Conventional and Electrically Heated Diesel Oxidation Catalyst Physical Based Modeling
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Conventional Oxidation Catalysts are widely used in Diesel engines for their capability to achieve a huge reduction of CO and HC and, at the same time, to improve SCR catalysts performance promoting NO conversion into NO<sub>2</sub> and generating exotherm. However, when the catalyst temperature is below the light-off, as it happens during a cold-start phase, the conversion performances are inadequate. This issue is further complicated by the upcoming regulations on CO<sub>2</sub> that, due to the required improvements on combustion efficiency and heat loss reduction, will cause gas temperature at engine-out to decrease. Furthermore, the heating of the Exhaust Aftertreatment System by fuel-based strategies should necessarily be limited to contain the fuel penalty. In this context, the Electrically Heated Catalyst (EHC) is a solution to quickly warm up the exhaust line and reduce CO<sub>2</sub> penalties.</div><div class="htmlview paragraph">The increasing complexity and the need to account for competing performances require the physical based numerical simulation to reduce time and cost of the entire Exhaust Aftertreatment System development process, from the hardware design to software calibration. This paper focuses on the physical based model development for both conventional and electrically heated DOC. A commercial code (GT-SUITE®) was selected as simulation environment. The main steps of the applied methodology are described, including the lab-scale characterization, the definition and calibration of the kinetic scheme and the setup of the substrate parameters set. Finally, the comparison between simulation results and experimental data is presented in driving cycle conditions, showing a good capability of the models in capturing the main features of the system behavior and proving then their suitability in supporting the various steps of the overall development process.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".