自由流
翼型
机械
NACA翼型
升力系数
物理
风洞
攻角
层流
气动中心
Lift(数据挖掘)
雷诺数
失速(流体力学)
升阻比
流动分离
空气动力学
降低频率
相对风
旋涡脱落
经典力学
俯仰力矩
空气动力
振幅
计算流体力学
起动涡流
后掠翼
阻力
阻力系数
涡流
压力系数
控制理论(社会学)
势流
气泡
数学
边界层
前沿
航空航天工程
流量(数学)
作者
Luca Damiola,Mark Runacres,Tim De Troyer,Nicholas J. Kay
出处
期刊:Journal of Aircraft
[American Institute of Aeronautics and Astronautics]
日期:2025-10-09
卷期号:: 1-14
摘要
This work combines wind tunnel experiments and numerical simulations to investigate the aerodynamic behavior of a symmetrical airfoil subjected to streamwise gusts. Specifically, this research examines the effect of sinusoidal freestream velocity oscillations on the unsteady lift of a NACA 0012 airfoil at a mean Reynolds number of 120,000. Experiments were conducted in a large closed-loop wind tunnel equipped with a louver system to generate a sinusoidal freestream velocity around the stationary wing model. Computational fluid dynamics simulations were conducted in parallel, although with the airfoil undergoing a streamwise oscillatory motion while the freestream velocity was held constant. This study investigates the impact of the surging frequency in pre-stall, post-stall, and near-stall flow conditions, considering velocity oscillations with an amplitude of 25% of the mean flow. For each scenario, quasi-steady cases were also analyzed to isolate steady effects from those due to flow acceleration. Results revealed lift enhancement during the deceleration phase under attached-flow conditions, with the converse occurring post-stall. The former was associated with cyclic shifts in the laminar separation bubble, the dynamics of which were largely driven by the time-varying pressure gradient. At the near-stall angle of attack, bubble bursting was observed during deceleration, leading to the formation and shedding of a leading-edge vortex. While Greenberg’s theoretical model was seen to be sufficient for predicting the lift coefficient magnitude and phase under attached-flow conditions, it proved ineffective when flow separation was present.
科研通智能强力驱动
Strongly Powered by AbleSci AI