MAE 3150

Hands-on Engineering Simulations and Design

Course Description

Course information provided by the 2025-2026 Catalog.

This course is aimed at preparing students to solve practical problems in structural mechanics using industry-standard simulation software. This is a problem-based course where students will learn both theory and applications by solving practical problems involving realistic 3D CAD geometries. Examples considered include pressure vessel and wind turbine blade static analysis as well as turbine disk vibration. Students will need to complete a final course project which will involve simulation-based design such as designing a bicycle frame. The focus will be on developing a strong conceptual understanding of what's inside the simulation black box so as to move beyond “garbage-in, garbage-out.” Substantial time will be spent on understanding the chain of reasoning and assumptions used to the develop the mathematical models underlying simulations as well as on interpreting the results obtained from solving these models using a finite-element solver. Verification and validation of simulation results is emphasized throughout. Examples, homework and project will use Ansys, an industry-standard simulation software. However, concepts and solution approaches learned will be applicable to other software for structural simulations.

Prerequisites MATH 2930, ENGRD 2020.

Last 4 Terms Offered (None)

Learning Outcomes

• Create reliable simulations of structures for a range of practical problems from different industries.
• Explain the mathematical model underlying each simulation including governing equations, boundary conditions, physical principles and assumptions.
• Explain the numerical solution strategy used to solve the mathematical model and how to reduce the numerical errors introduced.
• Predict expected results and trends using hand calculations.
• Defend simulation results by undertaking a “verification and validation” procedure that includes checking consistency with underlying mathematical model, assessing numerical errors and comparing with expected results and trends.

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