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Record W7006283010

Technical viability of underground hydrogen storage in cased borehole

2023· dissertation· en· W7006283010 on OpenAlexfundno aff

Bibliographic record

VenueSkemman · 2023
Typedissertation
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsBoreholeHydrogenWork (physics)Compressed hydrogenRenewable energyHydrogen storageCompressed air energy storageEnergy storage
DOInot available

Abstract

fetched live from OpenAlex

The decrease in generation costs of renewable energy, combined with advances in electrolyser technologies, suggest that green hydrogen production may be a viable option in the ongoing energy transition. Yet, a green hydrogen economy requires not only production solutions but also storage options, which prove to be challenging. An underexplored solution is the underground storage of hydrogen gas (H2) in cased boreholes or shafts. Its integration would bring versatility in the implementation, and large applicability since it does not require a particular geological context. The objective of this thesis is to evaluate the technical viability of this new storage technology. Accurate prediction of temperature and pressure variations is essential for design, materials selection, and safety reasons. This work uses numerical models based on mass and energy conservation equations to simulate hydrogen storage operations in cased boreholes. The study shows that the heat transfer at the cavity walls strongly affects temperature and pressure variations. This effect is accentuated by a borehole’s geometry providing significant contact area. Thus, such technology mitigates extreme pressure and temperature variations and yields a higher hydrogen density than conventional caverns for a given pressure constraint. Results show that with a radius of 0.2 m, a hydrogen density of 30 kg m−3 can be attained at a maximum pressure of 50 MPa. The response of the system in terms of maximum temperature and pressure is relatively linear with an injection over 4 ℎ but quickly becomes non-linear with a shorter injection time. The optimization of the initial storage conditions appears essential to minimize the cooling cost and maximize the storage mass.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.269
Teacher spread0.252 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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