Yield Stress, Strain-rate Dependency in MAT_024 and the significance of SIGY

*MAT_PIECEWISE_LINEAR_PLASTICITY (*MAT_024) is widely used material model for metals and in some cases plastics. Its popularity is widespread since it offers several plasticity models and can also be strain-rate dependent. One particle parameter, the Yield Stress, in the material card can appear in more than one place and can be sometimes confusing to know which value is used by LS-DYNA. Here is the hieararchy of the final value of the Yield Stress used in LS-DYNA.

Yield Stress Calculation
1. If LCSS is non-zero, the initial and evolving yield stress is always taken from either the Curve of Table that LCSS refers to.
2. If LCSS is zero AND EPS-ESS is defined, then the initial and evolving yield stress is determined by ESS
3. If LCSS is zero, EPS-ESS is zero, then the yield stress is obtained from SIGY parameter.

Strain-rate Dependency
In *MAT_024, there are three ways to define strain-rate dependency. Its hieararchy is defined below.
1. If LCSS refers to a table, then the strain-rate dependency is always computed from the table.
2. If LCSS is either a Curve or is zero AND LCSR is nonzero, then LCSR is used
3. If LCSS is either a Curve or is zero AND LCSR is zero and C & P is non-zero, then Cowper Symonds is used.

When using Cowper-Symonds method for strain-rate dependency and Viscoplasticity (VP) is turned on (equal to 1), SIGY, plays an important role in how the dynamic yield stress is determined. When VP=1, the strain-rate dependency is always based on SIGY which is then added to the static stress.
However, when VP=0, the dynamic stress is based on the static stress curve which is now a function of the effective plastic strain. For more information, please see the Remarks section of the LS-DYNA Keyword User’s Manual under MAT_024.

d3View v2.0beta – LS-DYNA Simulation Data Manager – Releasing Shortly

Since the first release of d3View, there have been many incremental but bold steps that were taken to tackle the problem of simulation data management especially related to LS-DYNA. I am proud to announce the next generation of simulation data manager that will be available shortly to worldwide users in the form of d3View v2.0 beta. It is a result of over 5 years of part time development with a simple goal of using web 2.0 technologies to help LS-DYNA users manage, mine, compare, and collaborate their simulations.

Many people have helped me in various ways (feedback, testing and simple encouragement), and I wish to thank them all because without that, it would not have been possible to sustain the development, especially when it was being done as a hobby.

If you are interested in beta testing, please send an email to info at d3view dot com.

New Home Page (Click for larger View)

Simulation View (Click for larger View)

Force Deflection to Effective Stress vs Effective Strain

Attached is a simple code that I wrote a while back to convert a force-deflection/engineering-stress_strain/true_stress_strain curve to effective stress vs effective strain curve. The input is a simple LS-DYNA valid keyword file with *define_curve keyword. Its not an elegant code but works in most cases. A sample curve is also included.

I am working on extending this to cover all materials with a HTML front-end. You should see this in the next release of d3View.

fd2stress_strainc.txt
sample_curve.txt

Other related post on a similar topic is here

Initializing Velocity

Initial velocity is always specified to a single node and can carry only one value. Multiple initial velocity definitions for the same node will always use the last encountered value. To illustrate this, if we have nodes N1,N2 and N3 and we define a initial velocity for all nodes to be ‘V1′ followed by N2 to have a velocity of 0 followed by N3 to have a value of ‘V3′, the final velocities for the nodes will be N1->V1, N2->0, N3->v3.

Simulation “Pack” to Perform Design or Numerical Variable Studies

In an iterative simulation process, the main problem or difficulty is to use the existing results to understand its dependency on certain variables (design or just numerical). This difficulty, often causes us to rerun the simulations in a more controlled environment. Let me illustrate this with a simple problem in which we have some v1-v10 variables that we are changing to improve r1-r10 responses in some fashion. Unfortunately, the v1-v10 variable magnitudes are very hazy at the beginning in many cases so we incrementally change them over a certain span of time. At the end of this period, if we were to answer the question of ‘what is the effect of v1 against r1′, we naturally are not comfortable to do this unless the entire simulations were done by an optimization tool.

One recent practice that has given me tremendous confidence to answer above question is to run a ‘pack’ of simulations and keep them aside for later retrieval. In a recent barrier development project, there are a number of variables that were constantly changing. One particle variable of interest was the shear yield of the honeycomb that was rather difficult to obtain. In one controlled ‘pack’ of simulations, while keeping every other variable constant, a 3 reasonably varying values of the shield yield was picked and its effects on key responses was quickly understood. This “pack” of simulations can then be often “put-away” such that we can come back at a later time to restudy the effects of the variable with confidence since no other variable was changed in the input file. For a multi-variable problem, LS-OPT is a fantastic tool. Since the number of runs is a function of the number of variables while the turnaround time depends on the size of the problem, it is often important to use a fairly small model that can best duplicate the original problem. In the honeycomb barrier problem, the block of 300,000 elements was easily duplicated with just a few thousand element problem.

Not sure if I conveyed the idea but if I can summarize it it would be to work in a ‘pack’ of similar simulations in a controlled environment to easily asses our findings with confidence. Contrary to this approach, if we were to make meaningful conclusions from a random set of results, the task of finding the changes between them is rather difficult if the changes were significant.