Engine Load Model for Flight Path Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current flight management systems lack accurate modeling of engine loads, leading to suboptimal flight profiles and increased operational costs due to simplifying assumptions and neglect of variable engine loads during flight planning.
Innovation Solution
An enhanced engine load model that accounts for mechanical, electrical, and pneumatic loads, using predictive modeling based on flight trajectory, weather, and historical data to refine engine subsystem utilization and optimize thrust and fuel flow, thereby improving flight control parameters and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If simplified engine load models are used in flight management systems, then computational complexity is reduced and processing speed is improved, but accuracy of flight profile prediction deteriorates and operational costs increase
Solution Approach 1:
The engine load model is segmented into distinct components: mechanical load model, electrical load model, and pneumatic load model. Each model handles specific aspects of engine loading separately, allowing for improved accuracy in predicting individual load components while maintaining overall computational efficiency through modular processing.
Solution Approach 2:
The system transitions from static engine load assumptions to dynamic modeling that accounts for variable engine loads throughout the flight profile. The models continuously adjust predictions based on changing operational conditions, weather data, and flight phase, enabling accurate real-time predictions without excessive computational burden.
2Measurement precision
If variable engine loads are accounted for in flight planning, then fuel management accuracy is improved, but computational complexity increases
Solution Approach 1:
The engine load models incorporate feedback mechanisms that use historical flight data, weather information, and real-time operational parameters to continuously refine predictions. This feedback loop enables accurate fuel management by adjusting predictions based on actual performance deviations from planned profiles.
Solution Approach 2:
The system performs preliminary computations of engine load profiles during flight planning phases, calculating expected mechanical, electrical, and pneumatic loads before the flight executes. These pre-computed profiles serve as baseline predictions that are then adjusted during actual flight operations, reducing real-time computational requirements while maintaining accuracy.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Methods 600, apparatus 100, and articles of manufacture to provide improved engine load models 330 are disclosed. An example apparatus 100 includes a model generator 305 to generate an engine load model 330 for an engine using flight information, weather information, and manifest information to predict a load on the engine from an engine subsystem utilization modeled for a flight. The example model generator 305 is to incorporate the engine load model 330 into an engine model 320, the engine model 320 representing engine behavior for the flight. The example model generator 305 is to determine a first measure of thrust from the engine and a second measure of fuel flow to the engine using the engine model 320 with the engine load model 330, the engine load model 330 to modify engine behavior by the predicted load on the engine from the engine subsystem utilization. The example model generator 305 is to generate flight parameters for a flight path using the first measure of thrust and the second measure of fuel flow for the predicted load on the engine based on the engine load model 330.