Optimal Top of Descent Analysis in Respect to Wind Prediction Errors and Guidance Strategies

Andreeva-Mori, Adriana; Uemura, Tsuneharu

doi:10.2322/tastj.15.a45

Cited by 3 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a myriad of factors can change where TOD locations occur, the earlier described macroscopic perspective is also compared to the microscopic viewpoint of the pilots training and decision making tools available. As exemplified by Adriana and Uemura [13] the formulas employed by the individual Flight management system (FMS) of each aircraft type should be primarily referenced. However, because they remain proprietary their use is often hampered and limited.…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Approach for the Prediction of Top of Descent

Benjamin

Alam

Yuan

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

View full text Add to dashboard Cite

Under the light of increasing air traffic congestion and reduced environmental impacts, the growing need for tailored approaches has arisen. To coordinate tailored approaches, accurate predictions of descent trajectories must be available. The ability of ground based automation for such endeavours however must be improved. Specifically in the ability to predict where descents begin, also referred to as the Top of descent (TOD). This paper attempts to provide a machine learning approach to TOD location prediction, considering the operational capabilities of modern-day air traffic management by working within the confines of parameters available to controllers and considerations of stakeholders. To identify the common TOD clusters and the resultant average TOD locations representing the controllers macroscopic perspective, an unsupervised learning approach is utilised. Specifically, an ensemble method of DBSCAN-K-means is used. This was compared to the more microscopic pilot controllers perspective and generic decision-making formulas to determine the proximity to expected TOD. Of which the microscopic option has proven to be more reliable in representing the individual Flight Management System (FMS) recommendations or pilots' decisions on aircraft. In the prediction of TOD location, operationally available factors such as proximity to expected TOD, current flight level, approach heading, ground speed, fleet and aircraft type are used to train a decision tree model. A Singapore flight information region (FIR) case study is conducted to illustrate the methodology. Results of the methodology employed on the inbound A380 fleet consisting a total of 79 flights, yielded a maximum prediction range of 31NM range from actual TOD occurrence. This paper has demonstrates the possibility of using a machine learning approach to predict TOD of a flight considering both prior cruise characteristics and operational considerations of pilots.

show abstract

Section: Methodsmentioning

confidence: 99%

A Machine Learning Approach for the Prediction of Top of Descent

Benjamin

Alam

Yuan

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

View full text Add to dashboard Cite

show abstract

“…5) Andreeva-Mori and Uemura analyzed the optimal top of descent in respect to wind prediction error and fuel burn. 6,7) Wickramasinghe et al provided an in-depth quantitative evaluation on potential benefits achieved by performing flight trajectory optimization, considering the tradeoff between fuel consumption and flight time. 8) Harada et al quantitatively evaluated the accuracy of the base of aircraft data model by comparing calculated fuel flow and total fuel consumption with flight data.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuel Efficiency Prediction Model to Enhance the Fuel Efficiency and Reduce the Carbon Emissions

Jang¹,

CHO

Yoo

et al. 2022

Trans. Japan Soc. Aero. S Sci.

View full text Add to dashboard Cite

This study proposes a fuel efficiency prediction model using a newly defined fuel efficiency index. The process is divided into three steps: (1) define a new fuel efficiency index after identifying the limitations of the existing fuel efficiency index; (2) set up a fuel efficiency prediction model; and (3) formulate possible actions for the airline to enhance fuel efficiency and reduce carbon emissions based on the model established in the previous step. The fuel efficiency prediction model was established using the actual flight data of Airbus 330-300 (engine type: PW4168A). The flight data were obtained from the fuel management and information system of an airline. The multiple regression model is used to identify the independent variables affecting the fuel efficiency and the degree of influence of each variable. The results indicate that variables such as payload, aircraft fuel mileage deterioration, center of gravity, extra fuel loaded, flight distance, and outside air temperature affect the fuel efficiency. Some variables can be controlled and managed by airlines, others are not. The proposed fuel efficiency prediction model is expected to be utilized as a measurable method for enhancing the fuel efficiency and reducing the carbon emissions.

show abstract

“…Further discussion and simulation analysis on this solution can be found in another work by the authors. 18)…”

Section: Strategy 1 Failurementioning

confidence: 99%

Efficiency Analysis of Speed Managed Descent in the Presence of Wind Prediction Error

Andreeva-Mori

Uemura

2018

Trans. Japan Soc. Aero. S Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Optimal Top of Descent Analysis in Respect to Wind Prediction Errors and Guidance Strategies

Cited by 3 publications

References 5 publications

A Machine Learning Approach for the Prediction of Top of Descent

A Machine Learning Approach for the Prediction of Top of Descent

Fuel Efficiency Prediction Model to Enhance the Fuel Efficiency and Reduce the Carbon Emissions

Efficiency Analysis of Speed Managed Descent in the Presence of Wind Prediction Error

Contact Info

Product

Resources

About