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Record W2166194595 · doi:10.1017/jfm.2013.634

The influence of Reynolds number on the triple point trajectories at shock reflection off cylindrical surfaces

2014· article· en· W2166194595 on OpenAlexaff
H. Kleine, E. Timofeev, Ali Hakkaki-Fard, B. W. Skews

Bibliographic record

VenueJournal of Fluid Mechanics · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsReynolds numberReflection (computer programming)Shock (circulatory)MechanicsTransition pointPhysicsPoint (geometry)Mach reflectionShock waveOpticsComputer scienceMathematicsGeometryOblique shock

Abstract

fetched live from OpenAlex

Abstract In the unsteady process of shock reflection off convexly curved surfaces, the Reynolds number can have an influence on the development of the irregular reflection pattern. Time-resolved visualizations of the reflection process and high-resolution numerical simulation are used in this investigation to quantify this influence, which manifests itself in a delayed growth of the shock pattern with decreasing Reynolds number. In order to conduct reliable and unambiguous measurements, the present study concentrates on observing the development of the established irregular reflection pattern rather than attempting to determine the transition point directly. It can be seen that the influence of the Reynolds number is highly nonlinear and that changes of two orders of magnitude or more are required to produce a reliably measurable difference in the triple point trajectories, which is considerably more than what has so far been reported in the literature. The results allow one to make inferences regarding the transition process and they help to clarify previously reported discrepancies between predicted and experimentally determined transition angles.

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)0.001
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 score0.282
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

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