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Record W2078610516 · doi:10.1002/cjce.5450780315

Transient simulation of a catalytic converter for a dual fuel engine

2000· article· en· W2078610516 on OpenAlexaffvenue
Benlin Liu, M. David Checkel, Robert E. Hayes, Mingbo Zheng, Edward A. Mirosh

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2000
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCatalytic converterTransient (computer programming)HoneycombMaterials scienceDiesel engineNuclear engineeringPlug flowAutomotive engineeringTransient responseFlow (mathematics)MonolithCatalysisSimulationMechanicsEngineeringComputer scienceChemistryComposite materialInternal combustion engineElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract An experimental and modelling study was performed for a catalytic converter attached to a natural gas/diesel dual fuel engine. The catalytic converter was a ceramic monolith honeycomb substrate coated with a washcoat of catalyst. A multiple segmented design of converter was used. This paper describes the application of a one‐dimensional finite element model for the transient and steady state operation of this converter. The model is a single channel model. The laminar flow was approximated using a dispersed plug flow model. The chemical kinetics were simulated using LHHW type expressions. Comparison of simulated results are made with experimental results for heating and cooling cycles that resulted from speed and load changes on the engine. These comparisons showed a maximum difference between experimental and predicted emission levels of about 10%.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.064
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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 teacher head, 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

Citations23
Published2000
Admission routes2
Has abstractyes

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