by 7.58%. Summing up, the enhancements in overall engine characteristics of preheated samples were accounted for by their improvised fuel injection characteristics due to their reduced density and viscosity, which ensured for their effective combustion.

This Power quality problem is a vital issue in current-day power system with the increased penetration of distributed generation (DG) systems which greatly affects the stability and reliability of the system. This study presents a comprehensive analysis of detection and classification strategies for monitoring power quality (PQ) disturbances. Specifically, the focus is on PQ disturbances caused as a result of load switching and variations in environmental factors, such as changes in solar irradiation and wind speed. Twelve different types of PQ disturbances under various operating scenarios are considered for the detection and classification study. Twelve different statistical features are extracted by passing the disturbances through robust H-infinity filter in order to minimize the data size and computational complexities. The statistical properties of PQ disturbances are initially categorized using the Fast Generalized Fuzzy-C means (FGFCM) clustering approach. Further, the disturbances are correctly classified by pattern classification approach like support vector machines (SVM) in order to distinctly classify them. The validity of the classification analysis is reinforced by the inclusion of experimental data gathered from a prototype wind energy system and photovoltaic (PV) system setup. Again, Genetic algorithm (GA) is used for extracting optimal features and Particle swarm optimization (PSO) is applied for optimising the heuristic parameters of the SVM in order to improvise the classification accuracy under normal, noisy as well as increased penetration of wind and photovoltaic (PV) systems.