Thank you very much for your help :) On Mon, May 14, 2012 at 3:47 PM, Jonathan Guyer <gu...@nist.gov> wrote: > > On May 12, 2012, at 9:44 AM, Matej Svejda wrote: > >> I'm using FiPy to solve a Convection Diffusion Reaction Equation (one >> spacial dimension, plus time). I want my source to: >> >> da^2(H(a, t)) * P(a, t) >> >> Where da^2(H(a,t)) is the second special derivative of a function H (a >> known function and NOT the function I'm solving for) and P the >> function I'm solving for. Both are represented as CellVariable. > > I think this is covered in > http://www.ctcms.nist.gov/fipy/documentation/FAQ.html#how-do-i-represent-a-term-that-doesn-t-involve-the-dependent-variable > > >> I have tried to write: H.getGrad().getGrad() * P but the ranks don't >> match (H has rank-0 and H.getGrad().getGrad() has rank-3). > > H.getGrad().getGrad() should be rank-2 (and that's what I get with both FiPy > 2.1 and trunk/) > >> I don't get >> why the rank is increased when I take the gradient of a function that >> is defined on a 1-d mesh. Any ideas? > > The gradient of a scalar is a vector. The gradient of a vector is a rank-2 > tensor. The dimensionality doesn't matter. Working in 1D can often mislead > like this. > > \nabla^2 H is not equivalent to \nabla(\nabla H). It is equivalent to > \nabla\cdot(\nabla H), and in FiPy you do that with > H.getFaceGrad().getDivergence() > > > _______________________________________________ > fipy mailing list > fipy@nist.gov > http://www.ctcms.nist.gov/fipy > [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ]
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