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A Data Model for Turbulence Analysis Downstream of an Ocean Current Turbine Rotor for Hydrokinetic Power Generation

[+] Author Affiliations
Yuchen Shang, Nikolaos I. Xiros

University of New Orleans, New Orleans, LA

James H. VanZwieten

Florida Atlantic University, Boca Raton, FL

Cornel Sultan

Virginia Tech, Blacksburg, VA

Paper No. DSCC2017-5371, pp. V003T41A004; 9 pages
doi:10.1115/DSCC2017-5371
From:
  • ASME 2017 Dynamic Systems and Control Conference
  • Volume 3: Vibration in Mechanical Systems; Modeling and Validation; Dynamic Systems and Control Education; Vibrations and Control of Systems; Modeling and Estimation for Vehicle Safety and Integrity; Modeling and Control of IC Engines and Aftertreatment Systems; Unmanned Aerial Vehicles (UAVs) and Their Applications; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Control of Smart Buildings and Microgrids; Energy Systems
  • Tysons, Virginia, USA, October 11–13, 2017
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5829-5
  • Copyright © 2017 by ASME

abstract

Neural networks are derived to be used as closed-form representations of mean hydrokinetic turbine performance variables. These representations can be used to obtain estimates of turbine performance when the ambient turbulence characteristics, namely in-flow velocity and turbulence intensity, are given. The neural networks were developed using a detailed hydrodynamic code, which simulates performance of a rigidly mounted hydrokinetic turbine where only rotor rotation is allowed (1-DOF). By varying the in-flow velocity (U) of the water current between 0.4m/s and 2.6m/s with a step of 0.2m/s, as well as Turbulence Intensity (TI) between 5% and 20% with a step of 2.5%, a set of variables including the output shaft power, shaft torque, force on a single blade and drag force were obtained for each case. The obtained data sets were used to train appropriately sized, feed-forward (i.e. without recirculation) neural networks. Four neural networks obtained, one for each output variable of the hydrodynamic code. Each neural net constitutes a closed-form, explicit mathematical relationship (equation) generating estimates for the corresponding dependent variable it has been trained to approximate, when presented with specific values for the independent (input) variables current velocity, U, and turbulence intensity, TI. Four output (dependent) variables of interest of the hydrodynamic code are considered: shaft power, shaft torque, force on a single blade and drag. The dependent variables are actually time-averaged steady-state values derived from each hydrodynamic code run. The results of the neural networks are validated using the background theory, as well as the data generated by the hydrodynamic code. Error of less than 1% has been achieved between the neural net output and the hydrodynamics code data values suggesting that the neural networks and the equations are usable in place of the hydrodynamic code for estimating time-averaged loadings and power production.

Copyright © 2017 by ASME

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