Experimental and modeling analysis of the transient spray characteristics of cyclopentane at sub- and transcritical conditions using a machine learning approach

Abstract

Although fuel spray parameters, such as spray cone angle and penetration length, are crucial for developing high-efficiency and high-performance combustion engines, general models for predicting transient characteristics of these parameters have not been suggested. In this study, the spray characteristics of cyclopentane at sub- and transcritical conditions relevant for IC engine and rocket injections were experimentally evaluated. A single simplified model for predicting the spray cone angles and spray penetration lengths over time was developed by adopting artificial neural networks (ANN). Spray measurements were conducted by shadowgraphy and Mie scattering techniques to recognize the phase change behavior of the spray, changing the injection and chamber conditions. The ANN model was developed using a multi-layer network with six normalized inputs and four outputs. It was trained with five transient spray datasets at two subcritical and three transcritical injection conditions. It was validated with one transcritical spray dataset. The ANN prediction was assessed, and the proposed approach represents the spray characteristics of cyclopentane at sub- and transcritical conditions. According to the model results, the predicted spray parameters are in good agreement with the experimental results over a useful pressure and temperature range of 40–55 bar and 465–564 K, mean absolute percentage errors of 2.25% (shadowgraphy) and 4.92% (Mie) for the spray angles, and 1.11% (shadowgraphy) and 3.44% (Mie) for the spray penetration lengths. Moreover, the developed ANN model can predict the penetration ratio, providing information on phase changes in sprays. The developed ANN model in this study is expected to become a universal model for transient spray characteristics near the critical point. By adding the database with various fuel types and spray conditions, the universal model can be used to develop high-efficiency and high-performance combustion engines or other combustors.