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Record W4293115978 · doi:10.11159/htff22.151

Surrogate Model for the Prediction of the Performance of a Tubular Pulsated Heat Pipe

2022· article· en· W4293115978 on OpenAlexvenueno aff
Mouret Gaëlle, Mira Ibrahim, Becerril César, Cataldo Filippo

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
FundersCleanskyEuropean Commission
KeywordsComputer science

Abstract

fetched live from OpenAlex

Power electronics like Insulated Gate Bipolar Transistors (IGBT), Central Processing Units (CPU), Graphics Processing Units (GPU), and diodes, among others are key pieces for the control of electronics embedded in the new generation (more electrical) aircraft. These power electronics are smaller and more and more efficient. However, the increase in their performance and their miniaturization also increases their heat release. A solution to cool down these power electronics is the Pulsated Heat Pipes (PHP), which are very efficient and passive two-phase flow heat exchangers. This work discusses the construction of a PHP predesign tool using surrogate modelling. For this purpose, the database generation, surrogate training, and performance in predicting the thermal resistance of different PHP geometries are presented.

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 score0.324
Threshold uncertainty score0.401

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 head0.009
GPT teacher head0.185
Teacher spread0.176 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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