MétaCan
Menu
Back to cohort
Record W2801113061 · doi:10.1139/tcsme-2017-1023

AN INTEGRATED NUMERICAL AND EXPERIMENTAL INVESTIGATION ON IMPROVING ACOUSTIC AND COOLING PERFORMANCES OF A HIGH POWER MOTOR

2017· article· en· W2801113061 on OpenAlexvenueno aff
Sheam-Chyun Lin, Ming-Chiou Shen, Chen-Min Li, Fu-Yin Wang, Hung-Cheng Yen

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNoise (video)Rotor (electric)Reduction (mathematics)Power (physics)Range (aeronautics)Noise reductionEnclosureComputational fluid dynamicsAutomotive engineeringMockupThermalMechanical engineeringAcousticsComputer scienceNuclear engineeringEngineeringPhysicsMathematicsElectrical engineeringAerospace engineeringThermodynamics

Abstract

fetched live from OpenAlex

This research focuses on noise reduction of a 175-Hp motor under the same cooling need. First, the electromagnetic field is calculated numerically to provide the total losses to be 5.7 KW. Later, based on the numerical predication, the appropriate design, which consists of an inline fan rotor with a 2-cm diameter reduction and a streamlined housing, successfully reduce the noise by 3–5 dBA over various locations under the 3,000 rpm. Furthermore, the motor mockup is manufactured for testing, and result indicates that a similar noise reduction (4–7 dBA) is obtained while an acceptable deviation range between CFD and test results is observed. Consequently, this design and analysis tool offers a rigorous and systematic scheme for the thermal management on the high-power motor.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.620

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 head0.009
GPT teacher head0.198
Teacher spread0.190 · 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 teacher head, 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicElectric Motor Design and AnalysisFrench-language works237,207