I plan to add this transformation to my upcoming book on computational geometry. This mapping provides a one-to-one correspondence between RGB color and locations on a dance floor for a positioning correction in a project on robotic square dancers. Fun!

# Category Archives: Research

Research related news, supplements, and documentation.

# Robot wheel control

# End-of-millennium in Japan: Women, food, and other observations from a first-time visiting scientist

Ed: This post shares an entertaining and insightful essay about customs, food, and women in Japan over 20 years ago. It was written by my friend and colleague, Mark Boslough, after he returned from an engineering business trip in Japan in 1994. I am curious how much has changed over the subsequent 20 years, so please comment if you know which of his observations or impressions no longer apply (or if they never applied in the first place too)! With Mark’s permission, this version has a few bits [in brackets] that have been altered to remove information that could identify particular individuals or organizations. It also has corrections of some minor typo/style issues. It is written in a style that mimics what Mark’s employer (a major US scientific research laboratory) required after any foreign travel.

CONTENTS:

- Status of Women in Japan
- Diary of Food Activities
- Unusual Japanese customs (by US standards)

# Happy Father’s day: Sharing tips from my Dad.

Shown below is a scan of a piece of paper (with transcription) that I found in a file of my father’s old belongings. Even though this was about learning mathematics, it applies equally well to engineering. Any student who *seriously* follows tip number 7 will be an A student and, ultimately, a very productive and trustworthy engineer!

# 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 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)

# I love my MoM

Some mechanical-engineering programs, like ours here at the University of Utah, use the phrase “Strength of Materials” to refer to what the majority of other mechanical engineers more accurately refer to as “Mechanics of Materials” (often locally shortened to “MoM”). The latter designation is more appropriate because this class typically is focused on an introduction to elementary elasticity with only very lightweight coverage of failure criteria (and almost never any post-failure theories, which are typically covered in upper-division and grad courses). Our University of Utah class, ME EN 3300, is locally called “Strengths” even though strength is barely covered (and only at an idealized level such as Tresca and von Mises criteria).

The following infographic furthermore shows that engineering textbooks appropriately and overwhelmingly favor MoM over Strengths (to see details, click to open in separate page and then zoom to fit the page):

Thanks go to Dr. Ashley Spear for stimulating this commentary/flame.