ProgramMaster Logo
Conference Tools for Materials Science & Technology 2020
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting Materials Science & Technology 2020
Symposium Probabilistic Life Prediction of Materials in Aging Systems
Presentation Title Mechanistic and Engineering-Scale Modeling of the Effect of High-altitude Environments on the Structural Integrity of Airframe Components
Author(s) James Burns
On-Site Speaker (Planned) James Burns
Abstract Scope Aerospace components operate at high altitude (low temperatures and water vapor pressures); such environments retard fatigue crack growth. Incorporating these benefits into structural life management requires an understanding of the governing damage physics, data generation protocols that ensure similitude, and integration of environmental effects into fracture mechanics-based life prediction software. This talk will outline relevant knowledge gaps and present the results of research efforts aimed at addressing these issues. Specific emphasis will be given to multi-disciplinary modeling being performed to better understand the important role of molecular transport from the bulk to the crack tip in these environments. Also, results from a novel LEFM-based software (AFGROW) module that was developed to integrate a coupled load-environment spectrum into fatigue life prediction methodologies. The talk will conclude with a discussion of the incremental and long-term needs to enable incorporation of these approaches into structural life management of airframe components.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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

Questions about ProgramMaster? Contact programming@programmaster.org