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Record W2952270536 · doi:10.1177/1468087419852841

Studies of anaerobic internal combustion engine using direct injection of liquid monopropellant

2019· article· en· W2952270536 on OpenAlexaff
Gabriel Vézina, Martin Brouillette

Bibliographic record

VenueInternational Journal of Engine Research · 2019
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMonopropellantPiston (optics)CombustionInternal combustion engineMaterials scienceEngine powerMean effective pressureIsopropyl alcoholAutomotive engineeringRotary engineChemistryNuclear engineeringPower (physics)ThermodynamicsCompression ratioEngineeringPhysicsComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

This article describes the design and experimentation of a new anaerobic internal combustion engine using direct injection of liquid monopropellant. Specifically, the use of monopropellant post-injection allows the control of the profile P - V of the cycle, which significantly increases the mean effective pressure (indicated mean effective pressure) and the power density of the engine. The monopropellant used was isopropyl nitrate, which is the first known demonstration of a piston engine using this monopropellant for anaerobic operation. The engine power has been modulated with post-injection strategy to modify the P - V cycle profile to obtain a constant-pressure combustion in anaerobic mode. Experiments were performed using an engine with a single piston with a effective swept volume of 45 cm 3 , at an operating frequency of 2 Hz. The prototype engine has achieved an indicated mean effective pressure of up to 4.4 MPa and an indicated power up to 400 W. The indicated thermodynamic efficiency of the engine is found to be about 13% based on the isopropyl nitrate heat of explosion. The indicated specific fuel consumption of the engine is 2.5 mg/J, which represents a conversion efficiency of 49% based on the isopropyl nitrate specific energy.

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)0.002
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 score0.202
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.101
GPT teacher head0.405
Teacher spread0.305 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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