Efficient and robust CNN-LSTM prediction of flame temperature aided light field online tomography

Light field tomography, an optical combustion diagnostic technology, has recently attracted extensive attention due to its easy implementation and non-intrusion. However, the conventional iterative methods are high data throughput, low efficiency and time-consuming, and the existing machine learning models use the radiation spectrum information of the flame to realize the parameter field measurement at the current time. It is still an offline measurement and cannot realize the online prediction of the instantaneous structure of the actual turbulent combustion field. In this work, a novel online prediction model of flame temperature instantaneous structure based on deep convolutional neural network and long short-term memory (CNN-LSTM) is proposed. The method uses the characteristics of local perception, shared weight, and pooling of CNN to extract the three-dimensional (3D) features of flame temperature and outgoing radiation images. Moreover, the LSTM is used to comprehensively utilize the ten historical time series information of high dynamic combustion flame to accurately predict 3D temperature at three future moments. A chaotic time-series dataset based on the flame radiation forward model is built to train and validate the performance of the proposed CNN-LSTM model. It is proven that the CNN-LSTM prediction model can successfully learn the evolution pattern of combustion flame and make accurate predictions.