MODELLING AND SIMULATION OF SUSTAINABLE SYSTEMS: AN ENGINEERING DESIGN COURSE PROJECT
Bibliographic record
Abstract
To enhance the learning objectives of the course: Systems Modelling and Simulation (MSE 380), a Design-Based Learning (DBL) project centered on sustainability was developed and has been implemented for five years. The project consists of designing, modeling, and simulating a renewable energy system that solves a particular problem within a predefined scenario. Thescenario changes every year: past scenarios are a remote dwelling, a camping trip, a daily-life setting, a village in an underdeveloped country, and the design of a device that assists in the fight against COVID-19. The design of the system comprises two phases; first renewable energy is harvested and stored, and later the stored energy is used to solve the problem. Students model the systems using statespace representation and linear graphs; and simulate the response as linear, linearized, and nonlinear problems. Project examples for the scenario of underdeveloped villages are: discharge of flooded rice fields during monsoon season in South Asia using pumps, irrigation during the drought season in Northern India using stupas (artificial glaciers), uncovering of a Russian village after sandstorm with mechanical shovels, irrigation in Sub-Saharan Africa using solar uplift towers, saving crops from freezing temperatures in rural Iran using solar collectors (as opposed to burning tires which is the current practice), and producing hydroelectricity to power a cooking device in an Amazonian village. Our pedagogical experience with this didactic approach has been very positive. Students are creative and engaged with their own designs. As the project description and requirements have evolved through the years, not only the quality of the projects has improved but also it has had a positive impact on the course learning.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".