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Record W3083045336 · doi:10.1177/1468087420951326

Large eddy simulation of a double-injection cycle and the impact of the needle motion on the sac-volume flow characteristics of a single-orifice diesel injector

2020· article· en· W3083045336 on OpenAlexafffund
Mohamed Chouak, Louis Dufresne, Patrice Seers

Bibliographic record

VenueInternational Journal of Engine Research · 2020
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersFonds de recherche du Québec – Nature et technologiesCanada Foundation for InnovationMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsMechanicsTurbulenceVolume of fluid methodDetached eddy simulationFlow (mathematics)Body orificeComputational fluid dynamicsLarge eddy simulationInjectorSimulationEngineeringMaterials scienceMechanical engineeringReynolds-averaged Navier–Stokes equationsPhysics

Abstract

fetched live from OpenAlex

Study of the flow within diesel injector has gained in importance over the years as several authors have reported that the injector flow in the sac volume highly influenced the nozzle-flow characteristics at low lifts. Few studies have, however, characterized the sac volume and its dynamics. Thus, this paper reports on a numerical characterization of the needle displacement effects (static vs dynamic) on the internal flow of a sac-volume, single-hole diesel injector. To this end, a transient double-injection “closing-opening-closing-opening” cycle was simulated with a monophasic incompressible CFD model in combination with a moving mesh strategy to capture axial needle displacement. A large eddy simulation (LES) approach was chosen to gain better insight into the complexity of this unsteady turbulent flow. The emphasis of the paper is on the dynamic effects of needle movement on the sac flow, while static needle LES results are also shown to illustrate differences. The main findings reported herein are that the dynamic model shows a hysteresis effect associated with the needle motion between opening/closing phases. Quantitatively, the transient needle movement caused a difference in mass flow rate between the sac entrance and exit that was found to reach a maximum of [Formula: see text]. The hysteresis effect was found to be more pronounced at low needle lifts; both static and dynamic models seem to have performed similarly at very high needle lifts. Qualitatively, the LES sac-volume flow representations revealed a fuel jet attachment/detachment with needle movement, while static partial-lift simulations always predict an attached fuel jet. Further analysis of sac vortex dynamics revealed a high-energy vortex-structure breakdown just before the nozzle entrance that could help explain the higher turbulence production reported in the literature, both experimentally and numerically, at low needle lifts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.322
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2020
Admission routes2
Has abstractyes

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