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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A gas turbine engine lifecycle decision assistance system for understanding and quantifying uncertainties during the lifecycle of one or more gas turbine engine components, the system comprising, embodied in one or more machine-accessible storage media: a bidirectional probabilistic analysis subsystem comprising a probabilistic model of conditional dependencies between a plurality of random variables associated with a plurality of different sources of uncertainty in the gas turbine engine component lifecycle, the probabilistic model arranged to connect at least two of the plurality of different sources of uncertainty by a common random variable, the bidirectional probabilistic analysis subsystem to: compute a joint probability distribution for the probabilistic model; periodically receive new evidence from one or more of the different sources of uncertainty over the course of the component lifecycle; and in response to the new evidence, re-compute the joint probability distribution. 2. The system of claim 1, wherein the bidirectional probabilistic analysis subsystem computes the joint probability distribution in response to a request for a quantification of uncertainty relating to an aspect of the gas turbine engine component lifecycle. 3. The system of claim 2, wherein the bidirectional probabilistic analysis subsystem receives the request from a component design subsystem and/or a field management subsystem and the bidirectional probabilistic analysis subsystem communicates the re-computed joint probability distribution to the component design subsystem and/or the field management subsystem. 4. The system of claim 1, wherein the bidirectional probabilistic analysis subsystem connects the plurality of different sources of uncertainty in the gas turbine engine component lifecycle to the probabilistic model, and wherein at least one of the different sources of uncertainty relates to a pre-production certification phase of the component lifecycle and at least one of the different sources of uncertainty relates to a post-production certification phase of the component lifecycle. 5. The system of claim 1, wherein the bidirectional probabilistic analysis subsystem connects the plurality of different sources of uncertainty in the gas turbine engine component lifecycle to the probabilistic model, and wherein at least one of the different sources of uncertainty relates to a pre-production certification phase of the component lifecycle including one or more of a design phase, a manufacture phase, and a test phase. 6. The system of claim 1, wherein the bidirectional probabilistic analysis subsystem connects the plurality of different sources of uncertainty in the gas turbine engine component lifecycle to the probabilistic model, and wherein at least one of the different sources of uncertainty relates to a post-production certification phase of the component lifecycle including one or more of a use phase, and a service phase. 7. The system of claim 1, wherein the bidirectional probabilistic analysis subsystem connects the plurality of different sources of uncertainty in the gas turbine engine component lifecycle to the probabilistic model, and wherein the plurality of different sources of uncertainty include at least one analytical model, at least one source of empirical data, and at least one source of expert knowledge. 8. A method for quantifying uncertainty during different phases of the lifecycle of a manufactured component, the method comprising, with at least one computing device: identifying at least two sources of uncertainty that are associated with different phases of the manufactured component lifecycle; connecting the at least two sources of uncertainty by a common random variable in a Bayesian network; and computing a joint probability distribution for the Bayesian network using the common random variable and at least one random variable associated with each of the at least two sources of uncertainty. 9. The method of claim 8, comprising receiving new evidence relating to at least one of the random variables and propagating the new evidence through the Bayesian network. 10. The method of claim 9, comprising forward-propagating the new evidence through the Bayesian network if the new evidence relates to an early phase of the gas turbine engine lifecycle. 11. The method of claim 10, comprising back-propagating the new evidence through the probabilistic model if the new evidence relates to a later phase of the gas turbine engine lifecycle. 12. The method of claim 8, comprising computing the joint probability distribution in response to a request for a quantification of uncertainty relating to an aspect of the manufactured component lifecycle. 13. The method of claim 12, comprising receiving the request from a component design subsystem and/or a field management subsystem and communicating the joint probability distribution to the component design subsystem and/or the field management subsystem. 14. The method of claim 8, comprising connecting a source of uncertainty relating to a pre-production certification phase of the component lifecycle to the Bayesian network. 15. The method of claim 8, comprising connecting a source of uncertainty relating to a post-production certification phase of the component lifecycle to the Bayesian network. 16. The method of claim 8, comprising connecting at least one analytical model, at least one source of empirical data, and at least one source of expert knowledge to the Bayesian network. 17. The method of claim 16, comprising propagating output of the at least one analytical model, output of the at least one source of empirical data, and output of the at least one source of expert knowledge through the Bayesian network. 18. A gas turbine engine lifecycle decision assistant for understanding and quantifying uncertainties during the lifecycle of a gas turbine engine component, the gas turbine engine lifecycle decision assistant comprising: computer program instructions embodied in one or more machine-accessible storage media and executable by at least one processor to: create a Bayesian network of conditional dependencies between a plurality of random variables associated with a plurality of different sources of uncertainty in the gas turbine engine component lifecycle, the Bayesian network arranged to connect at least two of the plurality of different sources of uncertainty by a common random variable; compute a joint probability distribution for the Bayesian network; receive new evidence from one or more of the different sources of uncertainty over the course of the component lifecycle; and in response to the new evidence, re-compute the joint probability distribution. 19. The gas turbine engine lifecycle decision assistant of claim 18, wherein the computer program instructions are to compute the joint probability distribution in response to a request for a quantification of uncertainty relating to an aspect of the gas turbine engine component lifecycle, and wherein the computer program instructions are to receive the request from a component design subsystem and/or a field management subsystem and communicate the re-computed joint probability distribution to the component design subsystem and/or the field management subsystem. 20. The gas turbine engine lifecycle decision assistant of claim 18, wherein the computer program instructions are to connect the plurality of different sources of uncertainty in the gas turbine engine component lifecycle to the Bayesian network, and wherein at least one of the different sources of uncertainty relates to a pre-production certification phase of the component lifecycle and at least one of the different sources of uncertainty relates to a post-production certification phase of the component lifecycle, and wherein the plurality of different sources of uncertainty include at least one analytical model, at least one source of empirical data, and at least one source of expert knowledge.