Small Wind Turbine Tower Structural Vibration
Bibliographic record
Abstract
A major barrier to the acceptance of small wind turbines is that they are perceived to be noisy particularly when mounted on monopole towers rather than traditional guy-wired ones. This paper discusses an aspect of noise propagation that has not been studied previously: vibration of the tower. To start studying the tower’s behavior, twenty four accelerometers were attached in two orthogonal lines along the 10 m tower of Southwest Windpower Skystream 2.4 kW wind turbine located at the edge of the city of Calgary. About 15 minutes of data were recorded in order to extract natural frequencies and corresponding mode shapes while the turbine was in operating. Operational modal analysis (OMA), in which input loads are considered the ambient input, is conducted to identify dominant modes up to 100 Hz. This range covers the infrasound region (<20 Hz) that might be perceived at sufficiently high sound pressure levels. The captured modal frequencies and modal shapes compared favorably to those predicted by a finite element analysis. Results indicate that a cluster of modes located around 10 Hz show significantly higher magnitude than other modes. This corresponds to the second bending mode. Short-time Fourier transform was used to distinguish natural and forced frequencies. It was seen that higher modes were exited less than lower ones. Original signals were decomposed using discrete wavelet transform to obtain different frequency bands. Relative root mean square values for each frequency band were calculated to determine the contribution to the vibration energy. It was observed that most of vibration energy occurs in the lowest frequency band which is in the infrasound region. The accelerometers were monitored while the blades and generator accelerated and decelerated as the wind speed changed, and only the first bending mode was excited significantly which apparently generates most of noise emission.
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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".