Unless there is some physical bound on the value of u it appears that the minimum can become as negative as you like by letting u become as large as you like and finding appropriate values for the other variables.
For example, with 0<Y<1, t=10, 0<g<10
{minvalue, Y, u, g}={-30.8269, 0.173812, 79.9291, 0.0294438} for 0<u<80
{minvalue, Y, u, g}={-62.0829, 0.216682, 158.867, 0.0124873} for 0<u<160
{minvalue, Y, u, g}={-125.834, 0.167075, 319.935, 0.0223538} for 0<u<320
etc.
Each of those were obtained from a very simple fast brute force Monte Carlo run, just to cross check other calculations.
Depending on the range of g and t your 1 - (1 - u)*Y - u*Sqrt[1 - g/(t*Y^.3)]*Y^.32 is complex 10-25% of the time. If that were not the case then a bisection root finder for Y would be simple. It is feasible to calculate the minimum value of Y to ensure that this will be Real with a lower bound on Y of (g/t)^(10/3). With that lower bound on Y bisection seems to work well on your problem.
So are there other constraints on u and g? Or is this appearing to be unbounded the end of your problem?