Turbomachine Risk Prediction Using Ambient Data
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Solution Overview
Problem
Turbomachines face operational issues due to ambient conditions such as ice formation, lean blow out, and emissions, which current methods address reactively and can lead to performance reduction and physical damage, with robust designs compromising between reliability and cost.
Innovation Solution
A system and method that utilize historical risk profile data and ambient conditions to develop risk thresholds for known operating profiles, employing statistical techniques and pattern recognition algorithms to predict and mitigate anomalies by adjusting operations, such as applying bleed heat or scheduling maintenance, to prevent damage and optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time temperature data is analyzed to prevent ice formation by applying inlet bleed heat, then ice damage is prevented, but excessive application of inlet bleed heat leads to reduction in performance of the turbomachine
Solution Approach 1:
The system performs preliminary actions by analyzing historical risk profile data and predicting future ice formation risks before they occur. It proactively identifies high-risk operating conditions and alerts operators to take preventive measures, such as adjusting operating parameters or applying inlet bleed heat only when necessary, rather than reacting after ice formation has occurred or continuously applying heat regardless of conditions.
Solution Approach 2:
The system establishes a feedback loop by continuously monitoring ambient conditions, comparing them against historical risk profiles, and adjusting recommendations in real-time. It feeds back operational data and risk assessments to operators, enabling dynamic decision-making that balances ice prevention with performance maintenance based on actual operating conditions and historical patterns.
2Reliability
If robust designs are used to handle varying weather conditions, then operational reliability is improved, but the designs are not optimized for performance and costs due to their robustness
Solution Approach 1:
The system changes operational parameters dynamically based on predicted risk levels and historical data patterns. Instead of designing for worst-case scenarios universally, it adjusts operating parameters such as inlet guide vane angles, rotor speeds, and temperature setpoints in real-time according to the specific combination of ambient conditions and operational state, optimizing performance for each scenario rather than using a one-size-fits-all robust design.
Solution Approach 2:
The system transitions from static robust designs to dynamic operational adjustments. It continuously adapts operating parameters based on real-time ambient condition monitoring and historical risk profile analysis, allowing the turbomachine to optimize its performance for current conditions while maintaining reliability through data-driven decision-making rather than relying on overly conservative design margins.
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
This approach improves turbomachine reliability and performance by accurately predicting and preventing anomalies, reducing trips, and optimizing water wash practices while maintaining emissions within limits.
Implementation Method 1
air traveling through the compressor can be directed to heat the inlet of the compressor
Data Source
AI summary
Systems and methods for determining risk to operating a turbomachine are provided. According to one embodiment of the disclosure, a method may include receiving historical risk profile data associated with a fleet of turbomachines by at least one processor from a repository. The method can also include receiving ambient conditions of an environment in which a turbomachine is to be operated. Based at least in part on the historical risk profile data and in view of the ambient conditions, at least one risk threshold for at least one known operating profile can be developed. The method may continue with determining that the at least one risk threshold for the at least one known operating profile is reached. Based at least in part on a determination that the at least one risk threshold is reached, a mitigating action associated with the turbomachine can be taken.