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Thermal management of high-powered short-duration electronics aided by a phase change material thermal energy storage

2024· article· en· W4399292100 on OpenAlexaff
D Martheleur, Dominic Groulx

Bibliographic record

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDuration (music)Thermal management of electronic devices and systemsElectronicsPhase-change materialThermalThermal energy storageEnergy storagePhase changeMaterials scienceThermal energyElectrical engineeringEngineering physicsMechanical engineeringEngineeringPhysicsThermodynamicsAcoustics

Abstract

fetched live from OpenAlex

Abstract Quantifying the performance of phase change materials (PCMs) for thermal energy storage in high-power short-duration electronics applications requires performing tests that push the material to its limit. This involves designing experiments to test solid-liquid PCMs in near real-world thermal load conditions to test its performance. Conventional cooling methods are sized to manage the maximum loading the cooling system will be subjected to in its operation, even if this loading occurs for a short period of time. This design ethos is inefficient and results in heavy and expensive equipment being used in an application where it will be underutilized most of its useful life. In this work, a PCM thermal energy storage (TES) device was built using a plate-and-frame heat exchanger and investigated to determine the suitability of this system to manage the temperature of high-power electronics with a low duty cycle. These experiments showed how a PCM-TES can be used in conjunction with conventional temperature management equipment to create a reliable cooling solution while allowing the conventional cooling components to be decreased in size and capacity. This work shows that, for a given application, a cooling system can be more efficiently designed with the use of a PCM-TES and opens the door to additional research on this topic.

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 score0.081
Threshold uncertainty score0.740

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 communication0.0000.001
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 head0.035
GPT teacher head0.280
Teacher spread0.246 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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