Multiphysics modeling with high priority research applications

Baccalaureate engineering graduates are facing an emerging class of design challenges that span multiple disciplines of science and technology. Sophisticated computational techniques, combining the representative physics of multiple domains, are needed to accurately model and predict results. Many engineering degree programs offer major specific modeling courses or embed simulations on a limited basis. For example, mechanical undergraduates may be exposed to solid modeling and computational fluid dynamics while electrical majors apply finite element techniques to electromagnetic problems. Few engineering curricula offer multiphysics design and research experiences. Where available, they are typically restricted to post-graduate studies; consequently, most baccalaureate graduates receive little or no exposure to areas of expertise outside of their discipline. This is inconsistent with the view that future graduates need to be more adaptable and versatile to succeed in a knowledge-based global marketplace. This paper describes an engineering undergraduate course that covers the methods and techniques of multiphysics modeling. Students become active participants in analysis and discovery by being challenged to solve a sequence of problems related to high priority technology areas. Projects range from power systems and thermal control of habitats to autonomous flight systems and harsh environment electronics. Working in a cooperative learning environment, teams encounter a series of assignments that build on existing skills while gradually expanding their knowledge and expertise in disciplines outside of their own. This project-based approach employs a scaffolding structure with assignments organized in progressively challenging modules supported by mentoring. Each project begins with a problem definition which requires consideration of factors and influences beyond a single discipline. Solution development then moves to setting material properties, boundary constraints and including the necessary physics engines. For many students, this is the first in depth exposure to problems with specialized terminologies, driving equations and limiting conditions. Lastly, solving and post processing are addressed exploring steady state, time-variant, frequency response, optimization and sensitivity methods. The paper discusses the teaching and learning strategies, course structure, outcome assessment and project examples.