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Record W2099189815 · doi:10.1115/imece2008-66769

Fluid-Structure Interaction Simulation With Free Surface Flows by Smoothed Particle Hydrodynamics

2008· article· en· W2099189815 on OpenAlexfundno aff
Mehrdad H. Farahani, Nima Amanifard, Hossein Asadi, M. Mahnama

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsSmoothed-particle hydrodynamicsFluid–structure interactionFree surfaceConstraint (computer-aided design)Surface (topology)Fluid simulationComputer scienceProjection methodPoisson's equationProjection (relational algebra)MechanicsComputational fluid dynamicsPoisson distributionMathematical optimizationFinite element methodPhysicsMathematicsAlgorithmMathematical analysisGeometryDykstra's projection algorithm

Abstract

fetched live from OpenAlex

Simulation of the fluid-structure interaction (FSI) and free surface flows includes an area of extremely challenging problems in the computational mechanics community. In this paper, a newly proposed SPH algorithm is employed to simulate FSI problems with complex free surface flows. In this way, fluid and elastic structure continua are coupled using a monolithic but explicit numerical scheme. The proposed method is similar to so-called SPH projection method and consists of three steps. The first two steps play the role of prediction, while in the third step a Poisson equation is used for both fluid and structure to impose incompressibility constraint.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.209
Teacher spread0.202 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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