Application of artificial neural networks to model macroscopic spray characteristics of alcohol fuels

In pursuit of global efforts to mitigate climate change and achieve net-zero carbon emissions by 2050, alcohol-based fuels are gaining attention as low-carbon alternatives for combustion engines. This study presents a novel application of Artificial Intelligence (AI) to predict the spray characteristics, specifically spray cone angle and penetration length, for a wide range of alcohol fuels and operating conditions. A robust Artificial Neural Network (ANN) model was developed using the Keras Application Programming Interface (API) on the TensorFlow platform, trained on a comprehensive dataset combining in-house experimental data for ethanol, octanol, and published data for methanol and butanol fuels. Spray behaviour was captured using shadowgraph technique under varied injection pressures (20–239 bar) and chamber pressures (9–21 bar). The ANN model demonstrated high predictive accuracy, with mean square error and coefficient of determination (R²) of 1.7189 and 0.9841 for spray penetration, and 6.3734 and 0.9163 for spray angles, respectively. The unified modeling framework effectively captures the complex interactions of different alcohol fuels and operating conditions, enabling advanced, accurate simulations of spray behaviour. This AI-driven approach could be a valuable tool for predicting the spray behavior of alcohol fuels, facilitating advanced modeling of low-carbon fuel combustion processes.