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Record W2935220514 · doi:10.4271/2019-01-0644

Investigation of Drag Reduction Technologies for Light-Duty Vehicles Using Surface, Wake and Underbody Pressure Measurements to Complement Aerodynamic Drag Measurements

2019· article· en· W2935220514 on OpenAlex
Fenella de Souza, Arash Raeesi, Marc Belzile, Cheryl Caffrey, Andreas Schmitt

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

VenueSAE International Journal of Advances and Current Practices in Mobility · 2019
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsTransport CanadaNational Research Council Canada
Fundersnot available
KeywordsDragWakeMarine engineeringAerodynamic dragAerodynamicsWind tunnelAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Amulti-year, multi-vehicle study was conducted to quantify the aerodynamic drag changes associated with drag reduction technologies for light-duty vehicles. Various technologies were evaluated through fullscale testing in a large low-blockage closed-circuit wind tunnel equipped with a rolling road, wheel rollers, boundary-layer suction and a system to generate road-representative turbulent winds. The technologies investigated include active grille shutters, production and custom underbody treatments, air dams, wheel curtains, ride height control, side mirror removal and combinations of these. This paper focuses on mean surface-, wake-, and underbody-pressure measurements and their relation to aerodynamic drag. Surface pressures were measured at strategic locations on four sedans and two crossover SUVs. Wake total pressures were mapped using a rake of Pitot probes in two cross-flow planes at up to 0.4 vehicle lengths downstream of the same six vehicles in addition to a minivan and a pick-up truck. A smaller rake was used to map underbody total pressures in one cross-flow plane downstream of the rear axle for three of these vehicles. The results link drag reduction due to various technologies with specific changes in vehicle surface, rear underbody and wake pressures, and provide a database for numerical studies. In particular, the results suggest that existing or idealized prototype technologies such as active grille shutters, sealing the external grille and ride height control reduce drag by redirecting incoming flow from the engine bay or underbody region to smoother surfaces above and around the vehicle. This mechanism can enhance the reduction in wheel drag due to reduced wheel exposure at lowered ride height. Sealing the external grille was found to redirect the flow more efficiently than closing the grille shutters, and resulted in greater drag reduction. Underbody treatments were also found in some cases to redistribute the flow around the vehicle to reduce pressure drag in addition to underbody friction drag. The magnitude and spatial extent of the measured pressure changes due to the various technologies were often consistent with the amount of drag reduction.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.517

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 communication0.0000.001
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 head0.079
GPT teacher head0.372
Teacher spread0.293 · 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