Today we are having the funeral for my father-in-law, Louis Forbes. He was a wonderful man who acted as an example in numerous ways. One part of his character was exceptional and an example for all of us. He was always learning. He was always open to new things, new experiences all the way to the end.
Lou didn’t have the advantage of a classical education growing up in rural depression-era North Carolina, but took the opportunities offered by military when he was drafted after WW2. His first assignment was to Los Alamos and then to the Atomic Weapons group at Sandia Base in Albuquerque. Along the way he learned a trade
that kept him employed into his 80’s and able to generously support his family.
One of the things that stand out to me about Lou was his use of new technology. He had a smartphone and communicated via text messages, was online every day web surfing, e-mailing and posting on Facebook. When so many elderly people are flummoxed by technology, Lou embraced it.
A lot of scientists would pride themselves on being technologically advanced; the reality is many get stuck within an era. Many people quit adopting new things at some point in their lives including scientists and engineers. We can all look to Lou as an example of someone who kept on learning, and experimenting with new things right up to the end.
It matters not how a man dies, but how he lives.
ch of these four categories. For example there might be a big difference between solutions that are wandering in phase space as opposed to one that is bouncing between several possible equilibrium points.
he point is that if your calculation runs to completion it could be in one of three states. You should be quite interested in which one. Verification is the vehicle to help you figure this out. You can really only start to sleuth out what state the calculation is in by doing a sequence of nearby calculations using variations in the models and methods. If you run a family of problems you will generally find that the easier problems converge to the exact solution without problems (assuming you have an exact solution). You can only distinguish between 1 & 2 when you have an exact solution.
As you raise the problem difficulty either by varying the parameters or geometry or materials, you will gradually get solutions that depart of the exact one, but converge to some solution. As the conditions for the problem become more extreme, the code will stop converging to a single answer, and ultimately encounter stability problems. For more complicated problems especially in multiple dimensions one may always be looking at problems that don’t converge to a single solution, nonetheless you should be asking these questions all the time.
itute proof, it’s a rough check. Sometimes it is offered as evidence because the author doesn’t want to look to close either because they are lazy, or they know that the result won’t bear up under scrutiny.
ctually is, the longer the speaker will leave the viewgraph up to be examined. If the result is poor, the viewgraph won’t be available for examination for more than a few seconds.
rs of technology, as well as a metaphor for the dehumanizing effect of the modern world. The zombie lives the existence of the undead driven by an inhuman hunger for flesh (or brains!). It has no other purpose, but to mindlessly consume the living.
sed. We don’t endeavor to have the World’s largest hammer or wrench; we recognize that these are tools whose function is essential. We don’t recognize this about computers, for some reason their status as tools has been lost. In this sense supercomputing research has become a bit of a fetish.
This blog is intended primarily as a place for me to focus on positive habits like writing every day, and establishing more of an online presence. I think its working well, but I need change it up. I’ve decided to move to a daily blog post instead of the weekly one. The writing is usually related to something I’m working on, or thinking about, or ne
discipline of computational fluid dynamics (CFD) defines this capability being responsible for many of the most iconic uses of computing and the visual power available today. Often when the results are given more to satisfy the eye rather than the mind CFD gets rechristened “Colorful Fluid Dynamics” as a slam. This is a sharp poke in the eye to the well-intentioned scientist seeking to impress someone through glitz rather than though convincing intellectual arguments.
of people’s real lives with a great deal of flourish added for ratings because reality is actually grim, monotonous and boring most of the time. Simulation is similar in that most of the important stuff is boring, dull and utterly essential, cool visuals avoid all of the reality and replace it with an advertisement.
agination without the slightest difficulty. Nothing creates patterns that wow the senses like the vortical motions that drive turbulence. These features are prevalent and have exemplified scientific visuals for hundreds of years. Fluid phenomena have stirred the curiosity of the greats including De Vinci who famously sketched the turbulence in a fountain. Every year we have a contest to create the most stunning and scientific compelling visuals put on by the American Physical Society in their Gallery of Fluid Motion taking off from Van Dyke’s classic book of the same name.
It should come as no surprise that computations of fluid dynamics have similarly stirred the artistic juices within modern science. The visuals arising from computation are compelling and seemingly reproduce the features seen in nature. Visualization software has become so good at creating beautiful images from computed data it actually works against aspects of science. In the sense of marketing the work of CFD, the visualization is the best advertising imaginable. Why rock the boat by asking whether the calculation is really mesh converged (what ever that means!), or whether the experimental data is modeling correctly? It just looks so damn cool! Our scientific management is simply so overworked and under-curious they are primed to accept the meager evidence offered by glimmering graphics as all the quality they need to see.
This is the problem that the tag colorful fluid dynamics is designed to project, an interest in how the computation looks over how good it really is. The colorful fluid scientists often reject the notion of looking deeply at the quality of what they do. This shouldn’t happen, but very often it does. Far too often they are successful and rewarded for their lack of focus on scientific content by rewarding showmanship and sleight of hand. The quality of computations is very important especially as computation becomes a greater player in decision-making across a vast variety of fields. Looking good and realistic is important, but that is a standard for Hollywood, not science.
intermittency, the large deviations from mean behavior. For visual purposes like Hollywood’s CGI, the methods need to add significant high-energy, small-scale content to get realistic-looking turbulent flow. These same mechanisms are not utilized in the scientific computation of turbulence although there might be virtue in examining the deeper meaning.
The bountiful success of CFD is do doubt in small part due to the appeal of visualizations. The problem comes when the use of computing becomes more about the appearance of the results than their scientific validity. In many cases the realistic appearance is all the proof needed to trust the results and little in depth analysis is done to establish the credibility of the calculations. This is a dangerous path to take especially as the role of computational simulation grows and the stake in its credibility similarly expands. The potential for misuse and disaster looms if the proper care and seriousness isn’t taken.
allowed the unhealthy obsession with computing hardware to take root, while starving the algorithm and method development of support. The bigger, faster computer has become the lynchpin to any ability to compute while other means to improvement have languished.
ment class who are susceptible to the shallow, deceitful
capability, but their solvers are mostly ancient garbage. Most of them rely upon methods that are more than two decades old. Even then the methods were not the best available then, but instead chosen for “robustness” rather than accuracy. One is fortunate if they can find a reliable second-order discretization and the standards for solving nonlinear systems of equations are pathetic (the community accepts a nonlinear residual that is enormous!). Turbulence is modeled using old antiquated methods of similar vintage. Of course, the acceptance of this ter
rible technology is aided by the relative lack of progress over the last couple of decades in no small part due to the shift in emphasis toward hardware and away from algorithms and methods. The real core of the problem is the willingness of the community to accept this crap as state of the art. Stunning visuals seal the deal and help craft the path toward mediocrity.
The Regularized Singularity 