Thermal Investigation of a Turbocharger Using IR Thermography
Bibliographic record
Abstract
An experimental thermal survey of a turbocharger was performed in an engine test cell using IR thermography. The emissivity coefficients of housings were specified using a furnace and camera. It was shown that the emissivity of the turbine, compressor, and bearing housings are 0.92, 0.65, and 0.74, respectively. In addition, thermocouples were mounted on the housing to validate the temperature of the thermal camera while running in an engine test cell. To compare the data of the thermocouple with data from the thermal camera, an image was taken from the sensor’s location on the housing. The experimental results show that the temperature prediction of the thermal camera has less than 1 percent error. Steady-state tests at various working points and unsteady tests including warm-up and cool-down were performed. The measurements indicate that the turbine casing’s maximum temperature is 839 °C. Furthermore, a thermal image of the bearing housing shows that the area’s average temperature, which is close to the turbine housing, is 7 °C lower than the area close to the compressor housing. The temperature of the bearing housing near the turbine side should be higher; however, the effect of the water passing through the bearing housing decreases the temperature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".