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Record W2116381028 · doi:10.1002/cjce.22366

Influence analysis of monolith structure on regeneration temperature in the process of microwave regeneration in the diesel particulate filter

2015· article· en· W2116381028 on OpenAlexvenueno aff
E Jiaqiang, Ming Liu, Yuanwang Deng, Hao Zhu, Jinke Gong

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaChina Scholarship CouncilNational University of Singapore
KeywordsDiesel particulate filterRegeneration (biology)MonolithParticulatesMicrowaveDiesel fuelFilter (signal processing)Materials scienceProcess (computing)MechanicsEnvironmental scienceChemistryWaste managementPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Based on models A, B, and C of three kinds of diesel particulate filter (DPF) with microwave regeneration, a DPF microwave regeneration model is established according to the laws of conservation of mass, momentum, and energy. The trends of internal temperature under different velocities of exhaust gas in channels are simulated and analyzed. The results show that: (1) Regeneration temperature in the process of microwave regeneration will begin to increase from the front to the rear end of along the axial direction, and the maximum temperature value will appear in the rear end of the monolith. (2) The internal flow velocity in the DPF of model C is the most uniform and the temperature gradient is the smallest among the three models. Therefore, it is the most useful for DPF regeneration. (3) The minimal thermal stress is exerted on the DPF of model C. Therefore, this model is most useful for prolonging the service life of a DPF.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.208
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations47
Published2015
Admission routes1
Has abstractyes

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Same venueThe Canadian Journal of Chemical EngineeringSame topicAerosol Filtration and Electrostatic PrecipitationFrench-language works237,207