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Record W4389540996 · doi:10.17118/11143/20994

Design of an experimental test fixture for investigations on earcanalthermal power capability and thermal comfort

2023· article· en· W4389540996 on OpenAlexaff
Tigran Avetissian, Aidin Delnavaz, Jérémie Voix

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversité du Québec à MontréalÉcole de Technologie Supérieure
Fundersnot available
KeywordsFixtureThermalTest fixtureTest (biology)Power (physics)Computer scienceEngineeringAutomotive engineeringMaterials scienceMechanical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

The growing need for and the perpetual development of new hearables, as cochlear implants or as digital hearing protectors, have stimulated research interest in enhancing these devices’ ergonomics and power management. Given the constraints on the latter imposed by the energy efficiency of state-of-the-art electronics, energy harvesting has become a promising alternative to increase the battery autonomy of hearables. Various studies have highlighted the abundance of energy sources around the human head, as well as those generated by it. More specifically, the thermoregulation of the body produces heat, a portion of which is dissipated from the earcanal. This thermal energy could be converted in electricity by thermoelectric generators to complement the power requirements of in-ear wireless hearables. The contact surface of such a thermal energy harvester must be maximized with the earcanal wall, while the device remains comfortable for the user. Thermal comfort is then an essential aspect that has to be clearly established. This study therefore presents a mechatronic test fixture designed to replicate a thermoregulated earcanal. The results may help to estimate the amount of thermal energy that can be generated from the human earcanal. Furthermore, as thermal energy harvesting induces temperature reduction in the earcanal walls, the temperature evolution of the fixture will be evaluated for further investigation on earcanal thermal comfort. The experimental mechatronic test fixture is based on the G.R.A.S. 45CB Acoustic Test Fixture, featuring cylindrical earcanals. The head’s thermal inertia is ensured by a solid steel bloc, heated by two resistive heating strips. The heating strips are powered by a controller set to function with a temperature target. Cold water, controlled by the vapor-water-ice triple point equilibrium, is circulated at a controlled flow rate through a hollow earplug placed within the earcanal of the mechatronic test fixture. The thermoregulation power is then electrically and thermically extracted from the experimental setup through a data acquisition system monitoring the heating strips’ consumed power as well as the temperatures in the earcanal, at the cold-water inlet and outlet.A theoretical thermoelectrical model of the mechatronic test bench is established using lumped-model elements. The experimental results are compared to those of the model and the thermal power capabilities for energy harvesting in a human earcanal are discussed.

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)0.002
metaresearch head score (Gemma)0.003
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: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.241
Teacher spread0.220 · 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
GenreMethods

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

Citations0
Published2023
Admission routes1
Has abstractyes

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