Neutron Radiography of Convective and Thermophoretic Diesel Engine Exhaust Soot Depositions in a Cooled Rectangular Chamber
Bibliographic record
Abstract
An investigation was performed to study the effects of convection, diffusion and thermophoresis on diesel exhaust soot deposition inside a plate-type rectangular cooling section for recirculation (EGR) applications since deposited soot can be detrimental to the heat transfer efficiency of EGR cooling devices. A non-destructive neutron radiography technique was used to measure the soot deposition thickness distribution on the plate surface inside the cooling chamber. The chamber cooled with an inlet water coolant of 20 and 40°C, was installed in a modified exhaust system of a 2.4kW diesel engine and subjected to a mass flow rate of 20kg/hr of diesel exhaust ranging 0 to 3 hours with the exhaust gas temperature at 260°C upstream of the cooling chamber. In this work, the effect of cooling temperature and operation time on thermophoretic deposition was investigated. Results show the mean soot deposition thickness increases with increasing engine operating time and decreases with increasing inlet coolant temperature. Further based on the soot deposition thickness distribution profile and outside wall surface temperature profile, the thermophoretic soot deposition was dominant since the soot deposition pattern qualitatively matched the temperature profile of the cooling wall. Mechanism of soot deposition will be discussed based on the observed soot deposition.
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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) | 0.000 | 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) | 0.001 | 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".