I enjoy collaborating with researchers from different disciplines. In this context, I have contributed in the numerical modeling and algorithm development related to the fluid mechanics and remote sensing.
Automatic Snow layer tracker
In this research, I developed an automatic snow-layer thickness tracker using the peaks method and Gaussian process modeling. Based on the airborne radar, the signals for the flight path are usually provided; however, one of the main challenges is to estimate the thickness of the snow by identifying the snow-air interface and snow-ground interface appropriately. Using a numerical model to identify the snow-thickness is advantageous in saving costs that is generally related to the in-situ experiments which are usually expensive and tedious.
Collaborators: The Remote Sensing Center, UA
Collaborators: The Remote Sensing Center, UA
Vortex detection in fluid flow using Kernel based Approach
In this study, I developed a kernel-based approach for automatic detection of the vortex in turbulent flows. Similar to the concept of kernels/filters in convolution neural networks, this kernel based approach uses a kernel with a fixed grid size which is then traversed along the pixels of the fluid flow images to identify the location of vortex locations. The vortex identification is based on the local velocity and location flow direction, and a demonstration of this novel approach is provided below. Based on the application to different experiments, it was able to detect the vortex precisely while neglecting the shear flow that is usually identified as artifact during the vortex identification in the other existing methods.
Collaborators: Leonardo Santos (Ph.D. Student and Engineer at Boeing)
Collaborators: Leonardo Santos (Ph.D. Student and Engineer at Boeing)