Boundary Layer Enhanced Thermal Recuperation for Diesel Particulate Filter Regeneration under a Periodic Flow Reversal Operation
Bibliographic record
Abstract
<div class="htmlview paragraph">Diesel Particulate Filters (DPF) are viable to reduce smoke from diesel engines. An oxidation process is usually required to remove the Particulate Matter (PM) loading from the DPF substrates. In cases when the engine exhaust temperature is insufficient to initiate a thermal regeneration, supplemental energy is commonly applied to raise the exhaust gas and/or the DPF substrate temperatures. A flow reversal (FR) mechanism that traps a high temperature region in the DPF substrate by periodically altering the gas flow directions has been identified to be capable of reducing the supplemental energy and thus to improve the overall thermal efficiency of the engine.</div> <div class="htmlview paragraph">However, extended operations with low exhaust temperature lowers the DPF boundary temperatures that defers the regeneration processes. Furthermore, the temperature fluctuations caused by the periodic FR operation also increase the thermal stress in the DPF. In this paper, two inert monoliths are used as the boundary layers of the DPF substrate in order to partially absorb the thermal energy and reduce the adverse effects of the periodic FR regeneration. A one-dimensional transient model is introduced to describe the gas flow, heat transfer, and chemical reactions during the regeneration. The model has been developed to analyze the effects of the inert parts on the DPF temperature distributions and soot layer profiles during regeneration when FR process is applied. The influences of the FR cycling period and the substrate thermal properties on the DPF regenerations are also discussed. The simulation results show that the periodic FR regeneration has higher regeneration efficiency than the conventional unidirectional flow regeneration and the use of inert parts is found effective to reduce the temperature gradients within the DPF substrate during the periodic FR regeneration processes. An exhaust FR setup has been built in authors' lab and part of the simulation results have been verified with the preliminary empirical data.</div>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".