燃料射流与空气协流混合中的自点火预测研究

预混燃烧室燃料与空气混合过程中出现的自点火会引起回火与挂火,烧毁燃料喷嘴。针对这一问题,利用实验台模拟贫燃燃烧室预混过程,燃料射流与预热后的空气协流同向喷入石英管预混段中,研究自点火现象。本文结合机器学习和物理规律分析,开展湍流混合过程的自点火预测研究。基于二元逻辑回归建立了机器学习模型,模型的特征由分析影响自点火的物理规律得到,训练和校验模型所需的数据由燃料射流-空气协流的自点火实验获得。结果显示,机器学习方法能快速、准确地预测混合过程中自点火的发生和火焰类型,并揭示其关键影响因素。与传统的数值计算方法相比,机器学习方法预测自点火所需的时间仅为传统数值模拟方法的几千分之一。

Prediction of the Autoignition of a Fuel Jet in a Confined Turbulent Hot Coflow

For advanced lean premixed gas turbine combustors that have high inlet air temperatures,autoignition may occur during the fuel/air mixing process,which can cause flame-holing inside the premixing device and burn the hardware.An experimental study was performed using a setup that mimics the fuel/air mixing process of lean-premixed combustors.In the present experiment,the preheated air was injected into a quartz tube,and a fuel jet was injected concentrically into the hot turbulent air coflow.This paper presents a study combining machine learning methods and physical analysis that is aimed at predicting autoignition in such flows.A model for the prediction of autoignition of a fuel jet in a flow configuration referred to as a’confined turbulent hot coflow’(CTHC)is developed using machine learning techniques based on binary logistic regression.Key factors that impact the autoignition phenomenon are identified by analyzing the underlying physics and are used to form the feature vector of the model.The model is trained using data from experiments and is validated by an additional set of data,which are selected randomly.The results show that the model predicts the autoignition event with satisfactory accuracy and quick turnaround.The trained model parameters in turn provide insights into the quantitative contribution of different factors that impact the autoignition event.Thus,the machine-learning based method can form an alternative to CFD modeling in some cases.