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Record W3204254692 · doi:10.1002/cjce.24320

A diffuse interface method for simulation‐based screening of heat transfer processes with complex geometries

2021· article· en· W3204254692 on OpenAlexafffundvenue
Elizabeth J. Monte, James Lowman, Nasser Mohieddin Abukhdeir

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaMinisterul Cercetării şi InovăriiCompute Canada
KeywordsPolygon meshComputer scienceConformal mapInterface (matter)Computational scienceHeat transferReduction (mathematics)Boundary value problemAlgorithmGeometryMechanicsMathematicsPhysicsMathematical analysisParallel computing

Abstract

fetched live from OpenAlex

Abstract Frequently, the simulation‐based design of physicochemical processes requires screening of large numbers of alternative designs with complex geometries. These geometries may result in conformal meshes which introduce stability issues, significant computational complexity, and require user‐interaction for their creation. In this work, a method for simulation of heat transfer using the diffuse interface method to capture a complex geometry is presented as an alternative to conformal meshing, with analysis and comparisons given. The methods presented include automated non‐iterative generation of phase fields from CAD geometries and an extension of the diffuse interface method for mixed boundary conditions. Simple measures of diffuse interface quality are presented and used to predict performance. The method is applied to a realistic three‐dimensional heat transfer problem (LED heat sink) and compared to the traditional conformal mesh approach. It is found to enable reasonable accuracy at an order‐of‐magnitude reduction in simulation time or comparable accuracy for equivalent simulation times.

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: none
Teacher disagreement score0.758
Threshold uncertainty score0.403

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

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