MétaCan
Menu
Back to cohort
Record W3044638872 · doi:10.1115/1.4047831

Inverse Heat Transfer Study of a Power Transmission Line Tower Foundation

2020· article· en· W3044638872 on OpenAlexafffund
Daqian Zhang, Xili Duan

Bibliographic record

VenueJournal of Heat Transfer · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeat transferTowerThermal conductivityLine sourcePermafrostElectric power transmissionThermalMechanicsGeologyEngineeringStructural engineeringMeteorologyPhysicsThermodynamicsOpticsElectrical engineering

Abstract

fetched live from OpenAlex

Abstract In some northern regions, power transmission lines are built with metal tower footings buried in the permafrost. With a high thermal conductivity, the tower footing has a significant thermal effect on the foundation and the nearby permafrost. Heat transfer models were previously developed to predict the thermal effect with line heat source assumptions, without knowing the exact spatial distribution and temporal variation of the heat source strength. This limited the accuracy of these heat transfer models. In this work, an inverse heat transfer method (IHTM) based on dynamic matrix control (DMC) theory is developed to better estimate the heat source strength representing the tower footing. The methodology is validated with numerical simulations and experimental data. It is found that the distribution of heat source varies spatially and temporally in a more complicated way than what was assumed in previous studies. The inversed heat source is then used to reconstruct the temperature fields in a tower foundation, which provides more accurate heat transfer analysis for design and maintenance of the foundation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.981

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)0.0200.000

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 head0.056
GPT teacher head0.262
Teacher spread0.205 · 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.

Study designObservational
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
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Heat TransferSame topicClimate change and permafrostFrench-language works237,207