Sea ice poses a substantial treat to any vessel attempting to navigate the waters around the Canadian Arctic, and renders the region virtually inaccessible to the plethora of shipping companies and nations looking to establish a more direct transport route through the northwest. Prior to developing an effective trade route, it is critical to procure information on the state and thickness of the ice. Currently, sea ice charts and climatologies are produced by the Canadian Ice Service (CIS), the U.S. National Ice Centre (NIC), and other organizations with a similar mission. These charts are generated manually by assimilating data from multiple sensors such as remote synthetic aperture radars (SARs) and moored Upward-Looking Sonars. There is inconsistency in the quality of analyses derived from these methods, largely because data is amalgamated from a large variety of sources and image analyses conducted by ice operators are highly subjective. Therefore, an automated algorithm that can generate regional ice charts with accuracy comparable to or greater than their manually produced counterparts is highly sought-after. Dr. Katherine Andrea Scott, assistant professor at the University of Waterloo in the Systems Design Engineering Department, is working with an efficient model which calculates sea ice thickness on the Canadian East Coast. The calculated ice thickness can be combined with other data in an automatic data assimilation algorithm.
Kevin Pauley, a 3A Mechanical Engineering coop student from the University of Waterloo, worked with Dr. Scott investigating an algorithm that computed the East Coast of Canada sea ice thickness using optical satellite data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the National Aeronautics and Space Administration’s Aqua satellite for the month of January, 2007.
This case can be used in courses that cover topics in one dimensional heat transfer. The solution modules present heat balance which is solved iteratively, using the Newton-Raphson method and the bisection method. The case can provide effective material for the Heat Transfer 1 (ME 353), Thermodynamics (SYDE 381), Transport Process Analysis (CHE 322), and other similar heat transfer courses. Upon completion of the case study, the intended learning outcomes include: