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Record W2587100625 · doi:10.1115/imece2016-65170

Study of Liquid Motion and Pressure Forces Applied on the Walls of Partially Filled Moving Tank

2016· article· en· W2587100625 on OpenAlexafffund
Mohamed Bouazara, Marc J. Richard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversité LavalUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSlosh dynamicsDamperSpring (device)StiffnessDiscretizationDisplacement (psychology)MechanicsStructural engineeringPhysicsEngineeringMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Various analytical, numerical and experimental studies have been developed to investigate the effect of liquid sloshing on the dynamic behavior of tank-trucks. However, this type of studies is still complex and expensive. Mechanical models are used to simulate complex phenomena. Using these models in the simulation of deformable bodies provides both geometrical and physical aspects. In this study, a new 3D mechanical model is applied to simulate liquid motion in partially filled tank. This model, which is developed in previous study, is able to simulate lateral, longitudinal and vertical displacements. It may also evaluate pressure forces applied on the tank walls. The main idea of this model is to represent the liquid as a mesh of spring-mass systems. The liquid was divided in multiple masses along each axis. The movement of each mass is simulated by displacement of its mass center; this constitutes the mesh nodes. Each adjacent two nodes are linked by flexible edges having a parallel spring and damper. The discretizing method of the liquid is applied; it is followed by computing of masses and initial coordinates of each node. We show, in detail, the method to obtain stiffness of the springs and damping coefficient of the dampers. Afterwards, equations of dynamic liquid motion are obtained. The system of equations is solved for some examples in order to compare results to the literature.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.138

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 integrity0.0000.000
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 head0.010
GPT teacher head0.220
Teacher spread0.209 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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