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Record W4211105636 · doi:10.3390/robotics11010023

A New Hyperloop Transportation System: Design and Practical Integration

2022· article· en· W4211105636 on OpenAlexafffund
Mohammad Abdul Bhuiya, Md Mohiminul Aziz, Fariha Mursheda, Ryan Lum, Navjeet Brar, Mohamed Z. Youssef

Bibliographic record

VenueRobotics · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsOntario Tech University
FundersTransport Canada
KeywordsPropulsionBattery (electricity)Linear motorInverterSystems designState of chargeControl engineeringState (computer science)EngineeringSynchronous motorSystem integrationAutomotive engineeringComputer scienceVoltageElectrical engineeringSystems engineeringAerospace engineering

Abstract

fetched live from OpenAlex

This paper introduces a new Hyperloop transportation system’s design and implementation. The main contribution of this paper is the design and integration of propulsion components for a linear motion system, with battery storage. The proposed Hyperloop design provides a high-speed transportation means for passengers and freights by utilizing linear synchronous motors. In this study, a three-phase inverter was designed and simulated using PSIM. A prototype of this design was built and integrated with a linear synchronous motor. The operation of full system integration satisfies a proof-of-concept design. A study of the inverter system in conjunction with a linear synchronous motor for a ridged Hyperloop system is made. The prototype of this system achieves propulsion for the bidirectional movements. Battery state of charge simulation results are given in a typical motoring and braking scenario.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.224
Teacher spread0.203 · 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 designBench or experimental
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

Citations13
Published2022
Admission routes2
Has abstractyes

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