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A surrogate model-based framework to calibrate the turbulence parameters of a vegetative canopy model for a marine turbine simulation

机译:A surrogate model-based framework to calibrate the turbulence parameters of a vegetative canopy model for a marine turbine simulation

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摘要

Generating sustainable energy from marine currents using marine turbines garners much attention in recent years. Assessments of marine turbine arrays require computationally expensive and very large domain simulations. This paper proposes a framework based on a surrogate model approach paired with optimization algorithms to calibrate the adjustable parameters value of the simulator and minimize the deviation between the simulation outputs and the physical experiment results. We find that the application of more advanced surrogate models and optimization techniques improved performance by 16.97% compared to the previous approach. We identify an easy-to-implement opportunity to further improve the performance. Based on descriptive statistics, we design a visual tool that evaluates the quality of sample data quickly and easily.
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