El Atwani, Osman | Engineering Papers

DOE OSTI2026

Thermal Stability and Ion Irradiation Response of Refined Grained V – 4Cr – 4Ti

Since the 1960’s, there has been interest in V-4Cr-4Ti and similar alloys as candidates for low activation structural materials for advanced nuclear reactors. V-4Cr-4Ti was produced using arc melting and subsequently subjected to large strain extrusion machining to produce a multimodal microstructure largely composed of nanocrystalline and ultrafine grain sizes. In-situ thermal stability of the multimodal V-4Cr-4Ti to 800 °C shows the formation of vanadium carbides with negligible grain growth. In-situ dual-beam 16 KeV He+ and 1 MeV Kr2+ ion irradiation performed at 700 °C to a final dose of ~5 displacements per atom show the formation of small He cavities well distributed throughout the system, with preferential clustering at grain boundaries. This work provides insight into how the increased fraction of grain boundaries affect the simultaneous dual beam ion irradiation response of multimodal V – 4 wt.% Cr – 4 wt.% Ti at elevated temperatures. Analysis of the cavities reveal an areal density of 0.024±0.007 cavities/nm2 and swelling of 0.236% after the dual-beam ion irradiation. Nanoindentation shows a ~50% increase in hardening after the ion irradiation at 700 °C.

TEM↗

DOE OSTI2025

Utilizing machine learning to predict tensile ductility and yield strength of CoNiV-based multi-principal elements alloys

This study explores the use of machine learning (ML) as a computational tool to accelerate the design of multi-principal element alloys (MPEAs) with improved tensile elongation. An ML model was trained using available experimental data from the literature along with theoretically derived features to predict yield strength (YS) and ductility. A subset of ML-predicted compositions—CoNiVFe, CoNiVTi, CoNiVTiFe, and CoCrNiVTi—was synthesized and evaluated through tensile testing. The ML model underpredicted YS by approximately 20–30 % and overpredicted ductility by 60–70 % for Ti-containing alloys. Microstructural analysis revealed that Ti segregation at interdendritic regions contributed to early fracture, leading to discrepancies in ductility predictions. Ti segregation at these regions likely drives the increased YS due to segregation strengthening. In contrast, the CoNiVFe alloy showed good agreement with both experimental YS and elongation, with prediction errors of ∼10.2 % and ∼20.7 %, respectively. Microstructural characterization revealed minimal segregation in this alloy, suggesting that the ML model can reliably predict the properties of alloys with little to no segregation. These findings highlight the capability of ML in predicting YS with good accuracy but underscore its limitations in capturing defect-driven failure mechanisms such as segregation-induced embrittlement.

36 MATERIALS SCIENCE↗