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Topology Optimization with Generative Design on Albatross Inspired Wing
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A
DOI:10.1007/s42496-026-00308-5.png)
Abstract
En 中文
The albatross wing profile, renowned for its superior lift-to-drag ratio at low angles of attack, presents a promising model for enhancing the aerodynamic efficiency of fixed-wing Unmanned Aerial Vehicles (UAVs). UAVs can achieve improved aerodynamic performance by mimicking the wing profile of the albatross wing. However, the challenge lies in ensuring that the wing and its supporting structures are as lightweight as possible while maintaining the structural integrity required to withstand the stresses of flight. This study applies cloud-based generative design for topology optimization to an albatross-inspired wing rib and spar structure-extending prior work that has predominantly focused on generic airfoil geometries. Generative design leverages the computational power of cloud-based platforms to automatically generate and evaluate numerous design iterations. In each iteration, the algorithm redistributes material from low-stress regions to high-stress regions, evolving the geometry to meet predefined constraints such as strength, stiffness, and manufacturing feasibility. Through this iterative approach the design converges toward an optimized structure that minimizes weight while maintaining structural integrity. As a result, the optimized rib achieved a 3.20 times mass reduction, decreasing from 247 to 77 g, with a maximum von Mises stress of 4.44 MPa, confirming structural adequacy for the designed load. Through the integration of the generative design method in the design process, the study aims to develop a wing structure that not only mimics the aerodynamic efficiency of the albatross but also meets the stringent weight and strength requirements of the UAVs. The results of this research have the potential to contribute significantly to the field of UAV design, leading to more efficient, lightweight, and robust aerial vehicles.
Keywords:
Generative design
Albatross
Biomimetics
Topology optimization
Journal
A
IF:
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Papers:
25
Citations:
0
