MétaCan
Menu
Back to cohort
Record W2071731313 · doi:10.1115/imece2005-79128

Analysis of Destabilizing Moments Due to Dynamic Fluid Slosh Within a Partly-Filled Vehicular Tank Under Braking and Turning Accelerations

2005· article· en· W2071731313 on OpenAlexaff
Korang Modaressi-Tehrani, Subhash Rakheja, Ramin Sedaghati

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsConcordia University
Fundersnot available
KeywordsSlosh dynamicsBaffleTransient (computer programming)MechanicsAccelerationStructural engineeringControl theory (sociology)EngineeringPhysicsClassical mechanicsComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

A general three-dimensional nonlinear model is formulated to study the liquid slosh inside a partly filled cylindrical tank with and without baffles, while subjected to lateral and longitudinal acceleration fields arising from braking and turning maneuvers of a tank vehicle. The analyses are performed to investigate the significance of resulting destabilizing forces and moments caused by transient fluid slosh using the FLUENT software. The analyses are performed under varying magnitudes of maneuver-induced planar braking-in-a-turn accelerations and different fill volumes. The influence of baffles is further investigated under time varying longitudinal and lateral accelerations. The deviations in transient lateral and longitudinal forces, as well as the roll, pitch and yaw moments imposed on the tank structure, are obtained with respect to the corresponding quasi-static solutions. The results show that the magnitudes of transient forces and moments are significantly larger than those obtained from steady-state or kineto-static solutions. The addition of baffles tends to limit the magnitudes of slosh forces not only in the longitudinal direction but also along the lateral axis.

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 score0.177
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicFluid Dynamics Simulations and InteractionsFrench-language works237,207