报告题目：Wind Effects on Bluff Bodies: Reduced-Order Modeling

报 告 人： Teng Wu Assistant Professor

Bio: Teng Wu is an assistant professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo. He obtained his Bachelor's Degree in Civil Engineering in 2007 from Tongji University, PROC. During that period he also completed the degree in Financial Engineering Minor at Fudan University, PROC. Dr. Wu earned his Master of Engineering in Bridge Engineering in 2010 from Tongji University, and a Ph.D. in 2013 from the University of Notre Dame.

时 间： 2015年7月7日（周二）上午9：00

地 点： 77779193永利官网风洞实验室（六号楼风洞210会议室）

Abstract:

Wind effects on structures governed by the Navier-Stokes equations are not adequately represented by the conventional linear analysis framework. This shortcoming is becoming important for contemporary structures, as their increasing span-lengths and heights make them more sensitive to nonlinear and unsteady aerodynamic/aeroelastic load effects.The primary goal of this seminar is to discuss effective analysis tools for better understanding and capturing nonlinear and unsteady features concerning bluff-body aerodynamics (gust-induced effects) and aeroelasticity (motion-induced effects) with immediate applications to the assessment of wind-induced effects on flexible bridges, stay cables, super tall buildings, airfoils in the transonic region or with high angle of attack, and wind turbines near dynamic stall conditions. These features are not fully captured in the state-of-the-art analysis procedures. To accomplish this goal, a systematic approach is detailed that focuses on identification and characterization of nonlinearity and unsteadiness in bluff-body aerodynamics and aeroelasticity, assessment of their impact on the performance, and development of an advanced analysis framework for modeling and analysis. In view of the complexity and intractability of nonlinear and unsteady fluid-structure interactions using governing equations of fluid and structural motions, reduced-order models are utilized and their efficacy is assessed.