|About this Abstract
|2022 TMS Annual Meeting & Exhibition
|AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|NOW ON-DEMAND ONLY – Uncertainty Quantification and Propagation in Prediction of Solid-liquid Interfacial Properties and Solidification Microstructures
|Sepideh Kavousi, Mohsen Asle Zaeem
|On-Site Speaker (Planned)
Molecular Dynamics (MD) calculations are often associated with aleatoric uncertainty arising from its intrinsic chaotic nature. In this study, we will first perform quantitative estimations of the uncertainty for different high-temperature material properties of Al-Cu binary system. We quantify how various parameters such as equilibration/simulation time, system size, and presence of defects alter different high-temperature material properties such as melting point, phase diagram, interface free energy, kinetic coefficient, and associated anisotropy parameters. These properties are essential for performing atomistic-informed quantitative phase-field modeling of solidification. By integrating the MD results with a quantitative phase-field model of solidification (Acta Materialia 211 (2021) 116885), we investigate how the uncertainty propagates through the modeling hierarchy. For each property, we select multiple samples from the input-space and perform phase-field simulations under different solidification conditions (temperature gradient, solidification velocity) to quantify how the uncertainty in each input property affect the model outputs.
|Solidification, Modeling and Simulation, Machine Learning