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Record W2225612121 · doi:10.4271/2000-01-0213

Experimental and Modelling Study of Variable Cycle Time for a Reversing Flow Catalytic Converter for Natural Gas/Diesel Dual Fuel Engines

2000· article· en· W2225612121 on OpenAlexaff
B. Liu, M. David Checkel, Robert E. Hayes, Minzhang Zheng, Edward A. Mirosh

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2000
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDual (grammatical number)ReversingDiesel fuelAutomotive engineeringCatalytic converterNatural gasVariable (mathematics)Computer scienceEnvironmental scienceEngineeringWaste managementInternal combustion engineMathematics

Abstract

fetched live from OpenAlex

This paper presents an investigation of a reverse flow catalytic converter attached to a diesel/natural gas dual fuel engine. Experimental data were obtained in a ceramic monolith catalytic converter with a palladium based catalyst. A variety of flow reversal cycle times were explored experimentally when the engine load was changed from a high load to a low load. A single channel numerical model was developed for the data set and the effect of reverse flow cycle time was studied using both physical and numerical model systems. The duration of the cycle time is shown to be an important parameter in the operation of the converter. Shorter cycle times produced the least fluctuation in reactor temperature and gave the highest time-averaged conversion. Intermediate cycle times gave the most rapid increase in the maximum reactor temperature.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 science0.0010.000
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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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 designSimulation or modeling
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

Citations24
Published2000
Admission routes1
Has abstractyes

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