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Record W3009005634 · doi:10.1139/tcsme-2019-0120

Drag-based aerodynamic braking system for the Hyperloop: a numerical study

2020· article· en· W3009005634 on OpenAlexvenueno aff
Mohammed Raihan Uddin, Tahsin Sejat Saniat, Sayedus Salehin, M. Hamidur Rahman

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
Fundersnot available
KeywordsAerodynamic dragDragAerodynamicsBrakeAutomotive engineeringPoint of deliveryAerospace engineeringTransonicDrag divergence Mach numberComputer scienceEngineering

Abstract

fetched live from OpenAlex

The Hyperloop promises to revolutionize the transport infrastructure of the 21st century by reducing travel time and allowing people to reach transonic speed on land. It carries with it the hope of a sustainable transportation system during an era of global energy crisis. Overall passenger safety in a high-speed pod necessitates a reliable braking system. This paper introduces the possibility of utilizing aerodynamic drag in the Hyperloop, anticipated to operate at high Mach and low Reynolds flow regime, to attenuate the speed of the pod. Numerical analysis was conducted to investigate the effect of incorporating an aerodynamic brake at different pod velocities (100, 135, and 150 m/s) and deployment angles (30°, 45°, 60°, and 90°). A detailed comparison between the proposed aerodynamic braking system (AeBS) and existing braking systems for the Hyperloop has been presented in this paper. The results demonstrate an increase in drag value of the pod by 3.4 times using a single 0.15 m 2 brake plate. When the brake plate was fully deployed at a pod velocity in excess of 112 m/s, the aerodynamic drag-based braking systems was shown to be more effective than the contemporary eddy current braking system.

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: none
Teacher disagreement score0.985
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.216
Teacher spread0.201 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicAerodynamics and Fluid Dynamics ResearchFrench-language works237,207