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Record W4317634092 · doi:10.2514/6.2023-2141

Passive and active flow control effects in the platoon and overtaking maneuvers

2023· article· en· W4317634092 on OpenAlex
Saber Karimi, B. Mohammadikalakoo, Paolo Schito

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
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsLakehead University
Fundersnot available
KeywordsOvertakingPlatoonFlow control (data)DragActuatorLift (data mining)EngineeringFlow (mathematics)Automotive engineeringComputer scienceSimulationControl theory (sociology)Aerospace engineeringControl (management)PhysicsElectrical engineeringMechanicsTelecommunications

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-2141.vid The current numerical study is dedicated to investigating the effect of passive, active, and combined flow control techniques on the performance of the vehicles in different maneuvers including, platoon and overtaking on critical highway velocity (70 miles per hour) for a reference bluff body vehicle called Ahmed body. The target passive flow control method is an innovative technique called Rear Linking Tunnels (RLTs), introduced previously by the group of authors. Studying the effect of the Single Dielectric Barrier Discharge Actuator (SDBD) as an active flow control method and its combined effect with RLTs on the drag and lift of controlled vehicles and surrounding vehicles in various maneuvers is one of the main aims of this research study.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.218
Teacher spread0.214 · 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