Education

• Ph.D., MIT, Civil and Environmental Engineering (2019)

• B.S., University of Michigan Ann Arbor, Civil and Environmental Engineering (2014)

• B.S., Shanghai Jiao Tong University, Mechanical Engineering (2014)

This project seeks to develop an analytical model that describes the efficacy of wave attenuation over partially vegetated waters. 

In areas with such vegetation, the wave amplitude is anticipated to diminish as waves propagate through the vegetation. Additionally, a transfer of wave energy is expected between the vegetated and non-vegetated regions. We will address this research question through theoretical derivations complemented by laboratory experiments. 

The research findings will also be used to calibrate the vegetation parameters in modeling software, such as XBeach.  

This project aims to develop a universal model for predicting wave attenuation over aquatic vegetation in orthogonal wave-current conditions.

Although wave damping by aquatic vegetation has long been noted, most researchers have only considered pure-wave conditions, and a handful of previous studies have considered co-directional wave-current conditions. To date, a predictor of wave damping under orthogonal wave-current conditions (i.e., the current is perpendicular to the direction of wave propagation), which usually correspond to wave-induced longshore currents near the coast, has not yet emerged. Through theoretical derivations and laboratory experiments, we will address this research question. 

We study the impact of hydrodynamics on sediment dynamics within coastal habitats.  The flow-vegetation-sediment model will be applied to predict the sediment erosion rate. 

The project aims at 1) examining the mechanical behaviour of hybrid solutions under various flow conditions 2) predicting how NbS within the hybrid solutions may respond to climate change impacts (sea-level rise, extreme scenarios) 3) developing theoretical and empirical models that can capture the important physics (wave attenuation, sediment transport) of wave-current flows interacting with hybrid solutions

AI techniques such as computer vision and machine learning can be incorporated into monitoring systems to understand the physics of fluid-structure interactions. Waves, currents, sediment transport, water quality monitoring, and surf forecasting all rely on rapid, accurate measurement of local water flows. 

Visual observations of the impact of water—for example, the swaying of flexible structures or aquatic vegetation—could be used to infer the local flow conditions. The intelligent monitoring framework serves as an inexpensive and ubiquitous alternative to conventional monitoring systems.