Experimental and numerical investigation on the heat transfer of an automotive engine’s turbocharger
Bibliographic record
Abstract
Measuring the temperature distribution in a complex and important engine part, such as a turbocharger, is essential for improving engine performance and efficiency. Heat transfer from the turbine to the compressor can strongly influence the turbocharger performance. One of the main measurement methods involves the installation of multiple K-type sensors. However, the location as well as the maximum and minimum temperatures of the turbocharger and the subsequent critical points may not be obtained by using sensors. In the current study, thermocouples, as well as an infra-red camera, are used to study the temperature distribution of the turbocharger housing in a spark ignition engine. A new method is introduced to determine the thermal radiation coefficient of the turbocharger housing by using a laboratory furnace and an infra-red camera. Together with experiments, the finite element method is used to find the temperature distribution in the turbocharger for all thicknesses. Comparing the temperature distribution obtained from simulation with experimental data, an acceptable level of agreement is observed. The location and temperature of the hottest area in experimental and numerical investigations are close to the waste gate. Temperatures using the finite element method for bearings exhibit maximum and minimum errors of 4.9% and 2.3%, respectively, indicating reasonable accuracy for the simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".