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![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
Time[log]
Intrinsic Uncertainty [-]
EllipSys
FR LES
SCADA
data
SCADA
data](https://image.slidesharecdn.com/fused-wake-130708042435-phpapp01/85/Fused-wake-8-320.jpg)
Uploaded byPierre-Elouan Réthoré
Fused wake
This document describes FUSED-Wake, a framework for running multiple wind farm wake models with the same inputs and outputs. It aims to standardize how wake models are run, facilitate model benchmarking and averaging, and enable multi-fidelity optimization and machine learning approaches. The framework is based on OpenMDAO and allows different wake, inflow, and power models to be combined. It can automatically run multiple wake models for benchmarking studies and has potential for uncertainty quantification and model calibration using field data.
Fused wake
- 1. FUSED-Wake Framework for UnifiedSystem Engineering and Design of wind farm Wake models Pierre-Elouan Réthoré Senior Researcher DTU-Wind Energy, Risø
- 2. 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 H A W C 2 FAST Wind Resource Model Flow M odel Wake Model Cost Model
- 3. 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
- 4. 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 Generator WS positionsWS positions Wake Accumulation Wake Accumulation Hub WSHub WS WT ModelWT Model Wake Model Wake Model Stream wise WTs Stream wise WTs Upstream WTsUpstream WTs RecorderRecorder RecorderRecorder Power Curve CFD ALFUGA BEMLog LawMann Precursor CFD LinearQuadraticMixed HAWC2 FUGA EllipSys AL/LES
- 5. 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)
- 6. DTU Wind Energy,Technical University of Denmark WindBench companion 6 July 8, 2013 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 Model FUSED-Wake Wrapping FUSED-Wake Wrapping
- 7. 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.
- 8. 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 Time[log] Intrinsic Uncertainty [-] EllipSys FR LES SCADA data SCADA data
- 9. 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
- 10. 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
- 11. 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