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Record W4417464749 · doi:10.33774/coe-2025-95gfz

Mathematical Modelling of Deposition and Erosion of Particles in Pipes

2025· article· en· W4417464749 on OpenAlexaff
C. Sean Bohun, Jake Bowhay, Thuy Duong Dang, Cameron L. Hall, Emmanuel Lwele, Brady Metherall, John C. Meyer, Philip Pearce, Clare R. Rees-Zimmerman, Matthew D. Shirley, Jesse J. Taylor-West, Alex Trenam, Edwina Yeo, Kieran Quaine

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsOntario Tech University
FundersUniversity of BristolDivision of Mathematical SciencesEngineering and Physical Sciences Research CouncilInternational Centre for Mathematical SciencesUK Research and Innovation
KeywordsCloggingDeposition (geology)ErosionWork (physics)Particle (ecology)TurbulenceFlow (mathematics)Particle deposition

Abstract

fetched live from OpenAlex

Paebbl are interested in the effective transport of a particle-laden fluid through a system of pipes. This transport has the potential to be disrupted if the particles sediment on the walls of the pipe, leading to pipe clogging and blockage. Motivated by this, we investigate the gravity- driven deposition and shear-driven erosion of solid particles carried in a turbulent flow in a pipe. We develop and solve a mathematical model for particle transport in the bulk of the fluid, and particle behaviour near the pipe walls, including deposition and erosion. We also model the chemistry related to the effective capture of CO2, which is important for the quality of the product. Our analysis is a good entry point for future work with Paebbl. More broadly, our work has relevance in wider industrial applications in relation to safety and efficiency, as well as the economic viability of industrial-scale production.

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 score0.305
Threshold uncertainty score0.141

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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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