{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/135948"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/135948","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Rotor-Airframe Interaction Noise: Predicting and Mitigating Noise with Artificial Neural Networks","abstract":"For small unmanned aerial vehicles, a rotor is typically mounted to the vehicle with a support rod. With the rotor operating in close proximity to the rod, an aeroacoustic installation effect known as the rotor-airframe interaction noise creates an unsteady deterministic loading noise that excites the harmonics of the blade passing frequency by as much as 20 dB, which meant that the installation effect could produce more noise than the rotor itself. As a result, it was imperative to parameterize and model the rotor-airframe interaction noise. Methods to predict the interaction noise were estimated using analytical and artificial neural network models. The artificial neural network required training data to correlate parameters with the acoustic noise, which was acquired from an experimental test campaign conducted within an anechoic chamber. Measurements were taken for numerous combinations of rotor radii, rotational speeds, rotor-rod proximities, and rod diameters to examine the parameter space expected of a rotor-rod configuration typically found on a small unmanned aerial system. Microphones were placed in a semi-hemisphere cluster to capture the directivity for the observer in-plane and below the plane of rotation. The analytical and artificial neural network models predicted the time-domain acoustic pressure emitted by the interaction, allowing for a more insightful inspection of the acoustic emission and providing a psychoacoustic analysis tool. In addition to acquiring an extensive database for training the artificial neural network, this database was also used to evaluate the prediction performance of the analytical model, which relies on a potential-flow model to represent the pressure fluctuations exhibited on the surfaces of the rotor and rod during the interaction. Results showed that the analytical and artificial neural network models had good prediction performance for lower harmonics. As the harmonic number increased above 11×BPF, the artificial neural network outperformed the analytical model due to the assumption built into the analytical model being invalid at higher harmonics. Efforts taken in the experimental test campaign also found that by curving the rod or sweeping the rotor blade, the rotor-airframe interaction noise was significantly reduced to the baseline case where the rotor operated without a rod within the flow. An analytical optimization method was developed to find the optimal rod shape to diminish the acoustic noise based on constraints, where the optimization algorithm can be quickly executed. In addition, a second study was conducted in an outdoor setting on the DJI S-1000 drone to demonstrate that the noise emitted by a drone could be reduced when a curved rod was installed instead of the conventional straight rod.","abstract_html":"For small unmanned aerial vehicles, a rotor is typically mounted to the vehicle with a support rod. With the rotor operating in close proximity to the rod, an aeroacoustic installation effect known as the rotor-airframe interaction noise creates an unsteady deterministic loading noise that excites the harmonics of the blade passing frequency by as much as 20 dB, which meant that the installation effect could produce more noise than the rotor itself. As a result, it was imperative to parameterize and model the rotor-airframe interaction noise. Methods to predict the interaction noise were estimated using analytical and artificial neural network models. The artificial neural network required training data to correlate parameters with the acoustic noise, which was acquired from an experimental test campaign conducted within an anechoic chamber. Measurements were taken for numerous combinations of rotor radii, rotational speeds, rotor-rod proximities, and rod diameters to examine the parameter space expected of a rotor-rod configuration typically found on a small unmanned aerial system. Microphones were placed in a semi-hemisphere cluster to capture the directivity for the observer in-plane and below the plane of rotation. The analytical and artificial neural network models predicted the time-domain acoustic pressure emitted by the interaction, allowing for a more insightful inspection of the acoustic emission and providing a psychoacoustic analysis tool. In addition to acquiring an extensive database for training the artificial neural network, this database was also used to evaluate the prediction performance of the analytical model, which relies on a potential-flow model to represent the pressure fluctuations exhibited on the surfaces of the rotor and rod during the interaction. Results showed that the analytical and artificial neural network models had good prediction performance for lower harmonics. As the harmonic number increased above 11×BPF, the artificial neural network outperformed the analytical model due to the assumption built into the analytical model being invalid at higher harmonics. Efforts taken in the experimental test campaign also found that by curving the rod or sweeping the rotor blade, the rotor-airframe interaction noise was significantly reduced to the baseline case where the rotor operated without a rod within the flow. An analytical optimization method was developed to find the optimal rod shape to diminish the acoustic noise based on constraints, where the optimization algorithm can be quickly executed. In addition, a second study was conducted in an outdoor setting on the DJI S-1000 drone to demonstrate that the noise emitted by a drone could be reduced when a curved rod was installed instead of the conventional straight rod.","abstract_has_math":false,"creators":["Wiedemann, Arthur David"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Fuller, Christopher R."],"committee_members":["Wicks, Alfred L.","Pascioni, Kyle Anthony","Southward, Steve C."],"year":2025,"date_issued":"2025-07-09","date_published":"2025-07-09","updated_at":"2026-07-22T22:20:35Z","subjects":["Aeroacoustic","Drone noise","Rotor-Airframe Interaction"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44398"],"render_values":[{"text":"vt_gsexam:44398","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/135948","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Fuller, Christopher R."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Wicks, Alfred L.","Pascioni, Kyle Anthony","Southward, Steve C."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Wiedemann, Arthur David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T08:00:49Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T08:00:49Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-07-09"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Aeroacoustic","Drone noise","Rotor-Airframe Interaction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44398"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/135948"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["For small unmanned aerial vehicles, a rotor is typically mounted to the vehicle with a support rod. With the rotor operating in close proximity to the rod, an aeroacoustic installation effect known as the rotor-airframe interaction noise creates an unsteady deterministic loading noise that excites the harmonics of the blade passing frequency by as much as 20 dB, which meant that the installation effect could produce more noise than the rotor itself. As a result, it was imperative to parameterize and model the rotor-airframe interaction noise. Methods to predict the interaction noise were estimated using analytical and artificial neural network models. The artificial neural network required training data to correlate parameters with the acoustic noise, which was acquired from an experimental test campaign conducted within an anechoic chamber. Measurements were taken for numerous combinations of rotor radii, rotational speeds, rotor-rod proximities, and rod diameters to examine the parameter space expected of a rotor-rod configuration typically found on a small unmanned aerial system. Microphones were placed in a semi-hemisphere cluster to capture the directivity for the observer in-plane and below the plane of rotation. The analytical and artificial neural network models predicted the time-domain acoustic pressure emitted by the interaction, allowing for a more insightful inspection of the acoustic emission and providing a psychoacoustic analysis tool. In addition to acquiring an extensive database for training the artificial neural network, this database was also used to evaluate the prediction performance of the analytical model, which relies on a potential-flow model to represent the pressure fluctuations exhibited on the surfaces of the rotor and rod during the interaction. Results showed that the analytical and artificial neural network models had good prediction performance for lower harmonics. As the harmonic number increased above 11×BPF, the artificial neural network outperformed the analytical model due to the assumption built into the analytical model being invalid at higher harmonics. Efforts taken in the experimental test campaign also found that by curving the rod or sweeping the rotor blade, the rotor-airframe interaction noise was significantly reduced to the baseline case where the rotor operated without a rod within the flow. An analytical optimization method was developed to find the optimal rod shape to diminish the acoustic noise based on constraints, where the optimization algorithm can be quickly executed. In addition, a second study was conducted in an outdoor setting on the DJI S-1000 drone to demonstrate that the noise emitted by a drone could be reduced when a curved rod was installed instead of the conventional straight rod."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["For small unmanned aerial vehicles, a rotor is typically mounted to the vehicle with a support rod. With the rotor operating close to the rod, an aeroacoustic installation effect known as the rotor-airframe interaction noise excites multiple tones by as much as 20 dB. This additional noise source rivals and sometimes exceeds the rotational noise emitted by the rotor. As a result, it was essential to investigate geometric and aerodynamic parameters that influence the rotor-airframe interaction noise to better understand which parameter most significantly contributes to the noise. Methods to predict the interaction noise were estimated using analytical and artificial neural network models. The artificial neural network required training data to correlate the aerodynamic and geometric parameters with the acoustic noise. The parameters rotor radius, rotational speed, rotor-rod proximity, and rod diameter are the geometric and aerodynamic parameters of interest that influence the strength of the rotor-airframe interaction noise. As such, a training database comprised of numerous combinations of these parameters was collected in an extensive experimental test campaign conducted inside an anechoic chamber. Microphones were placed in an inplane and below the plane of rotation because this is where a typical observer is expected to be. Analytical and artificial neural network models predicted the time-domain acoustic pressure emitted by the interaction, allowing for a more insightful inspection of the acoustic emission and providing a psychoacoustic analysis tool. In addition to acquiring an extensive database for training the artificial neural network, this database was also used to evaluate the prediction performance of the analytical model, which relies on a potential-flow model to represent the pressure fluctuations exhibited on the surfaces of the rotor and rod during the interaction. Results showed that the analytical and artificial neural network models had good prediction performance for lower harmonics. As the harmonic number increased above 11×BPF, the artificial neural network outperformed the analytical model due to the assumptions built into the analytical model being invalid at higher harmonics. Efforts taken in the experimental test campaign also found that the rotor-airframe interaction noise was significantly reduced by curving the rod or sweeping the rotor blade. Acoustic emissions from the curved rod were nearly identical to the baseline case, where the rod was absent from the system. This result suggested that the installation noise could be effectively eliminated by curving the rotor or rod. Since curving the rod proved to be an effective and practical means of reducing the installation noise, an analytical optimization method was developed to optimize the shape of the rod to diminish the interaction noise. In addition, an outdoor study was conducted with the DJI S-1000 drone to demonstrate that the noise emitted by a drone could be reduced when a curved rod was installed instead of the conventional straight rod."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Rotor-Airframe Interaction Noise: Predicting and Mitigating Noise with Artificial Neural Networks"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Fuller, Christopher R."],"dc:contributor.committeemember":["Wicks, Alfred L.","Pascioni, Kyle Anthony","Southward, Steve C."],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Wiedemann, Arthur David"],"dc:date.accessioned":["2025-07-10T08:00:49Z"],"dc:date.available":["2025-07-10T08:00:49Z"],"dc:date.issued":["2025-07-09"],"dc:description.abstract":["For small unmanned aerial vehicles, a rotor is typically mounted to the vehicle with a support rod. With the rotor operating in close proximity to the rod, an aeroacoustic installation effect known as the rotor-airframe interaction noise creates an unsteady deterministic loading noise that excites the harmonics of the blade passing frequency by as much as 20 dB, which meant that the installation effect could produce more noise than the rotor itself. As a result, it was imperative to parameterize and model the rotor-airframe interaction noise. Methods to predict the interaction noise were estimated using analytical and artificial neural network models. The artificial neural network required training data to correlate parameters with the acoustic noise, which was acquired from an experimental test campaign conducted within an anechoic chamber. Measurements were taken for numerous combinations of rotor radii, rotational speeds, rotor-rod proximities, and rod diameters to examine the parameter space expected of a rotor-rod configuration typically found on a small unmanned aerial system. Microphones were placed in a semi-hemisphere cluster to capture the directivity for the observer in-plane and below the plane of rotation. The analytical and artificial neural network models predicted the time-domain acoustic pressure emitted by the interaction, allowing for a more insightful inspection of the acoustic emission and providing a psychoacoustic analysis tool. In addition to acquiring an extensive database for training the artificial neural network, this database was also used to evaluate the prediction performance of the analytical model, which relies on a potential-flow model to represent the pressure fluctuations exhibited on the surfaces of the rotor and rod during the interaction. Results showed that the analytical and artificial neural network models had good prediction performance for lower harmonics. As the harmonic number increased above 11×BPF, the artificial neural network outperformed the analytical model due to the assumption built into the analytical model being invalid at higher harmonics. Efforts taken in the experimental test campaign also found that by curving the rod or sweeping the rotor blade, the rotor-airframe interaction noise was significantly reduced to the baseline case where the rotor operated without a rod within the flow. An analytical optimization method was developed to find the optimal rod shape to diminish the acoustic noise based on constraints, where the optimization algorithm can be quickly executed. In addition, a second study was conducted in an outdoor setting on the DJI S-1000 drone to demonstrate that the noise emitted by a drone could be reduced when a curved rod was installed instead of the conventional straight rod."],"dc:description.abstractgeneral":["For small unmanned aerial vehicles, a rotor is typically mounted to the vehicle with a support rod. With the rotor operating close to the rod, an aeroacoustic installation effect known as the rotor-airframe interaction noise excites multiple tones by as much as 20 dB. This additional noise source rivals and sometimes exceeds the rotational noise emitted by the rotor. As a result, it was essential to investigate geometric and aerodynamic parameters that influence the rotor-airframe interaction noise to better understand which parameter most significantly contributes to the noise. Methods to predict the interaction noise were estimated using analytical and artificial neural network models. The artificial neural network required training data to correlate the aerodynamic and geometric parameters with the acoustic noise. The parameters rotor radius, rotational speed, rotor-rod proximity, and rod diameter are the geometric and aerodynamic parameters of interest that influence the strength of the rotor-airframe interaction noise. As such, a training database comprised of numerous combinations of these parameters was collected in an extensive experimental test campaign conducted inside an anechoic chamber. Microphones were placed in an inplane and below the plane of rotation because this is where a typical observer is expected to be. Analytical and artificial neural network models predicted the time-domain acoustic pressure emitted by the interaction, allowing for a more insightful inspection of the acoustic emission and providing a psychoacoustic analysis tool. In addition to acquiring an extensive database for training the artificial neural network, this database was also used to evaluate the prediction performance of the analytical model, which relies on a potential-flow model to represent the pressure fluctuations exhibited on the surfaces of the rotor and rod during the interaction. Results showed that the analytical and artificial neural network models had good prediction performance for lower harmonics. As the harmonic number increased above 11×BPF, the artificial neural network outperformed the analytical model due to the assumptions built into the analytical model being invalid at higher harmonics. Efforts taken in the experimental test campaign also found that the rotor-airframe interaction noise was significantly reduced by curving the rod or sweeping the rotor blade. Acoustic emissions from the curved rod were nearly identical to the baseline case, where the rod was absent from the system. This result suggested that the installation noise could be effectively eliminated by curving the rotor or rod. Since curving the rod proved to be an effective and practical means of reducing the installation noise, an analytical optimization method was developed to optimize the shape of the rod to diminish the interaction noise. In addition, an outdoor study was conducted with the DJI S-1000 drone to demonstrate that the noise emitted by a drone could be reduced when a curved rod was installed instead of the conventional straight rod."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:44398"],"dc:identifier.uri":["https://hdl.handle.net/10919/135948"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Aeroacoustic","Drone noise","Rotor-Airframe Interaction"],"dc:title":["Rotor-Airframe Interaction Noise: Predicting and Mitigating Noise with Artificial Neural Networks"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:35Z"}