Research

 CFD Simulation of a Double-celled, Single-vortex Tornado

Simulation of a Multi-vortex Tornado and its Multiple 

Attacking on a Gable-Roofed House

Influence of Surrounding Structures on Tornadic Wind Field 

by Including both the Civil Structure of Interest and 

its Surrounding Structures 

  CFD Simulation of the Large-scale Tornado Simulators

Being Built in WHAM Lab

  Lab and CFD Simulations of the Wind Effects of Hurricanes on a Dome Structure

 Two-way Coupled FSI Simulation for Flexible Structures

Condition Assessment of Civil Large-scale Space Structures under Operational or Multi-hazard Environments

Civil large-scale space structures such as sports stadiums, arenas and auditoriums are usually built for venues where hundreds or even thousands of people assemble. Failure of this type of structure may risk many lives. The objective of this research is to detect damage and instability in space structures under operational or multi-hazard environments. The goal is to achieve automatic early-warning of damage, instability and potential collapse. Although the proposed approaches work on the shape/pattern changes of the structure, no direct displacement measurements will be required. The shape/pattern changes are strategically reflected in tilt angles, strains and accelerations, which can be measured easily. This research has been funded by National Science Foundation, the Hazard Mitigation and Structural Engineering program (Project No.:1405023).

 Wireless Sensor Networks for Structural Health Monitoring

Wireless sensor networks (WSNs) have become more and more popular in structural health monitoring due to their low cost, quick installation and easy configuration. Structural damage detection based on WSNs can be adversely affected by time synchronization errors (TSEs) among sensors. However, precise time synchronization over a long period of time is challenging in large WSNs. This study addressed this problem from a new perspective. Rather than to achieve a strict time synchronization among sensors, this study proposes a damage detection approach that is robust against TSEs in WSNs. This alternative approach relaxes the need for frequent sensor synchronization and can tolerate TSEs caused by node failures. The proposed approach is successfully validated through numerical simulations and experimental tests in a lab. For further information, please refer to the published journal paper.

 System Identification and Damage Detection 

of Nonlinear Systems

During the service life of structures, breathing-fatigue cracks may occur in structural members due to dynamic loadings acting on them. These fatigue cracks, if undetected, might lead to a catastrophic failure of the overall structural system. In this study, a simple and efficient approach to detecting breathing-fatigue cracks is developed based on dynamic characteristics of breathing cracks. First, considering that breathing cracks introduce bilinearity into structures, a simple system identification method for bilinear systems is proposed by taking best advantage of dynamic characteristics of bilinear systems. This method transfers nonlear system identification into linear system identification by dividing impulse or free-vibration responses into different parts corresponding to each stiffness region according to the stiffness interface. In this way, the natural frequency of each region can be identified using any modal identification approach applicable to linear systems. Second, the procedure for identifying the existence of breathing fatigue cracks and quantifying the cracks qualitatively is proposed by looking for the difference in the identified natural frequency between regions. Third, through introducing Hilbert transform, the proposed procedure is extended to identify fatigue cracks in piecewise-nonlinear systems. The proposed system identification method and crack detection procedure have been successfully validated by numerical simulations and experimental tests. For further information, please refer to the published journal paper. This research has been funded by National Natural Science Foundation of China.