atl_kerneldata_type Derived Type

type, public :: atl_kerneldata_type

This data type shares some properties with cube_container_type type like the number of elements on this refinement level of a cell type.


Inherited by

type~~atl_kerneldata_type~~InheritedByGraph type~atl_kerneldata_type atl_kerneldata_type type~atl_cube_container_type atl_cube_container_type type~atl_cube_container_type->type~atl_kerneldata_type kerneldata_list type~atl_varsys_solverdata_type atl_varSys_solverData_type type~atl_varsys_solverdata_type->type~atl_kerneldata_type kerneldata_listPtr type~atl_element_container_type atl_element_container_type type~atl_element_container_type->type~atl_cube_container_type cubes type~atl_varsys_data_type atl_varSys_data_type type~atl_varsys_data_type->type~atl_varsys_solverdata_type solverData

Components

Type Visibility Attributes Name Initial
integer, public :: nTotal

The total number of cells (including only fluid, ghost, halo and boundary cells).
integer, public :: nVars

The number of scalar variables of the current equation.
integer, public :: maxPolyDegree

Maximal polynomial degree of the data in this kerneldata.
integer, public :: poly_space

Chosen tensor kind of the polynomial representation in this kerneldata.
integer, public :: nDims

Number of dimensions of the polynomial in this kerneldata.
integer, public :: nDofs

The number of degrees of freedom per scalar variable of your equation.
integer, public :: nDervQuant

The number of derived quantities the kernel will use in the future.
real(kind=rk), public, allocatable :: state_der(:,:,:)

array of derived states. Could be anything like derivatives face values, etc. The only thing that is important is that the kernel has to handle the data consistently. The first dimension is the number of elemnts (including fluid, ghost, halo and boundary cells). The second dimension is the number of derived quantities (e.g. the fave value and derivatives at the faces). The exact meaning of this dimension is specified by the kernel. The third dimension is the number of informations per derived quantity per cell (e.g. the number of faces times the quadrature points). The exact meaning of this dimension is specified by the kernel. The fourth dimension is the number of varibales of the equation.
logical, public :: need_deviation = .false.

Flag to indicate, whether to compute the deviation.
logical, public :: need_maxgrad = .false.

Flag to indicate, whether to compute maximal estimates for derivatives.
real(kind=rk), public, allocatable :: deviation(:,:)

Maximal deviation bound of the polynomial in state.
real(kind=rk), public, allocatable :: maxgrad(:,:)

Limit for maximal size of derivative in state.