The Heat Transfer Characteristics of Exhaust Gas Recirculation (EGR) Cooling Devices
Bibliographic record
Abstract
A one-dimensional steady state model was developed to predict the heat transfer performance of a shell (liquid)-and-tube (gas) heat exchanger used as a cooling device for exhaust gas recirculation (EGR) application where there is a significant temperature drop across the device. The predictions of the model results were compared with experimental measurements and the trends were found to be in good agreement for most of the transitional and turbulent regimes. The results showed that the exit gas temperature increases with increasing gas mass flow rate at fixed gas inlet temperature and coolant flow rate. It was also found that the exit gas temperature was essentially independent of the coolant flow rate for the typical operating range but did depend on the coolant inlet temperature. It was observed that the pressure drop across the cooling device was not a strong function of the gas inlet temperature. The heat-transfer effectiveness of the cooling device was found to slightly depend on the gas mass flow rate and inlet gas temperature. A preliminary analysis showed that fouling in the EGR cooling device can have a significant effect on both the thermal and hydraulic performance of the cooling device.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".