Integrated FEM, CFD, and BIM Approaches for Optimizing Pre-Stressed Concrete Wind Turbine Tower Design
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".