基于B样条神经网络的熔铸装药温度场预测

doi:10.12382/bgxb.2022.0086

摘要/Abstract

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。

关键词: 熔铸装药, B样条神经网络, 水/油浴工艺, 温度场

Abstract:

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.

Key words: melt-cast explosive, B-spline neural network, water/oil bath process, temperature field

陶磊, 刘检华, 夏焕雄, 敖晓辉, 高丰. 基于B样条神经网络的熔铸装药温度场预测[J]. 兵工学报, 2023, 44(5): 1339-1349.

TAO Lei, LIU Jianhua, XIA Huanxiong, AO Xiaohui, GAO Feng. Temperature Field Prediction of Melt-cast Explosives Based on a B-spline Neural Network[J]. Acta Armamentarii, 2023, 44(5): 1339-1349.

图/表 13

参考文献 32

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项目	炸药	模具	冒口
材料	DNAN基炸药	Steel	铝合金
导热系数λ/ (W·m^-1K^-1)	0.72(20℃)	50	176
	0.70(65℃)
	0.64(110℃)
密度ρ/(kg·m^-3)	1858(20℃)	7940	2700
	1810(65℃)
	1700(110℃)
比热容c_p/(J·kg^-1K^-1)	1130(20℃)	450	900
	1320(65℃)
	1450(110℃)
固相线温度T_sol/℃	85
液相线温度T_liq/℃	90
浇注温度T_j/℃	100
与外界的换热系数h₁/ (W·m^-2K^-1)	20	300	200
与炸药的换热系数h₂/ (W·m^-2K^-1)		200	100
环境温度/℃	T₀

项目	炸药	模具	冒口
材料	DNAN基炸药	Steel	铝合金
导热系数λ/ (W·m^-1K^-1)	0.72(20℃)	50	176
	0.70(65℃)
	0.64(110℃)
密度ρ/(kg·m^-3)	1858(20℃)	7940	2700
	1810(65℃)
	1700(110℃)
比热容c_p/(J·kg^-1K^-1)	1130(20℃)	450	900
	1320(65℃)
	1450(110℃)
固相线温度T_sol/℃	85
液相线温度T_liq/℃	90
浇注温度T_j/℃	100
与外界的换热系数h₁/ (W·m^-2K^-1)	20	300	200
与炸药的换热系数h₂/ (W·m^-2K^-1)		200	100
环境温度/℃	T₀

试验组	水浴初始温度 T₁₀/℃	水浴降温速率v₁/ (℃·min^-1)	油浴初始温度 T₂₀/℃	油浴保温时间 t₂/h	油浴降温速率v₂/ (℃·min^-1)
1	90	0.15	95	4	0.5
2	90	0.2	100	5	0.6
3	90	0.25	105	6	0.7
4	95	0.15	95	5	0.6
5	95	0.2	100	6	0.7
6	95	0.25	105	4	0.5
7	100	0.15	100	4	0.7
8	100	0.2	105	5	0.5
9	100	0.25	95	6	0.6
10	90	0.15	105	6	0.6
11	90	0.2	95	4	0.7
12	90	0.25	100	5	0.5
13	95	0.15	100	6	0.5
14	95	0.2	105	4	0.6
15	95	0.25	95	5	0.7
16	100	0.15	105	5	0.7
17	100	0.2	95	6	0.5
18	100	0.25	100	4	0.6
19	92	0.175	102.5	5.5	0.55
20	95	0.225	100	5	0.6
21	98	0.2	97.5	4.5	0.65

试验组	水浴初始温度 T₁₀/℃	水浴降温速率v₁/ (℃·min^-1)	油浴初始温度 T₂₀/℃	油浴保温时间 t₂/h	油浴降温速率v₂/ (℃·min^-1)
1	90	0.15	95	4	0.5
2	90	0.2	100	5	0.6
3	90	0.25	105	6	0.7
4	95	0.15	95	5	0.6
5	95	0.2	100	6	0.7
6	95	0.25	105	4	0.5
7	100	0.15	100	4	0.7
8	100	0.2	105	5	0.5
9	100	0.25	95	6	0.6
10	90	0.15	105	6	0.6
11	90	0.2	95	4	0.7
12	90	0.25	100	5	0.5
13	95	0.15	100	6	0.5
14	95	0.2	105	4	0.6
15	95	0.25	95	5	0.7
16	100	0.15	105	5	0.7
17	100	0.2	95	6	0.5
18	100	0.25	100	4	0.6
19	92	0.175	102.5	5.5	0.55
20	95	0.225	100	5	0.6
21	98	0.2	97.5	4.5	0.65

测试组	参数	时间/s
测试组	参数	1000	6000	8000	10000	15000	25000
	μ	0.0778	-0.0921	0.0279	0.0578	0.0410	0.0704
19	σ	0.2202	0.4657	0.5285	0.3485	0.3903	0.3197
	μ_abs	0.1852	0.3434	0.3842	0.2260	0.2459	0.2070
	ΔT_max	0.5171	1.4519	1.6954	1.3802	1.3620	1.2470
	μ	0.1737	-0.0025	-0.0703	-0.0063	0.0219	-0.0286
20	σ	0.2219	0.6407	0.4891	0.3089	0.4052	0.1813
	μ_abs	0.2505	0.4450	0.3360	0.2283	0.2699	0.1153
	ΔT_max	0.5154	2.1489	2.6952	0.9250	1.5040	0.6326
	μ	-0.0060	0.0074	0.1287	-0.0456	0.0008	0.0145
21	σ	0.1800	0.3327	0.5125	0.3314	0.3535	0.1936
	μ_abs	0.1297	0.2368	0.3699	0.2586	0.2397	0.1574
	ΔT_max	0.5051	1.1007	1.8330	1.2420	1.2572	0.5140