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Record W4400498397 · doi:10.55037/lxlaser.21st.26

Implementation Of Optical Diagnostics For Study Of A Non-Canonical Hypersonic Geometry

2024· article· en· W4400498397 on OpenAlexaff
Steven J. Beresh, Anshuman Pandey, Daniel Richardson, Douglas Carter, Ashley J. Saltzman, Katya M. Casper

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsUniversité Laval
FundersNational Nuclear Security AdministrationU.S. Department of Energy
KeywordsHypersonic speedGeometryAerospace engineeringPhysicsComputer scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

The study of shock/boundary-layer interactions on a non-canonical hypersonic geometry involves physical complexities not found on the canonical investigations that dominate the literature. The present case examines a cone-slice-ramp, which combines an upstream asymmetric flow expansion with a downstream three-dimensional compression ramp. Optical diagnostics of the off-body flowfield are a necessary complement to surface instrumentation including high-frequency pressure sensors and Temperature Sensitive Paint (TSP). Focused Laser Differential Interferometry (FLDI) measured the flowfield distribution of second-mode instability waves responsible for transition. Filtered Rayleigh Scattering (FRS) was used for flow visualization of the separation region and unsteady shear layer. Velocimetry through the separation shear layer was provided by Femtosecond Laser Electronic Excitation Tagging (FLEET) whereas Coherent Anti-Stokes Raman Scattering (CARS) measured the thermal profiles through multiple flow features generated by the interaction. These flowfield diagnostics detail the alterations in the shock/boundary-layer interaction as the flow state moves from laminar to transitional to turbulent conditions, revealing behaviors absent from canonical flows.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.264

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 head0.380
Teacher spread0.349 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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