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Record W7116190740 · doi:10.82417/cm4c-1314

Experimental and numerical study on a pilot-scale high-grade rock bed thermal energy storage

2025· other· en· W7116190740 on OpenAlexfundno aff

Bibliographic record

VenueEspace ÉTS (ETS) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de Sherbrooke
KeywordsMass flow rateVolume (thermodynamics)Heat transferThermalInletThermal energy storageFlow (mathematics)CombustorVolumetric flow rate

Abstract

fetched live from OpenAlex

This study investigates the thermo-fluid performance of the SAUNA rock bed storage facility at Stellenbosch University. The facility features a 1.5 m³ storage volume filled with 2480 kg of dolerite samples to evaluate charging performance. A diesel burner downstream of the fan raises the inlet temperature to a maximum of 600°C. The experiments were performed for the highest temperature and maximum mass flow rate achievable based on the burner maximum capacity of 148 kW. The results are used as a benchmark against volume-averaged method predictions from Ansys Fluent, a commercial computational fluid dynamics software. The findings indicate that the volume average method accurately predicts temperature distribution provided that key design and flow parameters are considered. One key consideration, for instance, is that a two-dimensional axisymmetric flow assumption is inadequate, as it underestimates heat loss. Therefore, it is necessary to model the full three-dimensional model of the storage bed for an accurate prediction of the thermal performance. Another key observation is the necessity of implementing a fan intake boundary condition in rock bed simulations, especially for the purpose of numerical sensitivity analysis and optimal design considerations. Smaller particles for instance will obstruct flow, reducing mass flow rate and reducing the expected improvements in heat transfer for smaller particles. This will directly affect the optimal design for a rock bed storage. Not surprisingly, the local thermal non-equilibrium (LTNE) assumption is essential despite the use of relatively small particles. Interestingly, incorporating a modified specific surface area in the LTNE framework, considering particle non-sphericity, improves agreement with experimental data, which is a parameter that has received much less consideration in the literature. Incorporating particle non-sphericity, aligns well with results obtained using the Wakao correlation for particle-solid interfacial heat transfer. Additionally, shell conduction can be safely employed for outer surface heat transfer to account for the 15 cm insulation layer, significantly simplifying the simulation. Without this approach, numerical modeling would require coupling three surfaces: the fluid region of the packed bed, the solid region of the packed bed, and the solid insulation layer. The study concludes that radiation effects can be neglected even at high temperatures for the conditions considered in this study. However, further research is recommended, as radiation may become significant at lower mass flow rates or higher inlet temperatures. Overall, this research successfully validates the numerical model and provides insights for numerical implementation and optimization guidelines for rock bed storage systems.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.264
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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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