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Record W3209246400 · doi:10.3390/app112110350

A Latent Heat Storage System for Low-Temperature Applications: From Materials Selection to Prototype Performances

2021· article· en· W3209246400 on OpenAlexaff
Didier Haillot, Yasmine Lalau, Erwin Franquet, Sacha Rigal, Frédéric Jay, Jean-Pierre Bédécarrats

Bibliographic record

VenueApplied Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsÉcole de Technologie Supérieure
FundersAgence Nationale de la Recherche
KeywordsThermal energy storageProcess engineeringLatent heatWaste heatPhase-change materialMaterials scienceWaste heat recovery unitWork (physics)Energy storageThermal conductivityDimensioningEnvironmental scienceRange (aeronautics)Phase changeMechanical engineeringThermodynamicsEngineeringComposite materialEngineering physicsHeat exchanger

Abstract

fetched live from OpenAlex

The industrial sector is increasingly obliged to reduce its energy consumption and greenhouse gases emissions to contribute to the world organizations’ targets in energy transition. An energy efficiency solution lies in the development of thermal energy storage systems, which are notably lacking in the low-temperature range (50–85 °C), for applications such as district heating or low-temperature waste heat recovery. This work aims to bring a latent heat storage solution from material selection to prototype evaluation. The first part of this paper is dedicated to the characterization and aging of a phase change material selected from a screening of the literature (fatty acid mixture mainly composed by stearic and palmitic acid). Then, this material is encapsulated and tested in a prototype whose performances are evaluated under various operating conditions. Finally, a numerical model validated by the experimental results is used to explore the influence of a wider range of operating conditions, dimensioning choices, and material conductivity improvements.

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 score0.003
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.275
Teacher spread0.248 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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