6卷 第2期
1.Hunan Provincial Key Laboratory of Mechanical Equipment Health Maintenance
2.Hunan University of Science and Technology, Hunan Xiangtan 411201
纸质出版日期：2025-02-25
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(2025). Optimization of the Aerodynamic Shape of UAVs Based on Multiple Regression-SLSQP Algorithm. 科技理论与实践（英文版）, 6(2), 133-143.
Zhang Xu,Jiang Yongzheng,Tao Lian,et al.Optimization of the Aerodynamic Shape of UAVs Based on Multiple Regression-SLSQP Algorithm[J].Theory and Practice of Science and Technology,2025,06(02):133-143.
(2025). Optimization of the Aerodynamic Shape of UAVs Based on Multiple Regression-SLSQP Algorithm. 科技理论与实践（英文版）, 6(2), 133-143. DOI： 10.47297/taposatWSP2633-456926.20250602.
Zhang Xu,Jiang Yongzheng,Tao Lian,et al.Optimization of the Aerodynamic Shape of UAVs Based on Multiple Regression-SLSQP Algorithm[J].Theory and Practice of Science and Technology,2025,06(02):133-143. DOI： 10.47297/taposatWSP2633-456926.20250602.
In view of the problem that for the aerodynamic shape layouts formed by the interactions of different aerodynamic components under different flight indicators
there is a lack of a universal aerodynamic optimization method
making it difficult to achieve the optimal aerodynamic shape layout of the combination of various aerodynamic components according to the design indicators
an optimization design method for the aerodynamic shape layout of unmanned aerial vehicles (UAVs) based on the multiple regression - SLSQP (Sequential Least Squares Programming) algorithm is proposed. Taking a certain long - endurance fixed - wing UAV as an example
its aerodynamic shape layout is optimized to improve its maximum flight range performance. A database of aerodynamic performance influencing factors is constructed through the layout of each aerodynamic shape component
and a multiple regression model is constructed based on this to obtain the corresponding relational expressions. Finally
the SLSQP algorithm is used to solve for the optimal aerodynamic shape under the constraint conditions. Verified by calculations using CFD software
the results show that this method has high accuracy
with a prediction error of less than 1%. The maximum range performance has been improved by nearly 4 percentage points
providing a valuable reference for the design optimization of the aerodynamic shape layout of fixed - wing UAVs.
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