English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Low-fidelity wake models are used for wind farm design and controloptimization. To generalize to a wind farm model, individually-modeled wakesare commonly superimposed using approximate superposition models. Wake modelsparameterize atmospheric and wake turbulence, introducing unknown modelparameters that historically are tuned with idealized simulation orexperimental data and neglect uncertainty. We calibrate and estimate theuncertainty of the parameters in a Gaussian wake model using Markov chain MonteCarlo (MCMC) for various wake superposition methods. Posterior distributions ofthe uncertain parameters are generated using power production data from largeeddy simulations (LES) and a utility-scale wake steering field experiment. Theposteriors for the wake expansion coefficient are sensitive to the choice ofsuperposition method, with relative differences in the means and standarddeviations on the order of 100%. This sensitivity illustrates the role ofsuperposition methods in wake modeling error. We compare these data-drivenparameter estimates to estimates derived from a standard turbulence-intensitybased model as a baseline. To assess predictive accuracy, we calibrate thedata-driven parameter estimates with a training dataset for yaw-alignedoperation. Using a Monte Carlo approach, we then generate predicteddistributions of turbine power production and evaluate against a hold-out testdataset for yaw-misaligned operation. Compared to the deterministic predictionsof the baseline parameter estimates, we find that the MCMC-calibratedparameters reduce the total error of the power predictions by roughly 50%. Anadditional benefit of the data-driven parameter estimation is thequantification of uncertainty, which enables physically-quantified confidenceintervals of wake model predictions.

Data-driven wake model parameter estimation to analyze effects of wake superposition