MétaCan
Menu
Back to cohort
Record W4230480832 · doi:10.15407/fmmit2018.27.041

Investigation of an equilibrium state of water and two-component gas mixture of dry air and water vapor at the separation surface of phases in the conditions of phase transition Part II

2018· article· en· W4230480832 on OpenAlexaboutno aff
Taras Holubets

Bibliographic record

VenuePhysico-mathematical modelling and informational technologies · 2018
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsnot available
Fundersnot available
KeywordsThermodynamic equilibriumThermodynamicsCondensationEvaporationChemistryPhysics

Abstract

fetched live from OpenAlex

In this part of the paper, a new phenomenological approach to the description of the phase transformation of a liquid into a two-component gas mixture is proposed on the basis of the distribution function of the investigated macroscopic physical quantity from the thermodynamic variables (parameters) of the system. The equivalence of the mechanical and energy characteristics of the thermodynamic equilibrium on the interface between the liquid and gas phases is demonstrated. The conditions of the phase equilibrium between a liquid and a gas mixture at the superficial layer under conditions of evaporation or condensation are recorded. By the graphical methods the relative changes (displacement or fluctuations) of thermodynamic quantities in the phase transformation are depicted. In the model approximation, distortion of the meniscus for surface of the phase separation between the liquid and gas phases is calculated according to the balance (equilibrium) relations. References Braut, R. (1967). Fazovyie perehodyi. Moskva: Mir. Patashinskiy, A.Z., .Pokrovskiy, V.L. (1982). Fluktuatsionnaya teoriya fazovyih perehodov. Moskva: Nauka. Zubarev, D. N. (1971). Neravnovesnaya statisticheskaya termodinamika. M.: Nauka. Beck, С., Schlogl, I. (1997). Thermodynamic of chaotic systems. New York: Cambridge University Press. Holubets, T. V. (2016). Ymovirnistni metody opysu rivnovazhnoho termodynamichnoho stanu dvokhkomponentnykh vzaiemodiiuchykh sumishei. Fiz.-mat. mod. ta inf. tekhnolohii, 23, 61-79. Holubets, T. V. (2017). Doslidzhennia rivnovazhnoho stanu vody i dvokomponentnoi hazovoi sumishi sukhoho povitria ta vodianoi pary pry poverkhni rozdilu faz v umovakh fazovoho peretvorennia (Chastyna I). Fiz.-mat. mod. ta inf. tekhnolohii, 27, 51-71. CK12 Fundation. (2017). FlexBook Chemistry Concepts Retrieved from https://www.ck12.org/book/CK-12- Chemistry-Concepts-Intermediate (Intermediate: Section 17 Thermochemistry, Chapter 11). Sedov, L.I. (1973). Mehanika sploshnoy sredyi. (Vol. 1-2). Moskva: «Nauka». Bazarov, I. P. (1991). Termodinamika. Moskva: «Nauka». Shambadal, P. (1967). Razvitie i prilozheniya ponyatiya entropii. Moskva: «Nauka». Reid, R.C., Prausnit, J.M, Poling, B.E. (1987). The properties of gases&liquids. New York: McGraw-Hill. Abamson,A.W., Gast, A.P. (1997). Physical chemistry of surfaces. New York —Toronto: John Wiley & Sons. Shtrauf, E. F. (1949).Molekulyarnaya fizika. Leningrad-Moskva: Gos. izd. tehn.-teor. lit. Holovko, M, Shmotolokha, V., Patsahan, T. (2014). Hard convex body fluids in random porous media: Scaled particle theory. Journ. of Mol. liquid, 189(30), 115-133.https://doi.org/10.1016/j.molliq.2013.05.030 Kalyuzhnyi, Yu. V., Protsykevytch, I. A., Cummings, P. I. (2007). Thermodynamic properties and liquid-gas phase diagram of the dipolar hard-sphere fluid. Europhys. Letters Association, 80(5), 56002(1-6).https://doi.org/10.1209/0295-5075/80/56002 Thermophysical properties of humid air. M. CONDE ENGINEERING Zurich 2007. Retrieved from https://www.mrc-eng.com. ASHRAE-HANDBOOK-Fundamentals Atlanta 2017. Retrieved from https://www.ashrae.org/resources-publications/handbook/2017-ashrae-handbook-fundamentals. The Engineering ToolBox. Retrieved from http://www.engineeringtoolbox.com.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.251
Teacher spread0.230 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venuePhysico-mathematical modelling and informational technologiesSame topicFreezing and Crystallization ProcessesFrench-language works237,207