A novel analytical model for the thermal behavior of a fiber-reinforced plastic pulley in a front-end accessory drive
Bibliographic record
Abstract
This research proposes an innovative model for calculating the temperature distribution of a composite pulley inside a belt drive. The main advantage of the proposed model is a significant reduction in the costs of calculation resources and time. This model adopts two classical theories to determine the heat generation flux and subsequent thermal flow into the pulley. Then, ordinary differential equations are developed in this model according to the irregular geometric structures of a pulley to describe the thermal flow inside this component. Afterward, analytical solutions of the ordinary differential equations are derived to provide final temperature distributions of the pulleys. Moreover, measurements of thermal properties are conducted to reduce the influence of errors. To improve the reliability of the results, experimental temperature measurements were performed on a composite pulley of a designed belt drive system in an engine dynamometer system to validate this analytical model under various operating conditions. The temperature data measured at multiple locations indicate good agreement with the corresponding analytical results. Therefore, the temperature distribution provided by this model can be utilized for the development of high-thermal resistance composite. It can also be used for thermal fatigue simulations of composites under numerous load cases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".