Category Archives: Validation
Continuum homogenization and RVEs
For now (to help with a conversation that I’m having with a few collaborators) this post provides only the following “infographic” to illustrate the concept of approximating a periodic discrete system with an effective continuum over a sufficiently large scale. (More information will be added about this topic as needed and/or as requested).
Below is shown a five-link chain (in red-blue-green-orange-black). Immediately this colorful chain is a dark-gray plot of the exact (mesocale) lineal density, which is defined at a location “x” to be the mass within an infinitesimal segment dx at that location divided by the segment’s length dx. This local density is shown as the dark-gray shaded plot in the upper-left corner, and it is the slope of the black line in the graph of the lower-left corner.
The continuum concept. Homogenization and identification of an RVE is based on the value of x for which perturbations fall below numerical round-off error for a given application.
The exact homogenized (macroscale) lineal density at a location “x” is defined as the exact total mass falling inside the span from zero to x, divided by the chain’s length (x itself). While the mesoscale density is the local slope at location “x” of the black line in the graph, the macroscale density is the secant slope at location “x” of the same black line. The continuum (red-dashed) approximation of the local mass distribution ignores local fluctuations from the fact that the chain is actually heterogeneous. For short chain lengths, the exact macroscale density is significantly different from the continuum density, but this discrepancy asymptotes toward zero as the chain length is increased.
The theoretical representative volume element (RVE) size corresponds to the size for which the discrepancy (like the plot in the lower-right corner of the infographic) falls below some tolerable threshold, which is determined by considering the tolerable error in an engineering simulation.
These concepts apply to other properties besides density. For example, the macroscale elastic stiffness would be defined as the force applied to the chain divided by the corresponding induced displacement. Like density, this macroscale property varies with the number of links in the chain, but it asymptotes to a steady value as the chain length increases.
Density has a nice asymptotic continuum limit that isn’t sensitive to dilutely distributed statistical perturbations in the local (microscopic) density. If, for example, 1 in 10000 links is made of light aluminum while the others are made of heavy steel, then the continuum density will be nevertheless close to that of a chain that is made entirely of steel links. The continuum elastic stiffness is likewise not highly sensitive to slight variations in local constituent (link) stiffness. A chain’s failure strength, on the other hand, is profoundly affected by existence of even a miniscule fraction of weaker links. A mostly steel chain that contains relatively few aluminum links would have a continuum strength equal to the strength of the weaker (aluminum) link. That’s because (in the limit) an infinitely long chain would contain at least one aluminum link. For short chains that are made of, say, 10 links (each of which has a 1 in 10000 chance of being made of aluminum), the average macroscale strength would be higher on average than the strength of longer chains. The strength data for short chains would also be more variable.
These observations give insight into what a modeler must pay attention to when using continuum macroscale properties in simulations of engineering structures. To design for the structure’s daily (i.e., normal and therefore usually elastic) usage conditions, homogenized continuum properties would be fine. However, continuum strength properties would need to be appropriately perturbed based on the size of the finite elements. This explicit incorporation of statistical variability in continuum properties is required when those perturbations strongly influence the engineering objective of the analysis (such as computing failure risk). In fact, it can be argued that such revisions are crucial to predict fracture and fragmentation whenever the finite-element size is smaller than a few kilometers. For more details on scale-dependent and statistically variable macroscale properties, see Publication: Aleatory quantile surfaces in damage mechanics and the more recent 2015 IJNME article, “Aleatory uncertainty and scale effects in computational damage models for failure and fragmentation” by Strack, Leavy and Brannon.
The kinematic anomaly in MPM
The following Material Point Method (MPM) simulation of sloshing fluid goes “haywire” at the end, just when things are starting to settle down:
(if the animated gif isn’t visible, please wait for it to load)
Simulation of sand/soil/clay thrown explosively into obstacles
Here are a couple of cool movies created by CSM researcher, Biswajit Banerjee, in preparation for our project review this week:
- Clods of soil impact a plate: A major advantage of the Material Point Method (developed as part of this research effort) is that it automatically allows material interaction without needing a contact algorithm.
- Extrapolated buried explosive ejecta. The sample is in a centrifuge to get higher artificial gravity, so the particles move to the side because of the Coriolis effect!
Undergraduate researcher applies binning to study aleatory uncertainty in nonlinear buckling foundation models
Sophomore undergraduate, Katharin Jensen, has developed an easily understood illustration of the effect of aleatory uncertainty, which means natural point-to-point variability in systems. She has put statistical variability on the lengths of buckling elements in the following system:
Tutorial: A beginner’s introduction to the Material Point Method (MPM)
The presentation slides (downloadable as PowerPoint and PDF) describe the basic principles and application advantages of the material point method (MPM).
To follow this tutorial, you need to know basic equations for the finite-element method.
Finite sampling errors for Weibull distributions
Uintah Simulations of Perforation Experiments
ABSTRACT: A simulation of a simple penetration experiment is performed using Material Point Method (MPM) through the Uintah Computational Framework (UCF) and interpreted using the post-processing visualization program VisIt. MPM formatting sets a background mesh with explicit boundaries and monitors the interaction of particles within that mesh to predict the varying movements and orientations of a material in response to loads. The modeled experiment compares the effects of an aluminum sphere impacting an aluminum sheet at varying velocities. In this work, the experiment called launch T-1428 (by Piekutowski and Poorman) is simulated using UCF and VisIt. The two materials in the experiment are both simulated using a hypoelastic-plastic model. Varying grid resolutions were used to verify the convergent behavior of the simulations to the experimental results. The validity of the simulation is quantified by comparing perforation hole diameter. A full 3-D simulation followed and was also compared to experimental results. Results and issues in both 2-D and 3-D simulation efforts are discussed. Both the axisymmetric and 3-D simulation results provided very good data with clear convergent behavior.
See the link below for the full report.
Stretch, rotation, and intuition about strain rate approximations
The animation below shows
RED: simple shear in physical configuration
BLUE: simple shear with the polar rotation removed (i.e., the pure stretch)
GREEN: the deformation corresponding to the approximation that D-bar (given by the unrotated symmetric part of the velocity gradient) is actually the rate of reference logarithmic strain. This is found by integrating D-bar through time to obtain the apparent logarithmic strain, and then exponentiating this apparent strain to obtain an apparent reference stretch.
GRAY: rotation of the green deformation back to the spatial configuration.
Conference Poster: Scaled surrogate Hertzian bearing pairs for contact and wear testing
Contact pressures
Sanders, A. P., and R. M. Brannon. (2012). “ Scaled surrogate Hertzian bearing pairs for contact and wear testing.” Transactions of the Orthopaedic Research Society 2012 Annual Meeting, San Francisco, CA, Feb. 4-7, Poster 2070. 2012 ORS poster 01 small
Abstract
New implant bearing materials require extensive laboratory testing before clinical use, but the currently practiced contact and wear test methods impose limitations. Screening wear tests of prototype materials are typically done using simple bearing shapes (such as a ball-on-flat pair) and low loads. These tests are relatively simple and inexpensive, but they lack representative bearing shapes and contact stresses. Simulator wear tests on full-scale components overcome this shortcoming by implementing higher loads and complex, physiologic motion patterns. However, these tests are lengthy and expensive; so, they are reserved for final design testing. Surrogate test specimens that would mimic the contact mechanics of full-scale bearing pairs could improve the relevance of early screening tests. This research examines the hypothesis that a reduced-scale surrogate Hertzian contact pair can elicit a smaller scale, equal stress version of the contact response of a larger original contact pair. A chosen original contact pair mimics a knee implant femoral-tibial condylar interface, and a full-scale surrogate pair is found using recently published formulas. New formulas were derived to find a smaller version of the surrogate pair. The contact pairs were tested in quasi-static normal loading, and their contact patches were measured to evaluate the hypothesis.
University of Utah CSM Group 