Biological systems are interesting for their smart ways of relying on unsteady aerodynamics using flapping and choosing maneuvers to generate the necessary loads. Resolving the flow-field as the kinematic parameters vary and investigating the stability and bifurcations of the wake patterns are our interest. At the moment we are using particle based as well as grid based Navier-Stokes solvers to quantify the vortical flow-field. Lately, we have been trying to find out how fluctuating winds and gusts could affect the flow-field patterns and the aerodynamic loads.

The fluid dynamics associated with porous media is of special interest in various geotechnical applications. Obtaining such knowledge about porous media through experimental investigations is difficult considering the number of samples on which the experiments have to be performed. Computational studies on porous media are mostly based on averaging the Navier-Stokes equations over a small volume of the media. In the present study, a direct approach using the statistical properties of the media is used to reconstruct the media and do the subsequent analysis. The main advantage lies in the fact

that this also gives an analytical framework for the structure of the porous media in terms of the random fields that are produced.

Flow induced oscillations in engineering structures could spell fatigue and failure for the structural components. Modern wind turbine rotors with high flexibility is a good example. Dynamic stall, flutter, edge-wise oscillations are some of the examples of flow induced instabilities. We have a low speed low noise wind tunnel with a gust chamber at the Biomimetics & Dynamics Lab to run aeroelastic experiments.

In the compressible flow regimes, pressure fields can interact with the structural dynamics and can trigger a coupled behavior quite different from the individual systems. Presence of nonlinearities in such systems trigger interesting bifurcation patterns. The acoustic field is discretized using CFD based techniques.

In fluid structure instability systems, with system uncertainties, there is an increased risk of entering into the instability region challenging the strength of a deterministic prediction. As a result there is a chance of encountering fatigue damage or catastrophic failure due to such oscillations. It is of interest to understand how

the system uncertainties propagate through the system and influence the overall dynamics. In the presence of nonlinearities, it is also important to find for a correct definition of bifurcations. Is it really the bifurcation of the probability density function as some suggest? Does it really complete the picture? We have been working on Spectral uncertainty quantification tools and its variants. Recently, we have also started to look at the Probability density evolution technique.