Summary of Simulation, class Manager

Report file: /auto/tor/NO/NO/Work/Diffpack-3.5.00/NO/doc/Book/src/app/Pipeflow/Verify/SIMULATION-summary.html
generated: Mon Feb 5 15:03:37 2001.
Created by user: unknown user
running the program: /auto/tor/NO/NO/Work/Diffpack-3.5.00/NO/doc/Book/src/app/Pipeflow/../app (last modified , bytes)
on the i386-linux host: ampc-218.si.sintef.no.

PURPOSE:

exercise for IMA Workshop on Software for PDEs

In the tables below, some row names may be preceeded by "ML:". This means that the corresponding quantity has been varied in a multiple loop. Such quantities are always written in the summary table. If the programmer also writes the quantity, it will appear twice.

	run-1
ML: power law exponent	1
ML: alpha	0
alpha	0.00000e+00
power law exponent	1.00000e+00
initial sol. guess	Boundary_layer
mu_0	1.00000e+00
T_0	0.00000e+00
no of nodes	25
element type	ElmTensorProd2
no outer iterations	2 (ok)
total no iterations	6
w lin.system solver	GaussElim
w lin.system prec	None
w lin.system E[iter]	0
w Strategy used (no.)	1
w Number of switches	1
w Number of different Strategies	2
w Max allowed nonlinear difference	1.00000e-04
w inner iterations	converged
w pressure gradient	1.00000e+00
T lin.system solver	GaussElim
T lin.system prec	None
T lin.system E[iter]	0
T Strategy used (no.)	1
T Number of switches	1
T Number of different Strategies	2
T Max allowed nonlinear difference	1.00000e-04
T inner iterations	converged

	run-2
ML: power law exponent	0.5
ML: alpha	0
alpha	0.00000e+00
power law exponent	5.00000e-01
initial sol. guess	Boundary_layer
mu_0	1.00000e+00
T_0	0.00000e+00
no of nodes	25
element type	ElmTensorProd2
no outer iterations	2 (ok)
total no iterations	25
w lin.system solver	GaussElim
w lin.system prec	None
w lin.system E[iter]	0
w Strategy used (no.)	1
w Number of switches	1
w Number of different Strategies	2
w Max allowed nonlinear difference	1.00000e-04
w inner iterations	converged
w pressure gradient	1.00000e+00
T lin.system solver	GaussElim
T lin.system prec	None
T lin.system E[iter]	0
T Strategy used (no.)	1
T Number of switches	1
T Number of different Strategies	2
T Max allowed nonlinear difference	1.00000e-04
T inner iterations	converged

	run-3
ML: power law exponent	1
ML: alpha	1
alpha	1.00000e+00
power law exponent	1.00000e+00
initial sol. guess	Boundary_layer
mu_0	1.00000e+00
T_0	0.00000e+00
no of nodes	25
element type	ElmTensorProd2
no outer iterations	4 (ok)
total no iterations	18
w lin.system solver	GaussElim
w lin.system prec	None
w lin.system E[iter]	0
w Strategy used (no.)	1
w Number of switches	1
w Number of different Strategies	2
w Max allowed nonlinear difference	1.00000e-04
w inner iterations	converged
w pressure gradient	1.00000e+00
T lin.system solver	GaussElim
T lin.system prec	None
T lin.system E[iter]	0
T Strategy used (no.)	1
T Number of switches	1
T Number of different Strategies	2
T Max allowed nonlinear difference	1.00000e-04
T inner iterations	converged

	run-4
ML: power law exponent	0.5
ML: alpha	1
alpha	1.00000e+00
power law exponent	5.00000e-01
initial sol. guess	Boundary_layer
mu_0	1.00000e+00
T_0	0.00000e+00
no of nodes	25
element type	ElmTensorProd2
no outer iterations	4 (ok)
total no iterations	41
w lin.system solver	GaussElim
w lin.system prec	None
w lin.system E[iter]	0
w Strategy used (no.)	1
w Number of switches	1
w Number of different Strategies	2
w Max allowed nonlinear difference	1.00000e-04
w inner iterations	converged
w pressure gradient	1.00000e+00
T lin.system solver	GaussElim
T lin.system prec	None
T lin.system E[iter]	0
T Strategy used (no.)	1
T Number of switches	1
T Number of different Strategies	2
T Max allowed nonlinear difference	1.00000e-04
T inner iterations	converged

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