Assessing Metal Matrix Composites for Corrosion and Erosion-Corrosion Applications in the Oil Sands Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Erosion-corrosion that arises when materials are transporting aqueous slurries can be a significant problem in the oil sands industry. Interactions between erosion and corrosion are complex and, as such, it is difficult to determine the rate of material loss with sufficient accuracy for the reliable prediction of equipment lifetime. A combination of electrochemical and gravimetric techniques were used in this study to assess erosion-corrosion rates under liquid-solid impingement in a simulated recycle cooling water environment containing 5 wt% solids at 25°C and 65°C. One material that has been successfully used on critical production equipment is tungsten carbide (WC) metal matrix composite (MMC) applied to the surface as a weld overlay. Four WC-based hardfacing overlays with different particle size distributions were investigated in this study. These overlays comprised 65 wt% WC hard phase with a metal matrix binder consisting of mainly Ni, Cr, Si, B, and Fe. The MMC overlays were applied using the plasma-transferred arc (PTA) welding process. In static corrosion tests, little change in the corrosion rate with different WC grain sizes is observed. The smallest WC grain size distribution shows a slight decrease in corrosion resistance. In erosion-corrosion tests, the larger grain size WC-based MMC shows a slight reduction in erosion-corrosion resistance. The interactions between erosion and corrosion can be identified and are important in the MMC degradation. The corrosion mechanisms in static conditions and the erosion-corrosion mechanisms can be directly linked to the complex microstructure of the MMC.
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.
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 it