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Optimizing Water Disinfection: A CFD Study on Microorganisms Collision against a Triply Periodic Minimal Surface

2023· preprint· en· W4388802714 on OpenAlexaff
Leonardo Gadêlha Tumajan Costa de Melo, Frederico Duarte de Menezes, José Ângelo Peixoto da Costa, João Vitor Pereira Alves, Yi Wai Chiang, Rafael M. Santos

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsComputational fluid dynamicsCollisionPressure dropInfillEfficient energy useMaterials scienceMechanicsComputer scienceEnvironmental scienceMarine engineeringProcess engineeringMechanical engineeringEngineeringStructural engineeringPhysics

Abstract

fetched live from OpenAlex

Currently, point-of-use (POU) disinfection technologies have a significant impact addressing the control of microbial pathogens in water.Among the physical factors related to microbial filtration/inactivation, the rate of particles collision with the disinfecting surface is crucial to guarantee satisfactory efficiency.Through a Computational Fluid Dynamics (CFD) model run in Ansys CFX software, this study assesses the collision dynamics of particles configured according to E. coli cells' parameters against an infill mesh, designed to be manufactured as a metallic disinfectant.The mesh, created using Python coding due to its complexity, is based on the Triply Periodic Minimal Surface (TPMS) shape of the Schwarz P, providing a large surface area to volume ratio, with a geometry that allows high permeability.The efficiency of filtration was studied through analysis and comparison of different infill configurations, while also considering the pressure drop introduced by them, which is fundamental in the process' energy consumption.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.254
Teacher spread0.228 · 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.

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
Published2023
Admission routes1
Has abstractyes

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