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Record W4388906649 · doi:10.1021/acs.iecr.3c03742

Open Balance Point and Dry Pressure Drop of a Rectangular Float Valve Tray: Experiment and CFD Simulation

2023· article· en· W4388906649 on OpenAlexaff
Qingpeng Wu, Jiaxing Xue, Nan Hu, Yuyang Lai, Hongkang Zhao, Qunsheng Li, Junjie Gu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2023
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsCarleton University
FundersNational Natural Science Foundation of China
KeywordsMechanicsPressure dropTrayBody orificeFloat (project management)Drop (telecommunication)Materials scienceEngineeringMechanical engineeringMarine engineeringPhysics

Abstract

fetched live from OpenAlex

The open balance point (OBP) is the critical gas load at which the float valve tray transitions into or out of the all-open state, playing essential roles in pressure drop characteristics and engineering design. Its prediction is a typical fluid–solid interaction problem. A rectangular float valve tray’s Φ796 mm cold-model experiments demonstrated dry pressure drop and OBP differences between different gas-load-adjusting conditions (ascending or descending), leading to subsequent scaled-down experiments and numerical simulations. For the first time, the valve tray’s OBPs were predicted by numerical simulations, using the lattice Boltzmann method. The wall and adjacent valve effects positively influenced the opening of the valves, dry pressure drop, and vorticity above the tray deck. This study indicated that in multivalve systems, the valve with the highest partial gas load falls first as the gas load descends from the all-open state. Moreover, the difference in the OBP between the gas-load-ascending or gas-load-descending conditions is attributed to the synergistic effects arising from the frictional resistance between the valve legs and tray orifices in the inclined stationary state, along with the gas-load competition among the valves in the multivalve systems.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.109
GPT teacher head0.369
Teacher spread0.259 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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