Multi-Disciplinary Problem Based Learning in Computational Fluid Dynamics

Printer-friendly versionPDF version

KTH Royal Institute of Technology (KTH) is one of European’s key centers of innovation and intellectual talent for almost two centuries. Recognized as Sweden’s most prestigious technical university, KTH is also the country’s oldest and largest. Computerized simulation technique was recognized as a fundamental component of the higher-education sector at KTH as early as the fifties. Although the utilization of these tools in research is now considered to be standard, the educational aspects are not. In this field, the method of instruction followed by practice is considered to be a superior pedagogic method compared to lecture to many. The current method emphasizes on demonstrative activities of the learners known as phenomeno-graphic learning. However, phenomeno-graphic learning is viable if there are breadth and depth in the physic, mathematic, computer science and graphic, operational strategy and methodology, etc. illuminating weakness of learning by doing method.  

This work aims to report the success and progress of the course, numerical methods in energy technology, which utilizes Computational Fluid Dynamics cods such as STAR-CCM+ as the tool. Learning methodology is based on instructional design and andragogically approach that offers an elaboration on the mechanism of learning process and its premeditated in context of a prescribed framework. The results indicated the prominence of student sensitive and constructive learning process and the advantages of using a preferred framework in guiding the students in a pertinent context (area). The method particularly incorporates the constructivist principles that lead to enhance the learning process. In addition, the conclusions of this study similarly illuminate the vast potentials of computational fluid dynamic for research, evaluation and educational purposes.

Author Company: 
Royal Institute of Technology
Author Name: 
Reza Fakhrai, Bahram Saadatfar