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Record W2099259720 · doi:10.1109/tmag.2005.854990

Three-dimensional reluctance network analysis considering an iron loss characteristic for an EIE-core variable inductor

2005· article· en· W2099259720 on OpenAlex
K. Nakamura, S. Hayakawa, S. Akatsuka, Takashi Ohinata, Kazuo Minazawa, O. Ichinokura

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.

Bibliographic record

VenueIEEE Transactions on Magnetics · 2005
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsMagnetic reluctanceInductorControl theory (sociology)Magnetic coreCore (optical fiber)AC powerMagnetic circuitNonlinear systemVoltageVariable (mathematics)InductanceHysteresisTopology (electrical circuits)Computer scienceMechanicsPhysicsMathematicsMagnetElectrical engineeringEngineeringElectromagnetic coilMathematical analysisTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a quantitative analysis method for an EIE-core variable inductor, which is applied as a voltage regulator in an electric power system. We propose a three-dimensional nonlinear reluctance network analysis (RNA) model of an EIE-core considering magnetic hysteresis. The RNA model of the core is coupled with external electric circuits, in order to simulate an EIE-core variable inductor. Using the coupled model, we can calculate the operating characteristics of the variable inductor including an iron loss. Furthermore, we describe the development of a trial 6.6 kV-300 kVA reactive power compensator using an EIE-core.

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 categoriesInsufficient payload (model declined to judge)
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 score0.342
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.259
Teacher spread0.224 · 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