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Record W2102182813 · doi:10.5539/cis.v6n4p154

Partitioned Methods in Computational Modelling on Fluid-Structure Interactions of Concrete Gravity-Dam

2013· article· en· W2102182813 on OpenAlexvenueno aff
Wen Zyn Lim, Robert Xiao, Taehoon Hong, Chih-Cheng Chin

Bibliographic record

VenueComputer and Information Science · 2013
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsGravity damComputer scienceCoupling (piping)Fluid–structure interactionPartition (number theory)SoftwareFinite element methodFlow (mathematics)Fluid dynamicsComputational scienceMechanicsMechanical engineeringStructural engineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

Fluid-structure interaction (FSI) has resulted in both complex applications and computing algorithmic improvements. The aim of this paper is to develop a better understanding of the fluid-structure interaction behaviour and the numerical coupling methods which can be used in analysing the FSI problem of a multi-physics nature computationally. There are two different systems in partitioned methods for coupling the fluid and structural domains which use strong and weak couple algorithms. Numerical results have been obtained on the hypothetical models for the close and open-spillways concrete gravity-dam. The two-way coupling partition method has been applied to the dynamic velocity flow and pressure using the ANSYS FEA software. A close comparison between the weak and strong coupled systems of two-way partitioned method has been made for the consideration of both close and open-spillways concrete gravity-dam.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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