the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Feb 2025
On the Potential of Aerodynamic Pressure Measurements for Structural Damage Detection
Abstract. This study investigates the potential of using aerodynamic pressure time series measurements to detect structural damage in elastic, aerodynamically loaded structures. Our work is motivated by the increase in the dimensions of modern wind turbine blade designs, whose complex behavior necessitates the adoption of improved simulation and structural monitoring solutions. In refining the tracking of aerodynamic interactions and their effects on such structures, we propose to exploit aerodynamic pressure measurements, available from a novel, cost-effective and non-intrusive sensing system, for structural damage assessment on wind turbine blades. This study is based on a series of wind tunnel experiments on a NACA 633418 airfoil. The airfoil is mounted on a vertically oscillating cantilever beam with structural damage introduced in form of a crack by gradually sawing the cantilever beam close to its support. The pressure distribution on the airfoil is measured under diverse configurations of inflow conditions and structural states, including different angles of attack, wind velocities, heaving frequencies, and crack lengths. We further propose an algorithm, relying on convolutional neural networks, for damage detection and rating based on the monitored signals. Analysis of the dynamics of the system using reference acceleration measurements and a finite element model and application of the suggested method on the experimental data indicate that aerodynamic pressure measurements on airfoils can indeed be used as an indirect approach for damage detection and severity classification on elastic, beam-like structures in mildly turbulent environments.
- Preprint
(17622 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (extended)
-
RC1: 'Comment on wes-2025-26', Anonymous Referee #1, 24 Mar 2025
reply
The manuscript "On the Potential of Aerodynamic Pressure Measurements for Structural Damage Detection" investigates whether aerodynamic pressure measurements via an airfoil can be used to detect structural damage in a controlled laboratory setting. Developing effective methods for monitoring structural damage in wind turbine blades is a crucial and open research problem. However, from the reviewer's point of view, the applicability of the study's findings to real-world wind turbines is highly questionable due to the assumptions made in the laboratory experiment and the analysis methodology.
Key concerns:
Material and Damage Representation: The structural damage was introduced in metal rather than in composite materials, which are more representative of real-world turbine blades. Furthermore, a saw cut does not replicate the characteristics of a crack as it would naturally occur.
Experimental Conditions: Both the wind excitation and imbalance excitation were kept constant throughout the experiments, a condition not reflective of the variable nature of real-world wind turbine environments.
Evaluation Methodology: A supervised classification method based on CNNs was employed. For real applications, datasets typically do not include labelled damage states, necessitating unsupervised methods that do not rely on such data.
Given these points, the manuscript's findings have limited transferability to real-world settings. Furthermore, the impact and scope of the work may be misaligned with the target journal's focus. Specific points for improvement include:
- Line 281 mentions that the parameters of the CNNs are fewer compared to alternative methods. It would be helpful to specify the number of parameters used in the CNNs.
-Line 284 notes the use of the Adam algorithm. Given that AdamW is now the standard, why was the Adam algorithm chosen?
- Figure 8 suggests that labelled data is required. In real-world scenarios, where would labeled data come from? What can be done if no labelled data is available?
-Figure 10 is confusing due to the inconsistent decimal places, e.g. 0.99 and 0.011.
- While line 365 mentions fast classification times, how long does CNN training take? Showing a training loss curve would be beneficial.
- Section 5 would benefit from examining the model's performance when presented with data not included within the training data (e.g. other windspeed, bigger crack, etc.)
- Section 6.2 conducted studies under ambient excitation only, which depends on laboratory conditions and may not be comparable to the controlled excitation in wind and imbalance conditions in Section 5. This could also explain why certain frequencies were not identified for some damage states.
- The mode shapes in Figure 14 vary significantly between model orders. How were these complex mode shapes transformed into real space? The first mode shape seems improperly identified—what caused this?
- Line 482 suggests comparing identified mode shapes with those from the FE model using the MAC for clarity.
- Line 490 raises doubts regarding the assumption that Mode 4 results from the cut, as it appears in the stabilization diagram in Figure 12 for the healthy state and could be probably identified at higher model orders. The saw cut does not have the same dynamic characteristics as a real crack, which would only become apparent at higher vibration amplitudes.
Due to these concerns, particularly the limitation in the methodology's applicability to real-world wind turbines since the authors just presented a supervised damage detection approach and the fact that the submitted manuscript does not really match the journal's scope, the reviewer recommends rejecting the manuscript in its current form and considering a submission to a more suitable journal.
Citation: https://doi.org/10.5194/wes-2025-26-RC1
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 126 | 43 | 4 | 173 | 8 | 8 |
- HTML: 126
- PDF: 43
- XML: 4
- Total: 173
- BibTeX: 8
- EndNote: 8
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1