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Record W4293051303 · doi:10.1063/5.0106554

Air thermochemistry in the converging section of de Laval nozzles on hypersonic wind tunnels

2022· article· en· W4293051303 on OpenAlexaboutno aff
Sangdi Gu, Jiaao Hao, Chih‐Yung Wen

Bibliographic record

VenueAIP Advances · 2022
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsnot available
FundersHong Kong Polytechnic University
KeywordsThermochemistryNon-equilibrium thermodynamicsThermodynamicsThermodynamic equilibriumFlow (mathematics)Stagnation enthalpyMechanicsEquilibrium thermodynamicsNozzleRange (aeronautics)Section (typography)PopulationChemistryWork (physics)PhysicsMaterials scienceEnthalpy

Abstract

fetched live from OpenAlex

State-to-state simulations of nonequilibrium flow in nozzles are made for a range of reservoir conditions and geometries. The geometry of the converging section and throat has little influence on the thermochemistry of the flow. Higher reservoir pressure and temperature both drive the thermochemistry toward equilibrium. For reservoir temperatures of 1500, 4000, and 7000 K, the flow property that has the largest departure from equilibrium is the N2 vibrational temperature, the O mass fraction, and the N mass fraction, respectively. Even at the lowest reservoir pressure, these departures from equilibrium are only 14%, 8%, and 2% for the 1500, 4000, and 7000 K reservoirs, respectively. The differences in these flow properties at the throat between the nonequilibrium and equilibrium simulations are maintained throughout in the nonequilibrium simulations of the diverging section. Applying the simplification of equilibrium flow in the converging section and around the throat yields almost no observable errors in the vibrational population distributions in the diverging section. The simplification is recommended for most practical intents and purposes, and the current work provides important quantitative information to make informed judgments when applying it.

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 score0.448
Threshold uncertainty score0.214

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 head0.269
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 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

Citations5
Published2022
Admission routes1
Has abstractyes

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