As a result, definition of the geometric progress of the interface requires many gradient estimation computations, as is the case in moving and deforming bubbles or droplets, for example. In this respect, traditional gradient calculation methods based on 1D edge detection are not necessarily suited for the underlying physics, because there is no direction in which the gradients of the phase contours tend to evolve over time. A threshold is then applied to obtain an edge shape.įor multiphase flows, an edge or contour corresponds to the interface between the fluids. In this field, edge detection techniques rely on the application of convolution masks to provide a filter or kernel to calculate gradients in two perpendicular directions. Commonly used edge detection methods are Canny, Prewitt, Roberts and Sobel, which can be found in MATLAB’s platform. Typical applications are gradient reconstruction in computational fluid dynamics, edge detection in computer graphics and biomedical imaging, and phase boundary definition in the modeling of multiphase flows.Įdge detection, which is widely performed in image analysis, is an operation that requires gradient calculation. The design of discrete operators or filters for the calculation of gradients is a classical topic in scientific computing.
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