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Design and optimization of cost-effective coldproof portable enclosures for polar environment

2020· article· en· W2990245266 on OpenAlexaffabout
Behzad Parsi, Lihong Zhang

Bibliographic record

VenueInternational Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · 2020
Typearticle
Languageen
FieldComputer Science
TopicFuzzy Logic and Control Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsEnclosureResistorMultiphysicsComputer scienceElectronic componentElectronicsSoftwareHeating elementMechanical engineeringElectrical engineeringEngineeringFinite element method

Abstract

fetched live from OpenAlex

Based on the International Electrotechnical Commission standards, the electronic devices in the industrial class (e.g., integrated circuits or batteries) can only operate at the ambient temperature between -40°C and 85°C. For the human-involved regions in Alaska, Northern Canada, and Antarctica, extreme cold condition as low as -55°C might affect sensing electronic devices utilized in the scientific or industrial applications. In this paper, we propose a design and optimization methodology for the self-heating portable enclosures, which can warm up the inner space from -55°C for encasing the low-cost industrial-class electronic devices instead of expensive military-class ones to work reliably within their allowed operating temperature limit. Among the other options, ceramic thermal resistors are selected as the heating elements inside the enclosure. The placement of the thermal resistors is studied with the aid of thermal modelling for the single heating device by using the curve fitting technique to achieve uniform temperature distribution within the enclosure. To maintain the inner temperature above -40°C but with the least power consumption from the thermal resistors, we have developed a control system based on the fuzzy logic controller. For validation, we have utilized COMSOL Multiphysics software and then one prototyped enclosure along with the fuzzy control system. Our experimental measurement exhibits its efficacy compared to the other design options.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.006
GPT teacher head0.202
Teacher spread0.195 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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Same venueInternational Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer EngineeringSame topicFuzzy Logic and Control SystemsFrench-language works237,207