Machine learning models are applied to simulated electrospray particle data to investigate plume dynamics from emission to final particle properties. A limited set of final particle properties are successfully regressed exclusively from emission property inputs. Random Forest model feature rankings for final plume angle reveal that particle charge has dominant influence when emission velocity is strictly axial, while lateral emission velocity has dominant influence when particles are emitted with an off-axis velocity component. In addition to providing correlations between initial and final particle properties, the machine learning models also identify correlations between different final particle properties. These correlations reveal opportunities for experimental approaches and diagnostic design by determining experimental measurements that offer insight into desired final particle properties.

中文翻译：

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机器学习电喷雾羽流动力学

机器学习模型应用于模拟电喷雾粒子数据，以研究从发射到最终粒子特性的羽流动力学。一组有限的最终粒子属性完全从发射属性输入中成功回归。最终羽流角度的随机森林模型特征排序表明，当发射速度严格为轴向时，粒子电荷具有主导影响，而当粒子以离轴速度分量发射时，横向发射速度具有主导影响。除了提供初始和最终粒子属性之间的相关性之外，机器学习模型还识别不同最终粒子属性之间的相关性。这些相关性通过确定实验测量来揭示实验方法和诊断设计的机会，从而深入了解所需的最终颗粒特性。