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Record W7116122888 · doi:10.82417/dhjj-hr05

Analytical simulation of the thermal behaviour of parallel-series configurations of helical steel piles

2025· other· en· W7116122888 on OpenAlexfundno aff

Bibliographic record

VenueEspace ÉTS (ETS) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBoreholePileThermalHeat transferInletHeat exchangerFlow (mathematics)Series (stratigraphy)Line (geometry)

Abstract

fetched live from OpenAlex

The high initial cost of boreholes required for ground source heat pump (GSHP) systems is prompting a search for more cost-effective alternatives. This paper explores the use of helical steel piles (HSPs) as dual-purpose geo-exchange systems, offering both structural support and thermal energy exchange capabilities, and presenting a more economical option compared to traditional boreholes. In this study, an analytical approach utilizing the finite line source (FLS) model is developed to examine and compare the thermal performance of HSPs in various configurations—parallel, series, and mixed. For each configuration, the model dynamically computes the heat transfer rate of each pile as well as the overall inlet fluid temperature of the pile configuration. The proposed method has been validated against literature-reported test cases, demonstrating its accuracy and efficiency. The results indicate that the thermal efficiency of HSPs is similar to that of boreholes, and in the studied test case, HSPs were shown to perform better when the configurations were identical. However, comparisons may become unfair and are not advisable when the heights of the boreholes and piles are different. Moreover, the efficiency of various pile configurations varies significantly with their series or parallel arrangements as well as their flow rates, suggesting that the selection of pile arrangement and operational parameters should be carefully considered to optimize performance.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.294
Teacher spread0.275 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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