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Record W2616496519 · doi:10.1109/apec.2017.7931025

A cost effective magnetic/electronic design for the water pump application drive: Analysis, design, and experimentation

2017· article· en· W2616496519 on OpenAlexaff
Ahmed Abdelrahman, Mohamed Z. Youssef

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPrinted circuit boardDC motorComputer scienceMagnetic circuitSoftwareVoltageMagnetPower (physics)Battery (electricity)Automotive engineeringElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

In this paper, a thorough design for a cost effective BLDC drive in a water pump application has been performed. The novelty of this study can be interpreted in two facets. Firstly, a simple two-layer printed circuit board has been used to implement the sensor-less control of a brushless direct current machine. This new printed circuit board provides power and control voltages, thus eliminates the need for a control battery. Secondly, the brushless DC (BLDC) motor laminations have been manufactured in the same way using the same ferrite materials as existed in the conventional induction motor instead of using permanent magnet material which affects the total cost drastically. A detailed finite-element analysis has been executed through ANSOFT Maxwell's software and then an experimental verification has been carried out for the proposed drive. The overall wire to water efficiency in addition to the BLDC motor one was found to be increased simultaneously with a significant cost reduction for the proposed design.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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
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

Citations4
Published2017
Admission routes1
Has abstractyes

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