Altitude Change Route Profiles for Aircraft Fuel Burn Prediction
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Solution Overview
Problem
The aviation industry faces challenges in predicting and reducing fuel burn and carbon dioxide emissions, particularly during climb and descent phases of flights, due to the lack of quantified impact of step climb and descent profiles on emissions.
Innovation Solution
A data-driven sustainability solution using machine learning techniques to assess the impact of step climb and descent profiles on CO2 emissions, providing a quantitative value of savings in fuel burn and emissions by analyzing historical flight data and categorizing flights based on origin-destination pairs, aircraft type, and operator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional flight routes are used without optimization, then flight operations are simple and straightforward, but fuel burn and carbon dioxide emissions are high and unoptimized
Solution Approach 1:
The system performs preliminary calculations of fuel burn and CO2 emissions for multiple candidate routes before the actual flight. By pre-computing emissions data using machine learning models trained on historical flight data, the system enables informed route selection that optimizes environmental performance while maintaining operational simplicity during the actual flight.
Solution Approach 2:
The system creates a virtual model of flight routes by generating synthetic training data from historical flight information and using this to train machine learning models. This copying approach allows the system to learn from past flights and apply the knowledge to optimize future routes without requiring complex real-time calculations during actual flights.
2Measurement precision
If comprehensive multivariate flight data is collected and analyzed, then prediction accuracy of fuel burn and emissions is improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The system segments the complex prediction task into distinct components: data collection from multiple sources, data preprocessing and cleaning, feature extraction, machine learning model training, and prediction generation. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high prediction accuracy.
Solution Approach 2:
The system introduces machine learning models as intermediaries between raw flight data and emissions predictions. These models process and interpret complex multivariate data, transforming it into meaningful predictions while reducing the computational burden on the overall system. The ML models act as intelligent mediators that handle the complexity of data analysis.
3Object-generated harmful factors
If step climb and descent profiles are optimized, then fuel burn and CO2 emissions are reduced, but flight operation complexity increases
Solution Approach 1:
The system enables automated route selection and optimization without requiring manual intervention from pilots or operators. The machine learning models automatically evaluate candidate routes, predict emissions, and recommend optimal paths, allowing the flight operation system to self-optimize for environmental performance while maintaining ease of operation through automated decision-making.
Data Source
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AI summary
A method for selecting an altitude changing phase route for an aircraft is presented. The method comprises receiving sequences of multivariate flight data from at least one prior flight, receiving a set of flight parameters for the aircraft including at least a total altitude change and a takeoff weight, and receiving a set of candidate altitude changing phase routes having candidate step profiles. For each candidate altitude changing phase route, a sequence of fuel burn quantities is predicted for the respective candidate step profile based on at least the sequences of multivariate flight data and the set of flight parameters. The fuel burn quantities are summed over the candidate altitude changing phase route to obtain an estimated fuel burn. A preferred candidate altitude changing phase route having a lowest estimated fuel burn is indicated.