Optimized Design of Bionic Wings Considering Dynamic Soaring Performance

ZHANG Shenwei; XIANG Tao; ZHANG Hang

doi:10.7652/xjtuxb202601014

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Optimized Design of Bionic Wings Considering Dynamic Soaring Performance

Special Topic Pollution Control and Resource Utilization in the “Dual Carbon”Context | 更新时间：2026-01-15

- Optimized Design of Bionic Wings Considering Dynamic Soaring Performance
- Journal of Xi'an Jiaotong University Vol. 60, Issue 1, Pages: 137-149(2026)
- 作者机构：
  
  长安大学工程机械学院，710064，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202601014
  CLC： V221
- Received：31 March 2025，
  
  Published：10 January 2026
- 稿件说明：
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ZHANG Shenwei, XIANG Tao, ZHANG Hang. Optimized Design of Bionic Wings Considering Dynamic Soaring Performance[J]. Journal of Xi'an Jiaotong University, 2026, 60(1): 137-149.
DOI：

ZHANG Shenwei, XIANG Tao, ZHANG Hang. Optimized Design of Bionic Wings Considering Dynamic Soaring Performance[J]. Journal of Xi'an Jiaotong University, 2026, 60(1): 137-149. DOI： 10.7652/xjtuxb202601014.

摘要

为解决当前无人机续航能力有限的问题，基于信天翁的动态滑翔飞行机制，对考虑动态滑翔性能的仿生机翼进行优化设计，旨在提升无人机的动态滑翔性能与环境适应能力。根据信天翁的翅膀结构特点，建立了三段式仿生机翼的参数化模型，并选择展长、弦长、上反角和后掠角作为设计变量。基于已有研究成果，以最大升阻比为优化目标，结合动态滑翔的动力学模型构建了仿生机翼的优化模型。为了提高优化效率，利用神经网络拟合函数构建了仿生机翼的升力和阻力系数代理模型，并结合遗传算法对优化模型进行求解。仿真结果表明，优化后的机翼相较于基于图像测量的平直仿生机翼，在多个关键指标上实现显著提升：最大升阻比提高了13.73%，最小风梯度降低了16.23%，最大迎角增加了60%，最大升力系数增加了5.9%，最大总能量增加了33.61%。研究结果表明，优化后的仿生机翼具有更好的动态滑翔性能、更强的环境适应性及能量获取和利用能力。该研究为高动态滑翔能力的长续航仿生无人机的设计与优化提供了新的方法与思路。

Abstract

To address the limited endurance of current unmanned aerial vehicles (UAVs)，an optimized design of bionic wings considering dynamic soaring performance is proposed based on the dynamic soaring mechanism of albatrosses，aiming to enhance the dynamic soaring performance and environmental adaptability of UAVs.Based on the structural characteristics of albatross wings，a parametric model of a three-segment bionic wing is established，with wingspan，chord length，dihedral angle，and sweep angle selected as design variables.Building on existing research，an optimized model for bionic wings is constructed with the maximum liftto-drag ratio as the optimization objective，incorporating the dynamic model of dynamic soaring. To enhance optimization efficiency，a surrogate model for the lift and drag coefficients of the bionic wing is developed using neural network fitting functions，and the optimized model is solved using a genetic algorithm.Simulation results show that compared to a straight bionic wing based on image measurement，the optimized wing achieves significant improvements in multiple key performance indicators：the maximum lift-to-drag ratio increases by 13.73%，the minimum wind gradient decreases by 16.23%，the maximum angle of attack increases by 60%，the maximum lift coefficient increases by 5.9%，and the maximum total energy increases by 33.61%.The results demonstrate that the optimized bionic wing exhibits better dynamic soaring performance，stronger environmental adaptability，and enhanced energy acquisition and utilization capabilities.This study provides new methods and insights for the design and optimization of long-endurance bionic UAVs with high dynamic soaring capabilities.

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references

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