Adaptive Flow Model for Turbine Control Stability
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Solution Overview
Problem
Existing engineering control systems for fluid-based systems, such as gas turbine engines, face challenges in accurately and efficiently controlling heat transfer and clearance due to the complexity of phenomena involved, particularly under low-flow and choked-flow conditions, leading to instability and inefficiency in modeling and control.
Innovation Solution
A closed-loop control system utilizing a processor with a flow module, comparator, estimator, and control law to regulate fluid flow by positioning a control element, integrating heat transfer and clearance analysis for precise control, enabling real-time adjustments and improved system reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing flow parameter models are used under low-flow and choked-flow conditions, then the system can operate across a range of conditions, but the model becomes unstable and control precision deteriorates
Solution Approach 1:
The patent applies dynamics by making the flow parameter model adaptive to different operating conditions. The model dynamically switches between different mathematical formulations based on whether the system is in low-flow, choked-flow, or normal operating conditions. This allows the model to maintain stability and accuracy across the entire operational range by adjusting its behavior to match the current flow regime.
Solution Approach 2:
The patent changes parameters by introducing condition-dependent parameters that modify the flow model behavior. Specifically, it uses parameters such as the flow coefficient and pressure ratio thresholds to detect operating conditions and adjusts the mathematical model parameters accordingly. This enables stable modeling under varying flow conditions by transforming the model parameters based on the detected operating state.
2Device complexity
If complex phenomena of heat transfer and clearance are treated separately using independent sets of states, then the modeling approach is simpler, but the control accuracy and system fidelity decrease
Solution Approach 1:
The patent merges the previously separate heat transfer and clearance models into a unified integrated model. Instead of treating these phenomena independently with separate state variables, the patent combines them into a single cohesive mathematical framework that captures their interactions. This integration improves control accuracy by accounting for the coupled effects of heat transfer and clearance variations on system performance.
Solution Approach 2:
The patent creates a composite modeling approach by combining multiple physical phenomena (heat transfer, clearance effects, fluid dynamics) into a unified composite model. This composite model integrates different physical domains and their interactions, providing a more accurate representation of the actual system behavior than separate models could achieve alone.
3Adaptability or versatility
If existing models use different analysis methods for operational states and calibration data, then the modeling process is more flexible, but the overall system reliability and fidelity decrease
Solution Approach 1:
The patent creates a universal model that serves multiple functions: it can process both operational state data and calibration data using the same mathematical framework. The unified model eliminates the need for separate analysis methods by providing a single consistent approach that works across different data types and operating conditions, thereby improving system fidelity and reliability.
Solution Approach 2:
The patent inverts the traditional approach by not creating separate models for different data types, but rather creating a single model that can handle all data types uniformly. Instead of adapting the model to the data (separate methods for operational and calibration data), the data is adapted to fit the unified model framework, ensuring consistency and improving overall system reliability.
Data Source
AI summary
A system comprises an apparatus, an actuator and a processor. The apparatus defines a flow path through an aperture, the aperture defines a pressure drop along the flow path, and the actuator regulates fluid flow across the pressure drop. The processor comprises a flow module, a comparator, an estimator and a control law. The flow module maps a flow curve relating a flow parameter to a pressure ratio, and defines a solution point located on the flow curve and a focus point located off the flow curve. The comparator generates an error as a function of a slope defined between the focus and solution points. The estimator moves the solution point along the flow curve, such that the error is minimized. The control law directs the actuator to position the control element, such that the flow parameter describes the fluid flow and the pressure ratio describes the pressure drop.