Original poster: "Malcolm Watts" <m.j.watts@xxxxxxxxxxxx>
Hi Terry,
I have one difficulty with your model: Rl isn't present
all the time and I don't think an average value cuts it when
calculating Vs. Any comment?
Malcolm
On 18 May 2005, at 19:31, Tesla list wrote:
> Original poster: Terry Fritz <teslalist@xxxxxxxxxxxxxxxxxxxxxxx>
>
> Hi Marco,
>
> At 06:34 AM 5/18/2005, you wrote:
> >Hi Terry, all,
> >
> >Thanks a lot for your nice paper. It is very good to include in a
> >nutshell all what this ScanTesla stuff is about. Now even I can
> >figure it out :)
>
> ;-) It helps to sort of define the mess in a nice document.
>
>
> > > For a model of a Tesla coil, I think this is the "best" there is
> > > now (first figure):
> > >
> > > http://drsstc.com/~terrell/modeling/ScanTesla.pdf
> > >
> >
> >I have been doing a number of times with problems like this. This is
> >called "optimization", "linear programming" or "hill-climbing" in a
> >multidimensional space. You have:
> >
> >- a number of parameters (that's the multidimensional solution space)
> >- a number of constraints to be satisfied that bound the parameters -
> >a yield function to maximize (the output voltage, energy transfer,
> >whatever)
> >
> >Typically you can't perform an exhaustive search over all possible
> >solutions (not in a lifetime). So here is the way it's usually done.
> >
> >1. You write the required software to compute the yield function from
> >the problem parameters. That will be you LCC, Spice, whatever. 2. The
> >engine that decides what is the "next step" in the trial series is a
> >different beast. Usually it can just use C, Basic, Pascal, but surely
> >not Pspice.
> >
> >Now I give you two methods I have been using with success for this
> >engine:
> >
> >A. Genetic algorithm.
> >Basically you start from a random population of solutions (i.e.
> >parameters sets), calculate the yield for each of them. Then some of
> >them will pass away (worse yield), some will mutate (randomly) and
> >some will cross-combine with each other. The algorithm is quite easy
> >to implement, works and it's also intuitive.
>
> Using simply random numbers for the inputs could scan a lot of space
> ;-)) I had not thought of that *:-)
>
>
> >B. The simplex algorithm
> >There are many variations of this algorithm but basically you start
> >(again) from a random set of solutions and keep only the best 3 ones.
> >Using these 3 as the vertex of a triangle (in 3-D space) you
> >literally fold the triangle in the direction that gives the best
> >yield. You can visualize it as the triangle "climbing the hill" while
> >folding on itself, making its way to the top.
> >
> >The simplex method is older than genetic algorithms. I personally
> >prefer genetic algorithms: they are also fun to program if you only
> >have got the time.
> >
> >I suggest to search the net with these keywords. Literature abou them
> >can also easily to be found.
>
> Thanks! I am still trying to get it to work at all right now but
> hopefully I will soon have it basically working. Once it is going, I
> can figure out the next step.
>
> Cheers,
>
> Terry
>
>
>
> >Best Regards
>
>
>