Introductory Fluid Mechanics taught using a carburetor
Bibliographic record
Abstract
The typical fluid mechanics introduction in mechanical engineering covers basic fluid statics, forces on submerged bodies, control volumes, continuity, conservation of momentum, conservation of energy, Reynolds' transport theorem, internal and external flows. Students often struggle with the basic concepts and how they might apply to a real system.In the winter of 2015, a new set of open-ended labs were created for mechanical engineering students. Teams of 3 or 4 students in a third-year class of 110 were each given a small engine, and removed the carburetors. The series of labs had them discover some of the principles within the small carburetor from a single-cylinder 4-stroke gasoline engine. Students were asked to explore and explain how the carburetor worked, and then progressed through a set of hands-on labs culminating with a design modification and performance measurement of a real carburetor. At each stage, the students applied principles of fluid statics, control volumes, internal flow losses, and external flow drag to the physical device.The paper will describe the specific activities, and track the evolution of refinements to the experience through three implementations of the same course by three different instructors. Student feedback and measured evidence of learning will be reported to help justify the evolution of the activity. The scalability of the activity will be discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.017 |
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".