Optimizing Engineering Education with Simulation: Teaching Core Concepts through Integrated Case Studies and Ansys Analysis
Bibliographic record
Abstract
Engineering education traditionally relies on analytical methods and theoretical instruction, often lacking practical, hands-on learning experiences due to logistical and financial constraints. This study explores a novel approach that integrates Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) simulations with experimental and analytical methods through structured case studies. By leveraging Ansys software, this initiative aims to bridge the gap between theory and application, enhancing students’ understanding of fundamental engineering principles. The study involved a series of multi-day workshops at the University of Waterloo, engaging approximately 500 students from diverse engineering disciplines. These workshops incorporated analytical problem-solving, hands-on experimentation, and simulation-based validation. Case studies in structural mechanics, thermodynamics, and electromagnetics reinforced key engineering concepts across multiple disciplines. This study presents affective feedback from over 100 students across multiple disciplines who engaged in simulation-integrated workshops, evaluating their engagement, perceived relevance, and confidence in applying engineering concepts. Preliminary results indicate that integrating simulation-driven case studies enhances student comprehension and problem-solving skills. Workshop participants reported increased confidence in applying theoretical knowledge to real-world scenarios, recognizing the importance of correlating analytical and experimental data with simulation outputs. Combining case studies with industry-standard software, students develop a more intuitive grasp of complex engineering systems, better preparing them for both academic and professional challenges. Future work will focus on expanding this methodology across additional engineering curricula, refining assessment techniques, and further embedding simulation-based learning into undergraduate education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".