Saarblitz, Wind farm in HDR,, creative commons by-nc-sa 2.0

MesoWake (2014-2017)

Unified mesoscale to wind turbine wake downscaling based on an open-source model chain

MesoWake is a project sponsored by the European Commission's within an FP7 International Outgoing Marie Curie Fellowship granted to Javier Sanz Rodrigo, Senior Researcher at the National Renewable Energy Center of Spain (CENER). The outgoing phase of this fellowship, from August 2014 to July 2016, was hosted by the National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy. The reintegration phase is back to CENER until July 2017. The project counts with the National Center for Atmospheric Research (NCAR) and the Barcelona Supercomputing Center (BSC) as scientific partners.

The objective is to contribute to the development of an open-source model chain that can bridge the gap between mesoscale meteorological processes and microscale wind farm models.

The multi-scale modeling system spans domain sizes of the order of 1e7 to 10 m, at spatial resolutions ranging from 1e4 to 1 m and a temporal range from a few days to fractions of a second. The need for a multi-scale approach is the result of the evolution of wind turbine technology that, over the last decades, has been successful at exploiting wind turbine scaling designs with rotors already spanning more than 150 m diameter and hub heights above 100 m. Furthermore, large wind farm arrays can extend more than 10 km creating their own boundary layer structure with important interaction with the free atmosphere. This increasing range of scales is challenging traditional wind engineering models that consider the wind farm system as an idealized microscale system where surface-layer theories apply. This lack of appropriate physics often leads to wind farm underperformance and high project financing costs.

In effect, wind energy is the fastest growing renewable energy technology to increase the share of global energy demand from 2% in 2010 to 11% in 2030 (IRENA, 2016). To support this prosperous future, unprecedented research programs in the U.S. and Europe have been recently launched to improve our understanding of the complex flow physics around and within wind farms. Better insight into the flow physics has the potential of reducing wind farm energy losses by up to 20% according to the U.S. Department of Energy’s Atmosphere to Electrons (A2e) research initiative. Its European counterpart, the New European Wind Atlas (NEWA) project, leverages national funding from 8 EU Member States to reduce resource characterization uncertainties below 10%. Over the next years these two programs will improve our simulation-based design capabilities. This can accelerate significantly the development of new turbine prototypes, promote innovation and reduce wind farm design uncertainties provided the modeling tools are validated systematically with high-fidelity data.

In MesoWake, special focus is given to the characterization of mesoscale forcing to drive atmospheric boundary layer models using realistic boundary conditions. Turbulence modeling is based on large-eddy simulation (LES) to resolve the turbulent scales that affect turbine and wind farm performance. The open-source unified model is based on a combination of WRF-LES, developed by NCAR, and SOWFA (OpenFOAM-LES coupled to FAST aeroelastic code), developed at NREL. The codes are installed at the MareNostrum high performance computing facility managed by BSC, member of the Partnership for Advance Computing in Europe (PRACE). This will provide a virtual laboratory for high fidelity modeling of atmospheric physics applied to wind energy. This laboratory has effectively kicked-off in July 2017 with PRACE-MesoWake, a multi-year Project Access award with an initial allocation of 17 million cpu-hours for the first year. Scaling tests and a performance audit has been carried out, under the Performance Optimization and Productivity (POP) Centre of Excellence, to determine the suitability of the codes to run in high-performance computing systems.  

The MesoWake project is timely at creating a European hub for the development of high-fidelity modeling capabilities based on an open-source framework. This unified model will support ongoing activities in Europe and the U.S. and strengthen the cooperation in this strategic interdisciplinary research area that requires: cross-cutting knowledge across a wide range of atmospheric and engineering sciences, interfacing of models that have been developed separately, large computational resources, and high fidelity experiments for validation. The model evaluation strategy adopted in MesoWake follows the systematic verification and validation framework developed in the context of the International Energy Agency's IEA Task 31 Wakebench (Sanz Rodrigo et al., 2016a).   

Initial assessment of the meso-micro methodology around the GABLS3 diurnal cycle has shown good consistency of the methodology to couple mesoscale and microscale models asynchronously (Sanz Rodrigo et al., 2016b). The method is relatively straightforward to implement in existing microscale models. A benchmark revisiting GABLS3 for wind energy atmospheric boundary layer models has been launched within the IEA Task 31 to demonstrate the general applicability of the method for LES as well as Reynolds-averaged Navier Stokes (RANS) models (Sanz Rodrigo et al., 2016c). Ongoing work is directed towards demonstrating the general applicability in complex terrain based on simulations of CENER's test site for wind turbines in the Alaiz mountain range. The simulations will be used to support the design of a experimental campaign carried out in the frame of the NEWA project in 2017-18. The validation program will demonstrate the potential of using these high fidelity modeling framework to conduct cost-effective virtual experiments. These datasets will be useful for the calibration of engineering models, contributing to more reliable wind turbine and wind farm designs.     

The MesoWake research programme is complemented with a training programme focused on large-eddy simulation techniques, high performance computing and a Master in Business Innovation from Deusto Business School.


Sanz Rodrigo, J., Chávez Arroyo, R.A., Moriarty, P., Churchfield, M., Kosovic, B., Réthoré, R.-E., Hansen, K.S., Hahmann, A., Mirocha, J.D. and Rife, D. (2016a) Mesoscale-to-Microscale Wind Farm Flow Modelling and Evaluation. WIREs Energy Environ. doi: 10.1002/wene.214

Sanz Rodrigo, J., Churchfield M., Kosovic B. (2016b) Atmospheric boundary layer modeling based on mesoscale tendencies and data assimilation at microscale. Wind Energy Science, submitted

Sanz Rodrigo, J., Churchfield M., Kosovic B. (2016c) A wind energy benchmark for ABL modeling of a diurnal cycle with a nocturnal low-level jet: GABLS3 revisited. J. Phys. Conf. Ser. 2016, submitted

Sanz Rodrigo J., Cantero E., et al. (2015) Atmospheric stability assessment for the characterization of offshore wind conditions. J. Phys. Conf. Ser. 625, 012044, doi:10.1088/1742-6596/625/1/012044