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Design Methodology for Self-Similar Modular Assembly Lattice-Type Wind Turbine Supporting Structures Using Topology Optimization
1 School of New Energy, North China Electric Power University, Beijing, China
2 Department of Wind and Energy Systems, Technical University of Denmark, Kgs. Lyngby, Denmark
* Corresponding Author: Kai Long. Email:
(This article belongs to the Special Issue: Topology Optimization: Theory, Methods, and Engineering Applications)
Computer Modeling in Engineering & Sciences 2026, 146(3), 9 https://doi.org/10.32604/cmes.2026.078151
Received 25 December 2025; Accepted 09 February 2026; Issue published 30 March 2026
Abstract
Lattice-type ultra-tall wind turbine towers are popular in China for their modular benefits in fabrication, transportation, and installation. Nonetheless, their conceptual design remains predominantly dependent on engineering experience, and a generally applicable approach is still absent. This study proposes a self-similar modular topology optimization framework for lattice-type wind turbine support structures and develops software for its application. A minimum weighted compliance formulation with a prescribed volume fraction is developed utilizing the variable density approach, wherein modular constraints and their corresponding sensitivity expressions are explicitly included. The method is applied to a reference wind turbine model to generate modular lattice configurations. The novel structural models are evaluated under three representative design load cases outlined in IEC 61400 by finite element analysis. Compared with the reference structure, the 12-layer self-similar modular design reduces the maximum deformation and von Mises stress by 39.5% and 51.1%, respectively, demonstrating a substantial stiffness improvement while preserving modularity. The suggested approach provides an efficient and practical tool for the conceptual design of modular lattice-type wind turbine towers.Keywords
Cite This Article
Copyright © 2026 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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