Leveraging Lagrangian Analysis for Discriminating Nutrient Origins

Abstract

Understanding the origins of nutrients, e.g., nitrate, in ocean water is essential to develop an effective mariculture technique for free-floating macroalgae, which presents a potential solution to provide an alternative source of domestic renewable fuels to help reduce carbon emissions from automobiles. To study this problem, scientists simulate large-scale computational simulations with coupled flow and nutrient information. Since running the simulation multiple times is expensive, the scientists want to have efficient visual-analytic techniques that can analyze and visualize the simulation output quickly to investigate the reasons behind the existence of nitrate in different areas of ocean water. To address these needs, a mixed Lagrangian and Eulerianbased analysis technique is developed that leverages traditional Lagrangian analysis methods and fuses Eulerian information with it to comprehend the origins of nutrients in the water. The proposed method yielded promising results for the application scientists and positive feedback from them demonstrates the efficacy of the technique.