Journal
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION
Volume 33, Issue 4, Pages 547-571Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/09500693.2010.490570
Keywords
Hydrogeology; Numerical modelling; Fluid flow; Solute transport
Categories
Funding
- Directorate For Geosciences [1261005] Funding Source: National Science Foundation
- Division Of Earth Sciences [0747629] Funding Source: National Science Foundation
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Visualising subsurface processes in hydrogeology and building intuition for how these processes are controlled by changes in forcing is hard for many undergraduate students. While numerical modelling is one way to help undergraduate students explore outcomes of multiple scenarios, many codes are not user-friendly with respect to defining domains, boundary conditions, and coupling processes, and numerical modelling exercises are also often disconnected from systems that the students understand, limiting their ability to extrapolate what they have learned for other situations. Here, we test the hypothesis that hydrogeology students will better estimate rates of groundwater flow and contaminant transport and the magnitudes of the parameters that control flow and transport by linking physical and numerical models. We present an exercise that links physical and numerical modelling of fluid flow and solute transport using 2-D 'ant farm' sand tanks with parallel models in COMSOL Multiphysics. The sand tank exercises provide students with a way to visualise subsurface flow and transport processes, while COMSOL allows them to explicitly pull apart the mathematics associated with these systems and build intuition for their solutions. Given coupled experimentation and numerical exercises, we find that students will connect processes that they see in the laboratory with the outcomes of numerical models, and the post-exercise tests indicate that they have an improved understanding of: (1) the magnitude and importance of properties and parameters that control flow and transport and (2) the simplifications made in numerical models of physical systems.
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