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Record W2973239394 · doi:10.1139/tcsme-2018-0293

Performance and emissions of a diesel engine using Al–Fe–Mg–Si pistons contain up to 15.7% volume fractions of Fe-rich intermetallic compounds

2019· article· en· W2973239394 on OpenAlexvenueno aff
Karthikeyan Rangaraju, S. Neelakrishnan

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsIntermetallicPiston (optics)Materials scienceCombustionCylinderAlloyMetallurgyFour-stroke engineVolume (thermodynamics)Diesel engineDiesel fuelCombustion chamberThermodynamicsAutomotive engineeringMechanical engineeringChemistry

Abstract

fetched live from OpenAlex

Engine performance and emission characteristics were investigated using a single cylinder four-stroke diesel engine with different concentrated intermetallic-based Al–Fe–Mg–Si pistons. Three different alloy combinations (types A, B, and C) of Al–Fe–Mg–Si pistons were developed through the incremental alloying addition of Fe, Mg, Mn, Cu, and Ni. Piston types A, B, and C had Fe-rich intermetallic compounds (IMC), where types B and C had a higher density IMC distribution than type A. The influence of Fe, Mg, Mn, Cu, and Ni alloyed IMC pistons on engine performance and emissions was investigated at various loading conditions. Combustion characteristics such as cylinder pressure and net heat release rate for all piston types were investigated and compared. A similar duration of ignition was seen for all piston types. Frictional loss was reduced by ∼25% in types B and C in comparison to type A. Similarly, mechanical and thermal efficiency were enhanced considerably in types B and C compared to type A. Emission characteristics were also investigated for all piston types. Results showed that NO x was reduced by ∼17.3% with the use of types B and C.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.656

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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicBiodiesel Production and ApplicationsFrench-language works237,207