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Record W4225253466 · doi:10.3390/geosciences12050180

Estimation of In Situ Heat Capacity and Thermal Diffusivity from Undisturbed Ground Temperature Profile Measured in Ground Heat Exchangers

2022· article· en· W4225253466 on OpenAlexaff
Claude Hugo Koubikana Pambou, Jasmin Raymond, Mafalda M. Miranda, Nicoló Giordano

Bibliographic record

VenueGeosciences · 2022
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsThermal diffusivityThermal conductivityHeat capacityBoreholeGeothermal gradientHeat exchangerThermalVolumetric heat capacityGeothermal energyThermal effusivityMechanicsThermodynamicsEnvironmental scienceMaterials scienceGeologyGeotechnical engineeringHeat spreaderGeophysicsThermal resistancePhysicsThermal contact conductance

Abstract

fetched live from OpenAlex

Undisturbed ground temperature (UGT), thermal conductivity (TC) and heat capacity (HC) are essential parameters to design geothermal heat pumps and underground thermal energy storage systems, particularly borehole heat exchangers (BHE). However, field methods to assess the thermal state and properties of the subsurface are costly and time consuming. Moreover, HC is often not evaluated in situ but arbitrarily selected from literature considering the geological materials intercepted by boreholes. This work proposes an original empirical approach to reproduce a UGT profile and estimate in situ thermal diffusivity (TD) and HC in the scope of conventional thermal response tests (TRTs). Empirical equations were developed to reproduce a UGT profile measured along a BHE. Experimental coefficients are found with a non-linear least square solver optimization and used to calculate the damping depth, TD and HC. The suggested heat tracing method was verified and validated against other field methods demonstrating to be fast and reliable. The novelty of this new empirical approach relies on the use of a single temperature profile providing a simple way to better assess subsurface thermal properties.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.225
Teacher spread0.206 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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