α-IHS Sampling Method Based on Factor Function

In the probabilistic safety assessment of nuclear power plant, as for the uncertainty of data source, it’s essential to launch uncertainty analysis. As a key factor of uncertainty analysis, the distribution of sample space would have a great effect on results of uncertainty analysis. The Latin hypercube sampling method doesn’t make any improvement in space-filling of sample space, the improved distributed hypercube sampling method has improved it by keeping the optimal distance of sample points, but the optimal distance could only be achieved in ideal situation. In order to solve such a problem, α-IHS method based on factor function was put forward to optimize the optimal distance. The results prove that the α-IHS method has advantages over IHS method in a better distribution, and improves sampling efficiency.