How to handle discontinuity in diffusion coefficient?

Question

I am looking to solve the diffusion equation with a discontinuous jump in the diffusion coefficient. In 1D, the diffusion equation for $u(t,x)$ is: $$ \partial_t u = \partial_x (D \partial_x u), $$ where $D(x)$ is the spatially varying diffusion coefficient. Let's use $D(x) = 1$ if $x < 1$ and $D(x) = 3$ if $x > 1$.

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Question: Is there a better/smarter way to handle the discontinuity than approximating the jump with a continuous function? Is there a way to solve the equation in a piecewise manner, on $x \in [0,1)$ with $D=1$, on $x \in (1,2]$ with $D=3$, and somehow impose the condition that $D^\text{left} \partial_x u^\text{left} = D^\text{right} \partial_x u^\text{right}$ at $x=1$?

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What I am trying to avoid is the following approximation:

We can approximate the discontinuity in $D$ by a sharp continuous function.

diffConst[x_] := (1 + 2 LogisticSigmoid[50 (x - 1)])

Plot[diffConst[x], {x, 0, 2}, PlotRange -> {0, 3}]

Then we can solve the equation like so, with a sufficiently fine-grained spatial discretization:

fun = NDSolveValue[
  {
   D[z[x, t], t] == D[diffConst[x] D[z[x, t], x], x],
   z[0, t] == 2,
   z[2, t] == 1,
   z[x, 0] == 2
   },
  z,
  {x, 0, 2}, {t, 0, 20},
  Method -> {"PDEDiscretization" -> {"MethodOfLines", 
      "SpatialDiscretization" -> {"TensorProductGrid", 
        "MinPoints" -> 300}}}
 ]

Plot the solution:

Animate[
 Plot[fun[x, t], {x, 0, 2}],
 {t, 0, 5}
]

Answer 1

Yes, it is possible to solve the equation in a piecewise manner :

diffConst[x_]:=If[x<1,1,3];

fun = NDSolveValue[
  {
   D[z[x, t], t] == D[diffConst[x] D[z[x, t], x], x],
   z[0, t] == 2,
   z[2, t] == 1,
   z[x, 0] == 2
   },
  z,
  {x, 0, 2}, {t, 0, 20},
  Method -> {"PDEDiscretization" -> {"MethodOfLines", 
      "SpatialDiscretization" -> {"FiniteElement"(*, 
        "MinPoints" -> 300*)}}}
 ]
 Plot[{fun[x,0.4]},{x,0,2}]  

Note that the function diffConst[x] D[z[x, t], x] is continuous :

derivative = D[fun[x, .4], {x}]
Plot[{diffConst[x] derivative}, {x, 0, 2}]    

This is very likely the solution you are expecting, because it corresponds to most physical situations. (For example, in thermics, It means conservation of the flux of heat thru the point x=1)