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Record W3130973884 · doi:10.1109/tmag.2021.3060628

Efficient Solver for a Simplified Model of the Multi-Physics Heat Transfer Problem in Radio Frequency Ablation of Hepatic Tumors

2021· article· en· W3130973884 on OpenAlexafffund
Amir Akbari, Dennis D. Giannacopoulos

Bibliographic record

VenueIEEE Transactions on Magnetics · 2021
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolverScalabilityComputer scienceHeat transferCoupling (piping)Finite element methodRadio frequencyKey (lock)Computational scienceParallel computingPhysicsMechanicsMechanical engineering

Abstract

fetched live from OpenAlex

Mathematical modeling and computer simulation of heat transfer in the liver play a key role in prediction of radio frequency ablation outcome. While multi-physics modeling should be used for accurate simulation of heat transfer in a highly perfused organ like the liver, the computational cost of such a problem could have a negative impact on the simulation time. This article extends a highly parallel finite element-based method to simulate the multi-physics heat transfer problem in the liver efficiently. A message passing interface (MPI)-based strong coupling method is developed and tested on cluster nodes. The scalability of this work is assessed by performing simulations on up to 1024 cores.

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 score0.543
Threshold uncertainty score0.573

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 head0.033
GPT teacher head0.262
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.

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

Citations7
Published2021
Admission routes2
Has abstractyes

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