Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries.

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 NO 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.