the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Dec 2025
Enhancing yaw control resilience in wind turbines with CFD-informed digital twins
Abstract. Wind turbines are pivotal in the transition towards renewable energy. The operational conditions of these machines are continuously monitored through sensors that measure key indicators of efficiency and performance, including yaw angles, rotational speed, and vibrations. However, sensors are subjected to wear, degradation and consequent reduction in data reliability over time, which provides scope for developing a consistent and effective method to detect misinterpretation of turbine operating conditions caused by faulty measurements.
This research presents a novel method that integrates Computational Fluid Dynamics (CFD) simulations into a Digital Twin (DT) model to detect and correct yaw misalignment caused by faulty wind direction readings. Yaw error is estimated by interpolation across CFD-based performance data using live sensor measurements. The novel DT-based method was validated through experimental testing on a small-scale horizontal-axis wind turbine.
The results provide scope for a significant improvement in the resilience of wind turbines under conditions of sensor malfunctions, without the need for human intervention or supervision.
The proposed method is intended to be adaptable, enabling analysis of diverse failure modes under varying operational conditions. This work also advances condition monitoring and sustainable asset management, offering potential for a larger adoption across different turbomachinery applications.
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Status: open (until 28 Jan 2026)
- RC1: 'Comment on wes-2025-216', Anonymous Referee #1, 22 Jan 2026 reply
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RC2: 'Comment on wes-2025-216', Anonymous Referee #2, 23 Jan 2026
reply
General Comments
- The authors present an interesting experimental campaign of a small wind turbine rejecting yaw misalignments using a surrogate model created from CFD simulations. However, few details about the detection algorithm are provided and the results are sometimes redundant and difficult to understand. Please guide the reader through your process and experiment in your own words.
- The abstract would benefit from including more quantitative summaries of the results and methods.
- The manuscript would be strengthened by a clearer and more detailed explanation of the correction factor and misalignment detection process.
- Overall, the structure of the paper makes it difficult to follow. Most results are deferred to Chapter 3, while the reader is expected to accept claims in Section 2 without supporting evidence. A more integrated presentation, which introduces results alongside the methods would improve clarity and readability.
Methodology
- There is extensive discussion of implementation details (e.g., programming language, database structure, interpolation methods), but insufficient methodological detail for another researcher to reproduce the detection algorithm that is the primary contribution of this work. Consider refocusing this section on the scientific procedure and assumptions rather than software choices.
- It is not clear whether the mapping \gamma = f(C_p, TSR) yields a unique solution. Please address the potential for non-uniqueness and how it is handled.
Figures and Results
- Figure 5 would be clearer as a heatmap rather than a 3D surface plot. The current format makes it difficult to interpret gradients and trends.
- Consider combining figures where possible, as several convey largely overlapping information. For example, Figures 4, 5, and 6 appear to present similar content with limited incremental value.
- Figure 8 raises questions: Why does the CFD-derived yaw angle remain near zero for long periods and then exhibit sudden jumps? This behavior should be explained or justified.
- Please clarify your conventions for yaw angle, wind direction, and yaw misalignment.
- What are their respective bounds?
- Are any of these defined as inverses of one another?
- How are sign conventions handled consistently throughout the paper?
Citation: https://doi.org/10.5194/wes-2025-216-RC2
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The paper investigates whether parametric sweeps of steady-state CFD simulations can be used to detect and correct real-time wind turbine yaw misalignment. The authors test their method on a small-scale outdoor experimental rotor setup.
The authors strongly emphasize that they developed a digital twin of the rotor, however it seems that they are running steady-state CFD simulations and use linear interpolation to create a simple surrogate of the tsr, wind speed, yaw misalignment, Cp surface for the particular turbine they tested. They do not explain clearly how the turbine is controlled in the simulations. Simulations are performed for uniform inflow and without modelling turbulence, even though the turbine was tested close to the ground, which usually exhibits strong shear and variability. The wind speed reference is taken just above ground height, far below the hub height. Mostly 3D plots are used, which are difficult to interpret and it is difficult to follow how the surrogate model was built and how steady-state simulations should be adequate to correct yaw misalignment in real-time. It is not shown whether the yaw misalignment from their “digital twin” is actually correct. In the plots they seem quite off from the actual yaw misalignment. Without comparing their control method to other established forms of yaw control or more recent proposals, they claim that their “novel” method is superior, which is a strong claim.
The structure needs to be improved as well as the introduction that only focuses on very recent work. The conclusion seems to be written by an AI and is very general.
More detailed comments are given in the attached document.