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Modelling and optimization of helical steel piles as in-ground heat exchangers for Ground-Source Heat Pumps

2019· article· en· W2981825631 on OpenAlexaff
Sarah R. Nicholson, Aggrey Mwesigye, Seth B. Dworkin

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHeat exchangerBoreholePileHeat pumpGeothermal gradientGeothermal heatingFinite element methodMechanicsNuclear engineeringMaterials scienceEngineeringMechanical engineeringStructural engineeringGeothermal energyGeotechnical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract Ground source heat pumps are a sustainable way to provide building heating and cooling due to their efficient use of near-constant ground temperatures as mediums for heat exchange. Conventional in-ground heat exchangers are limited by the large size of required borehole field installations and the high economic costs, therefore the pairing of in-ground structural helical piles with these heat exchangers offers a system of geothermal heating and cooling which can be more accessible and lower cost than traditional equipment. In this research, a novel helical steel pile was modelled using a 3-D numerical model and finite element analysis. This model was first validated with experimental data from a double-tube pile, with 24-hr transient operation outlet temperatures accurate within 3%. Finally, the steady state heat exchange rate per unit area was calculated, with the new helical steel pile geometry yielding an increase of 8.6 W/m, 13.2 W/m, and 16.2 W/m (for 2 L/min, 4 L/min, and 8 L/min flowrates respectively) over the validation model.

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: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.605

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 head0.018
GPT teacher head0.213
Teacher spread0.195 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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