the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Wind Farm Structural Response and Wake Dynamics for an Evolving Stable Boundary Layer: Computational and Experimental Comparisons
Kelsey Shaler
Eliot Quon
Hristo Ivanov
Abstract. The wind turbine design process requires performing thousands of simulations for a wide range of inflow and control conditions. This necessitates computationally efficient yet time-accurate models, especially when considering wind farm settings. To this end, FAST.Farm is a dynamic wake meandering-based midfidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. This work is an extension to a study into constructing a diurnal cycle evolution based on experimental data. Here, this inflow is used to validate the turbine structural and wake meandering response between experimental data, FAST.Farm simulation results, and high-fidelity large-eddy simulation results from coupled SOWFA-OpenFAST. The validation occurs within the nocturnal stable boundary layer when corresponding meteorological and turbine data were available. To that end, load results from FAST.Farm and SOWFA-OpenFAST are compared to multi-turbine measurements from a subset of a full-scale wind farm. Computational predictions of blade-root and tower-base bending loads are compared to 10-minute statistics of strain gauge measurements during 3.5 hour of the evolving stable boundary layer, generally with good agreement. This time period coincided with an active wake steering campaign of an upstream turbine, resulting in time-varying yaw positions of all turbines. Wake meandering was also compared between the computational solutions, generally with excellent agreement. Simulations were based on the use of a high-fidelity precursor constructed from inflow measurements and using state-of-the-art mesoscale-to-microscale coupling.
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Kelsey Shaler et al.
Status: open (until 27 Dec 2023)
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RC1: 'Comment on wes-2023-138', Anonymous Referee #1, 21 Nov 2023
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The manuscript discusses a novel comparison FAST.Farm and SOWFA-OpenFAST against measurement data. The work is crucial for the community and very relevant to the readers of Wind Energy Science. I find that the description of the results, their presentation in the figures, and the main findings (what are the main benefits of the different approaches) can be improved; see my list of recommendations below. I hope these suggestions can help to enhance the presentation of the findings.
* Line 154: missing citations
* Line 162-165: without discussion of the actual parameters, this paragraph is rather vague.
* Figure 3: These results are very interesting. It would be great if you could find a way to better demonstrate the differences between the various models, which, in the current representation, is difficult to judge.
* Figure 4: It is stated that this figure demonstrates that the algorithm captures the wake center location accurately. This is not so clear from the figure; I would guess the wake centers should be a bit lower. Can you comment on this and how it may impact the final results?
* Figure 5: It is unclear why the results for Tr01 are not normalized.
* Section 3.1: The description of figures 5, 6, and 7 is unclear as their explanation is merged, and the reference to the different figures is unclear.Â
* Figure 6: "Vertical dashed lines indicate the 3P and 6P frequencies based on the average SOWFA-OpenFAST_AD rotor speed." --> This seems to be a typo.
* Figure 6: Are the lower panels normalized? This is not indicated on the vertical axis
* Figure 6: Define the meaning of the rad bands.Â
* Figure 6: Please define TS. Does this refer to time series?
* Figure 7: Make sure text and graphs are not overlapping
* Figure 7: The vertical dashed lines mentioned in the caption are (nearly) invisible. Please make these clearly visible.
* Figure 7: Define clearer what is defined by good and poorer agreement between model and observations. Looking at the spectra, the location of the peaks is captured better than in the top panels.
* Figure 8: Indicate vertical dashed lines.
* Figure 9: Improve alignment of the different panels.Â
* Line 260: "Though SOWFA-ALM results show more wake deflection that [typo: should be than] FAST.Farm results at 2D of Tr03, agreement 260 between the computational methods is very good at 5D downstream." --> Can this be discussed in more detail? [See left middle column]: This result suggests wake development in the different models is different.
* Conclusion: What is meant by terms like "good" or "strong" agreement should be more clearly defined.Â
* Conclusion: I missed a discussion summarizing the benefits and limitations of each approach.ÂTypos
Line 201: "and and"Citation: https://doi.org/10.5194/wes-2023-138-RC1
Kelsey Shaler et al.
Kelsey Shaler et al.
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