Modern airliners use the profiles calculated by the onboard flight management system (FMS) to execute safe and efficient descents. Since the wind often varies greatly between the cruising altitude and the end-of-descent altitude, the FMS uses both predicted and measured wind to determine the descent profile. Even so, the actual wind encountered along the descent changes the profile. When a constant descent speed is maintained at idling thrust, the aircraft deviates from its path and needs to either fly an additional steady level flight segment to the metering fix or deflect speed brakes to ensure the speed constraints at the metering fix are met. This research analyses the optimal top of descent in respect to such wind prediction error and fuel burn. Numerical simulations for the Boeing 767-300 are done and it is shown that an early descent of 0.5 nm would save, on average, 0.9 lb of fuel for an idling descent from 30,000 ft to 10,000 ft and at a constant speed of 280 kt, and decrease the number of cases where the necessary deceleration could not be achieved due to lack of enough lateral distance by 77%, thus improving safety and easing operations.
Efficient flight operations are crucial for the sustainable development of aviation. Continuous descent is a potential solution in a terminal airspace. At present, descent is evaluated based on the length or duration of level flight segments only. Typically, modern flight management systems calculate optimal descent profiles with level segments added only for necessary deceleration. If no wind disturbance is present, the aircraft can follow the path calculated and achieve the optimal descent. In practice, however, differences between the predicted and actual wind require changes to the descent profile such as adding thrust, drag, or flying additional level segments. This research analyses different descent control strategies and investigates their effects on fuel burn, flight time and path deviation. Monte Carlo simulations are conducted to account for various wind conditions. Results show that even strategies with level segments increased by an average of 22% can result in lower fuel burn. Therefore, it is concluded that level segments by themselves are not a sufficient metric for descent efficiency and a strategy for lower fuel burn descents is proposed.
A Neural Network (NN) model of a human operation is tried to construct to analyze pilot behavior. The flight data obtained by simulator operation are utilized to teach the NN model. The input data to the model are visual cues from the cockpit and the pilot column deflection history, and the output is the elevator angle. The Genetic Algorithms (GA) optimizes the NN model in order to obtain the appropriate structure and its parameters. It can be seen that the sensitivity analysis and the threshold analysis of the obtained NN model reveal the human pilot behavior.
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