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Record W2462445951 · doi:10.1115/1.4034162

Numerical Simulation of Piston Cooling With Oil Jet Impingement

2016· article· en· W2462445951 on OpenAlexaff
G. Nasif, R. M. Barron, Ram Balachandar

Bibliographic record

VenueJournal of Heat Transfer · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMechanicsHeat transferPiston (optics)Heat transfer coefficientMaterials scienceJet (fluid)NozzleReynolds-averaged Navier–Stokes equationsThermodynamicsComputational fluid dynamicsPhysicsOptics

Abstract

fetched live from OpenAlex

Convective heat transfer of an impinging jet is numerically evaluated for piston cooling process. A circular jet of subcooled engine oil that impinges normally onto the inner surface of the piston for an engine operating at normal condition is considered in the study. The k−ω shear stress transport (SST) based on transient three-dimensional governing Navier–Stokes (Reynolds-averaged Navier–Stokes (RANS)) equations are computationally solved using a finite-volume technique. The conjugate heat transfer method is used to obtain a coupled heat transfer solution between the solid and fluid regions, to predict the heat transfer coefficient at the piston walls and then the temperature distribution in the piston. It is shown that the cooling jet can significantly decrease the piston temperature. The location of the incidence of maximum heat transfer coefficient is moved away from the impingement point as the nozzle size increases.

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 score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.223
Teacher spread0.211 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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