MétaCan
Menu
Back to cohort
Record W4322831932 · doi:10.5281/zenodo.7693875

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

2020· article· en· W4322831932 on OpenAlexfundno aff
Weiran Chen, Tian Liang, Venkata Dinavahi

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmulationTransient (computer programming)SubmarineHardware-in-the-loop simulationPower (physics)Loop (graph theory)Nuclear powerComputer scienceEmbedded systemEngineeringElectrical engineeringPhysicsOperating systemMarine engineering

Abstract

fetched live from OpenAlex

A nuclear submarine has significant performance advantages over the conventional diesel-electric submarine. Its powerful nuclear propulsion can be operated independently of air and does not<br> 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 microsecond time-step and device-level transients (validated by SaberRD) at 400 nanosecond 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 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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score1.000

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)0.0010.003

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 head0.021
GPT teacher head0.212
Teacher spread0.191 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicReal-time simulation and control systemsFrench-language works237,207