Integrated FEM, CFD, and BIM Approaches for Optimizing Pre-Stressed Concrete Wind Turbine Tower Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Today, all over the world, people are looking for sustainable energy with modern systems for the coming generations. Wind energy plays a crucial role in supplying electricity to modern systems worldwide. Onshore turbines are necessary to ensure efficient and economical operation of taller wind towers, which can reach up to 100 m. However, building taller turbine towers faces many challenges, such as complex cross-sectional design, multiple stresses, and high construction costs due to different variables. To combat these challenges, this article proposes an optimization design aimed at enhancing the cost-effectiveness of the pre-stressed concrete wind turbine industry, making it accessible to the wind turbine market and design engineers. The main idea of the research is an integration of design criteria and cost conditions by creating a C# plugin to determine the optimal design with minimum cost as an add-in to a 3D software simulating program. This integration helps to calculate computational fluid dynamics (CFD) using the finite element method (FEM) and minimizes costs in building information modeling (BIM), which covers some gaps from the previous works. The study presents a methodology for designing concrete wind towers and facilitating data exchange between finite element software (Ansys) and BIM software by IFC files. The optimization problem in this article is a multi-objective problem, with an optimal design that minimizes costs by reducing the vibrational wear satisfied by suitable structural stiffness. Results showed an optimal design for the concrete wind tower, resulting in a 24% reduction in material costs for the same height compared to conventional alternatives. Doi: 10.28991/CEJ-2025-011-02-08 Full Text: PDF
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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 it