Thrust Link Hinge Positioning for Adaptive Engine Clearance Control
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Solution Overview
Problem
Existing active clearance control systems for aircraft engines set blade tip clearances without considering unique operational conditions, leading to inefficient fuel usage and potential rubbing issues due to fixed clearance targets.
Innovation Solution
Implementing a system with an adjustable coupling, actuator, sensors, and an electronic control unit that uses machine learning to predict clearance values based on real-time flight data, adjusting the hinge point of a thrust link to optimize clearances and improve centering of rotor to stator alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If fixed clearance targets are used in active clearance control systems, then the system structure remains simple, but fuel efficiency deteriorates and rubbing issues occur due to inability to adapt to unique operational conditions
Solution Approach 1:
The patent applies dynamics by transitioning from fixed clearance targets to dynamic, adaptive clearance control. The system continuously adjusts clearance targets based on real-time operational conditions (thrust, temperature, pressure) and machine learning predictions, allowing the clearance control to adapt dynamically rather than remaining static. This resolves the contradiction by enabling fuel efficiency improvement through adaptability without requiring complete system redesign.
Solution Approach 2:
The patent changes the parameter of clearance targets from fixed values to variable values that depend on operational conditions. By using machine learning models to predict optimal clearance targets based on thrust, temperature, and pressure parameters, the system dynamically adjusts clearance settings. This allows the system to optimize fuel efficiency across different operating conditions while maintaining a relatively simple overall system architecture.
2Reliability
If cold build clearances are increased to avoid rubbing, then reliability improves, but clearance optimization deteriorates due to excessively open clearances when the engine is running
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict the thermal expansion and shaft movement that will occur during engine operation. The system pre-calculates optimal clearance targets that account for anticipated thermal effects, allowing the clearance control system to set appropriate initial clearances that prevent rubbing while optimizing performance. This resolves the contradiction by eliminating the need for excessive cold build clearances.
Solution Approach 2:
The patent implements feedback by continuously monitoring actual clearance measurements and comparing them against machine learning predictions. The system uses this feedback to refine clearance targets and adjust the thrust link positioning in real-time. This closed-loop control ensures reliability by preventing rubbing while optimizing clearances during operation, resolving the contradiction between conservative cold build clearances and optimal running clearances.
3Measurement precision
If traditional clearance control algorithms are used, then computational requirements remain low, but accuracy deteriorates due to inability to account for unique operational conditions
Solution Approach 1:
The patent substitutes traditional mechanical clearance control algorithms with machine learning-based predictive models. Instead of using simple rule-based or lookup table approaches, the system employs trained machine learning models that can accurately predict optimal clearance targets based on multiple operational parameters. This substitution significantly improves measurement precision while the computational complexity is managed through efficient model deployment on the engine control unit.
Solution Approach 2:
The patent applies segmentation by dividing the clearance control problem into distinct functional modules: sensor data acquisition, machine learning prediction, clearance target calculation, and actuator control. This modular approach allows the system to achieve high accuracy through sophisticated machine learning models while keeping individual computational tasks manageable and efficient, resolving the contradiction between precision and computational complexity.
Data Source
AI summary
Systems and methods for optimizing clearances within an engine include an adjustable coupling configured to couple a thrust link to the aircraft engine, an actuator coupled to the adjustable coupling, where motion produced by the actuator adjusts a hinge point of the adjustable coupling, sensors configured to capture real time flight data, and an electronic control unit. The electronic control unit receives flight data from the sensors, implements a machine learning model trained to predict clearance values within the engine based on the received flight data, predicts, with the machine learning model, the clearance values within the engine based on the received flight data, determines an actuator position based on the clearance values, and causes the actuator to adjust to the determined actuator position.