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Record W4317583590 · doi:10.2514/6.2023-0910

Modeling of the Blackbird Wind-Powered Ground Vehicle

2023· article· en· W4317583590 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsWind powerAerospace engineeringEnvironmental scienceMarine engineeringComputer scienceAeronauticsAutomotive engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-0910.vid The Blackbird vehicle is a wind-powered ground vehicle purported to be capable of propelling itself directly downwind faster than the wind. This capability, demonstrated in 2010, has proven to be controversial because it is difficult to understand how a vehicle travelling faster than the surrounding air can collect energy from that air. This paper presents a mathematical model and corresponding simulation of the Blackbird vehicle. Our simulation results demonstrate that this vehicle should, indeed, be capable of travelling directly downwind much faster than the ambient wind speed. The simulation results show good correspondence with the few available experimental results. The simulation is also used to provide pointers to further improve the vehicle's performance. It is found that using variable gearing could improve the vehicle's acceleration, though would not significantly improve its terminal velocity. A sensitivity analysis is performed on the losses (rolling resistance, air drag and transmission efficiency) and it is found that improvement in the transmission efficiency would lead to the greatest increase in terminal velocity.

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.

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 score0.010
Threshold uncertainty score0.531

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 head0.010
GPT teacher head0.195
Teacher spread0.184 · 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