The current study assesses several water-based PVT system thermal absorber configurations. The thermal absorber in PVT system plays a vital role in efficiency evaluation as it lowers PV temperature and collects heat energy. The current study aims to discover and analyze advanced thermal absorber design by comparing well-received spiral circular absorbers and non-cooled PV with proposed semi-circular thermal absorbers with varying flow configurations. The proposed thermal absorber maintains surface contact with PV panels and improves heat transfer thereby yielding better thermal and electrical efficiency. Simulated PVT systems have a constant water flow rate and solar radiation. The CFD-FLUENT software was preferred to evaluate the PVT system in steady-state conditions for the investigation. Under constant ambient and inlet water temperatures of 299 K, the PV temperatures at the surface, water discharge temperature, and pressure drop were measured. It was discovered that a thermal absorber could effectively lower PV surface temperature by cooling. A zigzag thermal absorber was the most efficient since it produced the highest water outlet temperature and lowest PV surface temperature while also slightly raising the pressure drop. In comparison with a non-cooled PV system, a zigzag thermal absorber PVT system yields 11.97% more electrical efficiency, with an addition of 76.75% thermal efficiency. It was also noticed that a conventional spiral circular PVT system provides 13.5% electrical efficiency and 54.8% thermal efficiency while an electrical efficiency of 13.61% and thermal efficiency is 76.75% was obtained from a zigzag thermal absorber PVT system. The zigzag thermal absorber PVT system had a high initial investment of INR 38809.00. It showed a simple payback of 4.63 years, a 28% return on investment with a promising 2.1 Debt Service Coverage Ratio. It is advisable to consider incorporating zigzag semi-circular PVT in the prospective improvements of the PVT system.
This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, through the addition of nanoparticles to conventional fuels, improves fuel atomization, combustion efficiency, and emission control. Simultaneously, LSTM models are employed to analyze and predict the complex spray behavior under diverse operational and geometric conditions. Key parameters, including spray penetration, droplet size distribution, and evaporation rates, are modeled and validated against experimental data. The findings reveal that nanoparticle-enhanced fuels, coupled with LSTM-based predictive analytics, lead to superior combustion performance and lower pollutant formation. This interdisciplinary approach provides a robust framework for designing next-generation CI engines with improved efficiency and sustainability. Diesel engine performance and emissions were found to be influenced by variations in combustion chamber geometry, underwent validation through simulation using Diesel-RK. Re-entrant bowl profile in quaternary blend is found to exhibit 31.3% higher BTE and 8.65% lowered BSFC than the conventional HCC bowl at full load condition. Emission wise, re-entrant bowl induced 90.16% lowered CO, 59.95% lowered HC and 15.48% lowered smoke owing to improved spray penetration and faster burning of soot precursors. However, the NOx emissions of DBOPN-TRCC were found to be higher. The simulation outcomes, derived from Diesel-RK, were subsequently compared with empirical data obtained from real-world experiments. These experiments were systematically carried out under identical operating conditions, employing different piston bowl geometries.