Concept:
The directional derivative of \(f\) in the direction of a unit vector \(\hat u\) is
\[
\nabla f\cdot \hat u
\]
The maximum directional derivative at a point is the magnitude of the gradient:
\[
|\nabla f|
\]
Step 1: Find the gradient of \(f\).
Given,
\[
f=2x^2+3y^2+z^2
\]
Therefore,
\[
\nabla f=
\frac{\partial f}{\partial x}\hat i+
\frac{\partial f}{\partial y}\hat j+
\frac{\partial f}{\partial z}\hat k
\]
\[
\nabla f=4x\hat i+6y\hat j+2z\hat k
\]
Step 2: Substitute the point \((2,1,3)\).
\[
\nabla f(2,1,3)=4(2)\hat i+6(1)\hat j+2(3)\hat k
\]
\[
\nabla f(2,1,3)=8\hat i+6\hat j+6\hat k
\]
Step 3: Find maximum value.
\[
\text{Maximum directional derivative}=|\nabla f|
\]
\[
|\nabla f|=\sqrt{8^2+6^2+6^2}
\]
\[
=\sqrt{64+36+36}
\]
\[
=\sqrt{136}
\]
\[
=2\sqrt{34}
\]
Step 4: Final answer.
\[
\boxed{2\sqrt{34}}
\]