Design Space Exploration of Centimeter-Scale Wind Turbines using a Physics-Modified Optimization Formulation
Bibliographic record
Abstract
Abstract This paper explores the design space of centimeter-scale micro wind turbines to power wireless sensors through an experimentally validated modeling and simulation environment. A stochastic optimizer is used to obtain a functional relationship between the minimum wind velocity required to find a feasible design and multiple constraints relevant to turbine designers, such as the maximum turbine radius, electrical power required, minimum voltage required and available generators. This relationship is created from an optimization formulation that uses knowledge from the underlying physics and previous optimizations. It is shown that the design space of micro wind turbines is significantly different than large wind turbines due to the low Reynolds number regime. Also, a strong coupling exists between the choice of generator and optimal wind turbine geometry to minimize the wind required to meet the requirements. Smaller generators are more appropriate for micro wind turbines only if a constraint is applied on the maximum radius of the turbine and if no minimum voltage is required for a fixed power output.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".