Bibliographic record
Abstract
The SailRail air lubricated compliant bearing is a novel Canadian system based on the device originally developed in 1971. With only modest air consumption, this device can transport loads as high as 3000kg with an effective coefficient of sliding friction as low as 0.1%. Much of the previous development of this system has been based on trial and error. This report focuses on the use of Reynolds equations for compressible lubrication for fluid mechanics by using the commercial computational fluid dynamics software package, FLUENT, to obtain a better understanding of the flow in the seal region under the system's runner as well as using a previously developed flat plate model to study the effects of material properties on air-bearing performance. By modelling the gap profile in FLUENT, the question of where flow separation in the seal region occurs alonrf with validating several assumptions used for the original model for flow were examined. The results show that the assumption that the flow through the seal region is inertialess is valid. Several corrections to the original method for analysing data collected using the flat-surface model were implemented and more accurate results were obtained. It was confirmed that increasing the plies of tissue from 25 to 50 produced minimal performance enhancement. Comparing the tissue to foam showed that under uniform loading and applied longitudinal moments; the foam's air-bearing performance is comparable to that of the tissue.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".