I'm not sure what exactly you mean by saying scaling up the grid but keeping the fineness. Surely the solution domain remains constant. You are also leaving out many other potentially important details, such as the numerical method (space and time, steady/unsteady, boundary conditions, etc). I will proceed assuming that you mean coarsening the grid by a factor of 10.
What you see - better agreement between CFD and experimental data with coarse grids - is not unheard of. But it nevertheless is probably spurious in the sense that it does not accurately reflect what is the "true" numerical solution of your model. To obtain this "true" solution, you need to systematically refine your spatial and temporal resolution, i.e. you need to carry out a grid and time-step refinement study. When the numerical results become independent (or close to independent) of the resolution, you have obtained the "true" numerical solution of your model. Any deviations that remain between this solution and the experimental data must be due to the model you are solving. (In your particular case the modeling of the solid phase and the coupling of the phases.) Another source for deviations may be measurement errors in the experiment.
Refinement studies are usually carried out by halving the grid spacing in each coordinate direction for each refinement. This means that you will be carrying out simulations with your original grid of N points (or cells), then with a grid with 4N, 16N, etc. The same is done with the time step. If you solve the unsteady form of the equations, you want to keep the CFL number constant (and small) as you refine the grid. It is not difficult to find papers that use grids that are much too coarse. I remember recent articles on fluidized bed simulations in which it was shown that what was widely thought to be a model deficiency was in fact nothing but a lack of resolution.
Many multiphase flows are inherently unsteady, in which case you need to average your solution in some form. Then you need to be careful about your averaging duration and sampling frequency.
Finally, I base all of the above on the assumption that your code has undergone code verification. This is a prerequisite for any validation (comparison of simulation with experimental data). What I describe above is solution verification. If you are not familiar with these terms, I suggest you look at the book by Oberkampf and Roy.