Effect of Wear on the Performance of Non-Recessed Orifice Compensated Hybrid Journal Bearing
Bibliographic record
Abstract
A bearing subjected to frequent start/stop operations is worn progressively due to rubbing. As a consequence, the geometry of the bearing changes and the performance is affected. This paper presents a theoretical study of the performance of an orifice compensated worn non-recessed hole-entry hybrid journal bearing system. The finite element method has been used to solve the Reynolds equation, governing the flow of the lubricant in the clearance space between the journal and the bearing, along with a restrictor flow equation. The global system equation with the orifice restrictor is nonlinear, which is solved by an iterative technique using the Newton-Raphson method. Two types of journal bearing configurations, having symmetrical and asymmetrical distribution of supply holes around the circumferential direction, have been investigated in the present study. The effect of the wear depth on the journal bearing performance characteristics have been presented for a wide range of restrictor design parameters and external loads. The study demonstrates that the wear affects the bearing performance parameters and the degree of variation is affected by the operating condition, the bearing configuration, and the type of restrictor used. The influence of wear can be reduced by a proper selection of the bearing configuration (symmetrical/asymmetrical), the restrictor, and its design parameter.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".