MétaCan
Menu
Back to cohort
Record W4387454113 · doi:10.3390/en16196994

Laboratory Investigation of Impact of Injection–Abstraction Rate and Groundwater Flow Velocity on Groundwater Heat Pump Performance

2023· article· en· W4387454113 on OpenAlexaboutno aff
Taha Sezer, Abubakar Kawuwa Sani, Rao Martand Singh, Liang Cui

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwaterPlumeGroundwater flowHeat pumpEnvironmental scienceVolumetric flow rateThermalGroundwater dischargeMechanicsHydrology (agriculture)Environmental engineeringPetroleum engineeringHeat exchangerMeteorologyGeotechnical engineeringGeologyThermodynamicsAquiferGeographyPhysics

Abstract

fetched live from OpenAlex

Using low-temperature (shallow) groundwater as a heat source or heat sink is a common practice to supply space heating or cooling, especially in the United States, Canada, China, and several European countries. The groundwater heat pump (GWHP) system has been extensively studied in recent decades using numerical approaches, which have some limitations in understanding the soil’s thermal behavior. Therefore, a laboratory-scale experimental study involving cooling tests was carried out to investigate the impact of GWHP on system performance and sustainability with varying groundwater flow velocities and injection and abstraction rates. The results demonstrated that groundwater flow velocity, as well as injection and abstraction rates, significantly impact thermal plume development. Higher injection and abstraction rates create a larger thermal plume, thereby decreasing abstraction temperature. However, groundwater flow prevents heat development around the well by dispersing the heat in the groundwater flow direction. Furthermore, the results indicate that the energy gain only increased by 81% and 107%, with a respective increase of 100% and 200% in injection and abstraction rates.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.998

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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueEnergiesSame topicGeothermal Energy Systems and ApplicationsFrench-language works237,207