Date post: | 25-May-2015 |
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FUSED-Wake
Framework for Unified System Engineering and Design of wind farm Wake models
Pierre-Elouan RéthoréSenior ResearcherDTU-Wind Energy, Risø
DTU Wind Energy, Technical University of Denmark
FUSED-Wind
•Collaborative effort between DTU and NREL to create a Framework for Unified System Engineering and Designed of Wind energy plants.
•Based on OpenMDAO, a python based Open source framework for Multi-Disciplinary Analysis and Optimization.
PythonPython
HAW
C2
FAST
Wind Resource
Model
Flow
Mod
el
Wake
Mod
el
Cost
Mod
el
DTU Wind Energy, Technical University of Denmark
FUSED-Wake•The heart (and brain) of TopFarm II•Based on FUSED-Wind•Can run all the wake models of DTU with the same inputs and
outputs
FUGA
DWM
GCL NOJ(s)
EllipSys AD RANS
EllipSys AD LES
EllipSys AL LES
EllipSys FR LES
DTU Wind Energy, Technical University of Denmark
Research tool: Modularized concept
•The wind farm wake models are split into a generalized workflow
Inflow
Generator
Inflow
GeneratorWS positionsWS positions Wake
AccumulationWake
Accumulation Hub WSHub WS WT ModelWT Model
Wake
Model
Wake
Model
Stream wise WTs
Stream wise WTs
Upstream WTsUpstream WTs
RecorderRecorder
RecorderRecorder
DTU Wind Energy, Technical University of Denmark
Potential applications of the framework
•Model automatic selection•Machine learning (model recalibration)•Uncertainty quantification•Model Averaging (combining the information of several
models)•Multi-fidelity optimization•Standard way to run wind farm models•Bridging the gap between researchers and industry•“Companion” to WindBench
– Automatically running all the benchmarks with the same inputs / post processing
– Robust benchmarking (no expert user required)
DTU Wind Energy, Technical University of Denmark
WindBench companion
6 April 12, 2023
Benchmark manager
Post-processing script
Post-processing script
FUSED-Wake
Input
FUSED-Wake
Input
WakeBench
benchmark
WakeBench
benchmark
Cloud Cluster
Report
Web-graphs
For all models
Model manager
Wake ModelWake ModelFUSED-Wake
Wrapping
FUSED-Wake
Wrapping
DTU Wind Energy, Technical University of Denmark
Multi-fidelity & Machine Learning
•Each subcomponent, or several of them together can be swapped to a different level of fidelity.
•Each subcomponent level of fidelity produces an intrinsic uncertainty, dependent of the input-region.
•Swapping to higher fidelity might involve a computation cost and offer a reduction in intrinsic uncertainty in return.
•Running a higher fidelity can potentially re-calibrate the lower fidelity models, and lowering its intrinsic uncertainty within the specific input-region.
DTU Wind Energy, Technical University of Denmark
Input-region - Re-calibration cascade
FUGA
DWM
GCL NOJ
EllipSys AD RANS
EllipSys AD LES
EllipSys AL LES
Tim
e [
log
]
Intrinsic Uncertainty [-]
EllipSys FR LES
SCADAdata
SCADAdata
DTU Wind Energy, Technical University of Denmark
Parameter calibration
•Re-analysis of SCADA data using LES• Inverse uncertainty quantification
– Bias correction and Parameter calibration– Bayesian inference– Optimal maps– Kalman filters
Experiment
Variables ParametersModel
Experimental uncertainty
Bias function
DTU Wind Energy, Technical University of Denmark
Towards a higher level of science
• Including the uncertainty of the models in the results:– Parameter uncertainty estimation– Input uncertainty elicitation– Uncertainty propagation to the outputs– Model inaccuracy – Code inaccuracy
•Deterministic model => stochastic model– The framework could automatize this workflow
DTU Wind Energy, Technical University of Denmark
Next steps
•Gathering interest group •Alpha release to interest group•Public release of beta version•Forming a project portfolio to coordinate the efforts
Status
•Framework in alpha version is ready for testing– N.O. Jensen– G.C. Larsen– FUGA– EllipSys– DWM