Knowing that wind turbine and wind farm dynamics range in length scales, we used algorithms focused on these varying aspects to study the impact that flow dynamics have on wind turbines, blades and wind farms. Our High Performance Computing for Wind Energy (HPCWE) project addresses the development of algorithms for large-eddy simulations — a technique for simulating turbulent flows with high fidelity — to show the fluid-structure interaction (FSI) and aeroelastic coupling of wind turbine blades. By studying FSI, we are able to observe the interaction between a flexible structure and the fluid flow stream. Our research team also examined the development of wind turbine modelling methods for wind turbines and wind farms. This was accomplished by using mesoscale weather prediction models to generate the input data for the large-eddy simulations. Our final results discuss the development of uncertainty quantification (UQ) methods to determine how likely a certain outcome is if particular aspects are not known when processing complex data at high speeds — also known as high-performance computing environments (HPCE). We utilised the HPCE data to examine which parameters have the most significant influence on wind turbine performance.