Wear Resistance Performance of Conventional and Non-Conventional Wind Turbine Blades with TiN Nano-Coating
Bibliographic record
Abstract
Efficiency and durability are critical issues that affect widely-adopted aerofoil-power generator as a sustainable source of electrical power. Even though high wind power density can be achieved; installing wind turbines in desert condition has difficulties including thermal variation, high turbulence and sand storms. Sand blasting on turbine blade surface at high velocities causes erosion resulting turbine efficiency drop. Damage-induced erosion phenomena and aeroelastic performance of the blades needed to be investigated. Suitable coating may prevent erosion to a great extent. A numerical investigation of erosion on NACA 4412 wind turbine blade has been performed using commercial computational fluid dynamics software ANSYS FLUENT 14.5 release. Discrete phase model (DPM) has been used for modelling multi-phase flow of air and sand particles over the turbine blade. Governing equations have been solved by finite volume method (FVM). Conventional 30-70% glass fibre resin and non-conventional jute fibre composite have been used as turbine blade material. Sand particles of diameter have been injected from 20, 30, 45, 60 and 90 degree angles at 500C temperature. Erosion rate, wall shear stress and strain rate have been calculated for different wind velocities and impingement angles. Simulation results for higher velocities deviate from the results observed at lower wind velocities. In simulation, erosion rate is highest for impingement angle at low wind velocities, which has been validated by experiment with a mean absolute error (MAE) of 5.56%. Erosion rate and wall shear stress are higher on jute composite fibre than glass fibre resin. Developed shear stress on wind turbine blade surface is highest for impingement angle at all velocities. On the other hand, exerted pressure on turbine blade surface is found highest for 9 angle of attack. Experimental results, with or without Titanium nitride(TiN) nano-coating, also revealed that surface roughness augments with increasing impingement angles. Nano-coating (TiN) by RF sputtering technique reduced the surface roughness significantly as oppose to uncoated samples. Highest roughness has been observed on uncoated blade surface collided with 0.3-0.69 mm diameter brown aluminium oxide particles.
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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".