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Record W4400389093 · doi:10.47176/jafm.17.9.2595

Numerical Study on the Sloshing Behaviors of Dual Liquid Tanks with Gas Inflow

2024· article· en· W4400389093 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Fluid Mechanics · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSlosh dynamicsInflowMechanicsVolumetric flow rateMaterials scienceFlow (mathematics)Volume of fluid methodInletComputer simulationBoundary value problemVolume (thermodynamics)Impact pressureThermodynamicsPhysicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The finite volume method (FVM) is used to numerically investigate the sloshing behaviors of dual liquid tanks with gas inflow in this study. The sloshing process of a single liquid tank is simulated to verify the feasibility of the numerical method. Three different inlet boundary conditions are then discussed in order to obtain a reasonable gas flow rate. The sloshing process of a dual liquid tank with the gas inflow is simulated, and the effects of three different factors on the sloshing behaviors are investigated. The results indicate that the overload, flow rate, and filling ratio can affect the peak value of the impact force acting on the tank wall. The impact force is positively proportional to the overload (1G, 3G, or 5G). An increase in flow rate (50 g/s, 1000 g/s, or 5000 g/s) or a decrease in filling ratio (99.52%, 75.64%, or 63.69%) can increase the size and number of bubbles, leading to intensified sloshing behavior and increased impact force.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.385

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 integrity0.0000.001
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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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