Transient Temperature Estimation for Active-Flow Aftertreatment Control
Bibliographic record
Abstract
Diesel exhaust temperatures vary with engine load and speed thereby affecting the thermal behavior and thus performance of exhaust after-treatment systems. The determination of the transient temperature is needed to enable active-flow control after-treatment schemes that include parallel alternating flow, partial restricting flow, periodic flow reversal, and extended flow stagnation. The active schemes are found to be especially effective to treat engine exhausts that are difficult to cope with conventional passive-flow converters, by shifting the exhaust gas temperature, flow rate, and oxygen concentration to more favorable windows for the filtration, conversion, and regeneration processes. This paper reports a thermal-response model that uses the temperature data obtained with two high-inertia thermocouples of different sizes to estimate the diesel engine transient exhaust gas temperature. The thermal inertial difference of the two thermocouples is critical in predicting the transient temperature through a mathematical procedure. To validate the model, the exhaust gas temperature was simultaneously measured with a third thermocouple of high sensitivity that acquired temperature data approximating the real-time value.
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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 | 0.001 |
| 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 | 0.001 |
| 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".