An Efficient Hybrid Weno Scheme with a Novel Scale Separation Criterion

不连续性分类 变量(数学) 系列(地层学) 应用数学 计算 流量(数学) 算法 计算机科学 非线性系统 湍流 休克(循环) 数学优化 数学 数学分析 物理 机械 几何学 医学 古生物学 量子力学 内科学 生物
作者
Xuan Liu,Meiyuan Zhen,Jinsheng Cai,Fei Liao
标识
DOI:10.2139/ssrn.4693076
摘要

The WENO family schemes have been widely used in various compressible turbulence flow simulations due to its excellent shock-capturing capability and high resolution. However, due to the massive calculation is needed by WENO scheme, the variable-based flux is usually used in engineering numerical calculation, rather than characteristic-based reconstruction that require matrix operation. For problems containing strong shocks, variable-based reconstruction may produces numerical oscillations, while characteristic-based reconstruction rarely do. In this paper, a novel scale separation mechanism without free parameter is proposed to construct an efficient hybrid WENO scheme, i.e. WENO-H, by which the characteristic-based or variable-based reconstruction can be accurately selected for different region of the flow, so as to obtain a higher resolution and more stable shock capturing scheme while improving the efficiency. According to the novel scale separation mechanism the new scheme performs characteristic-based reconstruction near discontinuities and switches to variable-based reconstruction for smooth region. Linear variable-based fluxes with less computation are used directly in the smooth region, while nonlinear WENO-Z and more robust characteristic-base fluxes are used in the discontinuous region. Several one dimensional and two dimensional numerical tests are performed to validate and evaluate the scheme. Numerical results shows that WENO-H series schemes maintain essentially non-oscillatory flow filed near discontinuities. Besides, compared to the WENO-Z, the WENO-H series schemes are 10% faster in 1D problem and 30% faster in 2D problem, and saved more than 1.6 times computational cost compared with TENO.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
共享精神应助66采纳,获得10
2秒前
钟旭发布了新的文献求助10
3秒前
调研昵称发布了新的文献求助10
4秒前
sedrakyan发布了新的文献求助10
4秒前
大个应助毛啊毛啊毛采纳,获得10
5秒前
猫小曼完成签到,获得积分10
5秒前
6秒前
1234354346完成签到 ,获得积分10
7秒前
李知恩完成签到 ,获得积分10
9秒前
river123完成签到,获得积分10
9秒前
10秒前
10秒前
搜集达人应助大白采纳,获得10
11秒前
小团月完成签到 ,获得积分10
11秒前
Jasper应助Sun采纳,获得10
12秒前
富兰克林的薄荷糖给Starry的求助进行了留言
12秒前
自觉平露完成签到,获得积分10
12秒前
单纯夏云完成签到,获得积分10
14秒前
12345发布了新的文献求助20
14秒前
今后应助钟旭采纳,获得10
15秒前
15秒前
15秒前
16秒前
今后应助活力的秋烟采纳,获得10
16秒前
脑洞疼应助花花采纳,获得10
16秒前
无奈的台灯完成签到,获得积分10
16秒前
Cyanide完成签到,获得积分10
18秒前
Lvhao应助好好好采纳,获得10
18秒前
Foremelon发布了新的文献求助10
19秒前
cccx完成签到 ,获得积分10
19秒前
快乐应助咕噜咕噜采纳,获得10
19秒前
李爱国应助yuan采纳,获得10
20秒前
qiaoqiao发布了新的文献求助10
20秒前
22秒前
22秒前
24秒前
24秒前
花花完成签到,获得积分10
26秒前
27秒前
小二郎应助南禾采纳,获得10
28秒前
高分求助中
Evolution 10000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 600
Distribution Dependent Stochastic Differential Equations 500
A new species of Coccus (Homoptera: Coccoidea) from Malawi 500
A new species of Velataspis (Hemiptera Coccoidea Diaspididae) from tea in Assam 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3157519
求助须知:如何正确求助?哪些是违规求助? 2808900
关于积分的说明 7879102
捐赠科研通 2467351
什么是DOI,文献DOI怎么找? 1313394
科研通“疑难数据库(出版商)”最低求助积分说明 630395
版权声明 601919