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Empirical Equation for Determination of Alternate Bar Height

2019· article· en· W2969405787 on OpenAlexafffund
Yunshuo Cheng, Ana Maria Ferreira da Silva

Bibliographic record

VenueJournal of Hydraulic Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsFlumeReynolds numberBar (unit)MathematicsFlow (mathematics)Range (aeronautics)MechanicsShear stressGeotechnical engineeringGeometryGeologyMaterials sciencePhysicsMeteorologyTurbulence

Abstract

fetched live from OpenAlex

A new empirical equation for alternate bar height is introduced. It is assumed that the bars are formed under a steady and uniform flow; the stage of interest is that where bars have grown to their fully developed state. The equation is developed on the basis of dimensional considerations and all data available to the authors; the formulation also incorporates findings by stability analysis. The data result from a total of 191 flume experiments reported in 16 different works carried out from 1961 to 2014 using either sand or gravel as bed material. Both fully rough and transitionally rough flows were used in the experiments. Overall, width-to-depth ratios and relative depths varied from 3.5 to 54.4 and 3.8 to 191, respectively; the ratios of bed shear stress to critical bed shear stress ranged from 1.1 to 14.7. Data from the Naka River, Japan, are also used. The equation correlates bar height, normalized by flow depth, with the excess width-to-depth ratio (B/h) with regard to the smallest, or critical, value of B/h at which alternate bars occur as well as with the relative depth and, in the case of transitionally rough flows, also the grain-size Reynolds number. It is shown that when compared with previous empirical equations, the present equation produces a considerably improved overall alignment of the data with the perfect agreement line and a significant larger (nearly double) percentage of data falling within the 20% error range. The results by the present equation are also substantially more congruent with those derived from existing theoretical and numerical analyses of the development of alternate bars, and more specifically, those resulting from a stability analysis of the phenomenon. As a by-product of this work, a first comparative evaluation of existing equations for alternate bar height is also presented.

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: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.317

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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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

Citations5
Published2019
Admission routes2
Has abstractyes

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