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. / Doctor of Philosophy / 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.

published_or_final_version / Mechanical Engineering / Master / Master of Science in Engineering

The effect of the ground on a rotor wake in forward flight has been investigated experimentally in the working section of an 8ft x Oft straight-through wind tunnel. A three bladed fully articulated rotor with a solidity ratio of 0.07 and diameter of 1.06m, powered by a hydraulic motor, has been tested at a height of 0.47 rotor diameter above a solid ground board which has an elliptical leading edge. Tests have been run at various low advance ratios (<0.1) with two collective pitch settings. A three-element hot wire anemometer probe has been used to measure the average value of the three components of velocity simultaneously in the forward half (advancing side) of the rotor wake and in the main stream surrounding it. The rotor wake and the ground vortices have been visualized by smoke. Surface flow patterns on the ground board have located the interaction region between the rotor wake and the oncoming flow on the ground board. Theoretical estimates of the flowfield based on Heyson's vortex cylinder model (Ref. 2) are compared with the experimental results. Both experimental results and theoretical estimates show that the ground-induced interference is an upwash and a decrease in forward velocity. The upwash interference' opposes the vertical flow through the rotor, and have large effects on the rotor performance in producing thrust. The streamwise interference decelerates the mainstream and becomes more noticeable as the wake boundary is approached.

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The railroad industry is responsible for the mass transport of a vast numbers of goods throughout the United States. As needs and capabilities of the railroad industry have changed, the interest in reducing the resistance of locomotives and railcars has increased. This has become paramount as fuel prices have increased in recent years. Resistant forces can result from friction in mechanical components and aerodynamic drag of the moving train. As the average traveling speeds of trains have increased, aerodynamics are contributing a larger fraction of the overall resistance. For this reason, the aerodynamic profiles of trains have become a topic of research. Furthermore, current manufacturing practices of railcars provide an opportunity for research in modifications that reduce the aerodynamic drag. This thesis reports on research that has been done to reduce aerodynamic drag on automobile-carrying railcars. Data was collected by placing G-scale (1/29) models into a wind tunnel with a 0.74 m^2 test section. These models were tested for Reynolds Numbers ranging from approximately 2.05 x 10^5 to 2.79 x 10^5. Modifications were made to the models with the intention of reducing the drag. The profile features of the auto-carrying railcars were reviewed and three regions were chosen to be the focus of this study. The selected regions are the roof, side panels and structural chassis region. Special attention was given to the regulations of the railroad industry to ensure the tested modifications would be candidates for implementation. From the data, it was determined that drag could be reduced by modifying or covering the roof, side panels and chassis structure by nominally 20%, 5% and 15% respectively.

Thesis (M.S. in Aeronautical Engineering)--Naval Postgraduate School, September 1990. / Thesis Advisor(s): Healey, J. Val. Second Reader: Schmidt, L. V. "September 1990." Description based on title screen as viewed on December 18, 2009. DTIC Descriptor(s): Velocity, Air Flow, Flight Decks, Shear Properties, Measurement, Position(Location), Ships, Models, Layers, Edges, Boundary Layer, Flow Visualization, Tunnels, Curvature, Smoke, Stationary, Bubbles, Patterns, Helium, Flow, Ship Models, Video Signals, Anemometers, Earth Atmosphere, Deflectors, Photographic Equipment. DTIC Identifier(s): Wake, Naval Vessels(Support), Turbulence, Helicopters, Atmospheric Motion, Air Flow, Ship Models, Replenishment at sea, AOR Class Vessels, H-56 Aircraft, Wind Direction, Oilers(Naval Vessels), Rotor Blates(Rotary Wings), Impact, Vertical Replenishment, Blade Strike, Theses, Wind Tunnel Models. Author(s) subject terms: Flow Visualization, Simulated Atmospheric Boundary Layer, Helium Bubble Flow Visualization, Smoke Visualization, AOR Class ship. Includes bibliographical references (p. 84-85). Also available in print.

This thesis presents the work undertaken to assess potential improvements in high performance bicycles. There are several wheel options available for elite riders to use in competition and this research has investigated the aerodynamic properties of different wheel type. The research has also developed CFD and FEA models of carbon fibre bicycle wheels to assist in the wheel improvements process. An accurate and repeatable experimental test rig was developed to measure the aerodynamic properties of bicycle wheels in the wind tunnel, namely translational drag, rotational drag and side force. Both disk wheels and spoked wheels were tested. It was found that disk wheels of different hub widths have different aerodynamic properties with the 53mm wide Zen disk wheel requiring the lowest total power of the wheels tested. There was little difference between the translational power requirements of the wheels but there was greater variation in the rotational power requirements. Compression spoked wheels of 3 and 5 spokes were found to require less power than wire spoked wheels. There was little difference between the total power requirements of the compression spoked wheels tested, with the differences at 50km/hr being less than the experimental uncertainty. The Zipp 808 wheel demonstrated considerably lower axial force than all other wheels at 10° yaw angle, confirming Zipp design intention to have optimum wheel performance between 0-20°. The Zen 3-spoke wheel showed the lowest axial drag and side force at yaw of the compression spoked wheels tested and had similar side force results to the Zipp 808. CFD models of the disk and 3-spoke wheel achieved good agreement with the experimental results in terms of translational drag. Rotational drag did not agree so well, most likely due to the turbulence model being designed for higher Reynolds number flows. A FE model of the disk wheel was validated with experimental testing. In order to simplify modelling, the FE model of the 3-spoke wheel did not include the hub, which led to a large discrepancy with experimental results for the particular loading scenario. The experimental rig and CFD models were used to develop aerodynamic improvements to the wheel and the FE models were used to identify the implication of geometric changes to the wheel structural integrity. These improvements are not reported in this thesis due to the results being commercially sensitive.

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