Research digital skills training 2021
Optimisation of blades on large wind turbines with individual pitch control and trailing edge flaps
Zhenrong Jeremy Chen, PhD candidate, Dr Karl Stol, Senior Lecturer, Prof Brian Mace, Head of Department, Department of Mechanical Engineering
Figure 1 Overview of optimisation process.
Use of the University’s research virtual machines
The optimisation process developed makes use of virtual machines at several stages to parallelise task execution. Within the SQP optimisation, sensitivity analysis of each of the blade geometry design parameters is performed in parallel. Time domain simulations of wind turbines for system identification experiments at different wind speeds are also performed in parallel, along with additional simulations to determine the loads experienced by the turbine over its lifetime in a variety of wind fields and operational states as specified by international standards. From the results of these extensive simulations (typically around 80 separate simulations), constraints for the optimisation algorithms which correspond to limits on ultimate loads, fatigue loads and blade deflection are also calculated in parallel. The use of the computing resources from the NeSI Pan cluster greatly reduces the time required for each of these tasks, reducing time required for development of the optimisation process and allowing for results to be obtained within a realistic timeframe.
Figure 2 Sample results from NOMAD optimisation of blade layup.
The blade design and optimisation process is currently being used to optimise the blades on a baseline 5MW turbine with a standard collective pitch controller (no advanced load control). The result of this optimisation will be used as a starting point for optimisation of blades on a turbine with individual pitch control, with the use of localised actuators in the form of trailing edge flaps introduced at a later stage. Future work may also involve the optimisation of larger turbines (10-20MW) to assess the scale of benefits provided to large wind turbines which integrate advanced load control. Initial results have shown that there is room for the cost of energy of the baseline turbine to be reduced, and we aim to demonstrate how the benefits of advanced load control can extend past load reductions on existing blade designs.