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Record W3037136067 · doi:10.1177/1045389x20932220

An optical red green blue sensor for monitoring the degradation of magnetorheological fluids in flow-recirculating high-torque clutch actuators

2020· article· en· W3037136067 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2020
Typearticle
Languageen
FieldEngineering
TopicElevator Systems and Control
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsMagnetorheological fluidClutchActuatorTorqueMechanical engineeringEngineeringRotary actuatorAutomotive engineeringMaterials scienceStructural engineeringDamperElectrical engineering

Abstract

fetched live from OpenAlex

The aerospace industry is gradually moving toward “more electric aircraft” to reduce its environmental footprint. Developing all-electric actuators brings a reliability challenge because conventional geared-motor actuators are susceptible to jamming failures from their metal-to-metal gear contacts. Magnetorheological clutch actuators solve this challenge using a layer of fluid to transmit torque and are not exposed to potential jam failures, making them particularly attractive for high reliability applications such as primary flight controls. A key challenge that must be addressed for a widespread deployment of the magnetorheological fluid technology in aerospace is the ability to monitor magnetorheological fluid condition while it degrades in operation. To date, no such efforts have been reported in the literature. Recent studies on magnetorheological fluid durability have shown that mixing the magnetorheological fluid using a magnetic screw pump principle can significantly increase the life of the fluid by up to 50%. This article presents the design, prototyping, and testing of a proof-of-concept magnetorheological fluid condition monitoring sensor, capitalizing on the flow-recirculating properties of magnetic screw magnetorheological clutches to continuously provide a well-mixed and homogeneous fluid sample to the sensor. The proposed sensing principle uses optical red green blue sensor placed in the fluid circulation path to measure the fluid color during degradation. The sensor has been tested up to fluid failure on a high-torque (60 N m) magnetorheological clutch mounted on a fully instrumented durability test bench. Tests have been performed with two types of fluid: a commercially available fluid, the Lord 140CG, and a homemade fluid based on perfluoropolyether oil, the GPL-103. Results with the Lord fluid demonstrate a strong correlation between the decrease in torque-to-current performance with fluid brightness. The average of three aging tests on Lord 140CG fluid show a 12% ± 1% decrease in brightness at end-of-life regardless of operating conditions such as torque, shear rate, and dissipated power. These results suggest that, for the Lord 140CG fluid, brightness is directly linked to the fluid degradation state and independent of operating conditions, which makes it a more accurate metric to quantify durability than life dissipated energy since the latter can vary significantly depending on operating conditions. Tests made with the GPL-103 based fluid did not show such a strong correlation, which means that optical sensing of magnetorheological fluid condition must be carefully calibrated for each individual fluid and clutch design. Results from this study suggest that optical sensing is a relevant method to measure the magnetorheological fluid condition in flow-recirculating clutches.

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)0.000
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 score0.390
Threshold uncertainty score0.404

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 communication0.0000.000
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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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