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Random function in distribution material properties
Posted 2015年1月4日 GMT-5 14:47 Materials, Parameters, Variables, & Functions, Structural Mechanics Version 4.4, Version 5.1 2 Replies
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I would like to use random distribution of material properties.
In the user’s guide to version 4.3 it was written that I can add following functions to the material: analytic, interpolation, piecewise. It is possible to choose them in material tab.
Could you please tell me if you know that it is possible to use random function from the global definitions tab with material?
I tried to use it with Young Modulus distribution and it works only with the simplest model - fixed to ground cube with boundary load from the top. However it doesn’t work with any more complex models. There is an error “Maximum number of Newton iterations reached. Returned solution is not converged.” Increasing number of iteration doesn't help. Can you please give me some suggestions?
regards,
Maciej
Hello Maciej Nowakowski
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How does the random function work. For example, if I define a property of a rock permeability to be, say, 100 [mD]*(1+0.1*rn1(x)) then when does that random function get computed - ever iteration of the solver or just when the "Compute" button is launched. If I have that permeability being referenced by two different physics then does each get the same value of permeability or both cause the random number to be relaunched.
I guess what exactly fits my need is for the randomness to be at the instant I type it in as a material property and from then on to be constant for every other chain down the line.
And if so, then that does raise the question then of how to reseed the random number to run the chain on a different permeability.
Thanks, John
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I guess what exactly fits my need is for the randomness to be at the instant I type it in as a material property and from then on to be constant for every other chain down the line.
The random function is random but constant: rn1(x) is guaranteed to always return the same value. Without this property, random functions would become much less useful, especially in nonlinear models where it would be hard to get convergence.
There is no seed to the random function generator, but you can achieve the same effect by adding an offset to the argument: If you replace rn1(x) with rn1(x+0.1), you get the same effect. You can make this more explicit by defining a parameter called "seed" under Global Definitions > Parameters and using rn1(x+seed) where the function is used.