MétaCan
Menu
Back to cohort
Record W2055588906 · doi:10.2514/6.2010-635

Modeling of the Electric Field in a Hypersonic Rarefied Flow

2010· article· en· W2055588906 on OpenAlexfundno aff
Erin Farbar, Iain D. Boyd

Bibliographic record

Venue48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition · 2010
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsnot available
FundersNational Research Council CanadaZonta International FoundationNational Aeronautics and Space Administration
KeywordsElectric fieldHypersonic flowAerospace engineeringMechanicsHypersonic speedPhysicsEngineering

Abstract

fetched live from OpenAlex

the magnitude of convective and radiative heat transfer to the vehicle surface. The presence of this weak plasma necessitates the existence of an electric field in the shock layer to accelerate the charged particles. In this study, the structure of the self-induced electric field in a rarefied reentry flow is examined using a Direct Simulation Monte Carlo solver that is coupled to a Particle in Cell solver. Computations are performed for a one dimensional model of the stagnation streamline of the flow formed in front of a blunt body reentering the Earth’s atmosphere. The model parameters are chosen to produce a flow field structure similar to that experienced by the FIRE II reentry vehicle at an altitude of 85 km in its reentry trajectory. This is accomplished in a computationally tractable manner by varying the freestream density, the diameters of the particles, and the electron mass from the values at the actual FIRE II flight conditions. The flow field results are compared to results obtained using an approximate DSMC method used to incorporate the eect of the electric field on the structure of a hypersonic shock layer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.278
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venue48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace ExpositionSame topicGas Dynamics and Kinetic TheoryFrench-language works237,207