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