Teaching Fluid Mechanics for Undergraduate Students in Applied Industrial Biology: from Theory to Atypical Experiments

EBI is a further education establishment which provides education in applied industrial biology at level of MSc engineering degree. Fluid mechanics at EBI was considered by students as difficult who seemed somewhat unmotivated. In order to motivate them, we applied a new play-based pedagogy. Students were asked to draw inspiration from everyday life situations to find applications of fluid mechanics and to do experiments to verify and validate some theoretical results obtained in course. In this paper, we present an innovative teaching/learning pedagogy which includes the concept of learning through play and its implications in fluid mechanics for engineering. Examples of atypical experiments in fluid mechanics made by students are presented. Based on teaching evaluation by students, it is possible to know how students feel the course. The effectiveness of this approach to motivate students is presented through an analysis of students' teaching assessment. Learning through play proved a great success in fluid mechanics where course evaluations increased substantially. Fluid mechanics has been progressively perceived as interesting, useful, pleasant and easy to assimilate. It is shown that this pedagogy which includes educational gaming presents benefits for students. These experiments seem therefore to be a very effective tool for improving teaching/learning activities in higher education

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Computational Fluid Mechanics

A one-equation turbulence model based on the turbulent kinetic energy equation is presented. The model is motivated by the success of the Johnson-King model and incorporates a number of features uncovered by Simpson's experiments on separated flows. Based on the results obtained, the model duplicates the success of algebraic models in attached flow regions and outperforms the two-equation models in detached flow regions

IMPROVING THE STUDENTS’ LEARNING ACHIEVEMENT ON FLUID MECHANICS COURSE THROUGH THE USE OF INTERACTIVE MEDIA

This research aims at improving the quality of teaching and learning process in Fluid Mechanics course through the use of teaching media. The teaching of Fluid Mechanics needs communicative and authentic media. Interactive media which is made by using software is one of media that give chance to the students to learn, practice, and analyze the materials of mechanic and fluid dynamic outside the class independently and interactively. This study is categorized as an Action Research. The participants of this study were the semester three students of Diploma III of Civil Engineering Study Program of Yogyakarta State University who join Fluid Mechanics class. This research was conducted in 4 months. There were 2 cycles in this study. Each cycle consisted of planning, conducting, and controlling then the final stage was reflection. Data were analyzed using quantitative method and simple descriptive qualitative. The result of research shows that the implementation of interactive media can improve the quality of teaching and learning process on Fluid Mechanics course. The scores of the students had increased from the mean score of 68.41 on the first cycle into 75.37 on the second cycle. Moreover, 94.85% of the students responded well to the use of interactive teaching media as a medium of learning. Keywords: Fluid mechanics, learning media, action researc