基于神经网络的熔铸炸药热物性参数反演

蒋杰; 张向荣; 岳跃辉; 钟光天; 刘攀; 周霖

doi:10.12382/bgxb.2025.0797

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基于神经网络的熔铸炸药热物性参数反演

更新时间：2026-04-09

- 基于神经网络的熔铸炸药热物性参数反演
- Inversion of Thermophysical Properties for Melt-Cast Explosives via Artificial Neural Networks
- 兵工学报 2026年
- 作者机构：
  
  1. 北京理工大学机电学院,北京,100081
  2. 重庆红宇精密工业集团有限公司,重庆,402760
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0797
  中图分类号： TJ55
- 收稿：2025-09-01，
  
  网络首发：2026-04-09，
- 稿件说明：
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蒋杰，张向荣，岳跃辉，等. 基于神经网络的熔铸炸药热物性参数反演[J/OL]. 兵工学报, 2026(2026-04-09). https://doi.org/10.12382/bgxb.2025.0797.

JIANG J, ZHANG X, YUE Y H, et al. Inversion of thermophysical properties for melt-cast explosives via artificial neural networks[J/OL]. Acta Armamentarii, 2026(2026-04-09). https://doi.org/10.12382/bgxb.2025.0797. (in Chinese)
蒋杰，张向荣，岳跃辉，等. 基于神经网络的熔铸炸药热物性参数反演[J/OL]. 兵工学报, 2026(2026-04-09). https://doi.org/10.12382/bgxb.2025.0797. DOI：

JIANG J, ZHANG X, YUE Y H, et al. Inversion of thermophysical properties for melt-cast explosives via artificial neural networks[J/OL]. Acta Armamentarii, 2026(2026-04-09). https://doi.org/10.12382/bgxb.2025.0797. (in Chinese) DOI：

摘要

针对于传统热物性参数测定依赖于试验测量，耗时费力且难以获取连续温度函数的问题，通过一种基于贝叶斯正则化优化双层反向传播（BackPropagation

BP）神经网络的反演方法，可以精确获取熔铸炸药基体2

4-二硝基苯甲醚（2

4-Dinitroanisole，DNAN）在凝固过程中的关键热物性参数（导热系数与比热容）。通过构建以径向多个测温点冷却时间差为输入、对应温度下热物性参数为输出的映射关系，利用有限元仿真生成大量训练样本，并结合试验数据对网络进行训练与验证。研究结果表明，该神经网络模型在反演导热系数和比热容时决定系数达到0.994，反演结果与实测数据对比平均误差小于5%，具有很好的预测精度和良好的工程适用性，为热物性参数的获取提供了新途径。

Abstract

Conventionalmethods for determiningthermophysicalproperties predominantly rely on experimental measurements

which are often time-consuming

labor-intensive

and inadequate for obtaining continuous temperature-dependent functions. To address these limitations

usingan inverse method based on a two-layer Backpropagation (BP) neural network optimized with Bayesian regularization. This approach is designed to accurately retrieve keythermophysicalparameters—namely thermal conductivity and specific heat capacity—during the solidification process of melt-cast explosives. A mapping relationship was constructed using the cooling time differences from multiple radial temperature measurement points as inputs and the correspondingthermophysicalproperties at specific temperatures as outputs. A substantial set of training samples was generated via finite element simulation

and the network was subsequently trained and validated with experimental data. The results demonstrate that the neural network model achieves a coefficient of determination of 0.994 for the inversion of both thermal conductivity and specific heat capacity. The average error between the inverted results and the measured data is less than 5%

indicating high predictive accuracy and strong engineering applicability. This method provides a novel and efficient pathway for acquiring thethermophysicalparameters of melt-cast explosives.

关键词

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