While I can't give you a name, I can provide evidence that the problem is $\mathbf{NP}$-hard to approximate to any factor, and thus is likely to be unstudied in full generality in the literature (hinting that it may remain unnamed).
Indeed, consider the $\mathbf{NP}$-hard instance of the problem you have in mind, with source $s$ and destination $t$. Via standard arguments, there is a corresponding $\mathbf{NP}$-hard decision problem, of the form "is it possible to route at least $k$ units of flow from $s$ to $t$"? I'll assume here that $k$ is polynomial, since that seems to be the case for most reductions I can think of (such as the one through Max-3D-matching). We begin with this problem to construct our inapproximable example.
Let $u$ denote the maximum (unrestricted) flow routable from $s$ to $t$, which is an upper bound for the solution to your problem. Further, let $t'$ be a new vertex you tack onto the graph. For every possible value in the set $Q = \left(\{k, k+1, k+2, k+3, \cdots, 2k-1\} \cup \{2k, 4k, 8k, 16k, \cdots \}\right)$ no larger than $u$, add a parallel edge with that capacity from $t$ to $t'$. While this transforms your graph into a multigraph (we will fix this later), the number of parallel edges we added here is at most polynomial.
If the original problem had a solution with value $\geq k$, then there is some amount of flow routable from $s$ to $t'$. Indeed, route that flow to $t$, decompose the amount of flow at $t$ into a sum of values in $Q$, and then route it along the corresponding edges. Otherwise, it is impossible to route any flow between $t$ and $t'$, and thus the maximum flow routable (in your set up) from $s$ to $t'$ is $0$.
Since the dependency on parallel edges can be removed by subdividing each edge with a vertex, we conclude that it's impossible to distinguish cases where the optimal solution has value $0$ from that where it's at least some $k > 0$, and thus there is no multiplicative approximation. Standard modifications to this argument rule out additive (polynomial) approximations as well.
As a side note, this approach is pretty thematic in proving such hardness results, and in general problems that can encode an all-or-nothing "all solutions must be of size at least X" constraint for arbitrary X tend to be very inapproximable.