Probabilistic Life Prediction of Materials in Aging Systems: Probabilistic Life Prediction of Materials in Aging Systems
Sponsored by: TMS Corrosion and Environmental Effects Committee, TMS High Temperature Alloys Committee
Program Organizers: Narasi Sridhar, OSU; Raul Rebak, GE Global Research
Tuesday 2:00 PM
November 3, 2020
Room: Virtual Meeting Room 17
Location: MS&T Virtual
Session Chair: Narasi Sridhar, Ohio State University
2:00 PM Invited
Increasing Operation Life of Light Water Reactors by using Accident Tolerant Fuels: Raul Rebak1; 1GE Global Research
Nuclear power generated by using the heat from a fission reaction is clean, and it is safe. This form of commercial civilian electricity has been in use for over six decades mainly by means of light water cooled reactors. The type of alloys and materials employed for the construction of these reactors has changed little since their initial design and selection in the 1940s. Currently the international materials community is considering novel alternatives to the traditional materials for both the cladding and the fuel in the reactors. Three main options exist for the cladding; (a) coated zirconium alloys, (b) FeCrAl alloys, and (c) silicon carbide based composites. Newer fuel concepts include (1) modified urania, and (2) higher uranium density materials. The use of the newer proposed materials not only will increase the operation life of the reactors, but it will also make the reactors safer to operate.
2:30 PM
Mechanistic and Engineering-Scale Modeling of the Effect of High-altitude Environments on the Structural Integrity of Airframe Components: James Burns1; 1University of Virginia
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.
2:55 PM Invited
Probabilistic Life-cycle Decision Optimization with Bayesian Networks for Aging Fixed Equipment and Piping in the Energy Industries: Aaron Stenta1; Charles Panzarella1; 1The Equity Engineering Group
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.
3:15 PM
Probabilistic Prediction of Stress Corrosion Cracking of Oil & Gas Pipelines Using Bayesian Network: Narasi Sridhar1; Francois Ayello2; 1OSU; 2DNVGL
On-shore oil and gas pipelines have been known to exhibit stress corrosion cracking (SCC) under certain combination of circumstances. Two forms of SCC are recognized - high pH SCC that is intergranular in nature and near-neutral pH SCC that is transgranular in nature. Several factors contribute to SCC, including coating type, coating disbondment, applied cathodic potential, soil type, pressure fluctuation, and steel surface condition under the coating. Typically, SCC prediction involves considering one or two of these factors at the exclusion of others. In this paper, we consider all the relevant factors in a Bayesian network framework. The existing knowledge of cracking mechanisms are incorporated as conditional probability tables. The model predictions are compared to industry experience. The effect of improving knowledge and data acquisition on predicted probability are evaluated.