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Record W4388522308 · doi:10.1063/5.0171326

Do we understand the drying of porous materials?

2023· article· en· W4388522308 on OpenAlexaff
Linlin Fei, Jianlin Zhao, Feifei Qin, Aytaç Kubilay, Dominique Derome, Jan Carmeliet

Bibliographic record

VenueAIP conference proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversité de Sherbrooke
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsPorosityPorous mediumComputer scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Convective drying at pore scale is studied by a two-component two-phase lattice Boltzmann model at pore scale showing the important influence of capillary pumping from large to small pores and pinning of contact lines in dual porosity materials. Capillary pumping and pinning explains the first drying period with almost constant drying rate. The drying rate during the first drying period is found to depend on air velocity or Reynolds (Re) number, and a logarithmic relation between average drying rate and Re is found. This analysis allows to better understand first and second drying period and their dependence on air velocity and pore structure. In an upscaling example, the evaporative cooling effect of a two-layer porous pavement with optimal wetting protocol is analyzed for a square in Zurich. The top layer of the pavement enhances drying during first drying period due to capillary pumping, while the second layer prevents loss of sprayed water to the subsoil. The evaporative cooling from pavements enhances the thermal comfort, but has to be combined with other measures like shadowing from trees. The proposed multiscale approach upscaling from pore to continuum scale is believed to enhance the understanding of drying of porous materials and its application in urban and building physics.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.441

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 head0.037
GPT teacher head0.243
Teacher spread0.206 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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