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Record W2275821651 · doi:10.2174/1874447801004010023

An Analysis of Baffles Designs for Limiting Fluid Slosh in Partly Filled Tank Trucks

2010· article· en· W2275821651 on OpenAlexaff
Thirumalaisamy Kandasamy

Bibliographic record

VenueThe Open Transportation Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsConcordia University
Fundersnot available
KeywordsSlosh dynamicsBaffleAccelerationTruckStructural engineeringMechanicsEngineeringComputational fluid dynamicsTransverse planePhysicsMechanical engineeringAutomotive engineering

Abstract

fetched live from OpenAlex

This study presents an analysis of effectiveness of different designs of baffles, including the conventional, partial and oblique, in limiting the manoeuvre-induced transient as well as steady-state fluid slosh forces and moments in a partly-filled tank truck. The effect of an alternating arrangement of partial baffles is also explored. A three-dimensional computational fluid dynamics model of a partly-filled tank is developed to study the relative anti-slosh properties of different baffles designs and layouts under combined idealized longitudinal and lateral acceleration fields and different cargo loads. The analyses are also performed for a cleanbore tank, which is validated using the widely-used quasi-static slosh model. The results suggest that the conventional transverse baffles offer important resistance to fluid slosh under braking manoeuvres, while the obliquely placed baffles could help limit the longitudinal as well as lateral fluid slosh under combined lateral and longitudinal acceleration excitations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.298
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations39
Published2010
Admission routes1
Has abstractyes

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