Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks

Abstract

In this study, a transfer learning-based neural network approach to predict ignition delays for a variety of fuels is proposed to meet the demand for accurate combustion analysis. A comprehensive dataset of ignition delays was generated using a random sampling technique across different temperatures and pressures, focusing on hydrocarbon fuels with 1–4 carbon atoms. Two machine learning models, an artificial neural network and a graph convolutional network, are trained on this dataset, and their prediction performance was evaluated. A transfer learning framework was subsequently developed, enabling the models trained on smaller molecules (1–3 carbon atoms) to predict ignition delays for larger molecules (4 carbon atoms) with minimal additional data. The proposed framework demonstrated reliable and high prediction accuracy, achieving a high level of reliability for fuels with limited experimental measurements. This approach offers significant potential to streamline the prediction of ignition delays for novel fuels, reducing the dependence on resource-intensive experiments and complex simulations while contributing to the advancement of clean and efficient energy technologies.