ExaWind: A multifidelity modeling and simulation environment for wind energy

Sprague, Michael; Ananthan, Shreyas; Vijayakumar, Ganesh; Robinson, Michael C.

doi:10.1088/1742-6596/1452/1/012071

Cited by 72 publications

(64 citation statements)

References 13 publications

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“…Nalu‐Wind 51,52 is a multiphysics, massively parallel LES code used to simulate the ABL inflow and the wake. Nalu‐Wind uses a proportional controller with forcing source terms to drive the ABL velocity at a given height.…”

Section: Simulationsmentioning

confidence: 99%

Multimodel validation of single wakes in neutral and stratified atmospheric conditions

et al. 2020

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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.

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Section: Simulationsmentioning

confidence: 99%

Multimodel validation of single wakes in neutral and stratified atmospheric conditions

et al. 2020

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“…Turbine-wake interactions require high-fidelity simulations, including large-eddy simulations, to correctly resolve the highly complex flows within a wind power plant (Fleming et al, 2013;Churchfield et al, 2016); however, the large computational expense of these simulations limits their use in design optimization problems, which has encouraged the development of wind power plant simulation tools that straddle multiple levels of fidelity (Sprague et al, 2020;Réthoré et al, 2014). In this case study, we optimize the layout of turbines using multiple different wake models and resolutions to represent different levels of fidelity.…”

Section: Optimization Resultsmentioning

confidence: 99%

Effectively using multifidelity optimization for wind turbine design

Jasa

Bortolotti

Zalkind

et al. 2021

Preprint

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Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods for optimizing wind turbine designs by using information from both low- and high-fidelity models to find an optimal solution at reduced cost. Specifically, a trust-region approach is used with a novel corrective function built from a nonlinear surrogate model. We find that for a diverse set of design problems—with examples given in rotor blade geometry design, wind turbine controller design, and wind power plant layout optimization—the multifidelity method finds the optimal design using 38 %–58 % the computational cost of the high-fidelity-only optimization. The success of the multifidelity method in disparate applications suggests that it could be more broadly applied to other wind energy or otherwise generic applications.

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“…LES were performed using Nalu-Wind, a wind-focused fork of the incompressible flow solver developed by Sandia National Laboratories, Nalu (Sprague et al. 2020). Nalu-Wind solves the filtered Navier–Stokes equations using an unstructured finite-volume formulation that is second-order accurate in space and time.…”

Section: Methodsmentioning

confidence: 99%

Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine

2021

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The current study uses large eddy simulations to investigate the transient response of a utility-scale wind turbine wake to dynamic changes in atmospheric and operational conditions, as observed in previous field-scale measurements. Most wind turbine wake investigations assume quasi-steady conditions, but real wind turbines operate in a highly stochastic atmosphere, and their operation (e.g. blade pitch, yaw angle) changes constantly in response. Furthermore, dynamic control strategies have been recently proposed to optimize wind farm power generation and longevity. Therefore, improved understanding of dynamic wake behaviours is essential. First, changes in blade pitch are investigated and the wake expansion response is found to display hysteresis as a result of flow inertia. The time scales of the wake response to different pitch rates are quantified. Next, changes in wind direction with different time scales are explored. Under short time scales, the wake deflection is in the opposite direction of that observed under quasi-steady conditions. Finally, yaw changes are implemented at different rates, and the maximum inverse wake deflection and time scale are quantified, showing a clear dependence on yaw rate. To gain further physical understanding of the mechanism behind the inverse wake deflection, the streamwise vorticity in different parts of the wake is quantified. The results of this study provide guidance for the design of advanced wake flow control algorithms. The lag in wake response observed for both blade pitch and yaw changes shows that proposed dynamic control strategies must implement turbine operational changes with a time scale of the order of the rotor time scale or slower.

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ExaWind: A multifidelity modeling and simulation environment for wind energy

Cited by 72 publications

References 13 publications

Multimodel validation of single wakes in neutral and stratified atmospheric conditions

Multimodel validation of single wakes in neutral and stratified atmospheric conditions

Effectively using multifidelity optimization for wind turbine design

Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine

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