Gas Turbine Combustor Flame Blowout Detection
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Solution Overview
Problem
Combustors in gas turbine engines face challenges with flame blowout, which limits operational performance and safety, particularly under conditions of high flow rates and low pressures, and is exacerbated by stringent emissions regulations, leading to potential economic and safety concerns.
Innovation Solution
A system utilizing an acoustic sensor to measure combustor pressure and generate a raw data stream, processed by a blowout detection unit with ensemble analytics to predict blowout precursors, enabling timely control adjustments to prevent blowouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If designers include substantial safety margins in engine design to avoid blowout, then reliability is improved, but performance is reduced
Solution Approach 1:
The system performs preliminary detection of blowout precursors by continuously monitoring combustor parameters and analyzing acoustic signals. The blowout detection unit identifies precursor conditions before actual blowout occurs, allowing preventive action to be taken. This resolves the contradiction by enabling operation closer to blowout limits without sacrificing reliability, as the system provides advance warning to maintain safe operation.
2Object-generated harmful factors
If operators run the engine near flame blowout conditions to lower nitrous oxide emissions, then emissions are reduced, but the likelihood of flame blowout increases
Solution Approach 1:
The system implements continuous feedback monitoring of combustor conditions through acoustic sensors and pressure measurements. The blowout detection unit analyzes this data in real-time and provides feedback signals when precursor conditions are detected. This feedback mechanism allows operators to maintain operation near blowout limits for emissions reduction while having immediate warning to adjust conditions and prevent actual blowout, thus resolving the reliability concern.
3Reliability
If extra margin is built into the design to account for uncertainty in blowout conditions, then reliability is improved, but manufacturing precision requirements increase
Solution Approach 1:
The system replaces mechanical/design-based safety margins with an electronic detection and analysis system. Instead of relying on conservative design margins that require precise manufacturing tolerances, the system uses acoustic sensors, pressure sensors, and computational algorithms to detect blowout precursors. This substitution resolves the contradiction by achieving reliable blowout prediction through measurement and analysis rather than through manufacturing precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively predicts and prevents flame blowout conditions, enhancing operational safety and performance by allowing for more accurate monitoring of combustor stability and reducing the likelihood of costly shut downs.
Implementation Method 1
an acoustic sensor configured to periodically measure a pressure of the combustor and generate a raw data stream
Data Source
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AI summary
A system for controlling an operation of a combustor (13,30) in a gas turbine (27) that includes: an acoustic sensor (62) configured to periodically measure a pressure of the combustor (13,30) and generate a raw data stream having the pressure data points resulting from the periodic measurements; and a blowout detection unit (75) configured to receive the raw data stream from the acoustic sensor (62). The blowout detection unit (75) may include a processor (81) and a machine-readable storage medium on which is stored instructions that cause the processor (81) to execute a procedure related to a detection of a blowout precursor. The procedure may include an ensemble approach in which the detection of the blowout precursor depends upon a outcomes generated respectively by separate detection analytics.