Improvements for matrix-type implicit temporal scheme regarding entropy fix and local time step  

在线阅读下载全文

作  者:Jian LIU Jianqiang CHEN Yufeng YANG Bin MOU 

机构地区:[1]State Key Laboratory of Aerodynamics,Mianyang 621000,China [2]China Aerodynamics Research and Development Center,Mianyang 621000,China

出  处:《Chinese Journal of Aeronautics》2024年第11期138-146,共9页中国航空学报(英文版)

基  金:supported by the National Natural Science Foundation of China(Nos.12272397 and 11902334),the National Numerical Wind Tunnel Project,China。

摘  要:The matrix version of Symmetric Successive Over Relaxation(matrix-SSOR)scheme has been proved to be more efficient than the standard Lower-Upper Symmetric Gauss-Seidel(LUSGS),but less robust for high-speed flows.In order to ulteriorly improve the convergence rate as well as numerical stability of matrix-SSOR,two improvements regarding entropy fix and local time step have been proposed and validated.Firstly,an augmented entropy fix method is imposed on the inviscid Jacobian matrix and proved to be effective in two high-speed flows,in which the key parameter in entropy fix is discussed and found to be insensitive within appropriate range of values.Since the time step also has great effects on the numerical stability and convergence rate,a modified cell residual adapted local time step method with consideration of the residual history is developed,which is found to be effective for increasing the convergence rate when the matrix-SSOR is applied,but invalid when the LU-SGS is used.The proposed modified local time step method is also insensitive to the key parameter within appropriate range of values.The two modifications can be conveniently implanted into analogous matrix-type implicit schemes to improve the numerical performance.

关 键 词:Computational fluid dynamics LU-SGS Matrix-SSOR Entropy fix Local time step RESIDUAL 

分 类 号:O35[理学—流体力学]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象