Turbulence-parameter estimation for current-energy converters using surrogate model optimization. (May 2021)

• Record Type: Journal Article
• Authors: Olson, Sterling S.; Su, Jack C.P.; Silva, H.; Chartrand, Chris C.; Roberts, Jesse D.
• Is Part Of: Renewable energy. Volume 168(2021)
• Page Start: 559
• Page End: 567
• Publication Date: 2021-05
• Subjects: Actuator disc -- Marine energy -- OpenFOAM -- Gaussian process regression -- Surrogate -- Turbulence; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.12.036
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Surrogate models maximize information utility by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain simulations to estimate array efficiencies and environmental impacts. Meso-scale models typically represent turbines as actuator discs that act as momentum sinks and sources of turbulence and its dissipation. An OpenFOAM model was developed where actuator disc k -ε turbulence was characterized using an approach developed for flows through vegetative canopies. Turbine-wake data from laboratory flume experiments collected at two influent turbulence intensities were used to calibrate parameters in the turbulence-source terms in the k -ε equations. Parameter influences on longitudinal wake profiles were estimated using Gaussian process regression with subsequent optimization minimizing the objective function within 3.1% of those obtained using the full model representation, but for 74% of the computational cost (far fewer model runs). This framework facilitates more efficient parameterization of the turbulence-source equations using turbine-wake data.