Investigating the impact of aerodynamic devices on drag reduction in atransit GO-Bus using CFD
Bibliographic record
Abstract
In today’s world, natural resources are becoming exhausted and a shift to more renewable alternatives is quickly approaching. The transition to modern transportation methods strives to lower carbon emissions from vehicles and improve efficiencies on existing transit. Drag is the largest phenomenon that all moving vehicles must adhere to optimize its travel. Lowering the drag coefficient and forces experienced during travelling can ensure vehicle aerodynamics is improved. Resulting in more efficient travel and reducing amount of fuel consumed. This research aims at investigating the effectiveness of two aerodynamic modifications on the MCI-D4500, a GOBus model in Ontario. The modifications consist of a spoiler added to the top of the bus and a diffuser at the bottom of the rear end. The primary objective of these devices is to decrease the drag coefficient of the bus, leading to an improvement in fuel efficiency and a reduction in carbon emissions. The study involved the creation of three-dimensional models using SOLIDWORKS software and the use of ANSYS-Fluent to conduct computational fluid dynamics simulations. The simulation results were analyzed for both the original and modified bus designs, and drag and lift coefficients were determined at different speeds ranging from 40 to 120 km/hr. The addition of a spoiler on the top rear end of the bus yielded the greatest reduction in drag, with a decrease of 9.23%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".