Assessing Refractory High Entropy Alloys for Potential High Temperature Applications
Bibliographic record
Abstract
Abstract As the demand for higher turbine inlet temperatures increases, there is an ever increasing need to develop new materials capable of operating in these high temperature environments. Traditionally superalloys, Nickel or Cobalt-based, have been used in the hot sections of gas turbine engine. However, as operational temperatures begin encroaching on the upper temperature limits of superalloys, new classes of materials are being explored as potential replacements. One such class of materials is the High Entropy Alloy, a metal alloy composed of four or more principal alloying elements. This study will present the results from compression testing at room and elevated temperatures of high entropy alloys developed for potential high temperature applications. These alloys were specifically designed using refractory elements with high melting temperatures, such as tungsten and tantalum, as major components of its composition to increase the melting temperature of the resultant alloy, with the objective to elevate its potential operational envelope, while also including relatively soft elements, such as niobium, to ensure the alloys ductility. These results will be compared to yield strengths reported for existing superalloys, as well as other high entropy alloys, from literature. Additionally, this work will include a discussion of the difficulties observed during testing due to the alloy’s unique properties/behavior and their implications on the tested materials’ practicality. Some notable difficulties include the difficulty in manufacturing test coupons, and excessive oxidation resulting in early sample failure for high temperature tests conducted outside of a vacuum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".