Abstract
Generalised stacking fault energy surfaces (Γ-surfaces) are calculated for Co-Al-W-based and Ni-Al-based superalloys from first-principles calculations. A Special Quasi-random Structure is employed in the calculation of the ternary compound, Co 3(Al,W). Phase field simulations are used to compare dislocation cores present in Co-based and Ni-based superalloys. The higher planar fault energies of the Co-based system lead to a more constricted dislocation which can have implications on both the bowing of dislocations as well as cross-slip. Additionally, planar fault energies of various L1 2 compounds are compared to explain observed segregation pathways in both types of superalloy. Both the planar fault energies and the segregation pathways are discussed within the context of strengthening mechanisms in superalloys.
Original language | English |
---|---|
Article number | 100555 |
Number of pages | 13 |
Journal | Materialia |
Volume | 9 |
Early online date | 5 Dec 2019 |
DOIs | |
Publication status | Published - 31 Mar 2020 |
Funding
HH would like to gratefully acknowledge funding from the EPSRC Centre for Doctoral Training on Theory and Simulation of Materials at Imperial College London under grant number EP/L015579/1 . VAV would like to acknowledge support from Rolls-Royce plc and Imperial College London under the Imperial College Research Fellowship scheme. Calculations were performed at the Imperial College Research Computing Service, doi: 10.14469/hpc/2232 .
Keywords
- superalloys
- density functional theory
- stacking fault energy
- phase field simulation
- dislocations
Fingerprint
Dive into the research topics of 'Generalised stacking fault energy of Ni-Al and Co-Al-W superalloys: density-functional theory calculations'. Together they form a unique fingerprint.Datasets
-
Data for: Generalised stacking fault energy of Ni-Al and Co-Al-W superalloys: Density-functional theory calculations
Hasan, H. (Contributor), Mlkvik, P. (Contributor), Haynes, P. D. (Contributor) & Vorontsov, V. A. (Creator), Mendeley Data, 11 May 2023
Dataset