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

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

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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.

temperature predictionconvolutional neural networklong short-term memorylight field imagingonline tomography

NIU ZhiTian、QI Hong、SUN AnTai、REN YaTao、HE MingJian、GAO BaoHai

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School of Energy Science and Engineering,Harbin Institute of Technology,Harbin 150001,China

Key Laboratory of Aerospace Thermophysics,Ministry of Industry and Information Technology,Harbin 150001,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFoundation for Heilongjiang Touyan Innovation Team Program

5197604452227813

2024

10.1007/s11431-023-2466-7

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子：1.056
ISSN：1674-7321
年,卷(期)：2024.67(1)
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