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Record W1983431839 · doi:10.7158/m12-038.2013.11.2

Validation of KIVA II simulation results with experimental data

2013· article· en· W1983431839 on OpenAlexfundno aff
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Bibliographic record

VenueAustralian Journal of Mechanical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsnot available
FundersCanadian Bureau for International Education
KeywordsTurbulencePiston (optics)MechanicsComputational fluid dynamicsCombustion chamberTurbulence kinetic energyRoot mean squareRange (aeronautics)PhysicsOpticsEngineeringCombustionAerospace engineeringChemistry

Abstract

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A combined study based on laser Doppler velocimetry measurements and numerical simulation was undertaken in order to validate the KIVA II numerical code. A rapid intake and compression machine was used for the experimental studies, while the numerical calculations were performed with the KIVA II computational fluid dynamics code. The k-ॉ turbulence model was used to represent the effects of turbulence. The measured mean radial and tangential velocities were found to be generally higher than their computed counterparts. The differences between these velocities range from 3.5% to 7% in magnitude for the re-entrant bowl chamber while they vary from 5% to 11% for the bowl-in-piston combustion chamber configuration. The measured values of the turbulence intensity close to the bowl axis in the re-entrant bowl chamber configuration were fairly accurately predicted, but the quality of the prediction diminishes as the bowl entrance region was approached. The values of the turbulence intensity were however, poorly reproduced near the axis of the bowl-in-piston chamber assembly. The experimental and predicted values of turbulence were found to differ by between 7% to 20% (re-entrant chamber) and 13% to 20% (bowl-in-piston). The results of the experimental data that were obtained from this study show that the random uncertainties in the mean radial and tangential velocities for both chamber configurations range from ±13.2% to ±19.2%, while the uncertainties in the root mean square velocities were about ±14.1%.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.421

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 head0.030
GPT teacher head0.256
Teacher spread0.225 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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