Comparison metrics simulation challenge
In this project, a public "simulation challenge" for wind turbines in complex terrain is conducted, where participants submit simulation results in a predefined template. Hundreds of comparison metrics before and after running the simulations will be collected so that transfer functions can be developed to accurately predict the suitability of the tools and assist modellers in selecting the best model for a given wind energy project.
This public simulation challenge is open to the international community and consists of two phases:
Phase 1: Complex Site (Perdigão Site) - Apr 2020 - Sep 2020.
In this phase, participants will submit their results in a prepared template so that we can calculate weighted parameters in terms of qualification scores and costs both before and after the simulations are conducted. The simulation case will be clearly defined so that all results can be compared. The Perdigão site in Portugal, consisting of a flow over a double ridge, was chosen due to its relative complexity combined with the high number of field tests and open measurement data available for validation. All results are then plotted in a diagram and may look something like Figure 1(a), where each point represents a simulation tool. The clusters are expected based on different tool categories (e.g. linear model, RANS-CFD, LES-CFD). As can be seen, there will be a discrepancy between the predicted metrics and those determined based on the results of the simulations. Based on these results, transfer functions will be developed to better predict skill values and costs. More info and registration here.
Phase 2: Open test cases - Jul 2020 - Mar 2021
In this phase, participants will also submit their results in a prepared template so that we can calculate weighted parameters related to qualification values and costs before and after running the simulations. In this case, however, no specific test case will be predefined so that we can capture a much wider range of different locations and external conditions. The results are clustered according to different categories of input conditions and may look something like Figure 1(b). In this figure, only the most appropriate lines through the data are shown, and for simplicity, an example is shown for only two different categories. Based on these results, transfer functions will be developed to better predict skill values and costs.
This project is funded by the Swiss Federal Office of Energy and is carried out in collaboration with IEA Wind Task 31 (October 2019 to October 2021).