It has been a well-known problem in the G-framework that it is hard to compute the sublinear expectation of the G-normal distribution $\expt[\varphi(X)]$ when $\varphi$ is neither convex nor concave, if not involving any PDE techniques to solve the corresponding G-heat equation. Recently, we have established an efficient iterative method able to compute the sublinear expectation of arbitrary function of the G-normal distribution, which directly applies the Nonlinear Central Limit Theorem in the G-framework to a sequence of variance-uncertain random variables following the Semi-G-normal Distribution, a newly defined concept with a nice Integral Representation, behaving like a ladder in both theory and intuition, helping us climb from the ground of classical normal distribution to approach the peak of G-normal distribution through the iteratively maximizing steps. The series of iteration functions actually produce the whole solution surface of the G-heat equation on a given time grid.