Numerical Simulation and Analysis of Aerodynamic Characteristics of Road Vehicles in Platoon
Bibliographic record
Abstract
With the spiking of fuel price and increasingly stringent emission regulation requirements, it brings a more daunting challenge for researchers and engineers to reduce the aerodynamic drag of road vehicles.When a vehicle is traveling in a platoon, the wake flow of the leading vehicle can affect the aerodynamic characteristics of the following vehicle.Due to the interaction of the flow field of the involved vehicles, the aerodynamic drag of each vehicle changes, which results in the alteration of the vehicle's fuel consumption.In the study, a single MIRA model was generated using CATIA software.The external flow field of the MIRA was imitated by CFD simulation.The numerical result of the drag coefficient was compared with the wind tunnel test results of Hunan University, China.The drag coefficient errors between the simulated value and the experimental result are less than 6%.It implies that the simulation and tests achieve a good agreement.The benchmark indicates that the numerical simulation method is reliable.By means of CFD simulation, we explored the effects of separation distance, the number of vehicles in the platoon, the shape of the vehicle, and the speed of vehicle platoon on the aerodynamic properties of vehicles in platooning.The results of the numerical simulation demonstrate that although the influences of the aforementioned parameters on the aerodynamic properties of leading and trailing vehicles in the platoon are different, but the average drag coefficient of vehicle platoon is lower than that of a single vehicle, which is beneficial to improve the fuel economy of vehicle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".