In view of the consensus that the oversized initial combustion surface area of fractured propellant charge is the main cause of breech blow, the evaluation method for launch safety of propellant charge with initial dynamic vivacity ratio as the characterization parameter has drawn extensive attentions and inspired many variants. However, the characterization of propellant charge stacking configuration and the prediction of initial dynamic vivacity ratio are still two unsolved problems. To fill the gaps, this study provides a novel evaluation method for the launch safety of propellant charge through applying the machine learning and the error analysis methods. A new description with three parameters is proposed for dynamic compression and fracture process, and verified more accurate than the previous method with maximum compression stress as the single parameter. In addition, it identifies that the support vector regression is more suitable than back propagation neural network and least squares support vector machine in small sample training. And the corresponding model has been demonstrated by experiments to be of convinced accuracy and superior generalization capability. Through determining the model error distribution, this study makes it feasible to predict initial dynamic vivacity ratio and give an upper limit value with high confidence.

鉴于破裂装药初始燃烧表面积过大是造成后膛爆炸的主要原因这一共识，以初始动态活度比为表征参数的装药发射安全性评价方法受到广泛关注，并启发了许多变种。 . 然而，推进剂电荷堆积构型的表征和初始动态活力比的预测仍然是两个未解决的问题。为填补这一空白，本研究通过应用机器学习和误差分析方法为推进剂发射安全性提供了一种新的评估方法。提出了动态压缩和断裂过程的三个参数的新描述，并以最大压缩应力为单一参数验证比之前的方法更准确。此外，它确定支持向量回归比反向传播神经网络和最小二乘支持向量机更适合小样本训练。并且相应的模型已经通过实验证明具有令人信服的准确性和优越的泛化能力。本研究通过确定模型误差分布，使得预测初始动态活力比并给出高可信度的上限值成为可能。并且相应的模型已经通过实验证明具有令人信服的准确性和优越的泛化能力。本研究通过确定模型误差分布，使得预测初始动态活力比并给出高可信度的上限值成为可能。并且相应的模型已经通过实验证明具有令人信服的准确性和优越的泛化能力。本研究通过确定模型误差分布，使得预测初始动态活力比并给出高可信度的上限值成为可能。