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Record W4317424977 · doi:10.18280/mmep.090619

Experimental and Numerical Study of Open Channel Flow with T-Section Artificial Bed Roughness

2022· article· en· W4317424977 on OpenAlexvenueno aff
Hayder Q. Majeed, Ali M. Ghazal, Basheer Al-Hadeethi

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsSurface finishTurbulenceMechanicsComputational fluid dynamicsFlow (mathematics)Surface roughnessComputer simulationCross section (physics)Materials scienceChannel (broadcasting)GeologyGeometryGeotechnical engineeringMathematicsEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Experimental and numerical studies have been conducted on the effects of bed roughness elements such as cubic and T-section elements that are regularly half-channel arrayed on one side of the river on turbulent flow characteristics and bed erosion downstream of the roughness elements. The experimental study has been done for two types of bed roughness elements (cubic and T-section shape) to study the effect of these elements on the velocity profile downstream the elements with respect to different water flow discharges and water depths. A comparison between the cubic and T-section artificial bed roughness showed that the velocity profile downstream the T-section increased in smooth side from the river and decrease in the rough side from it compared with the case when a cubic artificial bed roughness is used. By comparing the results for the element shapes, it can be notices that the T-section bed roughness element more effective compared to cubic shape for both sides of the channel. The numerical method has been done using Computational Fluid Dynamic (CFD) method. A validation for the CFD model with the experimental study have been carried out for a specific flow discharge and water depth. The results indicated that the velocity distribution profiles downstream the bed roughness elements in both sides shown very good agreement for manning coefficients between the numerical and experimental studies. The range of errors between the experimental and numerical study have been calculated using Root Mean Square Error (RMSE) approach, which is found that the RMSE is approximately equal to 1 in case of cubic bed roughness and the RMSE is about 1.5 in case of T-section bed roughness for both smooth and rough sides. Furthermore, the influence of the velocity profile and the bed erosion downstream of the T-section element under the effect of tides have been investigated using the CFD method, which is commonly happened in Shat al-Arab south of Iraq. The results show that the tide of the flow has a reverse effect on the velocity profiles for both sides. Since the velocity profile downstream of bed roughness region increase in the rough side and decrease in the smooth side compared with the normal flow of the river.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.025
GPT teacher head0.224
Teacher spread0.198 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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