Ashok Kumar Dewangan

The thermal stability of lithium-ion batteries used in electric vehicles and energy storage systems is critical to their performance, reliability, and safety. This study proposes and evaluates a novel thermal management system that integrates a paraffin (RT44HC)/expanded graphite (EG) composite phase change material (CPCM) with liquid cooling. The proposed system is designed to effectively regulate battery temperature rise and minimize temperature non-uniformity within the battery module. The thermal performance of the system was evaluated at a 3C discharge rate based on the liquid volume fraction (LVF), maximum cell temperature (T_max), and temperature difference within the module (ΔT). Among the investigated configurations, the CPCM containing 12 wt.% expanded graphite exhibited the best thermal performance, providing effective temperature regulation and minimizing temperature differences within the module. These results demonstrate the potential of the paraffin/EG CPCM as an effective thermal management medium for lithium-ion battery systems. Furthermore, the temperature and energy consumption of the proposed coupled thermal management system were predicted using machine learning-based multi-output regression models, including a back-propagation neural network (BPNN) and Random Forest (RF). The RF model exhibited excellent agreement with the simulation results, demonstrating its potential for accurately predicting the thermal and energy performance of the proposed system. Overall, the integration of CPCM-based thermal management with machine learning-based prediction provides a promising approach for enhancing the thermal stability, efficiency, and reliability of lithium-ion battery systems.

The current study focuses on optimizing AC TIG welding parameters to achieve higher hardness and better control of the heat-affected zone (HAZ) using a Taguchi L16 orthogonal array on 10 mm-thick AA5083 alloy. The study establishes a heat-input-oriented optimization framework for AC TIG welding of thick AA5083 aluminum alloy by integrating statistical optimization with metallurgical characterization. The control parameters, including AC pulse modulation frequency, peak current, and welding speed, were selected to achieve improved hardness and a smaller heat-affected zone (HAZ). Qualitative microstructural characterization using optical microscopy, SEM, and XRD indicated comparatively finer weld morphology and fewer visible defects under lower heat-input conditions, while XRD confirmed the presence of the α-Al matrix together with Al₁₃Fe₄, Mg₂Si, and minor Si phases. The results indicate that lower heat input contributes to improved hardness retention and reduced HAZ width within the investigated parameter range, providing practical guidance for AC TIG welding of thick AA5083 aluminum alloy.

Hydrogen blending in natural gas pipelines is a promising decarbonization pathway. This study investigates a coaxial-swirl static mixer for hydrogen-natural gas mixing at ratios of 5% to 30% H₂. The mixer features nine ring-shaped cavities with 120° helical torsion to enhance turbulent mixing. A calibrated 2D axisymmetric computational model was developed and validated against experimental data. Results show that the configuration achieves 95% mixing uniformity within 8.2D to 9.0D across all blending ratios, meeting industry targets with minimal pressure penalty (<0.04% of operating pressure). Validation shows good agreement with literature, with mixing intensity profiles matching within 5%. This work supports the integration of hydrogen into existing infrastructure for near-term decarbonization.

Magnesium alloys, especially AZ80, are of great interest in the aerospace, automotive, and biomedical sectors because of their superior strength-to-weight ratio, corrosion resistance, and biocompatibility. Despite this, AZ80 is very difficult to machine due to its low melting point, high thermal conductivity, and reactivity, which result in problems such as poor surface finish, high tool wear, and even fire risks during traditional machining operations. To overcome these challenges, the noncontact machining process, W-EDM machining, is being researched more extensively for the accurate machining of magnesium alloys. In this investigation, the impact of WEDM process parameters on the machinability of magnesium alloy AZ80 was explored. Parameters such as [ ], [ ], wire tension, [ ], and [ ] were focused on optimizing the MRR, SR (Ra). Experiments were carried out on an ECO 35 CNC W-EDM to understand how these parameters affect the thermal and mechanical factors of the machining process. Due to the high thermal conductivity and low melting point of AZ80, undue heat may cause microstructural changes and defects such as recast layers and thermal cracks, and therefore, control of the process parameters is of utmost importance. Early experiments indicate that increased discharge energy, preferably obtained with longer [ ] and increased [ ], produces increased MRR but at the cost of surface quality. A greater pulse-off-time, on the other hand, enhances the effective flushing of chips from the machining area, which reduces wire breakage and enhances surface finish. It is found from the results that an optimum [ ] as medium and optimized pulse-off-time lead to a good trade-off between the MRR and surface quality with acceptable surface roughness (Ra) of [ ]m. The experimentation revealed that the optimal combination of pulse on time of 110 [ ]s, pulse off time of 50 [ ]s, current of 3 A, and voltage of 80 V resulted in a minimum Surface Roughness (SR) of 2.11 [ ]m and a maximum Material Removal Rate (MRR) of 8.47 mm³/min. The Taguchi L9 orthogonal array and grey relational analysis successfully identified the dominant parameters, with pulse on time having the highest influence on both SR and MRR.