MétaCan
Menu
Back to cohort
Record W2024234081 · doi:10.1260/1757-2258.4.1-2.41

On Numerical Techniques for Determination of the Sonic Point in Unsteady Inviscid Shock Reflections

2012· article· en· W2024234081 on OpenAlexfundno aff
Ali Hakkaki-Fard, Evgeny Timofeev

Bibliographic record

VenueInternational Journal of Aerospace Innovations · 2012
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInviscid flowMach numberShock (circulatory)Shock tubePerturbation (astronomy)MechanicsCompressible flowShock waveRegular polygonMathematicsPhysicsMathematical analysisAcousticsCompressibilityGeometry

Abstract

fetched live from OpenAlex

In this paper, three techniques for determination of the sonic/catch-up points in unsteady shock reflections based on numerical flowfield analysis are considered: the Mach-number-based technique, the characteristic-based technique, and the perturbation technique. These techniques are compared using the problem of shock reflection from a convex cylinder simulated with an inviscid, non-heat-conducting flow model and an ideal reflecting surface. It is shown that the sonic points obtained with the Mach-number or characteristic-based techniques, coincide with the catch-up point obtained by the perturbation technique. The obtained sonic point converges to the theoretical sonic point given by the steady two-shock theory as the grid is refined. Quantitative data are presented, which show that very fine meshes are needed to approach the theoretical value with good accuracy. Furthermore, potential sources of significant experimental errors when applying the perturbation technique in shock-tube experiments are ident...

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.560
Threshold uncertainty score0.314

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 head0.013
GPT teacher head0.298
Teacher spread0.285 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Aerospace InnovationsSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207