Bayesian Network for Gas Turbine Lifecycle Uncertainty
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Solution Overview
Problem
Existing decision tools for gas turbine engine components lack a closed-form mathematical solution, leading to inaccurate predictions and increased costs due to conservative design practices, as they rely on assumptions that constrain the design domain and fail to account for real-world variability and uncertainty over the component's lifecycle.
Innovation Solution
A bidirectional probabilistic analysis subsystem using a Bayesian network to connect various sources of uncertainty across different phases of the component's lifecycle, allowing for periodic re-computation of joint probability distributions based on new evidence, integrating empirical data, analytical models, and expert knowledge to refine design and field management decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing empirical models and system architectures are used to approximate system behavior, then decision-making can be supported with available tools, but the predictions become inaccurate and costs increase due to overly conservative design practices
Solution Approach 1:
The patent transforms the decision-making approach by changing from deterministic parameter estimates to probabilistic parameter distributions. Each uncertain parameter is represented as a probability distribution rather than a single value, allowing the model to capture variability and uncertainty explicitly. This enables more accurate predictions without requiring overly conservative designs, as the full range of possible outcomes is considered.
Solution Approach 2:
The patent adds a temporal dimension to the analysis by modeling uncertainty evolution over the component lifecycle. The probabilistic framework allows uncertainty to be updated as new information becomes available, transforming a static analysis into a dynamic one. This dimensional addition enables continuous refinement of predictions without increasing model complexity in the traditional sense.
2Adaptability or versatility
If boundary condition assumptions are made for representative testing and analysis, then the analysis can be performed with constrained parameters, but the design domain is constrained and real-world component behavior cannot be accurately determined
Solution Approach 1:
The patent makes the model dynamic by allowing boundary conditions and parameters to evolve over time rather than remaining fixed. The probabilistic framework enables the model to adapt to new information and changing conditions throughout the component lifecycle. This dynamic approach expands the design domain flexibility while maintaining prediction precision, as the model can accommodate a wider range of scenarios without being constrained by initial assumptions.
3Loss of information
If closed-form mathematical solutions are pursued for gas turbine engine decisions, then exact answers can be obtained, but no such solution exists for the complex nature of these systems
Solution Approach 1:
The patent introduces probabilistic graphical models as an intermediary framework that bridges the gap between complex physical systems and decision-making requirements. The Bayesian network structure serves as a mediator that organizes complex relationships between variables in a manageable format, enabling uncertainty quantification without requiring intractable closed-form solutions. This intermediary representation makes the complex mathematics computationally feasible while preserving the essential uncertainties.
Data Source
AI summary
Embodiments of a gas turbine engine lifecycle decision assistant apply a probabilistic-based process founded on a Bayesian mathematical framework to intelligently combine analytical models, expert judgment, and data during the development and field management of gas turbine engines. The process integrates physics-based and high-fidelity models with data and expert judgment that evolves over the course of the gas turbine engine lifecycle. Among other things, embodiments of the gas turbine engine lifecycle decision assistant can improve future predictive models and understanding while at the same time reducing risk and uncertainty in the service management of existing products.