Reduced-Order Model Control for Unsensed ACM Speed
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Solution Overview
Problem
Complex systems, such as those onboard aircraft, often lack sensors to measure critical parameters like Air Cycle Machine (ACM) rotational speed, making it costly and complex to add additional sensors for monitoring and control.
Innovation Solution
A reduced order model (ROM) sensor system that uses existing sensors to input data into a controller, which generates predicted parameters through a high-fidelity physics-based model, allowing for closed loop control and built-in testing without the need for additional sensors, using equations like y = b0 + ∑i bi xi + ∑j bj Xj to produce accurate predictions within +/- 10% error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional physical sensors are added to measure parameters like ACM rotational speed, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the physical sensor by implementing a reduced-order model that replicates the functionality of physical sensors (like ACM speed sensors) through mathematical relationships. This virtual sensor copy provides the necessary parameter measurements without requiring additional physical hardware, thereby maintaining measurement precision while avoiding increased device complexity and cost.
Solution Approach 2:
The patent introduces a reduced-order model as an intermediary computational layer that mediates between available physical sensor data and the desired parameter measurements. This intermediary uses mathematical relationships and existing sensor inputs to compute parameters that would otherwise require dedicated physical sensors, thus avoiding direct addition of complex sensor hardware while achieving the measurement objective.
2Measurement precision
If additional physical sensors are added to measure parameters like ACM rotational speed, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent creates a virtual copy of the physical sensor by implementing a reduced-order model that replicates the functionality of physical sensors (like ACM speed sensors) through mathematical relationships. This virtual sensor copy provides the necessary parameter measurements without requiring additional physical hardware, thereby maintaining measurement precision while avoiding increased device complexity and cost.
Solution Approach 2:
The patent employs computationally efficient reduced-order models that are inexpensive to implement and execute compared to physical sensor hardware. These computational models provide accurate parameter estimates at minimal cost, avoiding the need for expensive additional physical sensors while maintaining measurement precision requirements.
3Device complexity
If a reduced order model is used to predict parameters, then device complexity is reduced, but measurement precision may be compromised
Solution Approach 1:
The patent transforms the complex high-fidelity physics-based model into a reduced-order model by changing parameters such as reducing the number of state variables, simplifying differential equations, and using algebraic relationships instead of complex differential equations. This parameter transformation maintains sufficient prediction accuracy for control applications while dramatically reducing computational complexity and enabling real-time implementation.
Solution Approach 2:
The patent applies partial action by implementing a reduced-order model that captures the essential dynamics and relationships needed for control purposes without modeling all physical details. The model includes only the critical parameters and relationships necessary for accurate prediction and control, omitting less significant details, thus achieving sufficient precision with reduced complexity.
Data Source
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AI summary
A system (10) includes a plurality of sensors (16), a controller (12) and a component. The plurality of sensors (16) are configured to obtain sensed data indicative of characteristics of an environment. The controller (12) is configured with a reduced order model to output a predicted parameter based on the sensed data. The reduced order model is generated on an external computer system using a high-fidelity physics-based model. The controller is configured to control the component based on the predicted parameter.