By Kunstmann P.C.

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10a) −f f df ≈ (x +∆x ) (x−∆x ) (central). g. when Fick’s second law is applied for diffusion). The second derivative of a function, of which the values are know at discrete increments ∆x, can be approximated in a similar way as the first derivative: 2 Numerical methods ⎛ ∂f ⎞ ⎛ f(x +∆x ) − f(x ) ⎞ ⎛ f(x ) − f(x−∆x ) ⎞ ∂ ⎟ −⎜ ⎟ ⎜ ⎟ ⎜ ∆x ∆x ∂2 f ⎠ ⎝ ⎠ ⎝ ∂x ⎠ ⎝ = ≈ 2 ∆x ∂x ∂x f(x +∆x ) − 2 f(x ) + f(x−∆x ) = . 11) Fig. 16 To determine the local gradient at one point (x) on a lattice, one can use the difference with the next point (x + ∆ x; forward method), the previous point (x-∆ x; backward method), or the points on both sides (x- ∆ x and x + ∆ x; central method).

Using ready-made models as a “black box” can easily lead to erroneous results, if the boundary conditions and underlying assumptions are unknown to the user. 20 P. D. Bons et al. 2 Lattice data structures: Monte Carlo, Ising, Potts, etc. 1 Introduction to Monte Carlo Principle Monte Carlo models or stochastic models are models that involve probabilities. Deterministic equations of the type a=F(b) will always give the same value of a for a given value of b. If chance comes into play: a=P(b), the value of a can vary, depending on the probability function P(b).

12, namely the heat conductivity (κ). In the FD model, the system at t = 0 s can now be described by N nodes, lying on the x-axis with a spacing ∆ x. All nodes that lie within the dyke (xi ≤ w) have Tdyke, and all others have Twall. Here we use i as an index for the i-th node. Fig. 17 Set-up for the one-dimensional Finite Difference model to simulate the cooling of a dyke that intrudes at a temperature Tdyke To calculate the temperature of a node after a small time increment ∆ t, we must use Eq.

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