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Record W4405385078 · doi:10.23977/jeeem.2024.070308

Feasibility Analysis of Submarine Debye Effect Magnetic Field Detection Based on Fluent

2024· article· en· W4405385078 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2024
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsnot available
Fundersnot available
KeywordsSubmarineFluentField (mathematics)Magnetic fieldPhysicsEngineeringMarine engineeringMechanicsComputational fluid dynamicsMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

High-salinity seawater contains a large number of sodium ions and chloride ions. These ions not only carry charges but also have different masses. The thrust generated by a submarine moving underwater will inevitably accelerate the ions and cause the centers of mass of positive and negative ions to separate, forming a polarization current. This, in turn, generates a special magnetic field known as the Debye Effect magnetic field. Can the Debye effect magnetic field be detected in relation to the movement speed of seawater ions? This paper establishes a simulation platform for submarine motion, and the results show that during the submarine's impact with the water flow, there will be fluctuations in seawater velocity. This indicates that it is feasible to detect the changes in the Debye effect magnetic field during submarine motion.

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 score0.384
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.002
GPT teacher head0.196
Teacher spread0.194 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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