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Record W4376113903 · doi:10.1177/03064190231174438

On the origin of the ground effect

2023· article· en· W4376113903 on OpenAlexaff
Lisa Long, Jaime G. Wong

Bibliographic record

VenueInternational Journal of Mechanical Engineering Education · 2023
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGround effect (cars)AirfoilCommon groundCurvatureLift (data mining)Simple (philosophy)SolverCirculation (fluid dynamics)VortexComputer scienceMechanicsMathematicsPhysicsGeometryAerospace engineeringEngineeringPsychologySocial psychologyEpistemology

Abstract

fetched live from OpenAlex

The ground effect is well known to pilots and aerodynamicists alike. However, the current explanations found in undergraduate (and pilot-focused) textbooks can be inconsistent, often attributing the phenomena to the interaction between tip vortices at the ground. Others invoke the method of images to show that, when the flow is forced to have a straight streamline on the ground, ground pressure must increase. These must prescriptively choose an airfoil circulation. Meanwhile, a simple panel code can be used to show both that the lift on an airfoil in ground effect is significantly two-dimensional, and that the circulation about an airfoil near the ground is not constant. In particular, circulation will be found to grow as altitude decreases, magnifying the ground effect. A simple graphical panel method solver is provided, such that this exercise is accessible to students without the longer task of writing a panel code for themselves. This exercise can provide students with greater insight into the Kutta condition, the method of images, and panel methods themselves. The resulting streamline pattern can also be used to explain the phenomenon to more general audiences, by observing the relationship between lift and streamline curvature.

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 score0.423
Threshold uncertainty score0.170

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 science0.0010.000
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 head0.010
GPT teacher head0.279
Teacher spread0.269 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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