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Record W2622494001 · doi:10.1139/cjp-2017-0057

Transient flows past arrays of yawed finite prisms

2017· article· en· W2622494001 on OpenAlexvenueno aff
I-Han Liu, Bashar Attiya, Alparslan Öztekin

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPrismDragDrag coefficientPhysicsWakeOpticsTriangular prismMechanicsFlow (mathematics)

Abstract

fetched live from OpenAlex

Transient flows past arrays of yawed finite-length prisms in tandem and staggered arrangements are simulated. Large eddy simulation method is employed to characterize spatial and temporal characteristics of flows in the vicinity of prisms. Drag coefficients of prisms are determined for different spacing and yaw angle of prisms. Three-dimensional effects are investigated for different spacings of prisms in a tandem array. Drag coefficient of downstream prism is significantly lower compared to that of upstream prism when prisms are tightly spaced. Drag coefficient of downstream prism predicted by simulations in three-dimensional geometry is recovered fully as spacing approaches 10 times the height of the prism while drag coefficient predicted by simulations in two-dimensional geometry is only about 30% of the upstream prism even when two prisms are separated by 20 times the height of the prism. The wake flow patterns do not influence the drag coefficient strongly for the staggered arrays of yawed prisms. Prisms can be placed in a tighter arrangement without a significant penalty of drag reduction. Mathematical models and numerical methods employed are validated by comparing simulation results against experimental results. Drag exerted on these prisms can directly be related to power generation by devices containing translating blades of marine-current applications. This study demonstrates that arrangement, spacing, and yaw angle can be used to design and optimize energy harvesting devices and other offshore structures.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.573
Threshold uncertainty score0.303

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 head0.011
GPT teacher head0.195
Teacher spread0.185 · 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
Published2017
Admission routes1
Has abstractyes

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