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Record W2953593042 · doi:10.3390/en12122450

Tip-Bed Velocity and Scour Depth of Horizontal-Axis Tidal Turbine with Consideration of Tip Clearance

2019· article· en· W2953593042 on OpenAlexfundno aff
Tianming Zhang, Wei-Haur Lam, Y. Cui, Jinxin Jiang, Chong Sun, Jianhua Guo, Yanbo Ma, Shuguang Wang, Su Shiung Lam, Gerard Hamill

Bibliographic record

VenueEnergies · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersUniversiti Teknologi MalaysiaTianjin UniversityUniversiti MalayaScience Fund for Creative Research GroupsDalian University of TechnologyQueen's UniversityUniversity of OxfordNational Natural Science Foundation of ChinaNatural Science Foundation of Tianjin CityQueen's University Belfast
KeywordsTurbineTidal powerMechanicsFlow velocityFlow (mathematics)GeologyGeotechnical engineeringRADIUSMarine engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The scouring by a tidal turbine is investigated by using a joint theoretical and experimental approach in this work. The existence of a turbine obstructs a tidal flow to divert the flow passing through the narrow channel in between the blades and seabed. Flow suppression is the main cause behind inducing tidal turbine scouring, and its accelerated velocity is being termed as tip-bed velocity (Vtb). A theoretical equation is currently proposed to predict the tip-bed velocity based on the axial momentum theory and the conservation of mass. The proposed tip-bed velocity equation is a function of four variables of rotor radius (r), tip-bed clearance (C), efflux velocity (V0) and free flow velocity (V∞), and a constant of mass flow coefficient (Cm) of 0.25. An experimental apparatus was built to conduct the scour experiments. The results provide a better understanding of the scour mechanism of the horizontal axis tidal turbine-induced scour. The experimental results show that the scour depth is inversely proportional to tip-bed clearance. Turbine coefficient (Kt) is proposed based on the relationship between the tip-bed velocity and the experimental tidal turbine scour depth. Inclusion of turbine coefficient (Kt) into the existing pier scour equations can predict the maximum scour depth of a tidal turbine with an error range of 5–24%.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.473

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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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 designObservational
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

Citations16
Published2019
Admission routes1
Has abstractyes

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