Turbulence-parameter estimation for current-energy converters using surrogate model optimization
| Autor: | H. Silva, Chris Chartrand, Jesse Roberts, Jack C. P. Su, Sterling S. Olson |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
060102 archaeology
Renewable Energy Sustainability and the Environment Turbulence Estimation theory Computer science 020209 energy Energy current 06 humanities and the arts 02 engineering and technology Dissipation Physics::Fluid Dynamics Surrogate model Kriging Control theory Marine energy 0202 electrical engineering electronic engineering information engineering 0601 history and archaeology Actuator |
| Zdroj: | Renewable Energy. 168:559-567 |
| ISSN: | 0960-1481 |
| DOI: | 10.1016/j.renene.2020.12.036 |
| Popis: | 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-e 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-e 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. |
| Databáze: | OpenAIRE |
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