NlDEIDynamic

NlDEIDynamic	nsteps(in) #
	[renumber(in) #]
	dumpcoef(rn) #
	[deltaT(rn) #]

Represents the direct explicit nonlinear dynamic integration. The central difference method with diagonal mass matrix is used, damping matrix is assumed to be proportional to mass matrix, $\mbox{\boldmath$C$} = {\rm dumpcoef} * \mbox{\boldmath$M$}$, where $\mbox{\boldmath$M$}$ is diagonal mass matrix. deltaT is time step length used for integration, which may be reduced by program in order to satisfy solution stability conditions. Parameter nsteps specifies how many time steps will be analyzed. Nonzero value of optional parameter renumber turns on the equation renumbering to optimize the profile of characteristic matrix (uses Sloan algorithm). By default, profile optimization is not performed. It will not work in parallel mode.

$\langle$PNlDEIDynamic$\rangle$	$\langle$nsteps(in) #$\rangle$
	$\langle$dumpcoef(rn) #$\rangle$
	$\langle$[deltaT(rn) #]$\rangle$
	$\langle$*commode$\rangle$
	$\langle$[nonlocalext() #]$\rangle$

Represents the parallel direct explicit nonlinear dynamic integration. The central difference method with diagonal mass matrix is used, damping matrix is assumed to be proportional to mass matrix, $\mbox{\boldmath$C$} = {\rm dumpcoef} * \mbox{\boldmath$M$}$, where $\mbox{\boldmath$M$}$ is diagonal mass matrix. deltaT is time step length used for integration, which may be reduced by program in order to satisfy solution stability conditions. Parameter nsteps specifies how many time steps will be analyzed. The $\langle$*commode$\rangle$ keyword can be one from following:

• nodecutmode - indicates the node cut partitioning scheme is used. The cut is led along element edges or surfaces (see figures 5, 6, and 7).
• elementcutmode - the element cut partitioning is used. The partitioning cut led across the element edges or surfaces (see figures 8 and 9).

The nonlocalext turns on the nonlocal constitutive extension. The extension considers a band of remote elements involved in computation of nonlocal variables (see fig. 7 illustrating this approach for node-cut partitioning).