Three-Dimension Deposited Soot Distribution Measurement in Silicon Carbide Diesel Particulate Filters by Dynamic Neutron Radiography
Why this work is in the frame
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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.</div></div>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it