A new multi-fidelity flow-acoustics simulation framework for wind farm application

Jun, Cao; Nyborg, Camilla Marie; Feng, Jian; Hansen, Kurt Schaldemose; Bertagnolio, Franck; Fischer, Andreas; Sørensen, Thomas; Shen, Wen Zhong

doi:10.1016/j.rser.2021.111939

Cited by 6 publications

(10 citation statements)

References 39 publications

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“…This decision is made based on studies performed with point sources distributed at the rotor (Cotté , 2019;Barlas et al , 2017) and on the source positional study by (Oerlemans et al , 2007). In previous work with the WindSTAR model 36 point sources have been used (Cao et al , 2022). The computational time of running 36 individual computations for each octave band frequency is however too excessive for optimization purposes, and the use of the 3 distributed point sources has previously shown good comparison with field measurements (Nyborg et al , 2022).…”

Section: Sound Propagation Modelsmentioning

confidence: 99%

“…In previous work with the WindSTAR model 36 point sources have been used (Cao et al , 2022). The computational time of running 36 individual computations for each octave band frequency is however too excessive for optimization purposes, and the use of the 3 distributed point sources has previously shown good comparison with field measurements (Nyborg et al , 2022). The 3 point sources are assumed to be uncorrelated and the average L p (f k , d ij ) of the 3 point sources from the ith wind turbine to the jth receptor can be obtained by assuming equal source strength and uniform directivity…”

Section: Sound Propagation Modelsmentioning

confidence: 99%

“…In the work presented, the Gaussian wake deficit model developed by (Bastankah et al , 2014) is used. WindSTAR has previously been coupled with the engineering wake model by Qian (Qian et al , 2018;Cao et al , 2022;Nyborg et al , 2022). Besides the velocity deficit, the Qian wake model provides a turbulence intensity deficit which can be useful for noise source modeling.…”

Section: Wind Farm Flow Modeling In Pywakementioning

confidence: 99%

“…An upward refracting atmosphere can especially cause significant acoustic shadow zones in the far field of the wind turbine, and thereby result in highly reduced sound levels. In order to take phenomena like this, and in general more details about the flow and the terrain, into account, the WindSTAR model based on the Parabolic Equation (PE) method (Barlas et al , 2017;Barlas , 2017;Cao et al , 2022) is along with the ISO 9613-2 model used for optimization in this study. Both models are coupled to the Topfarm optimization framework (Pedersen et al , 2021;Réthoré et al , 2014), and the PyWake framework (Pedersen et al , 2019) is used for the wind farm modeling.…”

Section: Introductionmentioning

confidence: 99%

“…Both models are coupled to the Topfarm optimization framework (Pedersen et al , 2021;Réthoré et al , 2014), and the PyWake framework (Pedersen et al , 2019) is used for the wind farm modeling. The WindSTAR model has previously been validated against field measurements of sound propagation (Nyborg et al , 2022;Cao et al , 2022), and compared to other sound propagation models in order to further verify the model. These studies showed overall good results from the WindSTAR model.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of wind farm operation with a noise constraint

Nyborg

Fischer

Réthore

et al. 2022

Preprint

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Abstract. This article presents a method for performing noise constrained optimization of wind farms by changing the operational modes of the individual wind turbines. The optimization is performed by use of the TopFarm framework and the PyWake wind farm modeling as well as two sound propagation models: the ISO 9613-2 model and the Parabolic Equation model, WindSTAR. The two sound propagation models introduce different levels of complexity to the optimization problem with the WindSTAR model taking a broader range of parameters, like the acoustic ground impedance, the complex terrain elevation and the flow field from the noise source to the receptor, into account. Thus, as the WindSTAR model introduces a higher complexity of the sound propagation computations, it likewise introduces a higher computational time. Wind farm optimization using each of the two sound propagation models is therefore performed in different atmospheric conditions and for different source/receptor setups, and compared through this study in order to evaluate the advantage of using a more complex sound propagation model. The article focuses on artificial wind farms in flat terrain as well as arbitrarily chosen dwellings at which the noise constraints are applied. By this, the study presents the potential of an optimization algorithm focusing on the sound propagation and wind farm operation trade-off.

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Section: Sound Propagation Modelsmentioning

confidence: 99%

Section: Sound Propagation Modelsmentioning

confidence: 99%

Section: Wind Farm Flow Modeling In Pywakementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of wind farm operation with a noise constraint

Nyborg

Fischer

Réthore

et al. 2022

Preprint

Self Cite

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Development of a general sound source model for wind farm application

et al. 2022

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Predicting wind farm wake losses with deep convolutional hierarchical encoder–decoder neural networks

Romero,

Hasanpoor,

Antonini

et al. 2024

APL Machine Learning

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Wind turbine wakes are the most significant factor affecting wind farm performance, decreasing energy production and increasing fatigue loads in downstream turbines. Wind farm turbine layouts are designed to minimize wake interactions using a suite of predictive models, including analytical wake models and computational fluid dynamics simulations (CFD). CFD simulations of wind farms are time-consuming and computationally expensive, which hinder their use in optimization studies that require hundreds of simulations to converge to an optimal turbine layout. In this work, we propose DeepWFLO, a deep convolutional hierarchical encoder–decoder neural network architecture, as an image-to-image surrogate model for predicting the wind velocity field for Wind Farm Layout Optimization (WFLO). We generate a dataset composed of image representations of the turbine layout and undisturbed flow field in the wind farm, as well as images of the corresponding wind velocity field, including wake effects generated with both analytical models and CFD simulations. The proposed DeepWFLO architecture is then trained and optimized through supervised learning with an application-tailored loss function that considers prediction errors in both wind velocity and energy production. Results on a commonly used test case show median velocity errors of 1.0%–8.0% for DeepWFLO networks trained with analytical and CFD data, respectively. We also propose a model-fusion strategy that uses analytical wake models to generate an additional input channel for the network, resulting in median velocity errors below 1.8%. Spearman rank correlations between predictions and data, which evidence the suitability of DeepWFLO for optimization purposes, range between 92.3% and 99.9%.

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A new multi-fidelity flow-acoustics simulation framework for wind farm application

Cited by 6 publications

References 39 publications

Optimization of wind farm operation with a noise constraint

Optimization of wind farm operation with a noise constraint

Development of a general sound source model for wind farm application

Predicting wind farm wake losses with deep convolutional hierarchical encoder–decoder neural networks

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