To meet the demand for complex geometries and high resolutions of small-scale flow structures, a two-stage fourth-order subcell finite volume (SCFV) method combining the gas-kinetic solver (GKS) with subcell techniques for compressible flows over (unstructured) triangular meshes was developed to improve the compactness and efficiency. Compared to the fourth-order GKS-based traditional finite volume (FV) method, the proposed method realizes compactness effectively by subdividing each cell into a set of subcells or control volumes (CVs) and selecting only face-neighboring cells for high-order compact reconstruction. Because a set of CVs share a solution polynomial, the reconstruction is more efficient than that for traditional FV-GKS, where each CV needs to be separately reconstructed. Unlike in the single-stage third-order SCFV-GKS, both accuracy and efficiency are improved significantly by two-stage fourth-order temporal discretization, for which only a second-order gas distribution function is needed to simplify the construction of the flux function and reduce computational costs. For viscous flows, it is not necessary to compute the viscous term with GKS. Compared to the fourth-stage Runge--Kutta method, one half of the stage is saved for achieving fourth-order time accuracy, which also helps to improve the efficiency. Therefore, a new high-order method with compactness, efficiency, and robustness is proposed by combining the SCFV method with the two-stage gas-kinetic flux. Several benchmark cases were tested to demonstrate the performance of the method in compressible flow simulations.


翻译:为了满足对复杂的地貌和小规模流动结构的高分辨率的需求,一种两阶段四级次细胞亚细胞定流量(SCFV)法,将气体动力求解器(GKS)与可压缩流到(非结构的)三角三角模层的子细胞技术相结合,以提高紧凑性和效率。与基于GKS的第四级传统定流量(FV)法相比,拟议方法通过将每个细胞细分为一组子细胞或控制量(CVs),并只选择面部相邻电池来进行高档契约重建,从而有效地实现紧凑性。由于一套CVS公司在(非结构化的)三角模件上拥有一个解决方案,因此重建效率比传统的FV-GKS公司(每个CV-GKS)要分别重建。与单阶段三级的SCFVV-GKS传统定容量(FV-GKS)方法不同,拟议方法的准确性能和效率都通过两阶段新的四级时间级时间分解化而大大提高,而只需要第二级气体分配功能来简化结构结构结构结构的精度,因此,而不能简化的节流流到比更精度,而降低的节流和计算成本的方法也显示。

0
下载
关闭预览

相关内容

机器学习系统设计系统评估标准
【NeurIPS2021】去栅格化的矢量图识别
专知会员服务
14+阅读 · 2021年11月18日
专知会员服务
22+阅读 · 2021年9月5日
专知会员服务
10+阅读 · 2021年6月20日
专知会员服务
17+阅读 · 2021年3月16日
【AAAI2021】基于图神经网络的文本语义匹配算法
专知会员服务
47+阅读 · 2021年1月30日
专知会员服务
59+阅读 · 2020年3月19日
已删除
将门创投
7+阅读 · 2019年10月15日
Arxiv
0+阅读 · 2021年12月16日
VIP会员
相关资讯
已删除
将门创投
7+阅读 · 2019年10月15日
Top
微信扫码咨询专知VIP会员