Verifying Marine-Hydro-Kinetic Energy Generation Simulations Using SNL-EFDC

Conference Paper

Title: Verifying Marine-Hydro-Kinetic Energy Generation Simulations Using SNL-EFDC
Publication Date:
December 19, 2011
Conference Name: Oceans 2011
Conference Location: Waikoloa, HI
Pages: 9
Stressor:
Technology Type:

Document Access

Attachment: Access File
(406 KB)

Citation

James, S.; Lefantzi, S.; Barco, J.; Johnson, E.; Roberts, J. (2011). Verifying Marine-Hydro-Kinetic Energy Generation Simulations Using SNL-EFDC. Paper Presented at the Oceans 2011, Waikoloa, HI.
Abstract: 

Increasing interest in marine hydrokinetic (MHK) energy has led to significant research regarding optimal placement of emerging technologies to maximize energy capture and minimize effects on the marine environment. Understanding the changes to the near- and far-field hydrodynamics is necessary to assess optimal placement. MHK projects will convert energy (momentum) from the system, altering water velocities and potentially water quality and sediment transport as well. Maximum site efficiency for MHK power projects must balance with the requirement of avoiding environmental harm.

 

This study is based on previous modification to an existing flow, sediment dynamics, and water-quality code (SNL-EFDC) where a simulation of an experimental flume is used to qualify, quantify, and visualize the influence of MHK energy generation. Turbulence and device parameters are calibrated against wake data from a flume experiment out of the University of Southampton (L. Myers and A. S. Bahaj, “Near wake properties of horizontal axis marine current turbines,” in Proceedings of the 8th European Wave and Tidal Energy Conference, 2009, pp. 558- 565) to produce verified simulations of MHK-device energy removal. To achieve a realistic velocity deficit within the wake of the device, parametric studies using the nonlinear, modelindependent, parameter estimators PEST and DAKOTA were compared to determine parameter sensitivities and optimal values for various constants in the flow and turbulence closure equations. The sensitivity analyses revealed that the Smagorinski subgrid-scale horizontal momentum diffusion constant and the k-ε kinetic energy dissipation rate constant (Cε4) were the two most important parameters influencing wake profile and dissipation at 10 or more device diameters downstream as they strongly influence how the wake mixes with the bulk flow. These results verify the model, which can now be used to perform MHK-array distribution and optimization studies.

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