Integration of Computational Fluid Dynamics and Experimentation in Undergraduate Fluid Mechanics
Bibliographic record
Abstract
A combination of computational and experimental analyses with the conventional lectures and problem-solving in a fundamental course such as fluid mechanics can enhance students' learning enormously. This teaching model has been examined within the mechanical engineering curriculum at WSU Vancouver, and successful results have been obtained thus far. The goal in this course was first to seed concepts and theorems of fluid mechanics in general terms, followed by numerical solutions and hands-on experimentation on selective subjects. This would allow the students to gain a deep understanding of the contents within the course timeframe. For selective fluid problems with more complications, such as the flow in the entrance region of a pipe, a computational fluid dynamic (CFD) software known as FlowLab was used to obtain numerical solutions. The assigned computational projects could open the eyes of students to the world of CFD analysis in thermal/fluid systems design. The results of the numerical analysis were then compared to the theoretical and experimental results. For experimentation, the students were divided into groups to design experimental procedures, conduct experiments, collect and interpret data, and report the results in an appropriate format. The selective experiments were relevant to the course topics including Burdon pressure gauges, manometers, flow-rate measurements, pipe flow, and flow around immersed bodies in a water tunnel. The present study addresses the details, results, and advantages of such a multi-dimensional and more interactive learning model.
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 | 0.008 | 0.031 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".