Characterization of onset of dynamic stall for low Mach number flows

动态失速是机动气动物体(如昆虫/鸟翼、直升机/风力机叶片、飞机机翼)中的一种常见现象，其特征是非定常气动载荷的大幅增加。这种载荷可能会导致直升机和风力涡轮机中的机翼和旋翼叶片发生灾难性和疲劳故障。影响动态失速开始的流动物理仍不清楚，并且高度依赖于由几何形状、机动类型、俯仰率、雷诺数和马赫数组成的大参数空间。该项目将利用新的数据分析技术与高保真模拟相结合来了解动态失速开始。对失速开始有一个清晰的认识和描述，将为有效的控制和缓解动态失速策略铺平道路。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影响。此外，通过课程改进和招募妇女和代表性不足的少数民族，预计将产生更广泛的影响。该项目的目标是通过数据驱动的分析方法和实验验证，使用高效的流体模拟来了解和描述动态失速的开始。具体目标是：(1)通过使用一套基于物理、数据驱动和机器学习的方法来表征失速起始，从而确定失速起始的通用参数/标记物；(2)使用新的模拟策略，通过采用新的四维时空框架来去除瞬变并显著减少计算时间，并通过使用自适应、异步、贝叶斯采样策略来有效地探索参数空间，来有效地探索大参数空间；以及(3)使用实验数据和额外的模拟来验证通用参数，并研究声辐射在失速起始中的作用。这项拟议的工作包括在识别触发动态失速开始的潜在物理现象方面取得的进展，开发一种有效消除流动瞬变的创新方法，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NSF的法定使命，并通过使用基金会的智力优势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.