Kurganov.h File Reference

#include "General.h"
#include "Param.h"
#include "Arrays.h"
#include "Forcing.h"
#include "MemManagement.h"
#include "Util_CPU.h"
#include "Spherical.h"

Include dependency graph for Kurganov.h:

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Go to the source code of this file.

Functions
template<class T >
__global__ void	updateKurgXGPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *dtmax, T *zb)
template<class T >
__global__ void	AddSlopeSourceXGPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *zb)
template<class T >
__host__ void	updateKurgXCPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *dtmax, T *zb)
template<class T >
__host__ void	AddSlopeSourceXCPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *zb)
template<class T >
__global__ void	updateKurgYGPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *dtmax, T *zb)
template<class T >
__global__ void	AddSlopeSourceYGPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *zb)
template<class T >
__host__ void	updateKurgYCPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *dtmax, T *zb)
template<class T >
__host__ void	AddSlopeSourceYCPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *zb)
template<class T >
__global__ void	updateKurgXATMGPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *dtmax, T *zb, T *Patm, T *dPdx)
template<class T >
__host__ void	updateKurgXATMCPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *dtmax, T *zb, T *Patm, T *dPdx)
template<class T >
__global__ void	updateKurgYATMGPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *dtmax, T *zb, T *Patm, T *dPdy)
template<class T >
__host__ void	updateKurgYATMCPU (Param XParam, BlockP< T > XBlock, EvolvingP< T > XEv, GradientsP< T > XGrad, FluxP< T > XFlux, T *dtmax, T *zb, T *Patm, T *dPdy)
template<class T >
__host__ __device__ T	KurgSolver (T g, T delta, T epsi, T CFL, T cm, T fm, T hp, T hm, T up, T um, T &fh, T &fu)

Function Documentation

AddSlopeSourceXCPU()

template<class T >

__host__ void AddSlopeSourceXCPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	zb
	)

AddSlopeSourceXGPU()

template<class T >

__global__ void AddSlopeSourceXGPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	zb
	)

AddSlopeSourceYCPU()

template<class T >

__host__ void AddSlopeSourceYCPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	zb
	)

AddSlopeSourceYGPU()

template<class T >

__global__ void AddSlopeSourceYGPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	zb
	)

KurgSolver()

template<class T >

__host__ __device__ T KurgSolver	(	T	g,
		T	delta,
		T	epsi,
		T	CFL,
		T	cm,
		T	fm,
		T	hp,
		T	hm,
		T	up,
		T	um,
		T &	fh,
		T &	fu
	)

updateKurgXATMCPU()

template<class T >

__host__ void updateKurgXATMCPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	dtmax,
		T *	zb,
		T *	Patm,
		T *	dPdx
	)

updateKurgXATMGPU()

template<class T >

__global__ void updateKurgXATMGPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	dtmax,
		T *	zb,
		T *	Patm,
		T *	dPdx
	)

updateKurgXCPU()

template<class T >

__host__ void updateKurgXCPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	dtmax,
		T *	zb
	)

updateKurgXGPU()

template<class T >

__global__ void updateKurgXGPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	dtmax,
		T *	zb
	)

updateKurgYATMCPU()

template<class T >

__host__ void updateKurgYATMCPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	dtmax,
		T *	zb,
		T *	Patm,
		T *	dPdy
	)

Topographic source term

In the case of adaptive refinement, care must be taken to ensure well-balancing at coarse/fine faces

updateKurgYATMGPU()

template<class T >

__global__ void updateKurgYATMGPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	dtmax,
		T *	zb,
		T *	Patm,
		T *	dPdy
	)

Topographic source term

In the case of adaptive refinement, care must be taken to ensure well-balancing at coarse/fine faces (see [notes/balanced.tm]()).

updateKurgYCPU()

template<class T >

__host__ void updateKurgYCPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	dtmax,
		T *	zb
	)

Topographic source term

In the case of adaptive refinement, care must be taken to ensure well-balancing at coarse/fine faces

updateKurgYGPU()

template<class T >

__global__ void updateKurgYGPU	(	Param	XParam,
		BlockP< T >	XBlock,
		EvolvingP< T >	XEv,
		GradientsP< T >	XGrad,
		FluxP< T >	XFlux,
		T *	dtmax,
		T *	zb
	)

Topographic source term

In the case of adaptive refinement, care must be taken to ensure well-balancing at coarse/fine faces (see [notes/balanced.tm]()).