通过BP神经网络优化强度紧的DT中子发生器的目标材料
Pingwei Sun1, Jiayu Li1, Yingying Cao1
1School of Physics, Northeast Normal University, Changchun, 130024, China.
概括
这项研究使用了反向传播神经网络 (BPNN) 来确定中子发生器的最佳目标材料,减少了广泛的测试. 这些发现与模拟结果一致,并提高了中子产量.
科学领域:
- 材料科学 材料科学 材料科学
- 核工程 核工程是指核工程.
- 计算物理 计算物理
背景情况:
- 强烈的紧中子发生器需要具有优良热性能的目标材料来处理高功率负载 (千瓦).
- 目标系统通常由背面,目标膜和保护层组成,提供多种材料组合.
- 优化目标材料的传统方法涉及耗时和广泛的实验测试.
研究的目的:
- 通过反向传播神经网络 (BPNN) 评估不同目标材料的热特性.
- 确定目标材料的最佳组合,以提高中子发生器的性能.
- 为了验证模拟结果并讨论优化目标的冷却系统.
主要方法:
- 利用反向传播神经网络 (BPNN) 来评估目标材料的热性质.
- 使用FLUENT软件进行基于模拟的材料组合验证.
- 利用SRIM软件优化保护层和目标膜材料,并评估中子产量.
主要成果:
- BPNN成功地确定了目标材料的最佳组合.
- 来自BPNN分析的结果与FLUENT模拟结果一致.
- 在最佳配置下,SRIM软件有助于优化材料选择和评估中子产量.
结论:
- 开发的BPNN方法有效地确定了中子发生器的最佳目标材料组合.
- 优化的材料选择导致了更好的中子产量和热管理.
- 这种计算方法显著减少了在材料选择中进行广泛实验测试的需要.
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