Propagation of parameter uncertainties through large computer models can be very resource intensive. Frameworks and tools for uncertainty quantification are generally geared to individual codes, are research codes, or are single-purpose tools such as LHS matrix generators. The Reduced-Order-Clustering-Uncertainty-Quantification (ROCUQ) methodology discussed in this proposal is specifically designed to circumvent many of the issues associated with uncertainty quantification of large simulation codes. The ROCUQ methodology has been applied in several different physical disciplines with good results. The computational methodology is a combination of reduced-order modeling, stratified sampling (Latin Hypercube Sampling -- LHS), statistical clustering of results (K-means clustering) and a few (five to ten) full-physics runs of the high-fidelity model under investigation. The method should be applicable to hundreds of uncertain variables when required. ROCUQ enables estimates of system response quantities (SRQ) uncertainty distributions for situations where it is not feasible to use purely sampling, collocation, or other techniques where many runs would be required. For some organizations, uncertainty analysis has never been possible due to resource limitations, and thus is not part of the organizational culture. Many analysts know that uncertainties can be important, but have no way to expend sufficient resources (money, CPU cycles, time) to do the work needed to quantify uncertainties. A methodology such as ROCUQ promises to open doors in organizations that know that they have the need, and may now be able to actually perform the analyses. Successful completion of the Phase II project will produce not only new software that will be able to be used by researchers and industry, but will assemble insights on the use of reduced order models in a variety of disciplines, and provide guidance and rules for the use of ROCUQ for the estimation of SRQ uncertainty distributions.