高压工况下底排推进剂燃速的反向传播神经网络模型

 为了研究底排推进剂在火炮膛内随弹丸运动时的燃烧特性，采用密闭爆发器仿真实验技术，针对底排推进剂在膛内高压工况下的燃烧特性进行实验研究，获得了两种不同装填密度下平均压力随时间变化的关系，并对压力进行了全程热散失修正。采用多次平滑、滤波数据处理技术和发射药燃速处理方法，得到了燃速与压力（8~150 MPa）之间的关系。基于实验数据特征样本，建立并训练得到了底排推进剂高压工况下的反向传播（Back Propagation）神经网络燃速模型，该模型与传统的指数模型相比，具有拟合精度高和稳定性强的特点。

Back Propagation Neural Networks of Base Bleed Propellant Burning Rate under High Pressure Condition

To investigate the combustion characteristics of base bleed propellant moving with the projectile in the gun bore,the closed bomb semi-physical and experimental simulation technology was employed.The combustion property under the condition of simulative high pressure in the gun bore was studied.Two average pressure-time curves corrected by heat loss were obtained under different charge density of the closed bomb.The correlation data between burning rate and pressure(8-150 MPa) were processed by smoothing,filtering and data transformation.The burning rate model adopting back propagation(BP) neural networks describing,which is suitable for high combustion pressure condition,was built based on training data samples.Comparing with the exponential burning rate model,the BP neural networks burning rate model of base bleed propellant has higher fitting precision and stronger robust.