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Record W2294443540 · doi:10.3390/su8030267

Analysis of an Internal Combustion Engine Using Porous Foams for Thermal Energy Recovery

2016· article· en· W2294443540 on OpenAlexaff
M.A. Ehyaei, Mehdi Tanehkar, Marc A. Rosen

Bibliographic record

VenueSustainability · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsRegenerative heat exchangerThermal efficiencyCombustionInternal combustion engineHomogeneous charge compression ignitionExternal combustion engineMaterials scienceExhaust gas recirculationCompression ratioStirling engineExhaust gasThermalWaste managementCombustion chamberAutomotive engineeringHeat exchangerThermodynamicsMechanical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Homogeneous and complete combustion in internal combustion engines is advantageous. The use of a porous foam in the exhaust gas in an engine cylinder for heat recovery is examined here with the aim of reducing engine emissions. The internal combustion engine with a porous core regenerator is modeled using SOPHT software, which solved the differential equations for the thermal circuit in the engine. The engine thermal efficiency is observed to increase from 43% to 53% when the porous core regenerator is applied. Further, raising the compression ratio causes the peak pressure and thermal efficiency to increase, e.g., increasing the compression ratio from 13 to 15 causes the thermal efficiency and output work to increase from 53% to 55% and from 4.86 to 4.93 kJ, respectively. The regenerator can also be used as a catalytic converter for fine particles and some other emissions. The regenerator oxidizes unburned hydrocarbons. Meanwhile, heat recovered from the exhaust gases can reduce fuel consumption, further reducing pollutant emissions from the internal combustion engine.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.505
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.265
Teacher spread0.255 · 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 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

Citations9
Published2016
Admission routes1
Has abstractyes

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