Ultra-low NOx diesel aftertreatment: An assessment by simulation
Bibliographic record
Abstract
Upcoming Euro 7/VII regulations are under discussion, and, from available information, they will focus not only on reducing the current emission limits but also on all those operating conditions that are still responsible for high emission events (e. g. cold start or altitude) as well as regulating secondary emissions with a major focus on GHGs (N2O, CH4 and HCHO). In this perspective, robustness towards a broader range of operative and environmental conditions and high conversion efficiency against all pollutants species will be demanded to aftertreatment systems. In an engine development process, the activity of aftertreatment architecture selection requires huge efforts in terms of time, hardware procurement, facilities and resources. That is because different topological layouts, different technologies and different interactions between the engine and the After-Treament System (ATS) must be investigated to find the most suitable solution. In this perspective, virtual testing is a strong and precious tool to accelerate and substantially reduce development effort with respect to an experimental campaign. The present work aims at showing a deep dive into an aftertreatment modeling and simulation approach in which experimental data coming from steady state and dynamic characterizations are used at first to calibrate 1D catalyst kinetic models and in a second step as input to homologation cycles for ATS performance evaluations. Modeling, validation and an example of aftertreatment technology and layout screening in the context of Euro 7 future scenario proposed by CLOVE will be discussed as well, to clarify how a technology emission reduction walk could be built with such an approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".