基于CVAE的超高速碰撞碎片云运动过程的快速预测技术

在航天器防护构型设计中,需要快速、精确预测空间碎片超高速撞击防护屏产生碎片云的质量分布及其运动过程。采用深度学习方法,基于条件变分自编码器（CVAE）模型和大量铝球超高速正撞击铝板的光滑粒子流体动力学（SPH）方法的数值模拟结果,初步构建了碎片云空间质量分布与运动特征的快速预测模型。数值模拟中把铝球速度（3.00～8.00 km/s）、铝球半径（2.00～8.00 mm）、铝板厚度（1.000～4.000 mm）以及观测时间（1.0～12.0 μs） 4个变量作为输入控制参数,生成大量格式统一的训练集数据。模型隐藏层采用200个特征数据来描述碎片云质量分布,训练集参数范围内平均误差在0.6%以内,生成一个碎片云质量分布的平均时间小于7 ms。

Evolution prediction of HVI debris based on CVAE model

Efficiently and accurately predicting the evolution of hypervelocity impact debris is crucial in the design of spacecraft protective structures. To this end a deep learning model is constructed. The model is based on the conditional variation auto encoder (CVAE) and massive smoothed particle hydrodynamics (SPH) simulations. The model includes four controllable labels, namely projectile velocity (3.00-8.00 km/s) radius (2.00-8.00 mm), target plate thickness (1.000-4.000 mm), and time instant (1.0-12.0 μs). The model uses 200 parameters to describe the debris mass distribution, resulting in an average error less than 0.6%. It takes less than 7 micro seconds to predict one debris mass distribution.