Three-Dimension Deposited Soot Distribution Measurement in Silicon Carbide Diesel Particulate Filters by Dynamic Neutron Radiography
Bibliographic record
Abstract
Exhaust emissions are well known to have adverse impacts on human health. Studies have demonstrated that there is an association between ambient particulate matter (PM) levels and various harmful cardiopulmonary conditions. Soot exhaust from diesel engines can be a significant contributor to airborne pollutants. A key component in PM level control for a diesel engine is a diesel particulate filter (DPF). This device traps soot while allowing other exhaust gases to pass unhindered. However, the performance of diesel particulate filters can change with increasing soot loadings and thus may require regeneration or replacement. Improved understanding of diesel particulate filters is dependent upon the knowledge of the actual soot loading and the soot distribution within the DPF. Neutron radiography (NR) has been identified as an effective means of non-destructively identifying hydrogen or carbon adsorbed in PM. Previous work has shown this technique to be relatively successful for cordierite type DPFs. In this feasibility study, the neutron radiography method is used to image a clean SiC DPF and a 3g/L soot loaded SiC DPF to measure the three-dimensional soot deposition profiles. The attenuation coefficients of SiC and soot are determined and quantitative soot loadings are determined. The resulting images show that soot deposition is generally uniform although some of the modules have higher soot deposition than others. The deposited soot distribution appears to be relatively uniform along the axial length of the SiC DPF with some variability. Radiography image of the deposited soot distribution in SiC DPFs compares well with deposited soot distribution in cordierite DPFs by the neutron radiography method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".