Discrepancy Between Circuit Simulation and Reality

29 09 2011

Previously we modeled the polywell coils and power supply in SPICE.

Today I returned to that model.

All resistance values in the simulation are based on real world measurements with the exception of coil inductance (code).

Starting with an estimate for coil inductance of 0.1 mH the discharge current looks like this:

The simulation’s peak of 1.5 kA is nowhere near the 2.3 kA we are getting in the real world:

OK. Maybe the value for coil inductance is off?

I played around with the value for coil inductance but the simulation would not match reality.

As a control I replaced the simulated inductor with a 1 mΩ resistor (code). Looks like this:

The simulation predicts ~1.8 kA but in reality we see 2.3kA!

Where does this discrepancy come from?

 

 

UPDATE: Reader Andrew solved the mystery:

You could try changing the ON resistance of your switch/SCR to something a bit lower than 100mOhms

.model MySwitch SW(Ron=.1 Roff=1Meg Vt=3 Vh=0)

I can’t see the part number of your SCR but 2mOhms would seem reasonable.

I didn’t notice that rather high resistance lurking in the SCR model.

Now the simulation matches reality very closely with 0.06mH coil inductance (code):

Good work Andrew and the rest of the internet brain!





Computationally Intractable (or maybe not?)

26 03 2011

In Bussard’s 2006 Google tech talk  Should Google Go Nuclear? he talks about computer modeling of his reactor. He concludes that computer modeling is unfeasible. Beyond a handful of particles in the model, the computation slows down to the point of useless.

Now there may be a new approach to this type of problem.

http://news.stanford.edu/news/2011/march/airplane-aeroelastic-flutter-032411.html

Professor Charbel Farhat, chair of the Aeronautics and Astronautics Department at Stanford’s School of Engineering, and David Amsallem, an engineering research associate who worked on his PhD thesis with Farhat, have been studying and trying to solve aeroelastic flutter for years. Computers help, but only to a point.

Essentially it’s a story of the unfeasible made feasible by mathematical inovation:

How have Farhat and Amsallem succeeded where others have come up short? The answer sounds suitably complex: interpolation on manifolds. What it means, in essence, is approximating unknowns based on known information. The two engineers devised a system of mathematical approximations that break down complex, computationally demanding equations into smaller, more manageable parts. In mathematics, this is known as “reducing.” Reducing allows them to make some very educated guesses, very quickly.

I wonder if this technique could be applied to computer modeling of the Bussard reactor?

I suppose in our case we would be looking FOR the flutter, not trying to avoid it.

 

UPDATE:

Another good article: http://www.psc.edu/science/2001/farhat/








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