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Record W3210923022 · doi:10.32920/ryerson.14664894.v1

Nonlinear dynamic response of seismically excited rectangular concrete liquid filled tanks

2021· preprint· en· W3210923022 on OpenAlexaff
Arash Farzin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSlosh dynamicsNonlinear systemStructural engineeringFinite element methodImpact pressureGeologyDynamic pressureSeismic analysisBoundary value problemMechanicsGeotechnical engineeringMaterials scienceEngineeringPhysicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

The effect of nonlinearity on behaviour of rectangular concrete tanks partially filled with water is studied. The nonlinearity in the numerical modeling of the surface liquid sloshing performance and hydrodynamic pressure initiates from unknown boundary conditions of contained liquid volume. The nonlinear simulations are performed for Time-History seismic analysis using the finite element software ABAQUS/CAE. The nonlinear results are compared with linear analytical solutions and ACI 350.3-06 code. A Paramedic study is conducted to investigate the effect of tank plan dimension, frequency content of different seismic ground motions, nature of earthquake movements, and interaction of bi-directional component of earthquake on the maximum sloshing height of liquid. The results reveal that the nonlinearity is more significant in shallow tanks. Moreover, nonlinear hydrodynamic pressure distribution has no important difference with linear calculated pressure except for the surface sloshing pressure acting on the top of tanks. The linear ratio of depth of liquid to tank plan dimension used in ACI 350.3-06 formulation is found to be less accurate for calculating the maximum sloshing height of liquid.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.241
Teacher spread0.234 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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