The present work investigates the application of Machine Learning and Artificial Neural Networks for tackling the complex issue of transcritical sprays, which are relevant to modern compression-ignition engines. Such conditions imply the departure of the classical thermodynamic perspective of ideal gas or incompressible liquid, necessitating the use of costly and elaborate thermodynamic closures to describe property variation and simulation methods. Machine Learning can assist in several ways in speeding up such calculations, either as a compact, trained thermodynamic model that can be coupled to the flow solver, or as a surrogate predictive tool of spray characteristics. In this work, such applications are demonstrated and their performance is assessed against more traditional approaches. Such applications involve the prediction of macroscopic spray characteristics, for example, the spray penetration over time, or the spray distribution in space and time, and predictions of fluid properties for the thermodynamic states encountered in such applications. Macroscopic characteristics can be adequately predicted by relatively simple network structures, involving just a hidden layer of 3–4 neurons, whereas prediction of thermodynamic states requires several layers of 5–20 neurons each. The results of integrating Artificial Neural Networks in transcritical sprays are rather promising; prediction of thermodynamic properties at pressures greater than 1bar has effectively zero error, yielding simulations indistinguishable from standard tabulated approaches with minimal overhead. When used as a regression method for time-histories either of spray characteristics or spray distributions, the results are within experimental uncertainty of similar experiments, not included in the training dataset.

本工作研究了机器学习和人工神经网络在解决跨临界喷雾这一复杂问题方面的应用，这些问题与现代压燃发动机有关。这样的条件意味着理想气体或不可压缩液体的经典热力学观点的偏离，有必要使用昂贵且复杂的热力学封闭物来描述性质变化和模拟方法。机器学习可以多种方式帮助加快计算速度，例如可以与流量求解器耦合的紧凑，训练有素的热力学模型，或者作为喷射特性的替代预测工具。在这项工作中，展示了此类应用程序，并根据更传统的方法评估了它们的性能。这样的应用包括对宏观喷雾特性的预测，例如，随着时间的流逝的喷雾渗透或喷雾在空间和时间上的分布，以及在这种应用中遇到的热力学状态的流体性质的预测。宏观特征可以通过相对简单的网络结构适当地预测，其中仅涉及3–4个神经元的隐藏层，而热力学状态的预测则需要分别由5–20个神经元组成的多层。将人工神经网络集成到跨临界喷雾中的结果是很有希望的。压力大于1bar时对热力学性质的预测实际上具有零误差，从而以最小的开销将模拟与标准列表化方法区分开。