.png){kind=icon}
.png){kind=icon}
Return
Aerodynamic/control coupling optimization method for reusable vehicles using adaptive partitioning
.png){kind=icon}
OA
AI
DOI：10.1016/j.cja.2025.103899.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
To address the issues of poor lateral stability, strong interactions between channels, and the inherent trade-offs of global optimization methods in the aerodynamic shape design of reusable hypersonic vehicles, this study proposes an Adaptive Partitioning Optimization (APO) method for aerodynamic/control coupling design. The APO method explicitly integrates the interactions between aerodynamic configuration and longitudinal/lateral control performance, while introducing a variable correlation-based partitioning strategy. This enables multi-channel aerodynamic/control collaborative optimization while avoiding the performance compromises associated with global multidisciplinary optimization. To address the high computational cost of control performance evaluation, a sample augmentation strategy with interpolation correction is introduced, reducing cost while maintaining accuracy. Optimization of a representative reusable vehicle demonstrates that this framework achieves a 2.23% increase in lift-to-drag ratio, a 0.56% reduction in drag coefficient, and enhanced lateral stability. Moreover, it achieves better coordination between aerodynamic and control objectives compared to global optimization. These results highlight the practical value of the APO method in improving aerodynamic and control performance for reusable hypersonic vehicles, offering a scalable and computationally efficient solution for multidisciplinary aerodynamic/control co-design in hypersonic vehicle applications.
Keywords:
reusable vehicle
aerodynamic/control coupling
shape optimization
multichannel control
adaptive partitioning
small sample interpolation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.7
Papers:
4.7K
Citations:
1.4W