| 标 题： |
A Data-Driven Body Vortex Modeling for Engineering-Level Missile Aerodynamic Prediction
基于数据的体涡旋建模用于工程级导弹空气动力学预测 |
| 作 者： |
Daeyoung Jun
Hansol Nam
Hanphil Yoo
Bok Jik Lee
Hyoungjin Kim |
| 关键词： |
Missile aerodynamics [导弹空气动力学]
Missile DATCOM [ 导弹DATCOM]
Body vortex tracking [ 机身涡流追踪]
Artificial neural network [ 人工神经网络] |
| 出版年： |
2026 |
| 期刊名： |
International Journal of Aeronautical and Space Sciences
国际航空与空间科学杂志 |
| 年卷期： |
|
| 摘 要： |
Modeling body vortex is essential for accurate missile aerodynamic prediction at high angles of attack, where separated flow dominates. This study investigates the impacts of body vortex on engineering-level missile aerodynamic prediction and compares the results with those of a semi-empirical vortex model. A vortex-tracking model based on two-dimensional potential flow is implemented in Missile DATCOM, with vortex strength and separation locations provided by an artificial neural network trained on body-alone CFD databases. The implemented method improves the prediction of local angle-of-attack changes near the body and yields more accurate nonlinear aerodynamic forces across body-fin and body-canard-tail configurations. Compared to the semi-empirical model, it better captures vortex interference, particularly for fins with low semispan. Notable improvements are observed in high angle-of-attack regimes and under roll control conditions.
在攻角较大、分离流占主导的条件下，准确预测导弹气动性能的关键在于对机身涡流的建模。本研究探讨了机身涡流对工程级导弹气动预测的影响，并将结果与半经验性涡流模型进行对比。研究在Missile DATCOM中实现了一种基于二维势流的涡流追踪模型，其中涡流强度和分离位置由训练于仅包含机身CFD数据库的人工神经网络提供。该方法提升了对机身附近局部攻角变化的预测能力，并在机翼尾翼及机翼前缘-尾翼配置下，提供了更精确的非线性气动载荷。与半经验性模型相比，该方法能更好地捕捉涡流干扰效应，尤其在半展弦比较小的机翼上表现更优。此外，在高攻角区域以及滚转控制条件下，也观察到了显著的改进效果。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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