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Record W4412616944 · doi:10.1002/esp.70125

Flow and turbulence characteristics of bed load sediment transport for self‐cleansing without deposition

2025· article· en· W4412616944 on OpenAlexaff
Mehrnoush Kohandel Gargari, Ilayda Keskin, Mir Jafar Sadegh Safari, Babak Vaheddoost

Bibliographic record

VenueEarth Surface Processes and Landforms · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTurbulenceDeposition (geology)Flow (mathematics)Sediment transportBed loadHyperconcentrated flowSedimentGeologyEnvironmental scienceHydrology (agriculture)Geotechnical engineeringMechanicsGeomorphologyPhysics

Abstract

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Abstract Investigating the structure of flow turbulence and bed load sediment transport is crucial as it provides insights into the functioning of aquatic environments, where such variations can lead to changes in ecosystem dynamics. This study focuses on the impact of sediments on the hydraulic characteristics of flow at self‐cleansing without deposition conditions of sediment transport. The self‐cleansing without deposition is not only a mode of sediment transport in alluvial channels, but it also serves as a criterion for the design of lined channels. Among the various design concepts for lined open channels, such as sewers and drainage channels, self‐cleansing without deposition condition is implemented as the most conservative and reliable approach. However, most of the conducted experimental studies on self‐cleansing without deposition have focused on measuring the basic flow and sediment characteristics for modelling purposes and neglected the effect of bed load, sediment size, flow discharge, and channel bed slope on turbulence characteristics. This study addresses this gap by examining the impact of bed load, sediment size, bed slope, and discharge on turbulence characteristics through a series of experiments conducted in a 12.5 m flume with a rectangular cross‐section, equipped with an automatic control system (ACS) at the Hydraulic Laboratory of Yaşar University. The channel bed slope, sediment discharge, flow discharge, and depth were adjusted and measured using ACS. Discharge and flow depth were measured using an ultrasonic flow‐meter and depth sensors, respectively. Flow characteristics were measured using a Vectrino profiler device. The study reveals that bed load sediment transport reduces streamwise velocity, especially for coarse particles. Additionally, at a constant bed slope, velocity differences remain small at lower discharges but become more significant as discharge increases. Turbulence intensity rises with bed load motion, more in the streamwise direction than vertically. At a constant bed slope, increasing discharge enhances turbulence, but the effect is more pronounced at lower slopes and less significant at steeper slopes. Reynolds shear stress increases with particle size and steeper slopes, indicating greater shear production. These observations suggest critical implications for the design and optimization of open‐channel systems, emphasizing the need for detailed consideration of particle sizes and bed conditions in engineering practices.

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 score0.207
Threshold uncertainty score0.439

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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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