基于B样条神经网络的熔铸装药温度场预测  

Temperature Field Prediction of Melt-cast Explosives Based on a B-spline Neural Network

作　　者：陶磊[1] 刘检华[1,2] 夏焕雄 敖晓辉 高丰 TAO Lei;LIU Jianhua;XIA Huanxiong;AO Xiaohui;GAO Feng(School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China;Tangshan Research Institute,Beijing Institute of Technology,Tangshan 063015,Hebei,China)

机构地区：[1]北京理工大学机械与车辆学院,北京100081 [2]北京理工大学唐山研究院,河北唐山063015

出　　处：《兵工学报》2023年第5期1339-1349,共11页Acta Armamentarii

基　　金：基础加强计划项目(2019-JCJQ-ZD-314);国家自然科学基金项目(51905038、52105504)。

摘　　要：熔铸装药过程中模具内部温度场分布及其变化规律对装药质量具有重要影响。建立基于B样条神经网络的水/油浴熔铸装药工艺瞬态温度场预测模型,通过数值仿真的正交试验,获得不同工艺条件下熔铸装药温度场演变的数据样本;利用B样条神经网络对数据样本进行训练,得到水/油浴工艺的温控参数与药柱内部温度场之间的关系模型,实现温度场及其凝固前沿演变的快速准确预测。所得成果为熔铸装药的温控参数优化和在线控制提供了高效预测方法,为解决熔铸装药智能化发展中的物理场预测问题提供了方法的借鉴。The temperature field distribution and evolution inside the mold play a crucial role in determining the casting quality of melt-cast explosive processes.A fast prediction model is developed based on a B-spline neural network for the transient temperature field in a melt-cast explosive process with a water/oil bath.The model is created by first obtaining temperature evolution data samples under different processing conditions through orthogonal numerical experiments.The B-spline neural network is then trained on these data samples to establish a prediction model that represents the relationship between temperature-control parameters and the temperature field inside the grain.This model enables rapid and accurate prediction of the temperature field and solidification front,providing an efficient prediction method for parameter optimization and online control of melt-cast explosive processes.This study serves as a valuable reference for predicting other physical fields in the intelligent development of similar processes in the future.

关 键 词：熔铸装药 B样条神经网络 水/油浴工艺 温度场 

分 类 号：TJ55[兵器科学与技术—军事化学与烟火技术]