Yooyen, Soemsak

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Yooyen, Soemsak
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Yooyen, S.
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soemsak.yo@kmitl.ac.th

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    Item type:Publication,
    Predicting Fuel Burn with Neural Network to Adjust Contingency Fuel of Airplane
    (2023-01-01)
    Ounsrimoung, Pimolrat
    ;
    Nootyaskool, Supakit
    ;
    Atchariyachanvanich, Kanokwan
    ;
    Yooyen, Soemsak
    The amount of fuel in an airplane tank is very important for flying. however, flying a short distance by adding a fuel-full tank is not energy efficient because spending a lot of tons for holding fuel weight. The flight planners who consider the amount of fuel to add to the tank by using historical data, use fuel burn calculating and adjust contingency fuel. This research presents the neural networks to predict fuel burn, which learn from historical airplane data. The experiment applied to local and international flight data and used both Airbus and Boeing. The predicted model was swapped and tested on the outbound and inbound replacements for confirmation capable of the predicted mode.
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    A statistical model for estimating statistical contingency fuel
    (2022-01-01)
    Atchariyachanvanich, Kanokwan
    ;
    Kruaklai, Warune
    ;
    Chaipatchareekorn, Nattanan
    ;
    Sukteab, Nuttavadee
    ;
    Yooyen, Soemsak
    Contingency fuel is the amount of fuel used to compensate for unexpected events. This amount of fuel is equal to 5% of the trip fuel or 3% of the trip fuel when it has been determined to have an alternate airport on the route according to the rules of the Thai Civil Aviation Authority. Currently, contingency fuel planning determines the minimum and maximum values of contingency fuel based on aviation industry experience. As a result, the fuel supply may be either too much or too little on some flights. In this research, we aim to create a statistical model that can estimate the fuel required in the event of an emergency and measure the efficiency of contingency fuel with a loss function. The model uses statistical methods to calculate the contingency fuel in the form of Statistical Contingency Fuel (SCF) and monitors the fuel deviation for the planned and actual trip. We used fuel preparation data from 2018 and 2019 that was sourced from Thai Airlines data for six routes with a total of 4,184 flights. The results show that the SCF of flight A was at confidence of level 95, while that of other flights was at a confidence level of 99. The results obtained from the model can be used to assist flight planners to make better decisions concerning the determination of contingency fuel.