| Citation: | CHEN Cong, LI Haojie, SHI Lizhong, et al. Aircraft fuel flow prediction based on NBEATS-MARS and calculation methods for aviation emission[J]. Journal of Aerospace Power, 2026, 41（5）：20250218 doi: 10.13224/j.cnki.jasp.20250218
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To address the challenge of accurately predicting fuel flow under complex operating conditions that affects emission calculation precision, a NBEATS-MARS (Neural Basis Expansion Analysis for Time Series with Multi-variable Adaptive Rapid State-transition) model based on neural network basis function decomposition was proposed for aircraft fuel flow prediction. The model employed a multi-stack decomposition structure and designed multiple types of basis function systems to achieve interpretable high-precision prediction through basis function decomposition. Experiments demonstrated that the NBEATS-MARS model achieved a root mean square error of 59.49, a symmetric mean absolute percentage error of 5.75%, and a median error of only 0.29%. The model performed optimally during the climb-cruise-descent phase with a root mean square error of 32.75 and a symmetric mean absolute percentage error of 1.87%. Based on this, a comprehensive aviation emission calculation method was constructed by using predicted fuel flow data as the core input and combining engine exhaust temperature and other health status parameters to achieve precise quantification of various aviation emissions, including carbon dioxide, nitrogen oxides, black carbon, and organic carbon. The reduction in fuel flow prediction error significantly cut down the emission calculation uncertainty, with cruise phase emission calculation accuracy improved to within ±2%. The method effectively enhanced the accuracy and spatial resolution of downstream aviation emission assessment by improving the upstream fuel flow prediction precision.
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