Monday, February 22, 2016

How to add random geometric imperfections in Abaqus?

Besides using eign-mode analysis, the following procedures are recommended by Dr. Yinning Lv:
(1)in your prepared .inp text file, find the *node part and copy this part into an Excel spreadsheet.
(2)for each node, you have initial imperfection of a prescribed factor;
(3) add this imperfection to the coordinates of *Node segment manually in the Excel spreadsheet.
(4) copy this updated *node segment to revise the original .inp file.
Now, it'll be ok to run this .inp file.

You can run inp file by following way:
Open abaqusgo to Analysis..create job...in source option ..give the path of your input file.then submit for the analysis..thenAfter finishing the simulation.....go to results..then go to Output databaseselect ODB file which generated during ur simulation.


In addition to that you can import inp file by this way.
Go to file
import..model
pop up file filter select inp file..then ok.
Yourinp file will be imported in abaqus.

Right click "Models" - the parent of everything in model tree, then select import in the sub-menu. It should open a window, choose the filetype as "Abaqus input file, .inp". The window should now display all the input files in the working directory. Select your input file, this should import the model. 
You may then run by creating a new job and submitting the job.

A. Papa and S. Pellegrino, Systematically Creased Thin-Film Membrane Structure, Journal of Spacecraft and Rockets, 45, 2008:

Geometric imperfections were seeded into the model to facilitate the formation of buckles (wrinkles). Random imperfections z were seeded [8]
z􏲆=k*pi*h, (i=1, . . . , N􏲑)
where k􏲆 is a dimensionless amplitude parameter, pi is [-1, 1]􏲒 a pseudorandom number, h is the membrane thickness, and N is the total number of nodes in the model. A MATLAB script was written to generate a table of nodal geometric imperfections that were subsequently superposed to the heights of all of the nodes of the finite element model. The value 􏲆 􏲍k= 0.2 was used.