Gérard J. Poitras

A cognitive apprenticeship approach to engineering education: the role of learning styles

Abstract Prior to the creation of engineering schools, engineering was taught in an apprenticeship style. However, from the onset of formal engineering education, engineering curricula have been based largely on science and mathematical knowledge. Applied subject based learning (usually called traditional teaching methods) is still a common teaching model in engineering education programmes today. The professor or tutor passes information to the students, the newly acquired knowledge is applied to specific problems and communication between students and professor (and between students themselves) is limited. In order to better prepare future engineers for the workplace, many engineering educators are implementing innovative approaches to teaching and learning in their classrooms (e.g. problem based learning). In the work described in this paper, a cognitive apprenticeship approach is used. This teaching model includes the main assumptions of the problem based learning approach and also defines instructional methods for enhancing learning. The model was used for teaching two groups of civil engineering students enrolled in their third and fourth year. Results of the two experiments showed that the cognitive apprenticeship approach used for teaching undergraduate civil engineering students was favoured by most of the students, independent of their preferred learning style. The implications of these findings with regard to implementing the cognitive apprenticeship approach in civil engineering education are discussed.

Study of atmospheric boundary layer flows over a coastal cliff

The extent to which a wind energy site is affected by a coastal cliff is presented by studying numerically a neutral Atmospheric Boundary Layer (ABL) flow using an RNG k-ε model and different geometries. Initially, the classical flow over a forward-facing step is modelled, followed by the modelling of a neutral ABL over a rough plane in two and three dimensions with various types of ground conditions. Finally, the two and three-dimensional flows over a forward-facing step, representing a coastal cliff, in a neutral ABL were modelled and applied to the Atlantic Wind Test Site (AWTS), site composed of a wind turbine testing facility and a 13 MW wind farm, in the province of Prince Edward Island, Canada. After assessing that the model can predict classical flows and ABL with relatively good accuracy and robustness, the results show that the extent of the effect of a coastal cliff on the flow above the AWTS is limited to a distance of 5h downstream of the (height h = 10 m) cliff. Based on the sitting of the existing wind power infrastructure, it appears that the coastal cliff does not influence the power capacity of the site.

Wind Speed Prediction for a Target Station using Neural Networks and Particle Swarm Optimization

In this study, artificial neural networks (ANN) and particle swarm optimization (PSO) were applied to predict the average daily wind speed measured at a meteorological tower installed June 2005 at the Greater Moncton Sewerage Commission, in Moncton, New Brunswick, Canada. Wind speeds were collected covering the period between June 2005 and December 2008. Five reference airport meteorological stations were used as input for the neural network and PSO. The artificial neural network modeling was done using the Matlab® neural network toolbox, while the PSO algorithm is an in-house program written in Matlab®. The daily wind speeds generated by the ANN model and PSO were compared with the actual measured data. It was found that with six months of input data, both the ANN and the PSO were able to predict the short term daily wind speed for the following 36 months at the target station. The PSO obtained a smaller error compared to the neural network. The PSO algorithm was also able to find the best combination of input variables automatically, while the ANN used manually-selected input variables.

PIV measurements around 2D and 3D building models

Particle image velocimetry (PIV) measurements for three Reynolds numbers were performed around two-dimensional and three dimensional model buildings that were placed inside an Eiffel type wind tunnel. For the 2D model, the height was half the length while for the 3D model the height equaled half the width and the length. Along with the velocity measurements, pressure measurements on the center plane of the models were also taken. Noticeable differences in the pressure coefficient distribution between the 2D and 3D models were observed. However, there are no significant Reynolds number effects in the range studied for both types of models.