The US Air Force has added the ‘e’ designation to denote an aircraft or satellite having been designed using computer modeling and simulation, as well as interface technologies like virtual and augmented reality. This new digital aerospace engineering process, i.e. using computers to design, model, and test aircraft before an actual prototype is built, is so important that it drives the need for a different approach to the engineering curriculum. In response, our students are provided an opportunity to create and analyze throughout a lecture-based course. The availability of sophisticated software and its use outside of the classroom without prior training facilitates nonlinear learning, promotes exploration of alternative resources, from written material to the human network, and ultimately brings it all back to the theoretical underpinnings.

The Compressible Aerodynamics course in its first iteration featured the following simulation projects: (1) Incompressible flow through the converging nozzle, (2) Flying on earth, (3) Pitot tube, (4) Compressible flow over an airfoil, (5) Airfoil in different flow regimes, and (6) Rayleigh flows. They were chosen from six ANSYS Fluids Engineering Innovation Courses that are available free online. Each project originally included a video, handout, and pre-generated mesh, and was further customized with specifically focused grading criteria.

This study built on the authors’ previous work on the use of simulations in the individual courses and course sequences in the mechanical engineering program. The goals were to bring the elements of the new digital aerospace engineering process to standard lecture-based courses, ensure students’ engagement from the beginning, and increase the time students invest in studying. The anticipated challenges (from the perspective of Bloom’s Taxonomy) were (1) non-hierarchal structure of learning where students create and analyze while mastering the use of the tool, (2) integrated and balanced process of learning where every element of the framework is equally important, and (3) the differences that each learner brings, from motivation to the cognitive processing.

The significance of our approach and its success are illustrated with qualitative and quantitative evidence. Qualitative data provide examples of student work, while quantitative assessment of students’ understanding and confidence in comprehension of select concepts includes grades, student surveys, and course evaluations.

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