Experimental and Numerical Modeling of a Cross-Flow Turbine Runner Made of HDPE: Experimental and Numerical Approach
Bibliographic record
Abstract
This study investigates the viability of high-density polyethylene (HDPE) as a sustainable, low-cost alternative to conventional metallic materials for Cross-flow turbine runners in micro-hydropower systems. The primary goal is to design, manufacture, and validate the hydrodynamic and structural performance of an HDPE runner. A three-stage methodology was applied: CAD-based design, thermoforming fabrication, and performance evaluation through computational fluid dynamics (CFD) and finite element analysis (FEA) using ANSYS. Numerical predictions were validated against experimental data obtained from a hydraulic test bench. Mesh refinement and turbulence modeling were included to ensure numerical reliability. Results show that the HDPE runner achieved efficiencies of 80-83% compared to a geometrically identical steel runner under similar operating conditions. Structural analysis confirmed von Mises stresses (8.5 MPa) and deformations (0.12 mm) remained well below HDPE’s yield strength (22 MPa), validating its mechanical integrity. Statistical comparison revealed a deviation of less than 4% between numerical and experimental results. This research provides a validated framework for using recyclable HDPE in turbine manufacturing. It demonstrates that HDPE can deliver comparable power output to steel while reducing manufacturing costs and environmental impact, offering a sustainable pathway for rural electrification.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".