格子博尔茨曼方法的不确定性量化,重点研究人体规模的血管血流
Jon W S McCullough1, Peter V Coveney2,3,4
1Centre for Computational Science, Department of Chemistry, University College London, London, UK.
Scientific reports
|May 17, 2024
概括
在格子博尔茨曼法 (LBM) 中,不确定性量化 (UQ) 对于可靠的医疗器械设计至关重要. 算法参数,如时间步骤和网格间距,以及边界条件,对血管流动的模拟结果产生重大影响.
科学领域:
- 计算流体动力学 计算流体动力学
- 生物医学工程 生物医学工程
- 数字分析 数字分析
背景情况:
- 不确定性量化 (UQ) 对于在医疗器械设计等领域对数值模型的信任至关重要.
- 格子博尔茨曼法 (LBM) 是一种广泛使用的计算流体动力学 (CFD) 方法.
- 了解LBM的数值不确定性是其在科学和工业中的应用的关键.
研究的目的:
- 进行一个深入的UQ研究的LBM在一个规范的分叉几何体代表的血管结.
- 研究LBM算法参数和边界条件对感兴趣的量 (压力和速度) 的影响.
- 展示UQ技术在个性化血管几何形状上的应用.
主要方法:
- 应用UQ到LBM在一个分叉几何和个性化容器.
- 检查了算法参数 (时间步骤,网格间距) 和边界条件 (入口速度,出口压力) 对压力和速度的影响.
- 在没有代码修改的情况下,在高性能计算机上使用了UQ的开源工具包.
主要成果:
- 算法参数:时间步骤的数量和网格间距的大小是最有影响的.
- 边界条件:输入速度大小和平均输出压力产生了最大的影响.
- 参数可变性:输入速度变化显著放大了输出可变性.
结论:
- UQ洞察力指导血管LBM模拟输入参数和边界条件的选择.
- 更好地了解LBM的数值不确定性特征,以提高CFD和医疗应用中的可靠性.
- 开源的UQ工具包有助于在数值建模中采用不确定性分析,降低了复杂性.
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