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Record W7047246146

An Evaluation of Computational Fluid Dynamics for Spillway Modelling

2007· article· en· W7047246146 on OpenAlexaboutno aff

Bibliographic record

VenueQueensland's institutional digital repository (The University of Queensland) · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsComputational fluid dynamicsSpillwayComputational modelPhysical modellingHydroelectricitySoftwareFlow (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Computational Fluid Dynamics (CFD) is used extensively by engineers to model and analyse complex issues related hydraulic design, planning studies for future generating stations, civil maintenance, supply efficiency, and dam safety. The integrity of computed values from CFD models is of considerable economic importance in the design, upgrading and maintenance of hydroelectric generating stations. CFD models have the ability to predict many characteristics flow over a spillway and Manitoba Hydro has had good agreement with physical model results in the past. However, date there has not been a review that brings all the available information together for a comprehensive assessment. The objective of this research is to build upon previous investigations on the use of CFD modelling, by focusing specifically on the ability to accurately model spillways using CFD. This paper discusses three-dimensional numerical modelling of several different spillway configurations using the CFD software Flow-3D and compares the predicted rating curves, pressures, and water surface elevations to corresponding physical model experimental values. The numerical model results were generally in agreement with physical model data, however, the relative differences in discharges were found to have a P/Hd dependency. The accuracy for a given model resolution and associated computational time required was also considered.

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)0.001
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 score0.468
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.020
GPT teacher head0.254
Teacher spread0.234 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueQueensland's institutional digital repository (The University of Queensland)Same topicMagnetic confinement fusion researchFrench-language works237,207