2026 ASEE Annual Conference & Exposition

Simulations are only as meaningful as the experiments they reflect. In engineering education, this principle forms the foundation for building trust in digital tools that increasingly shape how students learn, design, and make decisions. This paper presents a framework for cultivating that trust through experimentally validated, simulation-aided case studies developed collaboratively between the a Canadian university and an engineering simulation company. These studies span multiple engineering domains; solid mechanics, fluid mechanics, thermodynamics, heat transfer, vibrations, electromagnetics, and biomechanics, and are implemented across both undergraduate and graduate levels through course-integrated and co-curricular experiential learning activities.

In each activity, students connect theory, experiment, and simulation through a structured feedback loop. A particularly striking moment occurs when they observe a live physical experiment, such as a vacuum-cleaner-driven Venturi tube or a 3D-printed heat exchanger, and watch how boundary conditions directly influence measurable outcomes. Small changes in flow restriction or induced backpressure cause visible shifts in readings, and students recognize that a simulation will only be as credible as its boundaries are accurate. When this same experiment is recreated in Ansys Discovery, the software’s exploratory mode allows them to manipulate parameters in real time, modifying geometry, flow rates, or thermal loads, and instantly visualize changes in pressure distribution, temperature gradients, or velocity fields. The ability to dynamically observe cause-and-effect relationships bridges the gap between analytical theory and physical experimentation. This rapid feedback fosters intuitive understanding of model sensitivity and reinforces the digital twin as a trustworthy representation of reality.
Traditional laboratory environments often encourage passive data collection, where students follow prescribed steps and trust the apparatus to produce the “correct” result. The simulation-integrated framework reverses this model by prompting students to interrogate systems directly. By varying materials, exploring extreme or non-ideal boundary conditions, and visualizing quantities that cannot easily be measured, such as local turbulence intensity, wall heat flux, or stress concentration, they gain deeper agency in the learning process.

For students with limited prior exposure to a particular domain, the real-time, exploratory environment of Ansys Discovery democratizes access to complex physical behavior and builds confidence through inquiry-driven experimentation. Affective data from over 800 participants across mechanical, mechatronics, civil, biomedical, and systems design programs show consistent increases in self-reported confidence, curiosity, and conceptual clarity after only three short workshops. Students frequently remarked that seeing simulated and experimental results evolve together “made the theory come alive” and improved their ability to reason about physical systems.
Once validated, these same models naturally evolve into open-ended design challenges. Students are encouraged to ask how performance might be improved or losses reduced; minimizing pressure drop in a heat exchanger, optimizing heat transfer in a barbecue, or improving structural efficiency in a walking crutch. These activities introduce trade-offs between performance, manufacturability, and material selection, embedding authentic design decision-making into the learning experience.

Across these implementations, simulation and experimentation operate as complementary learning modes. Students model what they test, test what they model, and refine understanding through reflection on their alignment. The outcome is more than technical accuracy; it is pedagogical credibility. When grounded in observable behavior, simulation becomes both a tool for analysis and a platform for discovery, helping students build the engineering intuition and judgment required to design confidently within complex, constraint-driven systems.