Aircraft Performance Modeling With Segmented Flight Optimization
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Solution Overview
Problem
Current methods for optimizing aircraft operations are limited by the lack of publicly available, specific performance data and dependency on manufacturer-provided information, with existing models being restrictive and prone to convergence issues due to their non-linear nature, especially when dealing with large-scale processes.
Innovation Solution
The method segments aircraft flight into distinct phases governed by different equations based on engine thrust mode and vertical guidance, using a combination of aerodynamic and engine-speed models, and applies a least-squares calculation to iteratively determine parameters, ensuring convergence and predicting trajectory points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-linear parameter-estimation methods (least-squares methods with gradient-descent algorithms) are used to optimize aircraft performance models, then the model can handle complex non-linear relationships, but the method may converge on local minima leading to poor parameter estimation
Solution Approach 1:
The flight trajectory is divided into multiple segments (climb, cruise, descent phases) with distinct sets of equations for each phase. This segmentation allows the optimization problem to be broken down into smaller, more manageable sub-problems that can be solved sequentially, reducing the risk of converging to local minima while maintaining accuracy in parameter estimation for each flight phase.
Solution Approach 2:
The patent applies preliminary actions by using coarse-grid search to establish initial parameter ranges before applying refined least-squares optimization. This preliminary exploration of the parameter space helps identify promising regions and provides better initial guesses for the gradient-descent algorithm, reducing the likelihood of converging to local minima.
2Productivity
If existing aircraft performance models (BADA, EUCASS) are used, then development time is reduced, but the models are limited in applicability (thrust and drag limitations, turbojet-only)
Solution Approach 1:
The patent creates a universal performance model framework that can accommodate different aircraft types and propulsion systems. By using a modular approach with phase-specific equations that can be configured for various engine types (turbofan, turbojet, propeller) and flight phases, the model achieves broad applicability while maintaining efficient development through reuse of common computational structures.
Solution Approach 2:
The model employs dynamic equations that adapt to different flight phases and operating conditions. The thrust and drag models are designed to be dynamically adjustable based on Mach number, altitude, and flight phase, allowing the same framework to accurately represent diverse aircraft behaviors without requiring separate dedicated models for each aircraft type.
3Reliability
If physical equation-based models are used to represent aircraft behavior, then the model reflects actual aircraft physics, but the model becomes sensitive to variability between actual and average aircraft behavior
Solution Approach 1:
The patent uses parameter changes to bridge the gap between idealized physical models and actual aircraft behavior. By introducing adjustable parameters that are optimized against real flight data, the model maintains the physical correctness of the equations while adapting to specific aircraft characteristics and variability, making it robust to differences between actual and average aircraft performance.
Data Source
AI summary
Methods and systems for optimizing the flight of an aircraft are disclosed. The trajectory is divided into segments, each of the segments being governed by distinct sets of equations, depending on engine thrust mode and on vertical guidance (climb, cruise or descent). By assuming two, aerodynamic and engine-speed, models, data from flight recordings are received and a number of parameters from a parameter-optimization engine is iteratively determined by applying a least-squares calculation until a predefined minimality criterion is satisfied. The parameter optimization engine is next used to predict the trajectory point following a given point. Software aspects and system (e.g. FMS and/or EFB) aspects are described.