论文标题
直接沉浸式几何流体流量和由点云代表的物体的传热分析
Direct Immersogeometric Fluid Flow and Heat Transfer Analysis of Objects Represented by Point Clouds
论文作者
论文摘要
沉浸式几何分析(IMGA)是一种几何灵活的方法,它使人们能够使用复杂的计算机辅助设计(CAD)模型直接执行多物理分析。在本文中,我们开发了一种新型的IMGA方法,用于模拟围绕点云代表的复杂几何形状的不可压缩和可压缩流。点云对象的几何形状使用欧几里得空间中的一组非结构化点表示,并以表面正态的形式(可能的)方向信息表示。由于在点云模型中没有拓扑信息,因此无法保证几何表示是水密或2个manifold或具有一致的正常性。要使用点云几何形状直接执行IMGA,我们首先开发了一种直接从点云估算内部外部信息和表面正常的方法。我们还提出了一种计算弱执行Dirichlet边界条件所需的表面积分(在点云上)的Jacobian决定因素的方法。我们通过比较从点云计算的几何量与从分析几何形状和镶嵌CAD模型获得的几何量来验证这些几何估计方法。在这项工作中,我们还开发了热力IMGA,以模拟在复杂几何形状上流动的情况下进行热传递。对所提出的框架进行了针对以点云表示的几何问题的多种雷诺和马赫数的测试,显示了该方法的鲁棒性和准确性。最后,我们通过在大型工业规模的建筑机械上执行IMGA来证明我们的方法的适用性,该机械使用超过1200万点的点云代表。
Immersogeometric analysis (IMGA) is a geometrically flexible method that enables one to perform multiphysics analysis directly using complex computer-aided design (CAD) models. In this paper, we develop a novel IMGA approach for simulating incompressible and compressible flows around complex geometries represented by point clouds. The point cloud object's geometry is represented using a set of unstructured points in the Euclidean space with (possible) orientation information in the form of surface normals. Due to the absence of topological information in the point cloud model, there are no guarantees for the geometric representation to be watertight or 2-manifold or to have consistent normals. To perform IMGA directly using point cloud geometries, we first develop a method for estimating the inside-outside information and the surface normals directly from the point cloud. We also propose a method to compute the Jacobian determinant for the surface integration (over the point cloud) necessary for the weak enforcement of Dirichlet boundary conditions. We validate these geometric estimation methods by comparing the geometric quantities computed from the point cloud with those obtained from analytical geometry and tessellated CAD models. In this work, we also develop thermal IMGA to simulate heat transfer in the presence of flow over complex geometries. The proposed framework is tested for a wide range of Reynolds and Mach numbers on benchmark problems of geometries represented by point clouds, showing the robustness and accuracy of the method. Finally, we demonstrate the applicability of our approach by performing IMGA on large industrial-scale construction machinery represented using a point cloud of more than 12 million points.