To further improve the energy conversion efficiency of internal combustion engine, the transient and complex air flow movement inside the cylinder needs to be better understood and controlled. Although the in-cylinder flow fields are highly stochastic with strong cycle-to-cycle fluctuations, machine learning can still provide an efficient way to learn and regress the complex flow movement process inside the cylinder. In this work, a bidirectional recurrent neural network (bi-RNN) model with long short-term memory was applied to predict the in-cylinder flow fields at different time steps using training data from multi-cycle particle image velocimetry (PIV) measurements. To evaluate the agreement between the true and predicted flow fields, structure and magnitude comparison indices are calculated both globally and locally. The comparison results show that the bi-RNN model can accurately predict the bulk flow and vortex motions from early intake stroke to compression stroke. This work demonstrates that the machine learning model has the potential to predict the underlying dynamics of the interaction between in-cylinder flows and provides a reliable way to improve temporal resolution in PIV flow data to better reveal transient in-cylinder flow features.

为了进一步提高内燃机的能量转换效率，需要更好地理解和控制气缸内部的瞬态和复杂的气流运动。尽管缸内流场是高度随机的，并且周期间波动很大，但是机器学习仍然可以提供一种有效的方式来学习和回归缸体内复杂的流动过程。在这项工作中，使用具有长短期记忆的双向递归神经网络（bi-RNN）模型，使用来自多周期粒子图像测速（PIV）测量的训练数据来预测不同时间步的缸内流场。为了评估真实流场和预测流场之间的一致性，可以全局和局部计算结构和大小比较指标。比较结果表明，bi-RNN模型可以准确地预测从早期进气冲程到压缩冲程的总流量和涡流运动。这项工作证明了机器学习模型具有预测缸内流动之间相互作用的潜在动力的潜力，并提供了一种可靠的方式来提高PIV流量数据中的时间分辨率，以更好地揭示瞬时缸内流动特征。