Finite Element Model of Concrete-Filled, Fiber-Reinforced Polymer Tubes for Small-Scale Wind Turbine Towers
Bibliographic record
Abstract
The finite element method was used to study the feasibility of concrete-filled, fiber-reinforced polymer tubes (CFFTs) for small-scale wind turbine towers in remote areas. Although CFFTs have been successfully employed for a variety of structural applications, their use for wind turbine towers is novel and has yet to be investigated in detail. The objective of the study was to identify, for the first time, the most important parameters for design and compare the behavior of CFFT towers versus conventional steel and concrete towers. The model was first validated using experimental results reported in the literature followed by a series of parametric studies to evaluate the importance of several key parameters. In the first phase, the effect of different geometric properties (taper and concrete filling ratio) and reinforcement configurations (FRP laminate configuration, steel reinforcement ratio, and prestressing level) were investigated for cantilever tower models with concentrated lateral loads. A 10 m high CFFT wind turbine tower model was subsequently modeled and studied under different loading configurations. The influence of the height-to-diameter (h/D) ratio on cantilever CFFT models was also studied and a conservative preliminary design that can be refined for specific turbine systems and wind conditions was adopted using the h/D ratio. The CFFT tower model was compared to concrete and steel tubular models with similar geometry to study the advantages of CFFT towers and showed that CFFTs can be an efficient alternative.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".