Load Controller for Aircraft Power Systems
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Solution Overview
Problem
Complex systems, such as aircraft power generation, face challenges in managing sub-system faults like overheating, where existing solutions often require complete power shutdown, lacking flexibility in response to fault severity and system load distribution.
Innovation Solution
A load controller that develops real-time models of sub-systems to predict future performance, allowing for graded responses to faults, load redistribution between sub-systems, and adaptive load management to maintain system operation within predetermined criteria, using an adaptive model and load allocator to optimize load delivery and redistribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a thermomechanical fuse is used to monitor sub-system temperature, then overheating protection is improved, but system flexibility deteriorates because complete power shutdown is required
Solution Approach 1:
The system dynamically adjusts the load on sub-systems based on real-time temperature monitoring and predictive modeling. Instead of a static binary on/off control, the load allocation is continuously optimized to maintain temperatures below thresholds while maximizing power delivery. This allows flexible, graded responses to thermal conditions rather than rigid shutdown decisions.
Solution Approach 2:
The system performs preliminary thermal analysis using predictive models to forecast future temperature trends before actual overheating occurs. By analyzing historical temperature data and load patterns, the system proactively adjusts load allocation to prevent threshold violations, enabling preventive rather than reactive control and maintaining system flexibility.
2Reliability
If complete power shutdown is implemented when temperature thresholds are exceeded, then safety is improved, but productivity deteriorates due to unnecessary load loss
Solution Approach 1:
The system changes operational parameters dynamically by adjusting load allocation across multiple sub-systems based on real-time temperature conditions. When one sub-system approaches thermal thresholds, the controller redistributes its load to other available sub-systems, maintaining overall power delivery while keeping temperatures safe. This continuous parameter adjustment replaces binary shutdown decisions.
Solution Approach 2:
The system creates virtual copies of thermal management decisions by developing predictive models that simulate future temperature behavior under different load scenarios. These models allow the controller to evaluate multiple potential outcomes and select the optimal load allocation strategy, effectively copying successful thermal management patterns without actual trial-and-error that would risk overheating.
3Adaptability or versatility
If real-time model development is implemented, then adaptability to faults is improved, but device complexity increases
Solution Approach 1:
The system implements continuous feedback loops where temperature measurements from sub-systems are fed back to the controller, which updates predictive models and adjusts load allocation accordingly. This closed-loop control enables automatic adaptation to changing thermal conditions and faults without requiring complex manual intervention or reconfiguration.
Solution Approach 2:
The controller performs self-adjustment by automatically updating its predictive models using incoming temperature data and autonomously redistributing loads across sub-systems. The system serves itself by detecting thermal issues and correcting them through intelligent load management without external intervention, reducing the need for complex external monitoring and control infrastructure.
Data Source
AI summary
There is provided a load controller for a system, the system comprising a first sub-system arranged to deliver a first load, the load controller being operable to: acquire a first target load profile, being the load initially desired for delivery by the first sub-system over an operational period; measure in real time during an update window within the operational period: a first parameter of the first sub-system, to obtain a first measured Load Controller monitor signal; and the first load, to obtain a first measured load signal; develop in real time a model of the first sub-system, using the first measured monitor signal and the first measured load signal, the model relating the first load to the first parameter; given the first target load profile, and the model of the first sub-system, generate for a future period a first predicted monitor signal, the future period being ahead of the update window; and determine whether the first predicted monitor signal satisfies at least one predetermined criterion.