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Record W4409036870 · doi:10.2514/1.j064036

Linear Analysis of Boundary-Layer Instabilities on a Finned Cone at Mach 6

2025· article· en· W4409036870 on OpenAlexfundno aff
Daniel B. Araya, Neal Bitter, Bradley M. Wheaton, Omar Kamal, Tim Colonius, Anthony Knutson, Heath Johnson, Joseph Nichols, Graham V. Candler, Vincenzo Russo, Christoph Brehm

Bibliographic record

VenueAIAA Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaAir Force Office of Scientific ResearchBoeing
KeywordsMach numberBoundary layerCone (formal languages)MechanicsPhysicsGeometryMathematics

Abstract

fetched live from OpenAlex

Boundary-layer instabilities for a finned cone at [Formula: see text], [Formula: see text], and zero incidence angle are examined using linear stability methods of varying fidelity and maturity. The geometry and laminar flow conditions correspond to experiments conducted at the Boeing Air Force Mach 6 Quiet Tunnel at Purdue University. Where possible, a common baseflow is utilized among the stability computations, and comparisons are made along the acreage of the cone where transition is first observed in the experiment. Stability results utilizing linear stability theory, planar parabolized stability equations, one-way Navier–Stokes, forced direct numerical simulation, and adaptive mesh refinement wavepacket tracking are presented. A dominant three-dimensional vortex instability occurring at [Formula: see text] is identified and correlates well with experimental measurements of transition onset. With the exception of linear stability theory, all of the higher-fidelity linear methods considered in this work were consistent in predicting the initial growth and general structure of the vortex instability as it evolved downstream. Some of the challenges, opportunities, and development needs of the stability methods considered are discussed.

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 score0.020
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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
Published2025
Admission routes1
Has abstractyes

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