神经网络宽度对燃烧室排放预测的影响

doi:10.7527/S1000-6893.2022.26816

Abstract

Abstract:

To study the effect of neural network width on aero-engine combustor emission prediction， a neural network prediction model is established based on the experimental data of full annular combustor emission. The inlet air temperature， inlet air pressure， fuel flow rate and fuel-air ratio are defined as the input parameters， and CO and NO _x emission index as the output parameters of the model. Results indicate that there is an optimal network width value， which makes the best goodness of fit and prediction accuracy of the neural network prediction model， and the optimal network width in this study is 2⁴. The accuracy and generalization of the neural network model are verified by goodness of fit， error analysis and sensitivity analysis. The neural network prediction model based on the optimal network width can well mine the mapping relationship between input parameters and emission index， and can be used as a prediction tool for combustor emission with given operating parameters. Finally， based on sensitivity analysis， the interpretability of the constructed neural network is discussed in combination with the combustion physical mechanism and experimental phenomena.

Key words: combustor, emission, neural network width, prediction model, interpretability, sensitivity analysis

CLC Number:

V231.2

Zhikai WANG, Sheng CHEN, Wei FAN. Effect of neural network width on combustor emission prediction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(5): 126816-126816.

Figures/Tables 12

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测试参数	测试精度
P_t3，P_t4/MPa	±0.5%
T_t3，T_t4/K	±0.5%
m_a3/（kg∙s^-1）	±1.0%
m_f/（g∙s^-1）	±0.5%

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Effect of neural network width on combustor emission prediction

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