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Effects of varying heat transfer rates for borehole heat exchangers in layered subsurface with groundwater flow

2024· article· en· W4392975784 on OpenAlexafffund
Yuting Guo, Jian Zhao, Wei Victor Liu

Bibliographic record

VenueApplied Thermal Engineering · 2024
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundAlberta Innovates
KeywordsBoreholeHeat exchangerHeat transferPetroleum engineeringGroundwaterEnvironmental scienceGroundwater flowMaterials scienceGeologyGeotechnical engineeringPetrologyMechanicsAquiferEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The existence of groundwater flow adds a convective heat component to shallow geothermal heat pump systems, significantly improving heat transfer efficiency. Numerous analytical and numerical methods have been proposed to assess the impact of groundwater flow on ground heat exchangers. However, numerical simulations often require significant resources and time, while conventional analytical solutions overlook the time and depth-varying heat transfer rates, leading to inaccurate temperature predictions. This study proposes a computationally efficient semi-analytical solution by extending the line source solution and coupling it with a thermal resistance model. This approach addresses variations in heat transfer rate over time and depth, overcoming the limitations of conventional analytical solutions. Compared to a three-dimensional numerical model constructed in COMSOL Multiphysics, the average absolute percentage error (MAPE) of borehole wall temperatures for a U-shaped borehole heat exchanger (BHE) is approximately 1 %, across a wide range of water flow velocities and thermal conductivity ratios between two contacting geological layers. A significant advantage of the proposed solution is its computational efficiency. The proposed solution can complete long-term simulations with over 9,000 time steps in about 10 min, as opposed to numerical models that typically require several hours. The excellent computational efficiency and substantial accuracy make the proposed solution a simple and effective tool for the design and optimization of BHEs in engineering applications.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.770

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 head0.006
GPT teacher head0.197
Teacher spread0.190 · 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

Citations18
Published2024
Admission routes2
Has abstractyes

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