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Improved Rock-based Thermal Energy Storage (RTES) with Perforated Plate Based on Double Thermocline Design

2024· preprint· en· W4391774747 on OpenAlexafffund
Hamidreza Ermagan, Leyla Amiri, Agus P. Sasmito

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsUniversité de SherbrookeMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de Sherbrooke
KeywordsThermoclineThermalGeologyEnergy (signal processing)Thermal energy storageThermal massEnvironmental scienceComputer scienceOceanographyMeteorologyPhysics

Abstract

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Advancing the transition to renewable energy necessitates significant investments, especially in energy storage solutions to mitigate the variability and intermittency of renewable electricity generation.Rockbased Thermal Energy Storage (RTES) offers the vital adaptability needed to incorporate significant amounts of renewable energy by harnessing excessive energy and storing it in rocks or geological formations.This stored energy can then be used for various applications like heating or generating electricity, providing a reliable and sustainable energy source.One of the main challenges in the widespread use of RTES is the thermal losses (mainly because of the diffusion of thermocline) and the high pressure drop in the packed beds.Hence, a novel RTES system is proposed to overcome these limitations.The design has a perforated plate that delivers air in a certain length from the inlet.The fluid flow and heat transfer inside the packed bed are studied using a 2D Computational Fluid Dynamics (CFD) model considering the local thermal non-equilibrium (LTNE) at the air-rock interface.For the optimal case (perforated length to bed length of 0.7), the proposed design is shown to increase the charging efficiency of the conventional RTES by 14 percent by decreasing the fan power requirement and increasing the stored thermal energy.This improvement is due to the emergence of a secondary thermocline that bypasses the main thermocline, and hence, utilizes the packed bed more efficiently while also reducing the fan power requirement.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.262
Teacher spread0.218 · 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 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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