Dynamic Tables for Accurate Flight Profile Prediction
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Solution Overview
Problem
Flight management systems (FMS) struggle to accurately compute predicted flight profiles and trip prediction parameters due to incremental changes in airplane aerodynamic and propulsion performance characteristics, leading to inefficient manual updates and limited computing power constraints.
Innovation Solution
The implementation of dynamic tables within the FMS database that are updated based on real-time fuel flow and drag data, allowing performance algorithms to reflect current airplane characteristics, reducing the need for manual recertification and optimizing computing resources by using precomputed values from these tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual updates and recertification of baseline performance data tables are performed, then accuracy of trip prediction parameters is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system automatically monitors actual airplane performance parameters (fuel flow, drag) during flights and self-updates the performance data tables without requiring manual intervention. The FMS compares actual performance with baseline data, automatically generates corrections, and updates the tables in service, eliminating the need for time-consuming manual recertification processes while maintaining high accuracy
Solution Approach 2:
The system implements a feedback mechanism where actual flight performance data is continuously collected, compared with baseline performance data, and used to automatically generate and apply corrections. This closed-loop feedback system ensures the performance tables remain accurate without manual intervention, resolving the contradiction between maintaining precision and reducing time investment
2Measurement precision
If frequent database updates are performed to reflect changing airplane characteristics, then performance accuracy is improved, but system complexity and recertification requirements increase
Solution Approach 1:
The system transitions from static baseline performance data to dynamic performance tables that automatically adapt to changing airplane characteristics. The performance data tables are designed to be updateable in service, allowing the system to dynamically reflect actual airplane performance without requiring complex recertification processes, thus improving accuracy while managing system complexity
3Measurement precision
If corrected performance data is constantly accessed and recomputed in real-time, then accuracy is improved, but computing power requirements exceed FMS limitations
Solution Approach 1:
The system pre-computes corrected performance data during flight operations and stores the results in updated performance tables. This preliminary action allows the FMS to use preprocessed correction factors during flight planning and prediction without requiring intensive real-time computation, thus maintaining accuracy while respecting computing power limitations
Solution Approach 2:
The system replaces intensive real-time computational processing with pre-computed lookup tables. Instead of constantly recalculating performance parameters using complex algorithms, the FMS uses the updated performance tables for efficient retrieval of corrected data, significantly reducing computing power requirements while maintaining accuracy
Data Source
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AI summary
A flight management system which is capable of monitoring changes in airplane characteristics such as fuel flow and drag. When a predetermined event is triggered, the flight management system (FMS) creates or updates a set of "dynamic" tables in a database which enable FMS performance algorithms to utilize the latest fuel flow and drag data. Using the updated aerodynamic and propulsion performance data in the dynamic tables, the flight management system is able to compute more accurate flight profile and trip prediction parameters such as estimated time of arrival and predicted fuel consumption quantity.