Turbine Engine Power Management via Model Predictive Control
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Solution Overview
Problem
The offline configuration of power management systems in turbine engines limits their performance capability due to increased complexity and multivariable interactions, especially in transient conditions where flight and engine controls are closely integrated.
Innovation Solution
Implementing a dynamic online power management system that uses a model predictive control to filter data inputs, predict engine operating conditions, and solve constrained optimizations for optimal engine control, allowing for real-time updates and adaptable operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If offline configuration is used for power management system, then system complexity is reduced and ease of manufacture is improved, but engine performance capability and adaptability deteriorate
Solution Approach 1:
The power management system transitions from static offline configuration to dynamic online optimization. The system continuously updates control reference schedules, open-loop schedules for inputs, and constraint limits based on real-time engine operating conditions, enabling the system to adapt to changing conditions while maintaining manageable complexity through automated algorithms.
Solution Approach 2:
The system implements continuous feedback loops where engine operating conditions are monitored, compared against optimal trajectories, and used to adjust control parameters in real-time. This feedback mechanism enables the system to maintain optimal performance across varying operating conditions without requiring complex manual reconfiguration.
2Device complexity
If offline configuration is used for power management system, then device complexity is reduced, but productivity and real-time optimization capability deteriorate
Solution Approach 1:
The system performs preliminary optimization offline to establish baseline control strategies and optimal trajectories for various operating conditions. During online operation, it only needs to track these pre-computed trajectories and adjust for deviations, significantly reducing real-time computational complexity while maintaining high productivity and optimization capability.
Solution Approach 2:
The system dynamically adjusts control parameters based on real-time engine state while maintaining a structured approach through pre-computed optimal trajectories. This dynamic adaptation enables real-time optimization without requiring overly complex computational resources, as the system follows predetermined optimal paths with real-time adjustments.
3Device complexity
If offline configuration is used, then system simplicity is maintained, but ability to handle transient conditions and multivariable interactions deteriorates
Solution Approach 1:
The system employs continuous feedback mechanisms that monitor engine operating conditions and automatically adjust control parameters to maintain optimal performance during transient conditions. The feedback loops compare actual engine state with desired trajectories and apply corrective actions, enabling the system to handle complex multivariable interactions and transient conditions effectively.
Solution Approach 2:
The system pre-computes optimal control strategies and trajectories for various operating conditions including transient scenarios. During actual operation, it selects and follows the appropriate pre-computed trajectory, significantly improving its ability to handle transient conditions and multivariable interactions without requiring complex real-time decision-making algorithms.
Data Source
AI summary
A method and system for online power management of a turbine engine is provided. The method includes operating an engine control system on a first bandwidth, filtering at least one data input from the engine control system to a second bandwidth, and receiving, by a power management system operating on the second bandwidth, the at least one filtered data input. The method also includes predicting an engine operating condition using the at least one filtered data input using a closed-loop engine model, determining an optimal engine power management based on the prediction, solving a constrained optimization for a desired optimization objective, and outputting the optimal engine power management to the engine control system.