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Building the Bridge between Meteorology and Wind Engineering

Mesoscale-to-microscale coupling of meteorological and wind engineering models allows wind energy researchers to improve their simulation-based design capabilities with more realistic models about the interplay between weather processes and site wind conditions. The MesoWake Marie Curie International Outgoing Fellowship has introduced a relatively simple interfacing methodology that allows wind engineers to overcome the traditional paradigm of design tools based on idealized inflow conditions.

MesoWake has run in parallel to unprecedented research programs in the U.S. and Europe devoted to understanding 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 few years these two programs will improve our simulation-based design capabilities with more realistic multi-scale modeling, supported by high-fidelity experiments and a validation framework unified under the umbrella of the International Energy Agency (IEA) Task 31 “Wakebench”. Working in between these two research programs has allowed the Marie Curie Fellow to understand how to best meet the different needs of wind energy modelers, either focus on mean flow (RANS-based) or time-resolved (LES-based) quantities. Both computational fluid dynamic (CFD) communities seek ways of driving their codes with inputs from mesoscale models in order to produce simulations with a more realistic physical insight (Sanz Rodrigo et al., 2016). The “tendencies” approach, originally adopted by the boundary-layer meteorology community to characterize large-scale forcing in the GABLS3 model-intercomparison benchmark (Bosveld et al., 2014), became an effective solution to interface CFD models with mesoscale outputs without the need of developing complex code coupling. Here, pressure gradient and advection forcing components of the momentum and energy equations are extracted from mesoscale simulations to obtain, after filtering out small-scale effects, suitable vertical profiles of large-scale body forces for microscale models (Sanz Rodrigo et al., 2017a). The GABLS3 benchmark was adopted to test this methodology and compare it with other meso-micro coupling methods in the wind energy context (Sanz Rodrigo et al., 2017b). The results show good consistency of RANS and LES models driven by the same input forcing demonstrating the modularity benefits of the tendencies approach: LES models can be used for high-fidelity modeling of relevant boundary-layer processes which serve as a reference to build better RANS models in connection to design tools. From the NEWA perspective, tendencies will be generated as part of the wind atlas output parameters. In this respect, time and height-dependent tendencies are a more elaborated version of the so-called generalized wind climate concept that was introduced in the original European Wind Atlas of 1989: a way of characterizing the background wind climatology that drives wind conditions affected by local topography and, eventually, wind farm wake effects at microscale site level. Ongoing work is now directed towards demonstrating the general applicability of the tendencies approach in other sites and wind climate conditions. To this end, the method will be tested within the validation strategy of the New European Wind Atlas (NEWA) model-chain, which will exploit a large database of field experiments in a wide variety of terrain and wind climate conditions. This validation program will be leveraged internationally through the IEA-Wind Task 31 Wakebench to produce a validation suite for meso-micro wind flow models that builds on the open-access evaluation procedures of the GABLS3 benchmark (Sanz Rodrigo, 2017). References MesoWake website: http://windbench.net/mesowake-2014-2017 Bosveld et al. (2014) https://doi.org/10.1007/s10546-014-9917-3 Sanz Rodrigo et al. (2016) https://doi.org/10.1002/wene.214 Sanz Rodrigo et al. (2017a) https://doi.org/10.5194/wes-2-1-2017 Sanz Rodrigo et al. (2017b) https://doi.org/10.1088/1742-6596/854/1/012037 Sanz Rodrigo (2017) https://doi.org/10.5281/zenodo.834356

Keywords

wind energy, mesoscale modeling, microscale modeling, CFD, RANS, LES, meso-micro, validation

Countries

Spain, United States

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