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Record W3121678881 · doi:10.1139/tcsme-2020-0038

Study of incident drag response of the Hyperloop pod based on change in aerodynamic brake plate orientation

2021· article· en· W3121678881 on OpenAlexvenueno aff
Shadman Mahmud, Adib Adnan, Shahnoor Shamim Khan, M. Hamidur Rahman

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
Fundersnot available
KeywordsDragPoint of deliveryBrakeAerodynamicsMechanicsAccelerationBoundary layerMach numberAerodynamic dragFlow separationParasitic dragStructural engineeringAerospace engineeringEngineeringPhysicsMechanical engineeringClassical mechanics

Abstract

fetched live from OpenAlex

The concept of the Hyperloop has received significant attention, with expectations of it becoming a revolutionary and potentially the fastest mode of land transportation on the planet. The low-pressure tube through which the pod travels at expected speeds close to Mach 1.0, presents a unique case among other transport models, and braking of the pod is of critical importance if passenger safety protocols are to be maintained. The high-speed flow around the pod exerts high adverse pressure gradients on the pod surface, resulting in boundary layer separation, increasing drag, and affecting the acceleration of the pod. Numerical simulations have shown that the placement of an aerodynamic brake plate on the pod surface at the point at which boundary layer separation occurs provides the necessary drag required for safe deceleration. This study aimed to find the best angle for the aerodynamic brake positioned at a fixed point on the pod, allowing for the maximum generation of drag, using numerical simulations. After various trials, it was observed that angling the brake 15° backwards while increasing its length to keep the incident brake profile constant, the drag value obtained was the highest.

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 score0.149
Threshold uncertainty score0.927

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)0.0000.000

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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