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Meeting Materials Science & Technology 2020
Symposium Probabilistic Life Prediction of Materials in Aging Systems
Sponsorship TMS Corrosion and Environmental Effects Committee
TMS High Temperature Alloys Committee
Organizer(s) Narasi Sridhar, OSU
Raul B. Rebak, GE Global Research
Scope The energy and infrastructure systems in developed countries are aging. They can continue to be operated in a sustainable way by judicious use of new materials and management of existing materials. Many exciting developments are occurring in materials and processes, such as high entropy alloys, additive manufacturing, smart coatings, thermomechanically controlled processing, etc. These and conventional materials must perform over relatively long periods of time in engineered systems. There are many uncertainties endemic to life extension of aging systems arising from changes in material properties, environment, and operational envelopes. In some cases, conservative, bounding analyses can be performed to ensure that the materials can perform adequately. However, in many cases, bounding analyses may inhibit materials innovation, require costly replacements, or generate other types of unforeseen risks, including adverse environmental emissions. Data in many systems are relatively sparse. Therefore, probabilistic methods are required to predict the behavior of materials under more realistic, but uncertain conditions. This symposium will focus on methodologies to predict the life of new and conventional materials using probabilistic methods combining multi-scale models and data. The symposium is by necessity interdisciplinary and aims to foster conversation among materials developers, application specialists, and reliability/risk assessment specialists. The symposium will include both invited papers and submitted papers.
Abstracts Due 05/31/2020

Increasing Operation Life of Light Water Reactors by using Accident Tolerant Fuels
Mechanistic and Engineering-Scale Modeling of the Effect of High-altitude Environments on the Structural Integrity of Airframe Components
Probabilistic Life-cycle Decision Optimization with Bayesian Networks for Aging Fixed Equipment and Piping in the Energy Industries
Probabilistic Prediction of Stress Corrosion Cracking of Oil & Gas Pipelines Using Bayesian Network

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