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Record W4221037460 · doi:10.1002/ese3.1121

Experimental investigation and performance evaluation of thermal energy management arrangements for robots

2022· article· en· W4221037460 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy Science & Engineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergyThermalMaterials scienceWoolHeat transferComposite materialThermal energyThermal management of electronic devices and systemsNuclear engineeringPolyurethaneMechanical engineeringEnvironmental scienceProcess engineeringThermodynamicsEngineering

Abstract

fetched live from OpenAlex

Abstract In this study, thermal energy management systems with the choices of three different thermal insulating materials are experimentally investigated for robotic applications. These insulating materials are stone wool, fiberglass and extruded polyurethane with air cooling and heating system which are evaluated in the low and high temperature environments to really assess the thermal behavior and performance in such extreme ambient conditions. In this regard, thermodynamic and heat transfer modeling studies are undertaken to investigate various performance parameters, including energy and exergy efficiencies. The experimental results showed that energy efficiencies of the thermal management methods are obtained 46.34% for stone wool, 31.15% for fiberglass, and 44.3% for air cooling system at 40°C. Moreover, the exergy efficiencies are 12.6% for stone wool, 15.08% for fiberglass, 18.91% for extruded polyurethane, and 3.86% for air cooling system.

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.

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)0.002
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 score0.224
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.025
GPT teacher head0.232
Teacher spread0.207 · 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