Numerical investigation of thermal losses within an internal gear train submerged in a multiphase flow and enclosed in a rotating casing
Bibliographic record
Abstract
This paper presents results of a numerical study of thermal losses generated within an internal gear train consisting of a pinion and an annular gear. The gear train is submerged in a multiphase flow of Air and Oil and enclosed within a horizontal rotating cylinder (casing) attached to the annular gear. The casing rotates at a constant speed and exchanges heat through thermal radiation and natural convection to the ambient air. The study has been carried out using KISSsys and KISSsoft computer software. Numerical results have been validated using published experimental data. The maximum deviation is about 9.3%. The effects of several operating parameters; including the casing rotational speed (N), the torque (ζ) and the oil level (OV) on the various thermal losses generated within the gear train have been investigated. The effects of N, ζ and OV have been investigated in the following ranges: 20–160 rpm, 13–100 N.m, and 0–100%, respectively. The types of thermal losses considered in the present study are the churning, the meshing and the bearing losses. The gear ratio used in the present study is 4.45, therefore, the pinion gear rotational speed varied from 90 to 712 rpm. The present results indicated that increasing the rotational speed or the torque increases the thermal losses within the gear train. Increasing the oil level leads to an increase in the churning losses, up to a specific value of OV of about 31%, above which churning losses remained constant. The oil level at the 31% OV value is the oil level required to just submerge the pinion gear. Increasing the casing rotational speed enhanced the rate of heat transfer to the ambient air which improved the overall thermal performance of the gear train by about 12% at N = 460 rpm, compared to the stationary casing case.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".