MétaCan
Menu
Back to cohort
Record W4297094633 · doi:10.1109/access.2022.3210140

Initial Propulsion System Study for the Futuristic Hyperloop Transportation System: Design, Modeling, and Hardware in the Loop Verification

2022· article· en· W4297094633 on OpenAlexafffund
Mohammad Abdul Bhuiya, Mohamed Z. Youssef

Bibliographic record

VenueIEEE Access · 2022
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsLakeridge HealthOntario Tech University
FundersTransport Canada
KeywordsPropulsionComputer scienceHardware-in-the-loop simulationController (irrigation)Power inverterControl engineeringDC motorSynchronous motorInverterEmbedded systemEngineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Recently, the proposal for a futuristic mode of transportation known as the Hyperloop has been popularized. Currently, there are only some reports regarding the design of the Hyperloop. More specifically, reports regarding the propulsion system design methodology for Hyperloop is minimal. Thus, this paper provides an initial steppingstone to modeling and simulation study for the propulsion system in a Hyperloop. The main contribution of this study is to provide a relatively simple design methodology for any future Hyperloop endeavors. This is shown using state of the art simulators to aid in designing the propulsion system. The design revolves around the linear synchronous motor based on field-oriented control through a three – phase inverter. PSIM is used to develop the model and design the full power system and controller. This includes the DC-DC converter, battery system model, three – phase inverter, and the motor controller. The motor used for modelling is a rotary permanent magnet synchronous motor. Finally, hardware in the loop technology is used to verify and validate the design. The controller design is tested through a Texas Instrument digital signal processor. The real time verification shows matching results with the offline simulation model.

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)0.001
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 score0.301
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.050
GPT teacher head0.287
Teacher spread0.237 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueIEEE AccessSame topicReal-time simulation and control systemsFrench-language works237,207