MétaCan
Menu
Back to cohort
Record W2525728949 · doi:10.11159/icmie16.122

Design and Deformation Analysis of Six-component Wheel Dynamometer

2016· article· en· W2525728949 on OpenAlexvenueno aff
Changhwan Shin, Hyunchang Yoo, Jinwon Joo

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsDynamometerComponent (thermodynamics)Deformation (meteorology)Computer scienceEngineeringAutomotive engineeringMaterials scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

Extended Abstract A wheel dynamometer is used to measure dynamic load that is transmitted from the road surface to vehicles while driving. In this paper, a six-component (three forces and three moments) wheel dynamometer utilizing shear deformation was designed and evaluated. The cross type structure having four or six radial bars was used as a basic sensing structure of the wheel dynamometer. The body of the dynamometer consists of a rigid central hub, a rigid outer annular ring and the radial bars extending between the hub and the ring. Although most of sensors are mounted conventionally to provide an output signal indicative of bending strains in the sensing bar, other forms of sensors such as those that provide an indication of shear strains was used in this paper for obtaining more stable signal against inevitable location error at bonding strain gauges. One aspect of the load transducer includes a structure of placing flexural members which are compliant for displacement of each corresponding radial bars. The flexural members on the end of sensing bars in the annular ring serve to produce sufficient shear strain of the sensing bars which lie at 90 degrees, but they can provide non-uniform distributions of shear strain. Since there is a possibility that such a non-uniform strain distribution can cause mutual coupling errors between the forces or moments, deformation analysis is needed thereof. Finite element analysis was performed to design the shape including flexural members and determine the size of sensing parts, and to predict output signals. In order to obtain adequate output signal and minimize coupling errors, the locations of strain gauges and their connection were determined so that Wheatstone bridge circuits with four or eight strain gauges would be balanced. Through the design modifications of the various shapes of flexural members, the coupling effect was minimized. The maximum capacity of the six-component wheel dynamometer designed in this paper is 25 kN in each force Fx, Fy, 20 kN

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.173
Teacher spread0.168 · 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 designSimulation or modeling
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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicTransport Systems and TechnologyFrench-language works237,207