Investigation of a Pico Turgo Turbine for High-Rise Buildings Using Computational Fluid Dynamics
Bibliographic record
Abstract
Thailand is a rapidly developing country, and many high-rise buildings are being constructed to satisfy the demands of the increasing populace. The country is located in tropical South East Asia, which means it experiences abundant rainfall during the rainy season. The design of a hydropower system from a waterfall is re-invented in this study using rainwater flowing from the rooftop of a high-rise building to drive a Pico Turgo Turbine. In the building under study, the rooftop is restructured to receive and store 57.6 m3 of rainwater, which is allowed to flow down through a designed pipe of 21 m head to a Pico Turgo Turbine of 1 kW capacity. The turbine is fitted with four 10-mm-diameter nozzles that have a 17 angle of attack between the water jets and the buckets, a 430-mm runner diameter, and 21 buckets with a total capacity of 0.007 m3. The power generated by the device is analysed and compared with a Computational Fluid Dynamics (CFD) simulation under certain boundary conditions. The simulations use the k- turbulent flow model. The bucket is designed according to hydrodynamic theory and parameterised using Bezier polynomials. The theoretical calculation yields 1,310 W of electricity, the CFD simulation suggests 1,037 W, and the experimental result gives 950 W. The results are analysed according to the efficiency of output, with the CFD simulation representing 79.21% efficiency whereas the experimental result suggests 72.51% efficiency. The efficiency of the model is also investigated with respect to flow design and recommendations for future optimisation are presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".