Modelling and Experimental Validation of a Controllable Energy Harvester for Pressure Regulation
Bibliographic record
Abstract
Abstract A pico-scale Francis turbine (or energy harvester) was designed, fabricated and tested for pressure regulation and power generation application. The prototype energy harvester contains pivotable guide vanes and a controllable load to change the runner speed. This allows the simultaneous variation of the pressure drop and the output power. A computational fluid dynamics (CFD) model of the turbine was developed in ANSYS CFX 18.1 to evaluate the turbine’s sensitivity to geometric parameters such as the clearance gap size of the guide vane and its modularity. In conjunction to the CFD model, the electric generator’s characteristics were used to predict the turbine performance at varying guide vane angles. The turbine was prototyped and tested using a custom-built experimental set-up. The pico-scale turbine, with a runner diameter of 1.42 inches, was able to output up to 100 W of electrical power at its rated flowrate of 29 GPM. By varying the guide vane angles, the pressure drop and the hydraulic efficiency varied between 3–22 psi and up to 60% respectively. When validated against the experimental results, the CFD model showed a good agreement despite its low computational cost. The energy harvester’s initial characteristics demonstrate its potential as a game changer in the control valve market.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".