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Record W1819106984 · doi:10.4271/2005-01-0656

Mitigation of the Diesel Soot Deposition Effect on the Exhaust Gas Recirculation (EGR) Cooling Devices for Diesel Engines

2005· article· en· W1819106984 on OpenAlexafffund
Basel I. Ismail, Fraser Charles, Daniel Ewing, James S. Cotton, Jen‐Shih Chang

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsExhaust gas recirculationDiesel exhaustSootDiesel fuelDiesel particulate filterEnvironmental scienceDiesel engineAutomotive engineeringParticulatesExhaust gasDiesel exhaust fluidDeposition (geology)Waste managementCombustionChemistryEngineering

Abstract

fetched live from OpenAlex

An investigation was performed to characterize the effect of the short twisted-tape inserts on the performance of EGR cooling devices for diesel engine applications. The results showed that the addition of the twisted-tape insert reduced the soot deposition and the blockage of the entrance region observed in the cooling devices tested without inserts. The addition of the inserts improved the thermal performance of the cooling devices for lower mass flow rates per tube with a relatively intermediate penalty of the long-time pressure drop build-up. At high flow rates, there was only smaller improvement of the heat transfer and a larger pressure drop penalty was observed. The results suggested that the addition of short twisted-tape inserts could be used to improve some current under performing designs of cooling devices. Optimization in terms of the tape-to-tube length ratio, tube diameter, and number of tubes is required in future studies for higher potential EGR rates.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.011
GPT teacher head0.246
Teacher spread0.234 · 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

Citations25
Published2005
Admission routes2
Has abstractyes

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