Aerodynamic Design of Heavy Vehicles Reporting Period January 15, 2004 through April 15, 2004
Bibliographic record
Abstract
Listed are summaries of the activities and accomplishments during this second-quarter reporting period for each of the consortium participants. The following are some highlights for this reporting period: (1) Experiments and computations guide conceptual designs for reduction of drag due to tractor-trailer gap flow (splitter plate), trailer underbody (wedges), and base drag (base-flap add-ons). (2) Steady and unsteady RANS simulations for the GTS geometry are being finalized for development of clear modeling guidelines with RANS. (3) Full geometry and tunnel simulations on the GCM geometry are underway. (4) CRADA with PACCAR is supporting computational parametric study to determine predictive need to include wind tunnel geometry as limits of computational domain. (5) Road and track test options are being investigated. All is ready for field testing of base-flaps at Crows Landing in California in collaboration with Partners in Advanced Transportation Highways (PATH). In addition, MAKA of Canada is providing the device and Wabash is providing a new trailer. (6) Apparatus to investigate tire splash and spray has been designed and is under construction. Michelin has offered tires with customized threads for this study. (7) Vortex methods have improved techniques for the treatment of vorticity near surfaces and spinning geometries like rotating tires. (8) Wind tunnel experiments on model rail cars demonstrate that empty coal cars exhibit substantial aerodynamic drag compared to full coal cars, indicating that significant fuel savings could be obtained by reducing the drag of empty coal cars. (9) Papers are being prepared for an exclusive conference session on the Heavy Vehicle DOE Aerodynamic Drag Project at the 34th AIAA Fluid Dynamics Conference in Portland, Oregon, June 28-July 1, 2004.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".