Numerical Simulation of Stresses Produced on Hydraulic Clutch Discs due to Heat Generated During Operation
Bibliographic record
Abstract
Modern clutches are a critical component of a machine/equipment because they are designed to transfer and control torque, consequently producing movements, enabling safe operation, and controlling movement when necessary. When a clutch engages it transfers torque and allows a machine to produce mechanical work as a final result. The clutch engagement process generates heat due to the slipping of clutch discs. Although pressure applied on clutch discs generates stresses in the disc material, it was not clear how severe a combination of pressure and heat might raise the stresses. This study aimed to produce a numerical simulation to determine the impact of temperature also torque changes in the clutch discs when it transfers movement. Some static and thermal numerical simulations by Finite Element Analysis (FEA) (linear-elastic analysis) were performed, which considered two scenarios; (1) first with only pressures applied on clutch disc's face; (2) where heat was added to pressures. These mathematical simulations revealed that discs stresses are highly sensitive to thermal variations since for some cases the maximum von Mises stresses exceeded discs material mechanical strength leading it to failure. To overcome this problem it is compulsory to consider heat when designing a clutch and a cooling system for it.
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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) | 0.000 | 0.000 |
| 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) | 0.002 | 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".