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Record W2887968021 · doi:10.11159/htff18.152

Experimental Study of Latent Heat Thermal Energy Storage System for Medium Temperature Solar Applications

2018· article· en· W2887968021 on OpenAlexvenueno aff
Ashish Kumar, Pardeep Shahi, Sandip Saha

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsnot available
FundersNational Renewable Energy LaboratoryIndo-US Science and Technology ForumU.S. Department of Energy
KeywordsThermal energy storageLatent heatSolar energyThermalMaterials scienceEnergy storageThermodynamicsNuclear engineeringEnvironmental scienceEngineering physicsComputer scienceEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

An experimental study of multitube latent heat energy thermal storage system (LHTES) for medium temperature solar applications is presented in this paper. Charging and discharging of commercial grade PCM (A164), which is stored in annulus of the shell and tube type LHTES, is performed by flowing HTF (Hytherm 600) through seven inner tubes. Process of heat transfer between HTF and PCM is observed by obtaining temperature contours with the help of interpolation technique. The impact of parameters, such as HTF inlet temperature and mass flow rate of HTF on the thermal efficiency of LHTES is investigated during charging and discharging. Maximum efficiency of 41.3% is found at the highest HTF inlet temperature (200 C) considered in this study. The performance is found to increase with increasing mass flow rate and maximum efficiency of 33% is found at the mass flow rate of 0.1 kg/s.

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 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 score0.004
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.237
Teacher spread0.224 · 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.

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

Citations7
Published2018
Admission routes1
Has abstractyes

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