Characterization of onset of dynamic stall for low Mach number flows

动态失速是机动气动体（如昆虫/鸟类翅膀、直升机/风力涡轮机叶片、飞机机翼）中常见的一种现象，其特征是非定常气动载荷的大幅增加。这样的载荷会导致直升机和风力涡轮机的机翼和旋翼叶片发生灾难性的疲劳失效。影响动态失速发生的流动物理仍然不清楚，并且高度依赖于由几何形状、机动类型、俯仰速率、雷诺数和马赫数组成的大参数空间。该项目将结合新颖的数据分析技术和高保真度模拟来了解动态失速。对失速开始的清晰理解和特征将为有效控制和减轻动态失速的策略铺平道路。该项目对包括固定翼飞机和旋转机械在内的广泛应用具有变革性意义。此外，通过加强课程和招收妇女和代表性不足的少数民族，预计将产生更广泛的影响。该项目的目标是利用数据驱动分析方法和实验验证的高效流体模拟来了解和表征动态失速的开始。具体目标是：(1)利用一套基于物理的、数据驱动的和机器学习的方法，通过表征失速发生来识别失速发生的通用参数/标记；(2)利用新颖的仿真策略，通过采用新颖的4-D时空框架来有效地探索大参数空间，以消除瞬态并显著减少计算时间，并通过使用自适应、异步、贝叶斯采样策略有效地探索参数空间；3)通过实验数据和附加仿真验证了通用参数，并研究了声辐射对失速发生的影响。提出的工作包括识别触发动态失速开始的潜在物理现象的智力进步，开发有效消除流动瞬态的创新方法，以及研究声学对层流分离气泡动力学的影响。该项目将利用爱荷华州立大学正在进行的外展和劳动力发展活动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Dynamic stall is a common occurrence in maneuvering aerodynamic bodies (e.g., insect/bird wings, helicopter/wind turbine blades, aircraft wings) which is characterized by large increases in unsteady aerodynamic loads. Such loads can lead to catastrophic and fatigue failure of aircraft wings and rotor blades in helicopters and wind turbines. The flow physics affecting the onset of dynamic stall remains unclear and is highly dependent on a large parameter space consisting of geometry, type of maneuver, pitch rate, and Reynolds and Mach numbers. The project will utilize a combination of novel data analysis techniques with high fidelity simulations to understand dynamic stall onset. A clear understanding and characterization of stall onset will pave the way for effective strategies to control and mitigate dynamic stall. The project has transformative implications for a broad range of applications including fixed-wing aircraft and rotating machinery. In addition, broader impacts are expected through curricular enhancements and recruiting women and underrepresented minorities.The goal of this project is to understand and characterize the onset of dynamic stall using efficient fluid simulations with data-driven analysis methods and experimental validation. The specific objectives are: (1) identify a universal parameter/marker of stall onset through characterization of stall incipience using a suite of physics-based, data-driven, and machine learning approaches, (2) use novel simulation strategies to effectively explore the large parameter space by employing a novel 4-D, space-time framework to remove transients and significantly reduce computation time and by efficiently exploring the parameter space using an adaptive, asynchronous, Bayesian sampling strategy, and 3) validate the universal parameter using experimental data and additional simulations, and investigate the role of acoustic radiation on stall onset. The proposed work consists of intellectual advances in identifying the underlying physical phenomenon that triggers dynamic stall onset, developing an innovative approach to efficiently remove flow transients, and investigating the effect of acoustics on laminar separation bubble dynamics. The project will leverage outreach and workforce development activities ongoing at Iowa State University.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.