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Record W4412699915 · doi:10.11159/ffhmt25.240

Velocity and Turbulent Kinetic Energy Prediction with DarcyForchheimer Model for Water Intakes

2025· article· en· W4412699915 on OpenAlexvenueno aff
Cumhur Ozbey, Serhat Küçükali

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsnot available
FundersHacettepe Üniversitesi
KeywordsKinetic energyTurbulence kinetic energyTurbulenceEnvironmental scienceMechanicsEnergy (signal processing)Atmospheric sciencesStatistical physicsPhysicsMathematicsStatisticsClassical mechanics

Abstract

fetched live from OpenAlex

The flow field and turbulent characteristics in the vicinity of water intakes are numerically modeled by using the Darcy-Forchheimer porous media approach coupled with LES.Two types of barriers used at hydropower plants were analyzed, namely the rectangular and Oppermann profiles, having clear bar spacing of b=20 mm and b=10 mm, respectively.For both profiles, simulations were run under two different angles of =30 and =45.The comparison between the experimental and CFD results revealed acceptable error ranges (<10%) for both velocity and turbulent kinetic energy, yielding reliable prediction accuracies of the Darcy-Forchheimer model.Also, treating the water intakes as a porous medium led to a significant reduction in the computational cost and time.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.025
GPT teacher head0.232
Teacher spread0.207 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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