QUANTIFYING THE UNCERTAINTY IN WIND POWER PRODUCTION USING METEODYN WT BRAY K. MOLL THOMAS L. ACKER, PhD UNDERGRADUATE RESEARCH ASSISTANT NORTHERN ARIZONA UNIVERSITY OCTOBER 31, 2017 PROFESSOR OF MECHANICAL ENGINEERING
NORTHERN ARIZONA UNIVERSITY DEPARTMENT OF MECHANICAL ENGINEERING CLEAN ENERGY RESEARCH ENERGY AND COMPUTATIONAL MODELING LAB OBJECTIVES Perform an Uncertainty Quantification in Wind Power Predictions from Meteodyn WT Use Computation Resources available at the Energy and Computational Modeling (ECM) Lab. Determine:
Numerical Uncertainty in Wind Speed Prediction Quantify Total Uncertainty in Model Results Velocity with 95% Confidence 2 BACKGROUND Simulatio n Error Modeling Error Numeric
al Error Input Error RoundOff Error Iterative Error Statistical Sampling Error
Discretizati on Error 3 SOFTWARE Geographic Information System (GIS) Used to develop the elevation file Computational Fluid Dynamics (CFD) Software Solves the Nonlinear, Steady, incompressible, isothermal Reynolds
Averaged Naiver Stokes (RANS) equations. Uses a one-equation closure model Matrix Laboratory Used for data reduction and visualization 4 LOCATION Grand Canyon
Flagsta 5 LOCATION Grand Canyon Flagsta 6 MESH
7 RICHARDSON EXTRAPOLATION 3 Systematically Refined Meshs Fine Med Intersectin g Nodes
Course 8 SYNTHESIS RESULTS Average Order of convergence = -1.33 9 SYNTHESIS RESULTS 1 0 SYNTHESIS RESULTS
11 CONCLUSION A significant difference between the theoretical and observed order of convergence Theoretical = 2 Observed= -1.33 Not in the asymptotic range Finer Mesh Iterative Error Influence Increase the number of iterations
12 ACKNOWLEDGEMENTS Dr. Tom Acker Energy and Computational Modeling (ECM) Lab Kathleen Stigmon & Dr. Nadine Barlow NAU Nasa Space Grant 13 Questions? 14
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C. Roy, Review of Discretization Error Estimators in Scientific Computing, AIAA Pap., no. January, pp. 129, 2010. [4] L. F. Richardson, The Approximate Arithmetical Solution by Finite Differences of Physical Problems involving Differential Equations, with an Application to the Stresses in a Masonry Dam, Trans. R. Soc. London, vol. 210, 1910. [5] W. Oberkampf and C. Roy, Verification and Validation in Scientific Computing. New York: Cambridge University Press, 2010.
[6] P. J. Roache, Perspective: A Method for Uniform Reporting of Grid Refinement Studies, J. Fluids Eng., vol. 116, no. 405, 1994. [7] Meteodyn, Technical note -meteodyn WT. [8] D. Martindale, Distributed Wind Resource Assessment Using Meteodyn Wt, Northern Arizona University, 2016. 15
