|About this Abstract
||Materials Science & Technology 2020
||Probabilistic Life Prediction of Materials in Aging Systems
||Probabilistic Life-cycle Decision Optimization with Bayesian Networks for Aging Fixed Equipment and Piping in the Energy Industries
||Aaron Stenta, Charles Panzarella
|On-Site Speaker (Planned)
In the aging energy industries, the time evolution of material damage provides ongoing struggles for risk based inspection planning and requires implementation of a thorough life-cycle management (LCM) program. Such programs can be overwhelming and may require a diverse team of engineering experts. However, with recent advances in technology, such an effort doesn’t need to be so daunting. Bayesian Decision Networks (BDN) are probabilistic computational algorithms that utilize disparate sources of knowledge to make more informed day-to-day decisions. Incorporating such BDNs into daily operations promotes pro-active decision-making and risk management to minimize the likelihood of potentially catastrophic, unexpected failures. These life-cycle BDNs are directly applicable to all failure modes throughout the aging energy industries. For demonstration, two examples are presented, i.e. i) long-term performance of nuclear spent fuel canisters subject to chloride stress corrosion cracking, and ii) managing plant-wide thickness data for facility equipment and piping subject to corrosion.