Doubrawa Moreira, Paula; Quon, Eliot; Martinez; Tossas, Luis (Tony) M.; Shaler, Kelsey; Debnath, Mithu; Hamilton, Nicholas; Herges, Thomas H.; Maniaci, David C.; Kelley, Christopher L.; Laros, James H.; Blaylock, Myra L.; Van Der Laan, Paul; Andersen, Soren J.; Krueger, Sonja; Cathelain, Marie; Schlez, Wolfgang; Jonkman, Jason; Branlard, Emmanuel; Steinfeld, Gerald; Schmidt, Sascha; Blondel, Frederic; Lukassen, Laura J.; Moriarty, Patrick
Previous research has revealed the need for a validation study that considers several wake quantities and code types so that decisions on the trade-off between accuracy and computational cost can be well informed and appropriate to the intended application. In addition to guiding code choice and setup, rigorous model validation exercises are needed to identify weaknesses and strengths of specific models and guide future improvements. Here, we consider 13 approaches to simulating wakes observed with a nacelle-mounted lidar at the Scaled Wind Technology Facility (SWiFT) under varying atmospheric conditions. We find that some of the main challenges in wind turbine wake modeling are related to simulating the inflow. In the neutral benchmark, model performance tracked as expected with model fidelity, with large-eddy simulations performing the best. In the more challenging stable case, steady-state Reynolds-averaged Navier–Stokes simulations were found to outperform other model alternatives because they provide the ability to more easily prescribe noncanonical inflows and their low cost allows for simulations to be repeated as needed. Dynamic measurements were only available for the unstable benchmark at a single downstream distance. These dynamic analyses revealed that differences in the performance of time-stepping models come largely from differences in wake meandering. This highlights the need for more validation exercises that take into account wake dynamics and are able to identify where these differences come from: mesh setup, inflow, turbulence models, or wake-meandering parameterizations. In addition to model validation findings, we summarize lessons learned and provide recommendations for future benchmark exercises.
Subscale wind turbines can be installed in the field for the development of wind technologies, for which the blade aerodynamics can be designed in a way similar to that of a full-scale wind turbine. However, it is not clear whether the wake of a subscale turbine, which is located closer to the ground and faces different incoming turbulence, is also similar to that of a full-scale wind turbine. In this work we investigate the wakes from a full-scale wind turbine of rotor diameter 80 m and a subscale wind turbine of rotor diameter of 27 m using large-eddy simulation with the turbine blades and nacelle modeled using actuator surface models. The blade aerodynamics of the two turbines are the same. In the simulations, the two turbines also face the same turbulent boundary inflows. The computed results show differences between the two turbines for both velocity deficits and turbine-added turbulence kinetic energy. Such differences are further analyzed by examining the mean kinetic energy equation.
Moriarty, Patrick; Hamilton, Nicholas; Debnath, Mithu; Herges, Thomas H.; Isom, Brad; Lundquist, Julie K.; Maniaci, David C.; Naughton, Brian T.; Pauly, Rebecca; Roadman, Jason; Shaw, Will; Van Dam, Jeroen; Wharton, Sonia
The American WAKE experimeNt (AWAKEN) is an international multi-institutional wind energy field campaign to better understand wake losses within operational wind farms. Wake interactions are among the least understood physical interactions in wind plants today, leading to unexpected power and profit losses. For example, Ørsted, the world’s largest offshore wind farm developer, recently announced a downward revision in energy estimates across their energy generation portfolio, primarily caused by underprediction of energy losses from wind farm blockage and wakes. In their announcement, they noted that the standard industry models used for their original energy estimates were inaccurate, and this was likely an industry-wide issue. To help further improve and validate wind plant models across scales from individual turbines as well as interfarm interactions between plants, new observations, such as those planned for AWAKEN, are critical. These model improvements will enable both improved layout and more optimal operation of wind farms with greater power production and improved reliability, ultimately leading to lower wind energy costs.
The development of a next generation high-fidelity modeling code for wind plant applications is one of the central focus areas of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative. The code is based on a highly scalable framework, currently called Nalu-Wind. One key aspect of the model development is a coordinated formal validation program undertaken specifically to establish the predictive capability of Nalu-Wind for wind plant applications. The purpose of this document is to define the verification and validation (V&V) plan for the A2e high-fidelity modeling capability. It summarizes the V&V framework, identifies code capability users and use cases, describes model validation needs, and presents a timeline to meet those needs.
Milestone Description: Enhance Nalu-Wind's actuator disc model through hardening, documenting, stress-testing, verifying, and validating. Existing workflows will be improved by reducing the data output stream, and by making the analysis capabilities more modular and generally better. These model capabilities are needed by other A2e areas, namely Wake Dynamics, AWAKEN, and VV&UQ.
Uncertainty is present in all wind energy problems of interest, but quantifying its impact for wind energy research, design and analysis applications often requires the collection of large ensembles of numerical simulations. These predictions require a range of model fidelity as predictive models, that include the interaction of atmospheric and wind turbine wake physics, can require weeks or months to solve on institutional high-performance computing systems. The need for these extremely expensive numerical simulations extends the computational resource requirements usually associated with uncertainty quantification analysis. To alleviate the computational burden, we propose here to adopt several Multilevel-Multifidelity sampling strategies that we compare for a realistic test case. A demonstration study was completed using simulations of a V27 turbine at Sandia National Laboratories’ SWiFT facility in a neutral atmospheric boundary layer. The flow was simulated with three models of disparate fidelity. OpenFAST with TurbSim was used stand-alone as the most computationally-efficient, lower-fidelity model. The computational fluid dynamics code Nalu-Wind was used for large eddy simulations with both medium-fidelity actuator disk and high-fidelity actuator line models, with various mesh resolutions. In an uncertainty quantification study, we considered five different turbine properties as random parameters: yaw offset, generator torque constant, collective blade pitch, gearbox efficiency and blade mass. For all quantities of interest, the Multilevel-Multifidelity estimators demonstrated greater efficiency compared to standard and multilevel Monte Carlo estimators.
Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018
Wind energy is stochastic in nature; the prediction of aerodynamic quantities and loads relevant to wind energy applications involves modeling the interaction of a range of physics over many scales for many different cases. These predictions require a range of model fidelity, as predictive models that include the interaction of atmospheric and wind turbine wake physics can take weeks to solve on institutional high performance computing systems. In order to quantify the uncertainty in predictions of wind energy quantities with multiple models, researchers at Sandia National Laboratories have applied Multilevel-Multifidelity methods. A demonstration study was completed using simulations of a NREL 5MW rotor in an atmospheric boundary layer with wake interaction. The flow was simulated with two models of disparate fidelity; an actuator line wind plant large-eddy scale model, Nalu, using several mesh resolutions in combination with a lower fidelity model, OpenFAST. Uncertainties in the flow conditions and actuator forces were propagated through the model using Monte Carlo sampling to estimate the velocity defect in the wake and forces on the rotor. Coarse-mesh simulations were leveraged along with the lower-fidelity flow model to reduce the variance of the estimator, and the resulting Multilevel-Multifidelity strategy demonstrated a substantial improvement in estimator efficiency compared to the standard Monte Carlo method.
The Department of Energy Atmosphere to Electrons (A2e) initiative has undertaken an experimental planning process for a validation directed program and an experimental planning process directed at improving simulations of wind plant performance. The validation process has been divided into two main sections: Integrated Program Planning, and Integrated Experiment and Model Planning and Execution. This document covers the Integrated Program Planning process in detail as it has been applied to the validation and assessment of models of various fidelity to predict wind plant performance. Three main parts of this process are presented in this document: the Phenomenon Identification and Ranking Table, the Validation Hierarchy, and the Prioritized Phenomenon and Experiment Mapping table. The document concludes with a description of validation program process next steps, which includes the planning and execution of integrated experiment and model campaigns