基于机器学习的煤油液滴蒸发模型探索

Based on the droplet evaporation theory of Thick Exchange layer and experimental data of kerosene，a kerosene droplet evaporation model was constructed by using linear regression，random-forest，support vector machine，extreme gradient boosting，and fully-connected neural network methods in machine learning theory to test the applicability and accuracy of the newly constructed model. Comparing the experimental data with the prediction results of traditional models and machine learning evaporation models，it was found that the evaporation models generated by the random-forest method and extreme gradient boosting method cannot be reasonably extrapolated. The extrapolation effect of support vector machine method was not good. The overall effect of the linear regression thick exchange layer model and the fully-connected neural network model was superior to others，with a mean square error of 2.71×10⁻² and 1.81×10⁻³ compared with the training data，respectively. Based on the prediction consequences of the deep learning model，it is feasible to construct a “digital evaporation model” stemmed from experimental data that can be reasonably extrapolated， and may have better realistic adaptability. Machine learning droplet evaporation model enriched extant droplet evaporation models，laying the foundation for machine learning droplet evaporation model research.