Investigating the Impacts of Longitudinal and Lateral Distances on theLift and Drag Coefficients of two Closely Moving Vehicles
Bibliographic record
Abstract
The limitations in using conventional wind tunnels and rapid developments in computer hardware have led to considerable efforts to study the vehicle aerodynamics using the computational fluid dynamic (CFD) capabilities for the last decade.The main objective of this paper is to investigate the changes in lift and drag coefficient of two closely moving vehicles subject to their lateral and longitudinal distances.We investigate two longitudinal distances of 0.2 and 2 m and two lateral distances of 0.2 and 1 m in this study.Simplified vehicle geometry, say the standard Ahmed body model, is used as the benchmark vehicle to carry on the investigation.The CFD methods are used to compute the flow patterns around the vehicle.The investigation in longitudinal distance shows that the drag coefficient of both vehicles significantly decreases, specifically the front one.Also, the lift coefficients of both vehicles reduce, and this force transforms to downforce for the rear vehicle.The investigation in lateral distance indicates that the drag coefficient depends on the attributed distance; however, the lift coefficient reduces in both distances.In the lateral distance, one expects equal coefficients for both vehicles; however, the results show that there is slight difference between them.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".