Experimental investigation on the urea injection and mixing module for improving the performance of urea‐SCR in diesel engines
Bibliographic record
Abstract
Abstract In a urea selective catalytic reduction (urea‐SCR) system for diesel engines, the atomization, evaporation, and mixing conditions of urea water solution (UWS) play a crucial role for NOX conversion and ammonia slip in the SCR process. In this paper, a series of novel mixing modules were proposed to improve UWS evaporation, mixing, and distribution for NOX reduction in the SCR system. Experimental investigations were conducted on the performances of the existing and developed mixing modules under various engine test conditions, including particle size test, NOX conversion efficiency, flow bench tests, etc. To determine ammonia slip, the urea and ammonia distribution patterns were constructed and the non‐uniformity index was then calculated. Compared with the baseline (without mixing module) and existing mixing module, the NOX conversion efficiency of the SCR system with a developed mixing module is improved by 37.7 and 14.1 % at maximum, respectively. Furthermore, ammonia slip and distribution are ameliorated significantly with low‐pressure drops simultaneously. Among the mixing modules, Concept 3 design exhibits superior performance under the engine test conditions.
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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) | 0.000 | 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".