Empirical and Theoretical Investigations of Active-flow Control on Diesel Engine After-treatment
Bibliographic record
Abstract
Empirical and theoretical studies are made between active-flow control and passive-flow control schemes in investigating the influences of gas flow, heat transfer, chemical reaction, oxygen concentration, and substrate properties. The exhaust active-flow control includes the parallel alternating flow, partial restricting flow, periodic flow reversal, and extended flow stagnation that are found to be especially effective to treat engine exhausts that are difficult to cope with conventional passive-flow converters [1, 2]. The tests are set up on a single cylinder Yanmar engine. Theoretical studies are performed with the one-dimensional transient modeling techniques to analyze the thermal behavior of the diesel after-treatment systems when active flow control schemes are applied. The combined use of active flow control schemes are identified to be capable of shifting the exhaust gas temperature, flow-rate, and oxygen concentration to more favorable windows for the filtration, conversion, and regeneration processes. The theoretical analysis indicates that the active-flow control schemes have fundamental advantages in optimizing the converter thermal management that includes the supplemental heating, thermal retention, thermal recuperation, and overheating protection.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".