A surrogate model for the prediction of turbine interaction loss
The combination of CFD and machine learning offers a solution. When trained on results from many CFD-based wind farm simulations, an appropriately designed machine learning model can encode the turbine interaction patterns evident in the CFD results. The model, which we call CFD.ML, can then use these encoded patterns to accurately predict CFD results for wind farm configurations not seen before.
The interaction between an array of turbines and the atmosphere is complex, involving a range of atmospheric phenomena and scales. CFD is capable of capturing the main physical drivers behind turbine interaction loss, but it requires the brute force of a supercomputer. CFD.ML, however, can deliver a similar level of accuracy at much greater speed using just a desktop computer—all while side-stepping many of the simplifying assumptions used in typical flow models. The speed increase is substantial: with just 2% of the computational resource, CFD.ML can deliver results 2 million times faster than the CFD model upon which it was trained.
CFD and CFD.ML form a symbiotic pair. With limited time and computational resource, CFD cannot practically cover the full range of wind farm operation; CFD.ML can quickly fill in the gaps between CFD simulations, increasing accuracy and reducing cost. In turn, training on an ever-growing set of CFD results progressively improves accuracy and range of applicability of the CFD.ML model.
CFD.ML, once trained, could also potentially be used without CFD. DNV’s WindFarmer Analyst software team is currently evaluating this possibility. A successful evaluation would signal a significant step forward in the design and analysis of wind farms, which has typically been the domain of simplified models. Unlike these models, CFD.ML accounts for wakes and blockage together while also representing the impact of important influences, such as atmospheric stability.
CFD.ML is a timely development and DNV believes there is significant market potential with many companies in the wind segment looking for a better, faster model of wind farm flows.