Penalty and Lagrangian methods

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In summary, the difference between Lagrange multiplier methods and penalty function methods for modelling contact interfaces lies in the approach to satisfying the contact constraint. Lagrange multipliers aim to satisfy the constraint exactly, while penalty function methods use a "spring" to simulate the contact force. Both methods have their own limitations and complexities, especially when the contact location is unknown.
  • #1
pukb
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Hi

Can somebody please explain fundamentally what is the difference between these two methods of modelling contact interfaces?
I would prefer a more qualitative explanation (physics concept based ) rather than a more mathematical description.
 
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Assuming "Lagrangian methods" mean "Lagrange multiplier methods" and not "Lagrangian dynamics in general:"

The basic idea of Lagrange multipliers is that you satisfy the contact constraint exactly (i.e. the two surfaces just touch) and calculate the force needed to make that happen. In the math, the force is the value of the Lagrange multiplier itself.

In penalty function methods, you do something like pretend there is a stiff spring in between the contact surfaces, and let the model do whatever it wants, according to the force in the spring. If the spring isn't stiff enough, the contact surfaces will overlap by an unreasonable amount. If it is too stiff, you will probably get some numerical problems in the solution. Finding a good value that lies between those two elephant traps is usually a matter of experience, (which is sometimes another name for "trial and error").

The above assumes you know where the contact will happen (if it happens at all), and that you can match up pairs of nodes (grid points) on the two surfaces. If you don't know that, both methods get more complicated to implement, but the basic ideas are still the same.

In both cases, the force is only applied if it is "pushing the contact surfaces apart", not "pulling them together".
 

Related to Penalty and Lagrangian methods

1. What are Penalty and Lagrangian methods?

Penalty and Lagrangian methods are mathematical optimization techniques used to solve constrained optimization problems. These methods involve adding a penalty term or a Lagrange multiplier term to the objective function in order to incorporate the constraints into the optimization process.

2. How do Penalty and Lagrangian methods work?

Penalty and Lagrangian methods work by converting a constrained optimization problem into an unconstrained optimization problem. The penalty term or the Lagrange multiplier term is added to the objective function, which penalizes the violation of constraints and guides the optimization process towards feasible solutions.

3. What are the advantages of using Penalty and Lagrangian methods?

The main advantage of Penalty and Lagrangian methods is their ability to handle constrained optimization problems, which are common in many scientific and engineering applications. These methods also provide a systematic approach to incorporating constraints into the optimization process.

4. What are the limitations of Penalty and Lagrangian methods?

One limitation of Penalty and Lagrangian methods is that they may require a large number of iterations to converge to a solution, especially for highly constrained problems. These methods also rely on the choice of the penalty parameter or Lagrange multiplier, which can affect the quality of the solution.

5. How are Penalty and Lagrangian methods used in practice?

Penalty and Lagrangian methods are commonly used in various fields such as engineering, economics, and physics to solve optimization problems with constraints. These methods are implemented using computer algorithms and software packages, making them accessible and easy to use for researchers and practitioners.

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