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Record W2936960798 · doi:10.1109/ecai.2018.8678975

The simulation of crude oil extraction in Canadian pumping with the DTC electric drive in a wide range of operating frequencies

2018· article· en· W2936960798 on OpenAlexaboutno aff
Boris Siro, Alexandru Săvulescu, Cornel Ianache

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsnot available
Fundersnot available
KeywordsRange (aeronautics)Automotive engineeringComputer scienceGenerator (circuit theory)Energy (signal processing)Electric motorInduction motorWork (physics)VoltageBrakeElectric machineEngineeringElectrical engineeringMechanical engineeringPower (physics)PhysicsStator

Abstract

fetched live from OpenAlex

The electric drive of field oil equipment in Canadian version requires that the electric drive's machine work, within each complete stroke of crude oil extraction, in a permanent dynamic regime, both in motor and generator mode or more precisely in that of an electromagnetic brake with a certain alternative of energy recovery mode. Under these circumstances, the present paper studies the possibilities of simulating of the electric drive with induction machine by using the DTC technique and analyzes the results obtained both on the quality of electric drive and on the level of energy parameters. The main conclusion is that the DTC drive version is valid for deployment in complex technologies when a certain level of speed has to be achieved and maintained with great accuracy and in a fairly wide range.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.669

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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