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Record W4394617561 · doi:10.4271/2024-01-2114

Downsizing a Heavy-Duty Natural Gas Engine by Scaling the Air Handling System and Leveraging Phenomenological Combustion Model

2024· article· en· W4394617561 on OpenAlexaff
Navid Balazadeh, Sandeep Munshi, Mahdi Shahbakhti, Gordon McTaggart-Cowan

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of AlbertaSimon Fraser University
Fundersnot available
KeywordsHeavy dutyNatural gasCombustionScalingEnvironmental scienceComputer scienceEngineeringWaste managementAutomotive engineeringChemistryMathematics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">A potential route to reduce CO<sub>2</sub> emissions from heavy-duty trucks is to combine low-carbon fuels and a hybrid-electric powertrain to maximize overall efficiency. A hybrid electric powertrain can reduce the peak power required from the internal combustion engine, leading to opportunities to reduce the engine size but still meet vehicle performance requirements. Although engine downsizing in the light-duty sector can offer significant fuel economy savings mainly due to increased part-load efficiency, its benefits and downsides in heavy-duty engines are less clear. As there has been limited published research in this area to date, there is a lack of a standardized engine downsizing procedure. This paper uses an experimentally validated one-dimensional phenomenological combustion model in a commercial engine simulation software GT-SUITE™ alongside turbocharger scaling methods to develop downsized engines from a baseline 6cyl (2.1 L/cyl, 26 kW/L) pilot-ignition, direct-injection natural gas engine. Since there is a reduced power demand from the engine in the hybrid powertrain over transient drive cycles, this study compares two methodologies to achieve a 230 kW engine: a reduction in number of cylinders at fixed displacement (4cyl- 2.1 L/cyl) and a reduction in cylinder displacement volume but retaining six cylinders (6cyl-1.4 L/cyl). The power for the downsized engine is reduced compared to the baseline engine since a future hybrid powertrain will not need as much power as a non-hybrid. By retaining similar total displacement and equivalent power rating, the impacts of engine size reduction can be distinguished from the scaling of the turbocharging and air handling system. The engines are evaluated over a series of steady-state and transient cycles based on a reduced load duty cycle for an engine in a hybridized vehicle. The results indicated that, as expected, downsized engines demonstrate increased peak cylinder pressure, exhaust gas temperature, boost pressure, and turbocharger speed compared to the baseline engine when all engines undergo the same reduced load duty cycle. Distinctly, the 6 cyl-1.4 L/cyl variant showed increased heat losses due to the higher surface area to volume ratio in the combustion chamber, while the 4 cyl-2.1 L/cyl variant had higher exhaust enthalpy losses. Both downsized engines showed lower friction losses than the baseline engine. Due to these offsetting effects, neither of the downsized engines showed a significant improvement in brake specific fuel consumption (BSFC). The change in mass due to the smaller engine offers only a minor improvement in payload capacity compared to the reduction in the maximum torque.</div></div>

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.834
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.004
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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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