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Record W2334719841 · doi:10.1103/physreve.89.063024

Mass flux in extended and classical hydrodynamics

2014· article· en· W2334719841 on OpenAlexafffund
Miroslav Grmela

Bibliographic record

VenuePhysical Review E · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Thermodynamics and Statistical Mechanics
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStatistical physicsPhysicsClassical mechanicsEntropy (arrow of time)Mass fluxTime evolutionEntropy productionMechanicsThermodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

In classical hydrodynamics, the mass flux is universally chosen to be the momentum field. In extended hydrodynamics, the mass flux acquires different terms. The extended hydrodynamics introduced and investigated in this paper uses a one-particle distribution function as the extra state variable chosen to characterize the microstructure. We prove that the extended hydrodynamics is fully autonomous in the sense that it is compatible with thermodynamics (i.e., the entropy does not decrease during the time evolution) and with mechanics (i.e., the part of the time evolution that leaves the entropy unchanged is Hamiltonian). Subsequently, we investigate its possible reductions. In some situations the emerging reduced dynamical theory is the classical hydrodynamics that is fully autonomous (i.e., all the structure that makes the extended theory fully autonomous is kept in the reduced theory). In other situations (for example, when the fluids under investigation have large density gradients) the reduced theories are not fully autonomous. In such a case the reduced theories constitute a family of mutually related dynamical theories (each of them involving a different amount of detail) that we consider to be a mathematical formulation of multiscale (or multilevel) hydrodynamics. It is in the reduced theories belonging to the multiscale hydrodynamics where the terms that emerge in the mass flux take the form of self-diffusion.

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: none
Teacher disagreement score0.917
Threshold uncertainty score0.505

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 head0.007
GPT teacher head0.283
Teacher spread0.277 · 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

Citations8
Published2014
Admission routes2
Has abstractyes

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