I have a constraint that I believe to be convex and not affine which I think means that I can implement a relaxation. I will first define the full constraint, and then build up my (informal) reasoning as to why I think it's convex. Hopefully the holes in my thinking can be pointed out and corrected.
$$ X_{t} = \frac{ Y_{t-1}^2 }{Y_{t-1}^2 + a^2}Z_{t-1}, \quad t=1,2,\dots,T \tag1 $$ $$ X_t,Y_t,Z_t \ge 0, $$ $$ X_0, Y_0, Z_0, \alpha \gt 0 $$
Argument 1: A quadratic polynomial is convex, so if the constraint was simply $ X_{t} = Y_{t-1}^2$ then the constraint would be convex.
Argument 2: By a similar extension, $ X_{t} = Y_{t-1}^2 + \alpha^2 $ would be convex.
Argument 3: The ratio of two convex functions, call it $F_{t-1}$, should also be convex.
Argument 4: If $F_{t-1}$ is convex, then multiplying $F_{t-1}$ by a continuous linear variable would not impose any non-convexity issues.
Conclusion: The original constraint is convex but not affine and as such we can apply a relaxation to change the problem into:
$$ X_{t} \le \frac{ Y_{t-1}^2 }{Y_{t-1}^2 + a^2}Z_{t-1}, \quad t=1,2,\dots,T \tag1 $$