Aircraft Performance Modeling With Real-Time Flight State Updates
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Solution Overview
Problem
Conventional aircraft performance modeling fails to accurately account for aircraft-specific variations and high-order effects, leading to inefficiencies in fuel consumption and operational costs due to over-smoothing or experimental fitting of models.
Innovation Solution
A computer-implemented method that identifies sample operating states during flight, generates an updated model using real-time flight performance parameters, and determines an enhanced operating state to reduce direct operating costs, accounting for individual aircraft-specific effects and high-order physical phenomena.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional performance models are used to determine optimal operating states, then the system is simple to operate, but the model accuracy is insufficient due to over-smoothing or experimental fitting that fails to account for aircraft-specific variations
Solution Approach 1:
The system dynamically adapts the performance model during flight by collecting real-time sensor data and updating model parameters. The model transitions from a static conventional approach to a dynamic system that continuously learns aircraft-specific characteristics, resolving the contradiction between model accuracy and system complexity through adaptive updating rather than requiring a completely complex system from the start
Solution Approach 2:
The system implements feedback loops where sensor measurements of actual aircraft performance are continuously compared against model predictions. This feedback is used to identify discrepancies and update the performance model parameters, enabling the system to account for aircraft-specific variations while maintaining a manageable level of complexity through iterative refinement
2Productivity
If existing performance models are used, then the system is easy to implement, but fuel efficiency is insufficient due to errors in operating cost determination
Solution Approach 1:
The system performs preliminary data collection and model calibration during flight by identifying sample operating states and gathering sensor data before final optimization calculations are made. This preliminary action allows the model to be pre-adjusted to aircraft-specific characteristics, improving both fuel efficiency and operating cost accuracy without requiring complete re-modeling
Solution Approach 2:
The system changes model parameters based on collected flight data, adjusting the performance model to reflect actual aircraft behavior. By modifying parameters such as drag coefficients, thrust characteristics, and efficiency factors based on real sensor measurements, the system improves operating cost determination accuracy and fuel efficiency while working within the existing model framework
Data Source
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AI summary
Systems and methods for enhancing aircraft performance are provided. In one example, a method 200 can include accessing 202 an initial model that defines operating cost for an aircraft at a series of model operating states. The method 200 also can include identifying 204 one or more sample operating states for analyzing aircraft performance during flight. The method 200 also can include receiving 206 one or more real-time flight performance parameters indicative of aircraft operating cost while the aircraft is operating at the identified sample operating states. The method 200 also can include generating 208 an updated model that defines operating cost for the aircraft using the initial model as well as data defined by the real-time flight performance parameters. The method 200 also can include determining 210 an enhanced operating state based at least in part on the updated model and outputting 212 the enhanced operating state for control of the aircraft.