ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Effect of neural network width on combustor emission prediction
Received date: 2021-12-14
Revised date: 2021-12-30
Accepted date: 2022-01-10
Online published: 2022-01-18
Supported by
National Level Project
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.
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 . DOI: 10.7527/S1000-6893.2022.26816
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