Statistical modeling of the fuel flow rate of GA piston engine aircraft using flight operational data

Abstract

The United States has the largest and most diverse general aviation (GA) community in the world, with more than 220,000 aircraft, flying almost 23million hours annually. Accurate and economic estimating the exhaust emissions of general aviation will effectively support the practice of mitigating the environmental impacts from general aviation. Fuel flow rate in each phase of the Landing and Takeoff Cycle is one of the necessary factors recommended by the International Civil Aviation Organization to be used to estimate the exhaust emissions. This paper explores statistical models of predicting the fuel flow rate of piston-engine aircraft using general aviation flight operational data, including the aircraft altitude, the ground speed, and the vertical speed. A machine learning technique is applied to adapt the variability of flight operational data due to flexible operations of general aviation and random errors in flight data. The Classification and Regression Trees (CART) and the Smoothing Spline ANOVA (SS-ANOVA) are adopted as the modeling approaches. The modeling results are compared and interpreted from the standpoint of general aviation phases of flight in the Landing and Takeoff Cycle. Both models demonstrate good accuracy in predicting the fuel flow rate. The CART model provides intuitive outputs by the phases of flight, and is more robust to flight data outliers. The SS-ANOVA model is relatively more accurate in predicting the fuel flow rate, and is better at explaining the interaction between variables. A robust fuel flow rate prediction model of predicting the fuel flow rate of piston-engine aircraft is believed to be practical and economic for GA exhaust emissions estimation.