Physical Models In an Age of Computers 78
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."
Re:Don't miss the Mississippi (Score:5, Informative)
More (much more) info [designobserver.com]
Re:Railroad scale (Score:5, Informative)
What model railroad scale is the closest? I have no interest in CA, so I don't know if 1.5 acres makes that bigger than G scale or smaller than Z scale or something in between. The live steamers might want to turn it into a live steam park, if allowed. Around here, the live steam parks are not quite as elaborate as this sounds.
You don't have to have an interest in CA to read the first few paragraphs of the article:
its 1.5 acres replicate a 1,600-square-mile area that runs from the Pacific Ocean to the Sacramento Delta
1600 mi^2 is 1024000 acres, so it's a 1.5:1024000 (or 1:682666) scale if you believe the article.
However, the bay model's webpage tells a different story:
http://www.spn.usace.army.mil/bmvc/bmjourney/the_model/facts.html [army.mil]
Model Scales (Model to the Bay)
Horizontal: 1 foot = 1000 feet
Vertical: 1 foot = 100 feet
Velocity: 1 foot/ second = 10 feet/second
So using their numbers, it's a 1:1000 scale.
I have no interest in model trains, but Wikipedia tells me that Z-scale is the smallest commercially available scale, and is 1:220, so this is a much smaller scale than any model train system.
Re:Railroad scale (Score:3, Informative)
The 1:682666 and 1:1000 aren't really very far apart...you based the 1:682666 on area, but scale is normally based on linear dimension. The square root of 682666 is about 825, so the two aren't really that different. Since the model isn't square, and actually twists part of it to make it smaller, the two are pretty close...
Utility of physical modeling (Score:4, Informative)
The days of large physical models of tidal hydraulics in large estuarine systems are past because properly calibrated/validated numerical models provide good results at a fraction of the cost.
However, it is paramount the the numerical models are capable of simulating the correct physical processes without over-simplication. For example, the flow hydrodynamics near the Port of Anchorage in the Knik Arm of Cook Inlet are dominated by large gyres that are shed off prominent headlands. A large physical model of the Knik Arm constructed by the Corps of Engineers at their Vicksburg, MS, research facility reproduced the large gyres with a good match to measured field data, and local tug pilots agreed that the flows resembled what they experience daily. Initial attempts at numerical modeling the flow fields produced no gyres, and it was not until a very sophisticated adaptive turbulence closure scheme was added that gyres formed in the numerical model. Both the physical and numerical models required good boundary and initial conditions for success.
Physical models are still useful for simulating processes that are beyond our ability to describe mathematically (required for numerical modeling). Examples in the field of hydraulic engineering include some sediment transport processes, stability of rubble-mound structures such as jetties and breakwaters, erosion of cohesive sediments, wave forces on structures, and resiliency of levee grasses subjected to wave overtopping, just to name a few.
Numerical modeling in hydraulic engineering is making rapid advances, and often any unknown processes can be adequately represented in the model by empirical formulations that have been developed based on physical model tests. Whatever the skill of the numerical model, it is imperative that engineers who apply the numerical model to a problem have a good understanding of what physics are being simulated and what compromises have been made during model development. Failure to understand what the model does will assuredly lead to disaster.
Finally, physical models can be successful provided: (1) The dominant forcing in the real world is correctly represented in the scaled model, (2) any forcing not correctly represented has minor influence, (3) laboratory and scale effects can be minimized or some compensation can be applied, and (4) model results have been validated to the extent possible. A similar set of criteria applies to numerical models.
In the future, physical modeling will continue to be used to validate numerical models, they will provide physical understanding and empirical formulas for use in numerical models, and physical models will continue to address those engineering problems that cannot be formulated mathematically.
Mythbusters (Score:3, Informative)
Isn't this the same model that was featured on the Mythbusters episode [wikipedia.org] about escaping from Alcatraz?
Re:Been there (Score:5, Informative)
The Navier-Stokes equations are definitely chaotic, but turbulence is itself chaotic. It's actually a wonderful example of it.
A physical model would have the same problems, yes, since you can't scale atoms and the sensitivity to initial conditions means that given a long enough run (which is going to depend on the exact nature of the system) the cumulative error will swamp the system. The advantage of physical systems (for now) is that both the step size and the particle size under consideration are considerably smaller and modeled to a far greater level of precision. Physical models are still imperfect and can lead to all kinds of false assumptions if relied upon too heavily, but as the step before the full-scale system, it's the best we currently have.
A 1:1000 model essentially treats a block of 1000 molecules on the full scale as being the same as 1 molecule on the small scale. This means that at 0'C and 1 atmosphere, such a model would consider 2.687 x 10^22 (remember, you're considering 1000 at a time) [wikipedia.org] molecules of an ideal gas per cubic metre of gas under consideration. In comparison, the world's fastest supercomputer can perform around 1 x 10^16 FLOPS and the cartesian form of Navier-Stokes [wikipedia.org] looks to me like you're going to need to perform around 24-25 floating point operations per iteration per molecule. So you're looking at around 600 million seconds per cubic meter of simulated gas flow to get CFD equal to a physical model, if no simplifying assumptions can be made, and that's only true if thee world's fastest supercomputer's FLOPS rating is with a level of floating-point precision great enough not to introduce rounding errors within those 600 million seconds.
Re:Been there (Score:4, Informative)
Neither. It's a computational capacity problem. The complexity and non-linearity of the equations means they cannot be feasibly solved in continuum for anything but the most simple of cases. That means you're left with an iterative, discretized version of them. In order to solve a flow directly using discrete Navier-Stokes, your computational grid must be sufficiently fine to resolve the smallest scales of turbulent mixing. Even with several decades of exponential growth in performance, we are still nowhere near what is needed to solve any real world solution with Direct Navier-Stokes (DNS).
That leaves us with generalization. Turbulence models designed to reproduce the results of physical simulations are applied, rather than direct solutions, easing the computational load for those small scales. More complex models can better model more complex flows, but in turn are more computationally intensive, giving us a sliding scale between solution quality, and solution speed.
Computer simulation was never intended to replace physical simulation, merely supplement it. Physical simulations will always be limited in the environments they can produce, the duration of testing, and the data that can be measured. Computer simulation works on inference. If the simulation matches the physical test's data points at certain test conditions, the rest of the flow field not physically measured and be considered reasonably accurate. The same simulation can be performed at different test conditions, and as long as they are bracketed by the testing, can also be considered reasonably accurate. The simulation can be performed at test conditions outside the parameters of the physical testing, extrapolating behavior with reduced accuracy.