ME 503 Smart Materials & Structures (3)
Provides and in-depth exploration of the fundamental principles, applications and design of smart materials and structures. We will cover the mechanics and design of smart materials, such as piezoelectric materials, shape memory alloys, magnetostrictive materials, and electrostrictive materials, and their integration into smart structures. The course also covers the principles of sensing and actuation, control systems and feedback, and the design and optimization of smart structures.
ME 516 Advanced Mechanics of Solids (3)
This course provides an advanced study of the mechanics of solids, covering topics such as stress and strain analysis, elasticity, plasticity, and fracture mechanics. The course will emphasize the development of mathematical models and their applications to engineering problems. The course will include lectures, homework assignments, quizzes, and a final project. The course meets for 3 hours per week.
ME 520 Advanced Fluid Mechanics (3)
Analysis and computation of steady flows in both internal and external geometries. Comparisons between experimental measurements and analytic solutions. Laminar and turbulent flows including boundary layers, duct flow, flow separation, and compressible flow. Prerequisite: ME 320.
ME 536 Wind Turbine Data Analysis and Statistics (3)
Course addresses methodology on data analysis of wind and wind turbine.
ME 571 Principles of Mobile Robotics (3)
Principles and approaches of mobile robotics are taught. The emphasis is placed on robot mobility which allows a mobile robot to move through an environment to perform its tasks, covering the aspects of locomotion, sensing, localization and motion planning. Also covered are computer modeling and programming of mobile robots. Prerequisites: Physics I and Calculus III, or approved by instructor.
ME 572 Principles of Robot Manipulators (3)
Principles of robot manipulators are taught, including the kinematics, dynamics, trajectory generation and control. Also covered are computer modeling and analysis of robot manipulators, and programming of robot manipulators. Prerequisites: Physics I and Calculus III, or approved by instructor.
ME 573 Introduction to Machine Learning for Engineering Applications (3)
Fundamental principles and approaches of machine learning are introduced. Topics include classification, regression, supervised learning, unsupervised learning, reinforcement learning, clustering, and neural networks. Engineering applications, such as data analysis and robotics, are explored.