About this Abstract |
Meeting |
2024 TMS Annual Meeting & Exhibition
|
Symposium
|
Advanced Characterization Techniques for Quantifying and Modeling Deformation
|
Presentation Title |
Microstructure-sensitive Modeling of Grade-91 Alloy with Uncertainty Quantification |
Author(s) |
Jobin Kolliyil Joy, Anjana Talapatra, Mariyappan Arul Kumar, Laurent Capolungo |
On-Site Speaker (Planned) |
Jobin Kolliyil Joy |
Abstract Scope |
Accelerated materials design and qualification entails that predictor for the creep and tensile responses of metals be fully validated against experimental data. One of the grand challenge lies in optimizing the datasets used for model calibration. Eventually, decision making relies on our ability to quantify model error and uncertainty. This study focuses on unraveling the effects of microstructure on the tensile and creep responses of grade-91 alloys and on proposing optimal design of experiments to minimize model uncertainty. To this end, a numerically efficient full field polycrystal model is used to generate synthetic databases of the mechanical response of Grade-91. Using a Bayesian approach for parameter estimation and uncertainty propagation, the variability in the tensile and creep responses will be studied. The work will finally demonstrate how to quantify the cost/benefit of each experimental calibration data point to propose optimal design of experiments. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Iron and Steel, Modeling and Simulation |