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Capillary performance of strut-based lattice wicks fabricated using laser powder bed fusion

2024· article· en· W4403761025 on OpenAlexafffund
Mohamed Hasan, Ahmed Elkholy, Morteza Narvan, Jason Durfee, Roger Kempers

Bibliographic record

VenueInternational Communications in Heat and Mass Transfer · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsMagna International (Canada)York University
FundersMitacsOntario Centre of Innovation
KeywordsMaterials scienceCapillary actionFusionComposite materialLaserLattice (music)OpticsAcoustics

Abstract

fetched live from OpenAlex

Laser powder bed fusion (LPBF) can be used to fabricate porous wicks with customized geometries for two-phase heat transport devices such as heat pipes; LPBF enables integration of these wicks into any two-phase transport device form factor in a single manufacturing step. Strut-based wicks with four different unit-cell geometries (simple cubic, body-centered cubic, face-centered cubic, and fluorite) with different porosities were designed and fabricated using LPBF. The capillary performance of the wicks was characterized using the mass rate-of-rise (m-t) method and quantified in terms of the ratio of permeability to effective pore radius ( K/r eff ). Both unit-cell geometry and porosity significantly affect the capillarity of these strut-based wicks, with K/r eff ranging from 0.05 to 1.43 μm, which is commensurate with conventional sintered metal wicks. This is due to a relatively high permeability, ranging from 39 μm 2 to 788 μm 2 , and an effective pore radius ranging from 233 μm to 1022 μm. The simple cubic 52 % and 65.1 % porous wicks exhibited the highest capillary performance with a K/r eff of 1.43 μm and 1.31 μm, respectively. These results suggest that modifying the LPBF process for finer feature resolution could result in higher capillarity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.365

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 head0.031
GPT teacher head0.271
Teacher spread0.240 · 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

Citations11
Published2024
Admission routes2
Has abstractyes

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