Turbine Engine Wash Scheduling Using Contamination Accumulation Modeling
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Solution Overview
Problem
Existing jet engine maintenance schedules do not consider the actual performance and condition of the engine, leading to inefficient and costly cleaning practices that do not effectively address contaminant accumulation, affecting engine efficiency and lifespan.
Innovation Solution
A system utilizing contamination accumulation modeling to predict particulate accumulations within a turbine engine, optimizing wash timings and methods based on sensor data to maximize hardware life and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If engine washes are performed regularly according to fixed schedules, then engine cleanliness is maintained, but maintenance costs increase and unnecessary washes are performed
Solution Approach 1:
The system transitions from fixed-time wash scheduling to condition-based wash scheduling by monitoring engine parameters (particulate matter accumulation, performance degradation) and triggering washes only when contamination thresholds are exceeded. This parameter-driven approach optimizes the balance between maintaining engine cleanliness and reducing unnecessary maintenance costs.
Solution Approach 2:
The system implements continuous monitoring of engine contamination levels through sensors and performance data, using this feedback to dynamically determine when washes are actually needed. This closed-loop feedback mechanism prevents both over-washing (wasting resources) and under-washing (allowing performance degradation).
2Reliability
If different types of engine washes are used, then washing effectiveness varies, but determining the appropriate wash type becomes difficult
Solution Approach 1:
The system automatically selects the appropriate wash type (water wash, chemical wash, steam wash, etc.) based on the monitored contamination characteristics and engine condition. This self-service approach eliminates the need for manual assessment and decision-making, reducing complexity while maintaining optimal washing effectiveness.
Solution Approach 2:
The system uses contamination parameters (particulate composition, accumulation level, engine performance impact) to automatically determine the appropriate wash type. This parameter-based selection methodology simplifies the decision process while ensuring the most effective wash type is chosen for each specific contamination scenario.
Data Source
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AI summary
A wash optimization system and related methods are provided that increase the efficiency and the effectiveness of engine washes. A system comprising at least one processor receives (402) sensor data representing one or more measured parameters of a turbine engine and determines (404) at least one performance parameter based on the sensor data. The at least one performance parameter represents one or more particulate values associated with the turbine engine. The system generates (406) a health state for the turbine engine based on the at least one performance parameter and generates (408) a wash identifier based on the health state of the turbine engine.