Anomaly Detection in Aeroacoustic Wind Tunnel Experiments
| dc.contributor.author | Defreitas, Aaron Chad | en |
| dc.contributor.committeechair | Alexander, William Nathan | en |
| dc.contributor.committeemember | Leman, Scotland C. | en |
| dc.contributor.committeemember | Devenport, William J. | en |
| dc.contributor.committeemember | Borgoltz, Aurelien | en |
| dc.contributor.department | Aerospace and Ocean Engineering | en |
| dc.date.accessioned | 2023-04-21T06:00:07Z | en |
| dc.date.available | 2023-04-21T06:00:07Z | en |
| dc.date.issued | 2021-10-27 | en |
| dc.description.abstract | Wind tunnel experiments often employ a wide variety and large number of sensor systems. Anomalous measurements occurring without the knowledge of the researcher can be devastating to the success of costly experiments; therefore, anomaly detection is of great interest to the wind tunnel community. Currently, anomaly detection in wind tunnel data is a manual procedure. A researcher will analyze the quality of measurements, such as monitoring for pressure measurements outside of an expected range or additional variability in a time averaged quantity. More commonly, the raw data must be fully processed to obtain near-final results during the experiment for an effective review. Rapid anomaly detection methods are desired to ensure the quality of a measurement and reduce the load on the researcher. While there are many effective methodologies for anomaly detection used throughout the wider engineering research community, they have not been demonstrated in wind tunnel experiments. Wind tunnel experimentation is unique in the sense that many repeat measurements are not typical. Typically, this will only occur if an anomaly has been identified. Since most anomaly detection methodologies rely on well-resolved knowledge of a measurement to uncover the expected uncertainties, they can be difficult to apply in the wind tunnel setting. First, the analysis will focus on pressure measurements around an airfoil and its wake. Principal component analysis (PCA) will be used to build a measurement expectation by linear estimation. A covariance matrix will be constructed from experimental data to be used in the PCA-scheme. This covariance matrix represents both the strong deterministic relations dependent on experimental configuration as well as random uncertainty. Through principles of ideal flow, a method to normalize geometrical changes to improve measurement expectations will be demonstrated. Measurements from a microphone array, another common system employed in aeroacoustic wind tunnels, will be analyzed similarly through evaluation of the cross-spectral matrix of microphone data, with minimal repeat measurements. A spectral projection method will be proposed that identifies unexpected acoustic source distributions. Analysis of good and anomalous measurements show this methodology is effective. Finally, machine learning technique will be investigated for an experimental situation where repeat measurements of a known event are readily available. A convolutional neural network for feature detection will be shown in the context of audio detection. This dissertation presents techniques for anomaly detection in sensor systems commonly used in wind tunnel experiments. The presented work suggests that these anomaly identification techniques can be easily introduced into aeroacoustic experiment methodology, minimizing tunnel down time, and reducing cost. | en |
| dc.description.abstractgeneral | Efficient detection of anomalies in wind tunnel experiments would reduce the cost of experiments and increase their effectiveness. Currently, manual inspection is used to detect anomalies in wind tunnel measurements. A researcher may analyze measurements during experiment, for instance, monitoring for pressure measurements outside of an expected range or additional variability in a time averaged quantity. More commonly, the raw data must be fully processed to obtain near-final results to determine quality. In this dissertation, many methods, which can assist the wind tunnel researcher in reviewing measurements, are developed and tested. First, a method to simultaneously monitor pressure measurements and wind tunnel environment measurements is developed with a popular linear algebra technique called Principal Component Analysis (PCA). The novelty in using PCA is that measurements in wind tunnels are often not repeated. Instead, the proposed method uses a large number of independent measurements acquired in various conditions and fundamental aspects of fluid mechanics to train the detection algorithm. Another wind tunnel system which is considered is a microphone array. A microphone array is a collection of microphones arranged in known locations. Current methods to assess the quality of the output data from this system require extended computation and review time during an experiment. A method parallel to PCA is used to rapidly determine if an anomaly is present in the measurement. This method does not require the extra computation necessary to see what the microphone array has observed and simplifies the quantities assessed for anomalies. While this is not a replacement for complete computation of the results associated with microphone array measurements, this can take most of the effort out of the experiment time and relegate detailed review to a time after the experiment is complete. Finally, an application of machine learning is discussed with an alternate application outside of the wind tunnel. This work explores the usefulness of a convolutional neural network (CNN) for cough detection. This can be similarly applied to detect anomalies in audio data if searching for specific anomalies with known characteristics. CNNs, in general, require much effort to train and operate effectively but are not dependent on the application or data type. These methods could be applied to a wind tunnel experiment. Overall, the work in this dissertation shows many techniques which can be implemented into current wind tunnel operations to improve the efficiency and effectiveness of the data review process. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:32721 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/114734 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | wind tunnel | en |
| dc.subject | anomaly detection | en |
| dc.subject | eigendecomposition | en |
| dc.title | Anomaly Detection in Aeroacoustic Wind Tunnel Experiments | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Aerospace Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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