Forward-swept wings offer unique advantages in the aerodynamic performance of air vehicles. However, the low-Reynolds-number characteristics of such wings have not been explored in the past. In this work, we numerically study laminar separated flows over forward-swept wings with semi aspect ratios $sAR=0.5$ to 2 at a chord-based Reynolds number of 400. Forward-swept wings generate wakes that are significantly different from those of backward-swept wings. For low-aspect-ratio forward wings, the wakes remain steady due to the strong downwash effects induced by the tip vortices. For larger aspect ratio, the downwash effects weaken over the inboard regions of the wing, allowing unsteady vortex shedding to occur. Further larger aspect ratio allows for the formation of streamwise vortices for highly-swept wings, stabilizing the flow. Forward-swept wings can generate enhanced lift at high angles of attack than the unswept and backward-swept wings, with the cost of high drag. We show through force element analysis that the increased lift of forward-swept wings is attributed to the vortical structure that is maintained by the tip-vortex-induced downwash over the outboard region of wing span. The current findings offer a detailed understanding of the sweep effects on laminar separated flows over forward-swept wings, and invite innovative designs of high-lift devices.
Vortices, turbulence, and center of pressure in flow over pitching swept wings
Center for Fluid Mechanics, School of Engineering, Brown University（布朗大学工程学院流体力学中心）；Department of Mechanical Engineering, University of California, Riverside（加州大学河滨分校机械工程系）
SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design
School of Aerospace Engineering, Tsinghua University, Beijing, China（清华大学北京航空航天工程学院）；School of Computation, Information and Technology, Technical University of Munich, Germany（慕尼黑技术大学计算、信息与技术学院）
Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows
Department of Aerospace Engineering, Tohoku University（东京东京大学航空工程系）；Department of Mechanical and Aerospace Engineering, Tokyo University of Science（东京科学大学机械与航空航天工程系）
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
University of Washington（华盛顿大学）；AI Institute in Dynamic Systems, University of Washington（华盛顿大学人工智能研究所）；RWTH Aachen University（亚琛工业大学）；Inha University（仁荷大学）；University of Michigan（密歇根大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Technical University of Munich（慕尼黑工业大学）；Arts et Métiers Institute of Technology（国立高等工程技术学校）；CNAM（法国国立工艺学院）；DynFluid, HESAM Université（HESAM大学流体动力学实验室）；Munich Institute of Integrated Materials, Energy and Process Engineering, Technical University of Munich（慕尼黑工业大学综合材料、能源与工艺工程研究所）；JARA Center for Simulation and Data Science, RWTH Aachen University（亚琛工业大学JARA模拟与数据中心）；MediaTek Research（联发科技研究中心）；German Center for Neurodegenerative Diseases（德国神经退行性疾病中心）
