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Record W4391301262 · doi:10.2514/6.2024-1292

A Comparative Study of Experimental and Simulation Data for Micrometer Particle Acceleration in a Low-Pressure Supersonic Jet Impingement System

2024· article· en· W4391301262 on OpenAlexaboutno aff
Austin J. Andrews, Nathan A. Bellefeuille, Ioannis Pothos, Hasan F. Celebi, Christopher J. Hogan, Thomas E. Schwartzentruber, Bernard A. Olson, Kaleb A. Siekmeier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
Fundersnot available
KeywordsSupersonic speedAccelerationJet (fluid)MechanicsMicrometerParticle (ecology)Aerospace engineeringMaterials sciencePhysicsClassical mechanicsEngineeringOpticsGeology

Abstract

fetched live from OpenAlex

Micrometer-sized particles impacting high-speed vehicles encounter a wide range of conditions, including gas rarefaction and high relative speeds, which significantly affect their drag. Recent attention has been given to understanding hypersonic vehicle behavior in particle laden environments, where accurate prediction of particle drag under a wide range pressure, temperature and gas velocities is paramount to predicting damage to surfaces. To this end, we design an experimental system to measure particle acceleration and declaration in a low pressure supersonic jet impingement with gas Mach reaching a value of 5. Pure nitrogen gas was used with gas pressure and gas temperature near standard conditions upstream of a de Laval nozzle and flow driven by a 266 Pa downstream pressure. Well controlled single sized solid spherical particles in the size range of 0.7 to 7 μm in diameter and density of 2.7 g cm⁻³ and 1.5 g cm⁻³ are generated by a vibrating orifice generator. The velocities of particles within the supersonic jet are measured using laser Doppler velocimetry at various downstream locations. Particle velocities are compared with numerical models that incorporate state-of-the-art drag laws suitable for the high Mach number and high Knudsen number regimes.

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 score0.448
Threshold uncertainty score0.348

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 head0.073
GPT teacher head0.346
Teacher spread0.273 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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