About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
High Performance Steels
|
| Presentation Title |
Predicting Nanoparticle Dispersion in Oxide Dispersion Strengthened (ODS) Alloys via Molecular Dynamics Simulations |
| Author(s) |
Alex Killips, Guan-Cheng Chen, Ertugrul Demir, Seung Min Ha, Anish Ranjan, Xingshuo Zhang, Jian Gan, Mukesh Bachhav, Lin Shao, Haiming Wen, Enrique Lavernia, Xiaochun Li |
| On-Site Speaker (Planned) |
Alex Killips |
| Abstract Scope |
Oxide Dispersion Strengthened (ODS) alloys offer exceptional high-temperature performance due to a fine dispersion of oxide nanoparticles. However, achieving this dispersion from a melt is challenging due to poor wetting and large van der Waals attractions between nanoparticles in the melt. This work presents a framework to predict the viability of small alloying additions for improving nanoparticle dispersion. As experimental data for contact angles and work of adhesion are limited for novel alloy-oxide systems, we utilize molecular dynamics simulations to obtain these values for molten metal droplets on a planar Y2Ti2O7 substrate, representing the fundamental nanoparticle-melt interaction. From the contact angles, the work of adhesion is calculated to quantify the interfacial energetics, which then predicts wetting behavior and van der Waals potential of the oxide with the melt. This provides a screening method to identify elements that create favorable scenarios, enabling the design of ODS alloy compositions for casting. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Iron and Steel, Nuclear Materials |