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The identification of variable shear strength of particles deposited on drinking water PVC pipes after the passage of a suspended particle plume in a full-scale laboratory system

2022· article· en· W4392420693 on OpenAlexaff
Artur Sass Braga, Yves Filion

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsFull scalePlumeParticle (ecology)Shear (geology)Materials scienceVariable (mathematics)Environmental scienceGeotechnical engineeringComposite materialStructural engineeringEngineeringGeologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Understanding the adhesion properties of sediments that accumulate on the wall of drinking water pipes is an important step in the development of mitigation strategies to prevent the formation of deposits and protect drinking water. Research based on flushing of operational pipe mains that mobilized particulate sediments from isolated pipe sections has shown that fine iron oxide particles are a recurrent major component of these deposits. In addition, it has been established that adhesion forces proportional to the flow wall shear stress (WSS) develop between the pipe wall and accumulated particles, which prevents the washing-off of particles during common conditioning flows and provokes a rapid resuspension during high-flow events that cause water discolouration. Discolouration models have also showed that sediments have a variable shear strength, and, therefore, a fraction of material may resist a first increase in WSS but then be mobilized after a second increase in WSS. To explain the variable shear strength of layers, researchers have hypothesized that sediments accumulate as cohesive layers, which might be explained by the growth of biofilm among particulate material. Although current models have successfully explained sediment mobilization during flushing, the prediction of material accumulation and its shear strength is more challenging due to the lack of a comprehensive understanding about the accumulation process. The aim of this paper is to examine how particulate iron oxide that are rapidly deposited on PVC pipes develop variable shear strength under common hydraulic conditions found in drinking water distribution networks. A set of experiments were performed in a full-scale laboratory facility, where selected iron oxide particles were controlled and used to amend the feed water at the entrance of a 200 m pipe loop during a short period of time to create a suspended sediment plume with constant concentration. Experiments were realized at three different concentrations and three different velocities. In each set, three sequential plumes were used to accumulate particles on the pipe walls, followed by three sequential flushing steps used to mobilize the particulate material. The deposition of iron oxides in the PVC pipes were assessed indirectly through suspended sediment concentration (SSC) and turbidity data. Results showed that iron oxide particles predominantly accumulated in the first sections of the pipe wall. Most sediments were found to have weak shear strength and were easily mobilized with the first flushing step. However, the mobilized load from the second and third flushing steps revealed a consistent mobilization of sediment with higher shear strengths. These shear strengths were higher in the experiments with a higher inoculation concentration, and they were lower in experiments performed with a higher conditioning fluid velocity. The results suggest that variable shear strength can develop without biofilm. Additional long-term experiments are still required to evaluate the evolution of sediment shear strength which possibly can increase with time.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.167
Teacher spread0.163 · 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 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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