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A Modular Real Time Simulation Solution for More Electric Aircraft Complex Architecture

2020· article· en· W3103809086 on OpenAlexaff
Quentin Dufour, Amine Yamane, Jean‐Nicolas Paquin, Kamal Al‐Haddad

Bibliographic record

VenueIECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society · 2020
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsOpal-Rt Technologies (Canada)École de Technologie Supérieure
Fundersnot available
KeywordsReal-time simulationField-programmable gate arrayElectric power systemComputer scienceHardware-in-the-loop simulationModular designConvertersDecoupling (probability)Power (physics)Embedded systemSimulationEngineeringControl engineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

This paper presents a Multi FPGA- based solution for More Electric Aircraft power systems real-time simulation. The proposed solution allows control and protection devices to be designed and tested in virtual power system before they can be implemented in a physical system. To demonstrate the effectiveness of the multi-FPGA solution, a model approximately representing the Boeing 787 More Electric Aircraft is considered. The B787 power system represents a challenge for any real time simulator. It includes a considerable number of fast and slow switching frequency converters, an extensive number of circuit breakers and very short cables that complicates the power decoupling task.Four complete feeders of the B787 power system are simulated using Dual-Kintex-7 FPGA boards. The controllers and mechanical parts of the Motorized Turbo Compressors are compiled, and real time simulated using a 3.5 GHz Intel processor. The performance of this overall Processor-In-The-Loop integration (PIL) is validated based on two criteria: 1) Evaluation of the results accuracy compared to the reference offline simulation. 2) Evaluation of the numerical stability of the simulation under steady state and fault conditions.

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 score0.214
Threshold uncertainty score0.894

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.245
Teacher spread0.209 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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