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Simulating the Entire Life of an Offshore Wind Turbine. Matthew Barone, Josh Paquette, and Brian Resor Wind Energy Technologies Department Sandia National Laboratories Lance Manuel and Hieu Nguyen Department of Civil , Architectural, and Environmental Engineering - PowerPoint PPT Presentation
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Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National
Nuclear Security Administration under contract DE-AC04-94AL85000..
Simulating the Entire Life of an Offshore Wind Turbine
Matthew Barone, Josh Paquette, and Brian Resor
Wind Energy Technologies Department
Sandia National Laboratories
Lance Manuel and Hieu Nguyen
Department of Civil, Architectural, and Environmental Engineering
University of Texas at Austin
High Performance Computing and Wind Energy
Vestas “Firestorm” Computer180 Tflop peak performance
#3 ‘fastest’ industry supercomputer in the world
From: Calaf, Parlange, & Meneveau, Phys. Fluids 23, 2011.
From: Larsen et al, European TOPFARM project final report.
Wind farm optimization LES of wind turbine arrays
Example Applications of HPC to Wind Energy
Growing Industry Resources
50-year recurrence ??
50-year recurrence? ?
Uncertainty in Wind Turbine Extreme Load Extrapolation
For DLC 1.1 the characteristic value of load shall be determined by a statistical load extrapolation and correspond to an exceedance probability, for the largest value in any 10-min period, of less than or equal to 3.8 x 10–7, (i.e. a 50-year recurrence period) for normal design situations.
From: IEC 61400-1 Ed. 3 – Wind Turbine Design Standards
6 weeks of simulation
2 different fits
128 hours of simulation (many different realizations)
Fits to 2 different realizations
Research Questions for the Computer
What are the probability distributions for various one-hour extreme turbine loads for an offshore wind turbine in shallow water?•Compute these down to fifty-year recurrence
probabilities. What turbulent wind inflow and wave conditions
lead to the largest turbine loads?•Save the input parameters for each simulation so
that select simulations can be reproduced later. What are the uncertainties for a given load
extrapolation procedure?
Sandia High-Performance Computing Resources Sandia continues to extend a distinguished record in high
performance computing. These resources are available for solving problems in
wind power.
ASCI RedWorld’s First Teraflop Computer
1.3 Teraflops*World Rank (1997): #1
Thunderbird Cluster
53 TeraflopsWorld Rank (2006): #6
450 TeraflopsWorld Rank (2010): #10
Red Mesa Partition:• Dedicated to energy-related work• NREL & Sandia users• 180 Teraflops
*1 Teraflop = 1 Trillion floating point operations per second
Turbine Aero-hydro-elastic Model NREL 5 MW offshore reference turbine
• 3-bladed HAWT with upwind rotor• Monopile foundation, water depth of 20 m• Rotor Diameter = 126 meters• Hub Height = 90 meters• Variable speed, collective variable pitch controller, no active yaw control• Cut-in, Cut-out, and Rated Wind Speed = 3 m/s, 25 m/s, 11.4 m/s
Aero-hydro-elastic Simulation Code• NREL FAST code• Equilibrium BEM ‘inflow’, or ‘wake’, model
Chosen to avoid instabilities associated with dynamic wake models• NREL Turbsim code used to generate inflow turbulence (Kaimal spectrum)• Incident wave field computed using JONSWAP spectrum in FAST
Site Definition Forschung in Nord-Ostsee (FINO) research
platform 45 km north of the Island of Borkum in the
North Sea Measurement Period: November 2003 –
May 2005 Wind: 10-minute mean values of the wind
speed at 100-m height Waves: 1-hour significant wave height from
wave buoy No data for turbulence intensity: we
assumed uniform 10% value for all wind speeds
http://www.dewi.de/dewi/index.php?id=152
Aero-hydro-elastic Load Simulations DAKOTA
• Simulation framework developed at Sandia National Laboratories• Enables large-scale parameter studies, sensitivity analysis, optimization, and UQ• dakota.sandia.gov
Simulation Procedure• DAKOTA samples two random wind seeds, two random wave seeds, and mean wind
speed for each sim using a Latin Hypercube sampling method• Significant wave height and period are taken as expected values conditional on mean
wind speed• DAKOTA asynchronously schedules a simulation on each available core• TurbSim and FAST are run in sequence for each simulation• Random seeds, mean wind speed, and 1-hour extreme values are saved by DAKOTA
Stats• 552,809 simulations performed (~63 years) in four separate batches• 1028 cores used on Red Sky• 5 days of total wall-clock time
Extreme Blade Tip Deflections
Extreme Blade Root Bending Moments
Extreme Tower Base Moments
Extreme Tower Torsional Moment
Extreme Tower Base Fore-Aft Moment vs. Mean Wind Speed
Max. load at U = 15.856 m/s
Extreme Tower Torsional Moment vs. Mean Wind Speed
Max. load at U = 22.915 m/s
Maximum Tower Base Fore-Aft Moment Case
Simulation No. 524,988
Hub Height Wind Speed (m/s)
Blade Pitch (deg)
Sea Surface Level (m)
Tower Fore-Aft Moment (kN-m)
Evaluation of Uncertainty in Load Extrapolation: How much simulation is needed?
128 Simulations 512 Simulations 2048 Simulations
1. The 63 years’ of simulation was used to generate subsets of N simulations
2. Each subset was used to estimate the 1- and 50 year loads using linear least-squares regression below a probability level of 0.1
3. Mean estimates and confidence intervals were generated for the 1- and 50-year load
Evaluating Load Extrapolation Uncertainty – Blade Root Flapwise Moment
Fifty-year Return Load
Evaluating Load Extrapolation Uncertainty – Tower Base Torsional Moment
Fifty-year Return Load
Simulation Challenges Large-scale loads simulations can be an “I/O bound”
supercomputing application rather than “CPU bound”•Many small files are written simultaneously to disk•Caused a problem on Red Sky’s parallel file system
Memory efficiency of sampling algorithm important for large numbers of simulations•Dakota’s Latin hypercube sampling algorithm limited the
number of samples in a single simulation batch
Future Directions Address dynamic wake robustness issue Treat turbulence intensity, significant wave height, wave
spectral peak period, wind shear probabilistically Examine fatigue load spectra Investigate concurrent extreme loads
•Example: what is the probability distribution of edge-wise blade root moment when flap-wise moment exceeds a given value?
Explore potential impact on wind turbine design standards
Acknowledgements Thanks to the Sandia Red Sky team: Steve Monk, Sophia
Corwell, Karen Haskell, Anthony Agelastos, Jeffrey Ogden, Joel Stevenson
Thanks to the DAKOTA team, Brian Adams and Mike Eldred Thanks to Jason Jonkman for assistance in modifying the FAST
code
Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National
Nuclear Security Administration under contract DE-AC04-94AL85000..
Thank You