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Record W3106879829 · doi:10.1109/ojies.2020.3036731

Comprehensive Real-Time Hardware-In-the-Loop Transient Emulation of MVDC Power Distribution System on Nuclear Submarine

2020· article· en· W3106879829 on OpenAlexafffund
Weiran Chen, Tian Liang, Venkata Dinavahi

Bibliographic record

VenueIEEE Open Journal of the Industrial Electronics Society · 2020
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmulationSubmarineTransient (computer programming)EngineeringHardware-in-the-loop simulationPropulsionPower (physics)Embedded systemNuclear powerElectrical engineeringComputer scienceAerospace engineeringMarine engineering

Abstract

fetched live from OpenAlex

A nuclear submarine has significant performance advantages over the conventional dieselelectric submarine. Its powerful nuclear propulsion can be operated independently of air and does not require refueling throughout the 25-year life span. Recent progresses of medium voltage direct current (MVDC) techniques applied in industry push the energy-saving and extremely long-time operation to a new stage with a feasible solution of smaller scale nuclear reactor embedded inside the vessel. Thus, there is a need to investigate the new MVDC-based nuclear submarine power distribution system and its real-time hardware-in-the-loop (HIL) emulation to test the control systems, device stresses, etc. This paper proposes a comprehensive MVDC-based nuclear submarine power distribution system emulated in real-time on a multi-FPGA hardware platform with system-level transients (validated by Matlab/Simulink) at 20 μs time-step and device-level transients (validated by SaberRD) at 400 ns time-step.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.028
GPT teacher head0.238
Teacher spread0.210 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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