Turbine Performance Model Calibration for Inter-Stage Condition Control
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Solution Overview
Problem
Conventional control systems for combustion-based power sources like gas turbines rely on pre-modeled and estimated qualities, which can lead to substantial economic shortfalls due to minor differences between the model and actual performance, especially when energy demand varies with season, time, and location.
Innovation Solution
A method and system that calculate inter-stage conditions of a turbine component using a performance model, calibrate the model based on differences between predicted and actual conditions, and adjust operating parameters to improve efficiency and energy output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-modeled and estimated qualities are used to calculate machine properties, then device complexity is reduced and ease of operation is improved, but measurement precision and reliability deteriorate due to differences between model and actual performance
Solution Approach 1:
The system continuously monitors actual machine performance parameters (temperature, pressure, flow rate at various stages) and feeds this data back to the performance model. The model is then calibrated by comparing predicted values with actual measured values, adjusting model parameters to minimize discrepancies. This closed-loop feedback mechanism maintains high measurement precision while preserving the ease of operation provided by using a performance model.
2Device complexity
If pre-modeled qualities are used for control calculations, then device complexity is reduced, but reliability deteriorates due to substantial economic shortfalls from model-actual performance differences
Solution Approach 1:
The system performs preliminary calibration of the performance model using actual machine data before critical operations. By pre-adjusting model parameters based on observed performance trends, the system ensures reliable predictions are available when needed, maintaining both low device complexity and high reliability through advance preparation rather than complex real-time adjustments.
3Device complexity
If conventional control systems with pre-determined time intervals are used, then device complexity is reduced, but productivity deteriorates due to inability to meet fluctuating energy demands
Solution Approach 1:
The system transitions from static pre-determined time intervals to dynamic adaptive monitoring and control. The performance model continuously updates predictions based on real-time sensor data, allowing the control system to dynamically adjust operating parameters (fuel flow, air intake, valve positions) in response to fluctuating energy demands. This dynamic approach maintains low device complexity while significantly improving productivity by enabling the system to adapt to changing conditions.
4Ease of manufacture
If pre-modeled performance parameters are used, then ease of manufacture is improved, but manufacturing precision deteriorates due to differences between model and actual machine performance
Solution Approach 1:
The system maintains ease of manufacture by using a standard performance model but compensates for manufacturing variations through parameter changes during calibration. Actual machine data (temperature, pressure, flow rate measurements) are used to adjust model parameters specific to each machine instance, accounting for manufacturing tolerances and variations. This approach preserves the ease of manufacture of using standardized models while achieving manufacturing precision through customized parameter calibration.
Data Source
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AI summary
Embodiments of the present disclosure include methods, systems, and program products for controlling a machine (100). Methods according to the present disclosure can include: calculating, using a performance model (306) of the machine (100), a set of inter-stage conditions (152A-E) of the machine (100) corresponding to one of a set of input conditions (302) and a set of output conditions (304) during an operation of the machine (100), wherein the machine (100) includes a turbine component having a fluid path therein traversing a plurality of turbine stages and a plurality of inter-stage positions (152A-E); calibrating the performance model (228) of the machine (100) based on a difference between a predicted value in the performance model (306) of the machine (100) and one of the set of input conditions (302) and the set of output conditions(304); and adjusting an operating parameter of the machine (100) based on the calibrated performance model (228) and the calculated set of inter-stage conditions (152A-E) of the machine (100).