Evaluation of the Effectiveness of Diesel Particulate Filter Cleaning Methods and their Effect on Fuel Consumption
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The negative effects of long-term exposure to soot and particulate matter emissions from diesel exhaust on the human health have been widely acknowledged due to the harmful substances that exhaust gas contains. Regulators have established strict programs to determine the performance and reliability of emissions after-treatment systems and devices. Tests were conducted to evaluate the efficacy of three diesel particulate filter (DPF) cleaning methods: 1) thermal and pneumatic cleaning, 2) ultrasonic cleaning, and 3) aqueous cleaning methods. A novel non-destructive method to quantify soot and ash deposits in the filters was developed, validated and used to determine the effectiveness of the identified cleaning methods. Given the number of different cleaning methods available, testing against a set of standard parameters provided accurate comparative results. Test results showed at least satisfactory ratings for all cleaning systems that were evaluated, while two systems stood out with very good ratings, with up to 96% of deposits removed by one cleaning system. Fuel consumption tests based on SAE J1321 and TMC RP 1102A Fuel Consumption Test Procedure – Type II were also performed to determine the effect of diesel particulate filter cleaning methods on fuel consumption. The tests were conducted on a high-speed test track at a constant speed of 105 km/h (65 mph). Test results showed that excluding the fuel consumed during passive regeneration, the level of obstruction of the diesel particulate filter showed little impact on fuel consumption. Results comparing the fuel consumption of a cleaned DPF versus that of a used DPF ranged from 0.65% to 1.43%.</div></div>
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".