Machine learning-based assessment of storm surge in the New York metropolitan area.

Storm surge generated from low-probability high-consequence tropical cyclones is a major flood hazard to the New York metropolitan area and its assessment requires a large number of storm scenarios. High-fidelity hydrodynamic numerical simulations can predict surge levels from storm scenarios. However, an accurate prediction requires a relatively fine computational grid, which is computationally expensive, especially when including wave effects. Towards alleviating the computational burden, Machine Learning models are developed to determine long-term average recurrence of flood levels induced by tropical cyclones in the New York metropolitan area. The models are trained and verified using a data set generated from physics-based hydrodynamic simulations to predict peak storm surge height, defined as the maximum induced water level due to wind stresses on the water surface and wave setup, at four coastal sites. In the generated data set, the number of low probability high-level storm surges was much smaller than the number of high probability low-level storm surges. This resulted in an imbalanced data set, a challenge that is addressed and resolved in this study. The results show that return period curves generated based on storm surge predictions from machine learning models are in good agreement with curves generated from high-fidelity hydrodynamic simulations, with the advantage that the machine learning model results are obtained in a fraction of the computational time required to run the simulations.

Material and geometric effects on propulsion of a fish tail.

We investigate the effects of material flexibility and aspect ratio on the propulsion of flapping tails. The tail, which is assumed to deform in the bending direction only, is modeled using the Euler-Bernoulli beam theory. The hydrodynamic loads generated by the flapping motion are calculated using the three-dimensional unsteady vortex lattice method. The finite element method is used to solve the coupled time-dependent equations of motion using an implicit solver for time integration. The results show improvement in the thrust and propulsive efficiency over a specific range of non-dimensional flexibility defined by the ratio of the elastic forces to fluid pressure forces. Structural and flow characteristics associated with the improved performance are discussed. As for geometric effects, the performance depends on the excitation frequency. At low frequencies, the improvement is continuous with increasing the aspect ratio in a manner similar to that of rigid tails. At higher frequencies, the improvement is limited to a region defined by aspect ratios that are less than 0.5. The extent of the improvement depends on the flexibility.

A novel imaging technique for measuring kinematics of light-weight flexible structures.

A new imaging algorithm is proposed to capture the kinematics of flexible, thin, light structures including frequencies and motion amplitudes for real time analysis. The studied case is a thin flexible beam that is preset at different angles of attack in a wind tunnel. As the angle of attack is increased beyond a critical value, the beam was observed to undergo a static deflection that is ensued by limit cycle oscillations. Imaging analysis of the beam vibrations shows that the motion consists of a superposition of the bending and torsion modes. The proposed algorithm was able to capture the oscillation amplitudes as well as the frequencies of both bending and torsion modes. The analysis results are validated through comparison with measurements from a piezoelectric sensor that is attached to the beam at its root.

The need for higher-order averaging in the stability analysis of hovering, flapping-wing flight.

Because of the relatively high flapping frequency associated with hovering insects and flapping wing micro-air vehicles (FWMAVs), dynamic stability analysis typically involves direct averaging of the time-periodic dynamics over a flapping cycle. However, direct application of the averaging theorem may lead to false conclusions about the dynamics and stability of hovering insects and FWMAVs. Higher-order averaging techniques may be needed to understand the dynamics of flapping wing flight and to analyze its stability. We use second-order averaging to analyze the hovering dynamics of five insects in response to high-amplitude, high-frequency, periodic wing motion. We discuss the applicability of direct averaging versus second-order averaging for these insects.