INTEGRATING A SHORT SIMULATION PROJECT INTO AN INTRODUCTORY TRANSPORT PHENOMENA COURSE
Bibliographic record
Abstract
Transport phenomena is an integral part of many engineering curricula. Some programs, particularly in chemical engineering, teach transport phenomena in an integrated manner, but most teach heat, mass and momentum transfer in separate courses. Since powerful computers and software packages are now available to solve transport problems numerically, many current transport phenomena courses incorporate these tools for demonstration purposes. Many institutions also offer advanced level courses focusing on the use of this type of software, but the intuitive nature of modern software packages also permits the integration of these tools directly into introductory courses.This paper summarizes efforts made in the Department of Process Engineering and Applied Science at Dalhousie University to integrate one such tool, COMSOL Multiphysics, directly into the introductory transport phenomena course. In an introductory course, the time available to study software tools is inherently limited, and therefore it is necessary to introduce these topics as efficiently as possible. In our course, several hands-on tutorials are used to introduce students to COMSOL. We avoid focusing too much on numerical methods, error estimation techniques, or even detailed model validation techniques. Instead, these tutorials focus on using commercial software to solve transport problems and demonstrating good modeling practices. Near the end of the course, students are asked to complete a short modeling project. In the latest iteration, students were asked to reproduce a simulation in a recent journal publication, to expand on the results, and comment on the significance of their findings. The main challenges related to implementing such a project include selecting problems that do not have unreasonably high computational requirements, the increased workload for teaching assistants, the required level of expertise among teaching assistants, and the increased workload for students.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".