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Record W7024257579

A Reduced Model of Borehole Thermal Energy Storage Thermal Response

2023· dissertation· en· W7024257579 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsThermal energy storageHeat exchangerVolume (thermodynamics)Heat transferEnergy recoveryEnergy storageThermal energyWork (physics)BoreholeThermal
DOInot available

Abstract

fetched live from OpenAlex

In Canada 15% of greenhouse gas (GHG) emissions are produced by the residential sector’s energy demand. The majority of the energy demand is space heating which is primarily met with natural gas combustion. Motivation exists to reduce GHG emissions due to their contribution to climate change. Integrated Community Energy Harvesting (ICE-Harvest) systems seek to integrate thermal and electrical energy production, storage, redistribution, and consumption in a way that reduces GHG emissions. Borehole thermal energy storage (BTES) is implemented in ICE-Harvest systems as seasonal thermal energy storage. This thesis presents a novel model of BTES thermal response with reduced complexity to aid in early siting, design, optimization, and control systems development work for ICE-Harvest systems. The reduced model can be used to approximate periodic steady state BTES thermal response. The model provides information on average ground storage volume temperature, outlet fluid temperature, heat exchanger fluid to storage volume heat transfer rate, storage volume top loss heat transfer rate, storage volume side and bottom loss heat transfer rate, and annual thermal energy storage efficiency which aids system modelling efforts for BTES in solar thermal and ICE-Harvest systems. The reduced model is formed from a solution of the thermal energy balance equations for the BTES ground storage volume and heat exchanger fluid with simplified operating conditions for a yearly BTES charging and discharging cycle. Ground storage volume temperature is lumped as a single value. Heat transfer rates between the storage volume and the heat exchanger fluid and the storage volume and its surroundings are modelled with periodic steady state thermal resistance values for the charging and discharging timesteps. A TRNSYS DST simulation of BTES is validated against measurements from a BTES installation and TRNSYS DST is used to generate the periodic steady state thermal resistance values the reduced model requires. The periodic steady state thermal resistance values of BTES charging and discharging are dependent on BTES design parameters (spacing between boreholes, number of boreholes, borehole depth, and storage volume size) and ground thermal properties (thermal capacity and thermal conductivity) which is presented in a series of parameter sweeps with respect to a reference simulation. The reduced model predicts periodic steady state average storage volume temperature with a RMSD of 0.96°C for charging and 1.3°C for discharging when compared to the TRNSYS DST reference simulation. The reduced model predicts the periodic steady state heat exchanger total energy transfer within 1.8% for the charging timestep and 2.8% for the discharging timestep when compared to the TRNSYS DST reference simulation. The reduced model’s periodic steady state thermal resistance values are demonstrated to be independent of heat exchanger fluid inlet temperature except for the side and bottom loss thermal resistance during discharging. The reduced model cannot replicate the change in heat transfer direction that occurs during BTES discharging when the temperature of the storage volume decreases below the temperature of the surrounding ground, however, the magnitude of the energy transfer that would occur is negligible compared to the magnitude of the BTES heat exchanger total energy transfer.

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: Other
Teacher disagreement score0.821
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0140.000

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 head0.016
GPT teacher head0.209
Teacher spread0.193 · 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
Published2023
Admission routes1
Has abstractyes

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