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Physical Models In an Age of Computers
Harperdog points out this article "about the Bay Model in Sausalito, California, which was built in 1959 to study a (terrible) plan to dam up San Francisco Bay. The model was at the forefront of research and testing on water issues that affected all of California; its research contributions have been rendered obsolete by computer testing, but there are many who think it could contribute still. Now used for education and tourism, the model is over 1 1/2 acres and replicates a 24-hour tidal cycle in just 14 minutes. Good stuff."
There was also the Army Corps of Engineers model of the entire Mississippi/Missouri/Ohio/Arkansas/Red river basins. It was built by POW labor near Clinton, MS. See what's left of it here. http://maps.google.com/maps?ll=32.30606,-90.316173&spn=0.003922,0.006335&sll=36.977452,-121.987122&sspn=0.118622,0.202732
What's interesting is that real life models sometimes still yields a better result than the computer simulated, but both have advantages and disadvantages.
Especially streaming water is a tricky thing to model entirely right and you can get surprises when you have a complex situation and try it in a physical model compared to a computer simulation.
If builders built buildings the way programmers wrote programs, then the first woodpecker would destroy civilization.
The model cover about a 3000 sq mile area. The Golden Gate Bridge is about 6 inches long IIRC.
You have to see the Bay Model if you visit the Bay Area. There is a fully restored Nike missile silo nearby that is also a must-see of Bay Area nerdy sights.
Give a man a fish and you have fed him for today. Teach a man to fish, and he'll say "WHERE'S MY FISH, YOU IDIOT?"
I imagine testing of scale (including full scale) models is still used in most industries. Computers help you reduce the number of mistakes and shorten the iterative development cycle so you make fewer elementary errors.
In Oil & Gas, almost all of the major manufacturers have been modeling all their new components in 3D for the last decade. You can have whole departments dedicated to running Finite Element Analysis on these 3D models. It's not as simple as putting in your constraints (loads, fixed and pinned reaction points, etc) and hitting "solve". You still have to make certain assumptions, tailor the matrix (the 3D lattice/approximation made of pins and beams) or make simplifications to the model if you want it to converge toward a solution. The computer solves the cases iteratively. Once you think you're close, you perform a test at scale and verify your assumptions. Then your match it to your computer model and hopefully you're not too far off. For example, things like incorrect friction factors between materials matter a lot when they are amplified by huge forces. The manufacturer of a particular coating may claim it's .07 (if they even tell you). You learn it's more like 0.08 through repeated testing. At this point there is no way of calculating things like that.
Of course, this assumes that you have the time, money, and server time to run such experiments. Most of the time you focus on the one or two critical areas of a design for FEA and use past experience or classic formulas on the rest. Customers still want final tests before product ships, so technically you have one last point to catch a failure/issue before it ships.
For what it's worth, this is the same Bay Model the Mythbusters used in their season 1 episode Alcatraz Escape.
The accurate reconstruction of the tidal effects allowed them to convincingly show that a raft would be unable to reach Angel Island, and that a more plausible route would have been toward the Marin Headlands - before confirming the model's result experimentally.
Any model that is inherently chaotic (read: almost all of them) cannot be simulated on a computer accurately. There will be a cumulative error which will grow extremely rapidly (this is known as the "Butterfly Effect" after a well-meaning but ultimately damaging title of a research paper on the subject). Fluid dynamics is a major headache when it comes to chaotic systems, since the equations are violently unstable at most points most of the time. The only reason anyone can get any work done at all with CFD is that most research groups simplify the equations to one very narrow, very specific range of conditions. Even then, CFD is treated as a first approximation. A physical model of some sort is almost always built, somewhere along the line.
Indeed, one of the unsolved Millenium Challenges is to find out just how bad the Navier-Stokes equations really are. I'd have thought a more interesting challenge would be to find an alternative way to model chaotic systems. (Chaos means that the system is deterministic but not predictable. Which is no different from saying that you have a superposition of probablistic outcomes where the actual outcome is only knowable by observing the system. A field that is of interest to all kinds of people, including physicists and sci-fi writers. A standardized way of dealing with such systems that can yield any additional information at all - doesn't matter what or how little - would have a lot of appeal.)
It's a small world and it smells funny; I'd buy another if it wasn't for the money; Take back what I paid (SoM)
Something I've always wondered about physical models is, how can you compare them to real situations at different scales?
IIRC the folks who design America's Cup sailboats have (had) a rule of thumb that a hull model less than 1/3 scale would not accurately predict the drag of the full-size design. Off-topic: if you think water and fluid dynamics are tough to figure out, take a look at what little we know about how ice skating works -- or to be exact, just how the surface of ice behaves to allow gliding motion.
https://app.box.com/WitthoftResume Code: https://github.com/cellocgw
Any model that is inherently chaotic (read: almost all of them) cannot be simulated on a computer accurately...Indeed, one of the unsolved Millenium Challenges is to find out just how bad the Navier-Stokes equations really are.
Is that really a chaotic system problem, or an incomplete model problem? Taking the Navier-Stokes equations as an example, they actually are surprisingly accurate at describing turbulence, even though, as you've mention, we haven't proven that they describe turbulent flow properly in all cases. So typically problems with fluid flow models come from making too many simplifications, having incorrect initial conditions, or making bad assumptions, not a problem with the Navier-Stokes equations per se.
In fact, as far as chaotic systems go, wouldn't a physical model have the same problems as well? The challenge there is a very high sensitivity to certain variables, so your physical model of the system may turn out to not scale at all.