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1-Visitor
January 23, 2015
Solved

Contact algorithm

  • January 23, 2015
  • 1 reply
  • 2008 views

Dear Sir/Madam,

There is a couple of questions and I appreciate if someone give any advice. We are now trying to handle a contact problem between spheres.

First question is what kinds of algorithm is used in Creo simulate. Some possibilities are Lagrange multiplier or penalty methods I guess and I wanna know this to make my numerical analysis by Creo be more reliable.

Second is how to defined master and slave relation.

Please advice me if you know any information. Thanks!

Best,

Masa


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Best answer by 346gnu

Masa,

A partial answer perhaps

Simulate uses the penalty method and solution by Newton-Raphson reducing residual enery to zero (small number). I'm not sure if it's 'Pure penalty' or Augmented Lagrange. It would seem sensible if it were augmented as this is less sensitive to normal stiffness.

By slave and master do you mean source and target contact surfaces as in Ansys? I think surface-surface and 'symmetric' is used as it more general than node-node and doesn't require a compatible mesh.

Regards

Charles

1 reply

346gnu13-AquamarineAnswer
13-Aquamarine
January 23, 2015

Masa,

A partial answer perhaps

Simulate uses the penalty method and solution by Newton-Raphson reducing residual enery to zero (small number). I'm not sure if it's 'Pure penalty' or Augmented Lagrange. It would seem sensible if it were augmented as this is less sensitive to normal stiffness.

By slave and master do you mean source and target contact surfaces as in Ansys? I think surface-surface and 'symmetric' is used as it more general than node-node and doesn't require a compatible mesh.

Regards

Charles

masa1-VisitorAuthor
1-Visitor
January 26, 2015

Charles,

Thanks for your reply.

It is very helpful to know that Simulate uses the penalty method and that's engouh for me.

About slave and master one, I don't mean the case like Anysis. Your advise, such as suface-surface and symmetric is common, is usefuls comment.

Thanks again!

Best,

Masa