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Record W2290711170 · doi:10.1109/tmag.2015.2477083

Analytical Calculation of the Magnetic Vector Potential of an Axisymmetric Solenoid in the Presence of Iron Parts

2015· article· en· W2290711170 on OpenAlexaff
Hossein Vahid Alizadeh, Benoît Boulet

Bibliographic record

VenueIEEE Transactions on Magnetics · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsSolenoidMagnetic potentialBoundary value problemComputationVector potentialPhysicsMagnetostaticsMagnetic fieldElectromagnetic coilMagnetic coreMaxwell's equationsMathematical analysisFinite element methodMechanicsComputer scienceClassical mechanicsMathematicsAlgorithmThermodynamics

Abstract

fetched live from OpenAlex

This paper presents an analytical calculation method for the computation of the magnetic vector potential of an axisymmetric solenoid in the presence of an iron shield and a ferromagnetic core. The proposed method can be used as a fast analytical computation technique for accelerating the design optimization process of the solenoid systems. In this paper, the analysis of the current carrying coil in the presence of the ferromagnetic materials is treated as a boundary value problem. The solution approach is based on partitioning the solution domain into distinct regions. The general form of the solution to Maxwell's equation in each region along with the corresponding boundary conditions is obtained using the Fourier analysis and the separation of variables. The final solution to the boundary value problem is constituted by considering the continuity of the magnetic vector potential, as well as the magnetic field, on the interfaces between the regions. Finally, the proposed analytical computation method is applied on an electromagnetic actuator. The magnetic vector potential is computed over the entire solution domain, and the result is compared with that of the finite-element method.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.223
Teacher spread0.208 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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