Optimization and System Identification of a Variable Pico-Scale Hydro Turbine for Pressure Regulation
Bibliographic record
Abstract
Abstract Recent studies from the European Commission estimate that more than 20% of global energy is consumed by pumping systems. Significant research has focused on increasing pump efficiency to lower energy consumption; however, few have looked at the energy lost in use of pressure regulating devices (PRDs). This paper proposes a novel pico-scale hydro turbine that could effectively replace PRDs and generate power while regulating pressure. The proposed hydro turbine has an outer diameter of 4″ and a total length of 5.4″. The turbine uses 14 rotating guide vanes and is attached to a generator with a variable load. To maximize power recovery and pressure control range of the turbine, a non-dominated sorting genetic algorithm was used for multi-objective geometry optimization. Then, to build a dynamic model for control system design, parameter identification was conducted using a Gaussian process surrogate model and stochastic search algorithms: particle swarm optimization and genetic algorithm. The optimized turbine showed good agreement between simulated and experimental results and achieved a power output of 120 W, pressure drop range of 6 to 27 psi, and maximum hydraulic efficiency of 75% at the rated flow rate of 27 GPM. The optimized turbine shows the potential of pico-turbines for pressure regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".