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Model Investigation of the Performance of SI ICE Fueled by Gasoline, Methane, Hydrogen and SOFC Anode Off-Gas

2023· article· en· W4391306996 on OpenAlexaff
Tsvetomir Gechev, Plamen Punov, Dalibor Barta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsTransport Canada
Fundersnot available
KeywordsAnodeMethaneInternal combustion engineGasolineHydrogenCombustionCogenerationSolid oxide fuel cellMaterials scienceProcess engineeringWaste managementNuclear engineeringMechanical engineeringEngineeringChemistryElectricity generationThermodynamicsElectrodePhysicsPower (physics)

Abstract

fetched live from OpenAlex

The paper presents a simple 1- D model of a single cylinder spark-ignited internal combustion engine (SI ICE) created by means of the software product Ricardo WaveBuild. The model is applied for the combustion of the anode off-gas (AOG) emitted from a pre-defined solid oxide fuel cell (SOFC) that is modelled in a previous study by the same authors. The selected cell is an intermediate-temperature, anode-supported, planar cell with internal reforming of 100% methane fuel. Firstly, the geometrical and operating parameters of the cell and the engine, as well as the composition of the AOG, are presented. Then the resultant chemicals in the AOG are modelled as a synthetic fuel mixture in Ricardo WaveBuild. Finally, analysis of the performance of the modeled engine with four fuels (gasoline, methane, hydrogen and AOG synthetic fuel) is conducted. The engine model assumptions as well as the SOFC-ICE system integration for a cogeneration combined cycle, are also briefly discussed in the paper.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.337

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 head0.015
GPT teacher head0.223
Teacher spread0.208 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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