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Record W2754169350 · doi:10.1115/1.4037901

A Robust Three-Phase Isenthalpic Flash Algorithm Based on Free-Water Assumption

2017· article· en· W2754169350 on OpenAlexaff
Ruixue Li, Huazhou Li

Bibliographic record

VenueJournal of Energy Resources Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFlash (photography)Isothermal processComputer scienceEnthalpyPhase (matter)Flash evaporationMaterials scienceThermodynamicsAlgorithmChemistryPhysics

Abstract

fetched live from OpenAlex

Isenthalpic flash is a type of flash calculation conducted at a given pressure and enthalpy for a feed mixture. Multiphase isenthalpic flash calculations are often required in compositional simulations of steam-based enhanced oil recovery methods. Based on a free-water assumption that the aqueous phase is pure water, a robust and efficient algorithm is developed to perform isenthalpic three-phase flashes. Assuming that the feed is stable, we first determine the temperature by solving the energy conservation equation. Then, the stability test on the feed mixture is conducted at the calculated temperature and the given pressure. If the mixture is found unstable, two-phase and three-phase vapor–liquid–aqueous isenthalpic flash can be simultaneously initiated without resorting to stability tests. The outer loop is used to update the temperature by solving the energy conservation equation. The inner loop determines the phase fractions and compositions through a three-phase free-water isothermal flash. A two-phase isothermal flash will be initiated if an open feasible region in the phase fractions appears in any iteration during the three-phase flash or any of the ultimately calculated phase fractions from the three-phase flash do not belong to [0,1]. A number of example calculations for water/hydrocarbon mixtures are carried out, demonstrating that the proposed algorithm is accurate, efficient, and robust.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.237
Teacher spread0.224 · 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 designOther design
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

Citations19
Published2017
Admission routes1
Has abstractyes

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