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Record W4390970666 · doi:10.1080/23744731.2023.2299172

Simulation and performance assessment of a heat pump coupled to a sub-slab thermal storage system

2024· article· en· W4390970666 on OpenAlexaffabout
Luminita Dumitrascu, Ian Beausoleil-Morrison

Bibliographic record

VenueScience and Technology for the Built Environment · 2024
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsHeat pumpSlabHeat exchangerEnvironmental scienceEnergy consumptionParametric statisticsThermal energy storageSensitivity (control systems)Heat recovery ventilationApartmentAir conditioningEngineeringCivil engineeringMechanical engineeringStructural engineeringThermodynamics

Abstract

fetched live from OpenAlex

The paper explores the performance of a sub-slab ground heat exchanger, coupled to a heat pump, and the ability of the system to provide space conditioning and domestic hot water (DHW) for a multi-unit residential building (MURB) located in Ottawa, Ontario. The parametric analysis conducted to optimize the design of the building, as well as the sensitivity analysis performed to optimize the configuration of the sub-slab ground heat exchanger is also presented. According to the simulated results, the ground layer can store some of the rejected energy over medium term, without reducing the long-term performance of the heat pump. The annual energy consumption per dwelling unit (including heat pump, all auxiliary equipment, appliances, and lights) was estimated to be 28.4 GJ, significantly lower than the average annual energy consumption for a low-rise apartment in Canada (46.7 GJ per apartment). To assess the sensitivity of the system to environmental conditions, the simulation was conducted using the weather files for other five locations in Canada.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.103
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes2
Has abstractyes

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