Probabilistic Failure Estimation for Gas Turbine Components
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Solution Overview
Problem
Current methods fail to accurately quantify the risk of failure in components subjected to cyclic stress, particularly in gas turbine components, as they do not effectively combine crack initiation and subsequent crack propagation processes in probabilistic fracture mechanics, leading to conservative design and potential underestimation of component lifespan.
Innovation Solution
A computer-implemented method that virtually divides components into domains, determining domain-specific probability density functions for crack initiation and propagation, and convolutes these to obtain a combined cumulative distribution function for failure, allowing for a comprehensive probabilistic estimation of the total probability of failure under cyclic stress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deterministic approach is used to assess manufacturing anomalies, then calculation is simple, but accuracy of failure risk quantification is insufficient
Solution Approach 1:
The component is divided into multiple domains, each representing a specific region with potentially different stress states and flaw characteristics. This segmentation allows the probabilistic method to be applied locally to each domain, improving overall accuracy while managing computational complexity through modular processing.
Solution Approach 2:
The invention transitions from deterministic parameter assessment to probabilistic parameter distribution assessment. Instead of using single values for flaw sizes and stress concentrations, the method employs probability density functions to represent the uncertainty and variability in these parameters, thereby improving failure risk quantification accuracy.
2Measurement precision
If crack initiation and propagation are treated separately, then calculation is simplified, but accuracy of total failure probability is reduced
Solution Approach 1:
The invention merges the separate assessments of crack initiation and crack propagation by convoluting their respective probability density functions. This combination accounts for the sequential nature of these processes and their interaction, providing a more accurate total failure probability than separate treatments would yield.
Solution Approach 2:
The probability density function serves as an intermediary that connects crack initiation and crack propagation assessments. By representing both processes in the probabilistic domain and using convolution to combine them, the method accurately captures the combined effect while managing computational complexity through mathematical transformation.
3Reliability
If conservative design assumption is made, then safety is ensured, but component lifespan is underestimated
Solution Approach 1:
The invention changes from using conservative deterministic parameters to using probabilistic parameter distributions. This allows the method to quantify reliability more accurately without excessive conservatism, thereby providing more realistic component lifespan predictions while maintaining appropriate safety margins through the probabilistic framework.
Data Source
AI summary
A computer-implemented method for probabilistic quantification of probability of failure of a component, especially a gas turbine component, which during operation is subjected to cyclic stress, wherein the component is divided virtually in one or more domains. The method includes: providing or determining for at least one domain, a domain probability density function for crack initiation and providing or determining for the considered domains a domain probability density function for subsequent crack propagation induced failure. Determining for each considered domain a combined domain cumulative distribution function for failure or its probability density function is done by convoluting either both the considered domain probability density functions for crack initiation induced failure and the respective domain probability density function for subsequent crack propagation induced failure, or their integral function. Alternatively, numerical methods for said component failure probabilities include domain-based Monte-Carlo schemes.