基于机器学习的多目标优化直刀式混合机,用于在造爆炸物系统中高效混合
Chong Li1, Jian Wang2, Junjiong Meng3
1Xi'an Modern Chemistry Research Institute, Xi'an, 710065, China. MCRIchong@163.com.
Scientific reports
|December 11, 2025
概括
这项研究利用机器学习优化了用于造炸药的直刀混合器,如三二烯-环二甲二二胺 (TNT-RDX). 开发的算法平衡了混合效率和压力,提高了产品的性能.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 材料科学 材料科学 材料科学
- 计算流体动力学的流体动力学.
背景情况:
- 有效的混合对于造爆炸物的性能至关重要.
- 三托烯-循环三甲烯-三丁胺 (TNT-RDX) 系统面临着特殊的混合挑战.
- 混合的冲突目标包括最大化流速和最小化压力差异.
研究的目的:
- 开发一套基于机器学习的多目标优化算法,用于直刀混合机.
- 为了解决最大化平均流速和最小化最大压力差的相互矛盾的要求.
- 为了优化混合器的设计,以提高造爆炸物生产.
主要方法:
- 利用历史实验和模拟数据来训练随机森林回归模型.
- 采用基于机器学习的替代模型与非主导排序遗传算法II (NSGA-II) 相结合.
- 使用量子化学方法研究非牛顿流体的形成,并进行数值计算.
主要成果:
- 基于结构和操作参数,准确预测混合结果.
- 确定混合机设计的帕雷托最佳解决方案.
- 实验验证证了优化直系统的有效性.
- 证明了非牛顿流体系统的设计方法的多功能性.
结论:
- 开发的机器学习算法成功优化了用于造炸药的直刀式混合器.
- 优化的设计实现了高效率,与其他先进的轮相提并论.
- 该研究为设计系统提供了一种多功能方法,即使是复杂的非牛顿流体.
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