Performance Evaluation of Cross-Flow Turbine for Low Head Application
Bibliographic record
Abstract
Pico hydro generators are a promising means of providing cost-effective electricity to locations with limited or no availability of grid-supplied electricity.The Firefly design has been employed throughout rural areas of Cameroon and used as a light battery charger.It is hoped to extend the turbine capacity to provide steady baseload output between 100-500 W operating at low head sites (2-10 m).Suitability of the Firefly under these conditions is currently unknown, and the turbine has not been evaluated under conditions of very low head.The study objective was to characterize the performance through laboratory testing under conditions of low head and variable flow rate, in order to determine if the Firefly turbine meets the requirements of users in Cameroon.At this time, construction has been completed of the Firefly turbine and testing apparatus.The testing process and initial Firefly performance results, as well as lessons learned to date, are the focus of this paper.This is the first phase of a larger project seeking to design an optimized pico hydro turbine that balances performance, reliability and ease of manufacture and installation.The Firefly results will be used as a baseline in comparisons to new turbine designs.
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 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".