Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
Bibliographic record
Abstract
The serpentine belt drive system is used in the auto industry. To avoid thermal destruction inside the belt drive and improve the thermal fatigue life of pulley materials under a variety of operating conditions, the temperature information for each load case must be determined within only a few seconds. To this end, this paper proposes an advanced thermal model to calculate the temperature distribution of a serpentine belt drive at static state operating conditions in an efficient manner. In this model, using analytical and numerical methods, a set of equations is developed according to the thermal flows and heat exchanges occurring in the system. After calculating the thermal flows of each pulley and the belt temperature, the baseline numerical simulations are modified to output the temperature distribution for each pulley. In this manner, the time-consuming numerical calculations for each pulley are performed only once and then analytically modified to provide the temperature predictions for various designed load cases, which dramatically reduces the computational time while maintaining the accuracy. Furthermore, experiments were performed to obtain the temperature data, and the results exhibited a good agreement with the corresponding calculated results. The proposed model can thus be effectively utilized for several types of belt systems and the material development of pulleys.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".