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Record W3016883800 · doi:10.1063/1.5143428

Compressible flow in a Noble–Abel stiffened gas fluid

2020· preprint· en· W3016883800 on OpenAlexafffund
Matei I. Radulescu

Bibliographic record

VenuePhysics of Fluids · 2020
Typepreprint
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCompressibilityEquation of stateCompressible flowFlow (mathematics)Isentropic processRiemann problemRiemann hypothesisPerfect gasJumpFluid dynamicsMathematicsApplied mathematicsPhysicsMechanicsMathematical analysisThermodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

While the compressible flow theory has relied on the perfect gas model as its workhorse for the past century, compressible dynamics in dense gases, solids, and liquids have relied on many complex equations of state, yielding limited insight into the hydrodynamic aspect of the problems solved. Recently, Le Métayer and Saurel studied a simple yet promising equation of state owing to its ability to model both the thermal and compressibility aspects of the medium. It is a hybrid of the Noble–Abel equation of state and the stiffened gas model, labeled the Noble–Able Stiffened Gas (NASG) equation of state. In the present work, we derive the closed form analytical framework for modeling compressible flow in a medium approximated by the NASG equations of state. We derive the expressions for the isentrope, sound speed, isentropic exponent, Riemann variables in the characteristic description, and jump conditions for shocks, deflagrations, and detonations. We also illustrate the usefulness by addressing the Riemann problem. The closed form solutions generalize in a transparent way the well-established models for a perfect gas, highlighting the role of the medium’s compressibility.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score1.000

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 head0.029
GPT teacher head0.250
Teacher spread0.221 · 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.

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
Published2020
Admission routes2
Has abstractyes

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