Design is at the core of engineering. Therefore, exposing students to design helps them realize the purpose of engineering, motivates them to see the relevance of learning, and pushes them to higher achievement levels. In addition, experiential learning practices, and new design methods, including design thinking, are reshaping design courses. After carefully considering trends in design theory and practice, we restructured our first-year design experience for mechanical engineering students at Saint Louis University. The paper outlines the overall structure of this innovative course. The course is structured around projects, while activity-based lectures provide the necessary background to execute them. In these projects, students identify customer needs and create, develop, test, and refine innovative concepts with a win-win value proposition for the stakeholders. This paper details the projects and lecture topics to help others develop and implement similar courses. Further, based on the survey of students and external evaluators, the value proposition is the most challenging aspect to learn and incorporate into the project.
The vast majority of undergraduate engineering students struggle to acquire a deep understanding of complex topics presented in mechanical vibrations and control theory courses due to their highly mathematical nature, limited resources on the use of commercially available turn-key laboratory equipment, and lack of innovative teaching tools to improve student understanding. In this study, we developed open-source stand-alone virtual laboratory exercises for vibrations and control theory courses using Matlab Simscape. The virtual labs enable students to change the system parameters, develop a feel of the material, and record and observe the system response through mechanics explorer.
In mature oil fields, the success of gel treatment results depends on the ability of the gel to reduce the high permeable formation without damaging to low permeable formation. Formation damage refers to the extent of damage reservoir rocks face from various drilling techniques and/or chemical treatment during well completion. A dynamic filtration test was used to investigate this effect using distinct core samples, brine concentrations and preformed particle gels. The effect of high pressures applied on the particle gels on various core samples with various permeability ranges was determined. These gels were pushed into the core holder with samples and the core permeability change was calculated. Different constant pressures were used to push the piston behind the gel samples. Then, the gel was flown around the core sample and collected in the outlet container. Various hardware was used to tighten the apparatus and provide connection between brine source, syringe pump, piston accumulator, core holder, and flow outlet container. The damage on the core was evaluated by comparing the original core permeability and the core permeability after gel treatments. Pressure gauges were used to measure the pressure drop across the core samples. The penetration of the particle gels into the low permeable formations can be decreased by the best selection of gel types, particle sizes, and brine concentrations under the reservoir condition. This work results can be used to select the best gel types for the right reservoir condition such as reservoir permeability, and reservoir pressure.
Mechanical engineering concepts such as Heat Transfer and FEM are better understood by visualizing and getting hands-on experience on the problem where many of the students fail. One such process that helps the students to visualize, understand and feel the concepts is to participate in student-level design and development challenges. There are many organizations spread across the world that conduct student-level design and development challenges. In India, Fraternity of Mechanical & Automotive (FMAE) is a private organization that focuses on student development in the automobile sector by conducting various types of student-level motorsport events like the design and development of Quad Bike, Baja Buggy, Formula Student, etc. Taking part in these competitions creates a platform for the students to form a team, design and develop vehicles, and compete against other teams nationally and internationally. It also increases the student’s ability to work in a team, take responsibility for a mission-driven project, and also increase leadership and managerial skills. During the process, the students enhance their skills of in-depth understanding, visualization, and knowledge of various engineering concepts.
To improve the integration of data science into thermal fluids education, a technical elective course is developed to introduce a wide range of machine learning and deep learning algorithms to engineering students, including principal component analysis, multiplayer perceptron, convolutional neural networks (CNN), long short-term memory (LSTM) networks, reinforcement learning (RL), generative algorithms (GA), and generative adversarial networks for mechanical engineering applications, including visualization-based physical quantity predictions, dynamic signal classification, and prediction, data-driven control of dynamical systems, surrogate modeling, dimensionality reduction, among others. The lectures cover the fundamental concepts and examples of developing machine learning models using Python and MATLAB. To facilitate students’ practice of applying data science in solving mechanical engineering progress, this course has touchpoints in several key areas of mechanical engineering, including fluid mechanics, heat transfer, materials science, design, and dynamics/control. Twenty-five students, including seven undergraduate and eighteen graduate students, took the course and the outcomes are very fruitful and encouraging. A variety of data science algorithms have been leveraged to solve mechanical engineering research problems, including generative designs of air-cooled heat sinks, Gaussian process regression for battery lifetime prediction and femtosecond laser manufacturing parameters, GA for two-phase cooling heat exchanger design, coupled PCA and LSTM for microcapsule deformation prediction, CNN for boiling regime classification and laser-manufactured textures classification, bidirectional recurrent neural networks basecalling of DNA and RNA sequences, coupled PCA and MLP porous medium morphology classification, RL for the control of soft robotics. The course has so far led to 25 student-centered machine learning projects, two conference papers, and an Honors thesis.
