Mobile Flight Segment Recording for Real Aircraft Performance Prediction
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Solution Overview
Problem
Existing systems fail to accurately determine current and predict future performance specifications of aircraft under conditions not previously experienced by the pilot or aircraft, particularly in general aviation, commercial, and military aviation, due to aging effects and modifications over time.
Innovation Solution
A mobile computing device application that receives static and dynamic data to determine aircraft performance, using sensors and network connectivity to provide real-time performance data and predictions, integrating with a graphical user interface for pilots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If performance data is provided from Pilot Operating Handbook or Aircraft Flight Manual based on test flights under ideal conditions, then the data represents ideal performance specifications, but the data does not reflect actual performance under aging effects, modifications, or unexperienced conditions
Solution Approach 1:
The system records actual flight data from sensors during real operations and uses this feedback to continuously update and refine performance predictions. The recorded data including aircraft state, environmental conditions, and performance metrics feeds back into the machine learning models to improve future predictions, resolving the contradiction between ideal handbook data and actual performance under varying conditions.
Solution Approach 2:
The system performs preliminary recording of flight segments and accumulation of operational data before predictions are needed. By continuously collecting and storing flight data in advance during normal operations, the system builds a comprehensive database that enables accurate predictions when pilots encounter unexperienced conditions, without requiring additional test flights.
2Adaptability or versatility
If aircraft operate in locations and weather conditions not previously experienced, then operational flexibility is improved, but performance criterion reliability deteriorates
Solution Approach 1:
The system introduces mobile computing devices with sensors and machine learning algorithms as intermediaries between the pilot and the aircraft performance prediction. This intermediary layer processes actual flight data and environmental conditions to generate reliable performance predictions for unexperienced conditions, bridging the gap between operational flexibility and reliability.
Solution Approach 2:
The system adapts performance predictions by dynamically adjusting parameters based on recorded flight data and environmental conditions. By changing performance parameters according to actual operational experience rather than fixed handbook values, the system maintains reliability across diverse conditions including unexperienced locations and weather.
3Measurement precision
If flight data is recorded and stored for analysis, then predictive accuracy is improved, but device complexity and data management requirements increase
Solution Approach 1:
The mobile computing device performs multiple functions: it records flight data, stores information, processes predictions, and provides user interface interactions. By consolidating these functions into a single multi-functional device rather than separate specialized systems, the reduces overall complexity while maintaining predictive accuracy.
Solution Approach 2:
The system automatically records flight data from sensors, manages storage, and performs predictions without requiring complex manual data management. The self-service capability of automatic data capture and processing reduces the complexity burden on users while improving predictive accuracy through consistent data collection.
Data Source
AI summary
Disclosed herein are methods, devices, and systems for facilitating pilots in determining current and predicted performance specifications of their aircraft. According to one embodiment, a method is implemented on a mobile computing device. The method includes receiving static pilot data, receiving static aircraft data associated with an aircraft, receiving dynamic aircraft data over a time period, determining aircraft performance data based on the static aircraft data and the dynamic aircraft data, providing the aircraft performance data to a graphical user interface (GUI) associated with the mobile computing device, and storing the static pilot data and the aircraft performance data.