Aircraft Energy State Model Feedback for Performance Capability
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Solution Overview
Problem
Conventional methods for determining aircraft performance capability are limited by reliance on accurate weight and condition information, and fail to account for variations caused by incorrect cargo or fuel loads, ice buildup, and wind shear, leading to overly optimistic predictions that may result in failing to clear obstacles.
Innovation Solution
An avionics system that includes a storage device and data processors to monitor actual aircraft performance by determining measured flight characteristics, executing flight maneuvers, generating predicted energy changes based on an energy state model, and adjusting the model to account for real-world variances, such as incorrect weights or performance degradations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional energy state models are used to determine aircraft performance capability, then the computational complexity is reduced and ease of operation is improved, but the measurement precision and reliability of performance predictions deteriorate due to inaccuracies in weight and condition information
Solution Approach 1:
The system continuously compares predicted flight characteristics from the energy state model with actual measured flight characteristics, generates performance variance information based on the difference, and feeds this back to adjust the model. This closed-loop feedback mechanism maintains measurement precision without increasing computational complexity, resolving the contradiction between ease of operation and measurement precision.
2Device complexity
If conventional energy state models are used to determine aircraft performance capability, then the device complexity is reduced, but the reliability of performance predictions deteriorates due to failures to account for real-world variances such as incorrect weight information, ice buildup, and wind shear
Solution Approach 1:
The energy state model performs self-correction by automatically detecting performance variances through comparison of predicted versus actual flight characteristics, and adjusts its own parameters without external intervention. This self-service mechanism enhances reliability while maintaining low device complexity, as the model adapts itself using existing sensor data and computational resources.
3Measurement precision
If complex equations with large computational capacity are used onboard the aircraft to predict aircraft performance, then the measurement precision and reliability of performance predictions are improved, but the device complexity and computational requirements increase significantly
Solution Approach 1:
The system replaces complex mechanical/computational performance prediction equations with a simplified energy state model that uses basic energy balance principles. Instead of solving complex aerodynamic equations requiring large computational capacity, the system uses energy methods (potential energy, kinetic energy, work done) to predict flight characteristics, achieving adequate precision with minimal computational resources and device complexity.
Data Source
AI summary
Systems and aircraft are provided. An avionics system includes a storage device and one or more data processors. The storage device stores instructions for monitoring an actual performance of the aircraft. The one or more data processors are configured to execute the instructions to: determine a first measured value of a flight characteristic of the aircraft at a first position of the aircraft; execute at least one flight maneuver between the first position and a second position of the aircraft; generate a predicted energy change between the first position and the second position based on the at least one flight maneuver and an energy state model; determine a second measured value of the flight characteristic of the aircraft at the second position; and generate an adjustment to the energy state model based on the first measured value, the second measured value, and the predicted energy change.