Research on transient aerodynamic characteristics of windshield wipers of vehicles
Bibliographic record
Abstract
Purpose The main purpose of this paper is to gain a better understanding of the transient aerodynamic characteristics of moving windshield wipers. In addition, this paper also strives to illustrate and clarify how the wiper motion impacts the airflow structure; the aerodynamic interaction of two wipers is also discussed. Design/methodology/approach A standard vehicle model proposed by the Motor Industry Research Association and a pair of simplified bone wipers are introduced, and a dynamic mesh technique and user-defined functions are used to achieve the wiper motion. Finite volume methods and large eddy simulation (LES) are used to simulate the transient airflow field. The simulation results are validated through the wind tunnel test. Findings The results obtained from the study are presented graphically, and pressure, velocity distributions, airflow structures, aerodynamic drag and lift force are shown. Significant influences of wiper motion on airflow structures are achieved. The maximum value of aerodynamic lift and drag force exists when wipers are rotating and there is a certain change rule. The aerodynamic lift and drag force when wipers are rotating downward is greater than when wipers are rotating upward, and the force when rotating upward is greater than that when steady. The aerodynamic lift and drag forces of the driver-side wiper is greater than those of the passenger-side wiper. Originality/value The LES method in combination with dynamic mesh technique to study the transient aerodynamic characteristics of windshield wipers is relatively new.
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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 | 0.000 | 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) | 0.001 | 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".