摘要 :
A low-fidelity toolchain was used to predict broadband self-noise trends for three proprotors in axial flight: a baseline (C24ND) and two acoustically constrained proprotors (OPT-III and COPR-3). Rotor loads were predicted with th...
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A low-fidelity toolchain was used to predict broadband self-noise trends for three proprotors in axial flight: a baseline (C24ND) and two acoustically constrained proprotors (OPT-III and COPR-3). Rotor loads were predicted with the ANOPP Propeller Analysis System (PAS) and self-noise was predicted with the semiempirical method of Brooks, Pope, and Marcolini (BPM) implemented in the ANOPP2 Self-Noise Internal Functional Module (ASNIFM). Comparisons to experimental data revealed that trends for turbulent boundary layer trailing edge (TBL-TE) noise could be modeled across several flight conditions by increasing the boundary layer thicknesses via the trip setting. Since the Mach number range of the BPM method is exceeded in these predictions, a dependence of boundary layer displacement thickness on blade station Mach number was suggested as a possible reason for needing to model thicker boundary layers, suggesting that the TBL-TE model needs to be developed further. Bluntness vortex shedding noise (BVS) predictions required tuning the trailing edge thickness and trailing edge closure angle for each flight condition to match experimental trends, demonstrating that the BYS noise model is incomplete and that BVS noise may vary with the angle of attack. This study indicates that the BPM self-noise method needs to be improved, which will lead to more accurate broadband predictions.
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This work compares artificial neural network and multi variate orthogonal function modeling methodologies for the prediction and characterization of isolated hovering sUAS rotor aerodynamics and aeroacoustics. Design of Experiment...
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This work compares artificial neural network and multi variate orthogonal function modeling methodologies for the prediction and characterization of isolated hovering sUAS rotor aerodynamics and aeroacoustics. Design of Experiments was used to create input feature spaces over 9 input features: the number of rotor blades, rotor size, rotor speed, the amount of blade twist, blade taper ratio, tip chord length, collective pitch, airfoil camber, and airfoil thickness. CAMRAD Ⅱ and AARON were executed at the points defined by the input feature space to predict aerodynamic and aeroacoustic quantities. These predicted aerodynamic and aeroacoustic data were then used to generate artificial neural networks and polynomial response surface models. The two prediction model methodologies were evaluated over test data previously unseen by the models, which showed good prediction capabilities for both model types, with slightly lower prediction error for the artificial neural networks. A characterization study was performed, which showed that input features correspondent to the spanwise sectional blade lift and drag were the most significant factors to the aerodynamic thrust and power, respectively. It was also shown that the aeroacoustic quantities were highly dependent on variations in rotor speed and size, which affect the Doppler factor for tonal noise and the spanwise Reynolds number for broadband noise.
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A series of experiments was conducted in an anechoic chamber and wind tunnel to investigate the noise and performance of an optimum hovering rotor design. The optimum hovering rotor experimental data set presented in this paper pr...
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A series of experiments was conducted in an anechoic chamber and wind tunnel to investigate the noise and performance of an optimum hovering rotor design. The optimum hovering rotor experimental data set presented in this paper provides the community with a rotor that is theoretically easier to model. Isolated rotors were tested in an anechoic hover chamber as well as on a representative quadcopter vehicle. In the anechoic chamber, performance and acoustic measurements were taken at various rotor speeds to compare to those of a commercial-off-the-shelf (COTS) rotor. In the wind tunnel, free-stream velocity, vehicle pitch, and rotor rotation rates were varied to achieve various hover and forward flight operating conditions. Previous investigation of a small quadcopter in the Low Speed Aeroacoustic Wind Tunnel (LSAWT) had identified possible broadband and interactional noise sources due to rotor airframe interaction and rotor-rotor interaction. These publications identified separation, turbulent boundary layer trailing edge, and bluntness vortex shedding as the main sources of self-generated airfoil noise. By replacing the COTS rotor with an optimum hovering rotor design, self-generated broadband noise was reduced for both hover and forward flight conditions for isolated rotor runs. However, the optimum rotors only reduced noise levels for full-vehicle hover conditions, and had little to no reduction in full-vehicle forward flight conditions.
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This work experimentally investigates the aerodynamic behavior of proprotors across a wide range of angles of attack. These flight conditions are intended to be representative of Urban Air Mobility (UAM) vehicle platforms that uti...
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This work experimentally investigates the aerodynamic behavior of proprotors across a wide range of angles of attack. These flight conditions are intended to be representative of Urban Air Mobility (UAM) vehicle platforms that utilize articulating propulsors to transition from a vertical takeoff and landing (VTOL) phase typical of a conventional rotorcraft to an axial mode of forward flight typical of a fixed-wing aircraft. These data are used to identify the potential limits of lower-fidelity aerodynamic modeling tools, as well as to inform future acoustic phases of testing. Tests were conducted on two proprotor designs in the NASA Langley 14- by 22-Foot Subsonic Tunnel using an articulating propeller test stand. Hover results identified unique flow physics on one of the proprotors, including severe outboard flow separation and perpendicular blade-vortex interactions on the outboard portions of the blades. Transition and forward flight conditions yielded informative trends in terms of both on- and off-axis forces and moments against which low-fidelity prediction models were compared.
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In this work, elastic microfences were generated for the purpose of measuring shear forces acting on a wind tunnel model. The microfences were fabricated in a two part process involving laser ablation patterning to generate a temp...
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In this work, elastic microfences were generated for the purpose of measuring shear forces acting on a wind tunnel model. The microfences were fabricated in a two part process involving laser ablation patterning to generate a template in a polymer film followed by soft lithography with a two-part silicone. Incorporation of a fluorescent dye was demonstrated as a method to enhance contrast between the sensing elements and the substrate. Sensing elements consisted of multiple microfences prepared at different orientations to enable determination of both shear force and directionality. Microfence arrays were integrated into an optical microscope with sub-micrometer resolution. Initial experiments were conducted on a flat plate wind tunnel model. Both image stabilization algorithms and digital image correlation were utilized to determine the amount of fence deflection as a result of airflow. Initial free jet experiments indicated that the microfences could be readily displaced and this displacement was recorded through the microscope.
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摘要 :
In this work, elastic microfences were generated for the purpose of measuring shear forces acting on a wind tunnel model. The microfences were fabricated in a two part process involving laser ablation patterning to generate a temp...
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In this work, elastic microfences were generated for the purpose of measuring shear forces acting on a wind tunnel model. The microfences were fabricated in a two part process involving laser ablation patterning to generate a template in a polymer film followed by soft lithography with a two-part silicone. Incorporation of a fluorescent dye was demonstrated as a method to enhance contrast between the sensing elements and the substrate. Sensing elements consisted of multiple microfences prepared at different orientations to enable determination of both shear force and directionality. Microfence arrays were integrated into an optical microscope with sub-micrometer resolution. Initial experiments were conducted on a flat plate wind tunnel model. Both image stabilization algorithms and digital image correlation were utilized to determine the amount of fence deflection as a result of airflow. Initial free jet experiments indicated that the microfences could be readily displaced and this displacement was recorded through the microscope.
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This work computationally investigated the rotor blade vortex-induced separation recently observed during an aerodynamic rotor test campaign in the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel. Two separate approach...
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This work computationally investigated the rotor blade vortex-induced separation recently observed during an aerodynamic rotor test campaign in the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel. Two separate approaches (i.e., airfoil modification and blade tip modification) were studied to mitigate the vortex-induced separation. Mid-fidelity tools based on blade element momentum theory were shown to mispredict the rotor inflow and were also shown to not capture the vortex-induced separation caused by perpendicular blade-vortex interaction. This mis-prediction was exploited to isolate the aerodynamic thrust deficit caused by the vortex-induced separation (20%) from the thrust deficit due to inflow variation (31%). High-fidelity tools were shown to reasonably predict aerodynamic forces within 13% and flow separation when compared to experimental results. The modified airfoil variant of the baseline rotor effectively mitigated the vortex-induced separation, while the blade tip modified variant still showed separation, though the size and strength of the vortex was reduced. Acoustic predictions were underpredicted by 10 dB from preliminary measurements taken in the untreated wind tunnel. Broadband noise contributions from different rotor blade sections showed that self-noise due to flow separation and other turbulent boundary layer mechanisms was the dominant noise source for all three rotor cases, followed by blade-wake interaction noise caused by perpendicular blade-vortex interactions.
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This work illustrates the effect of a rotor blade's boundary layer on the broadband laminar boundary layer vortex shedding (LBL-VS) self-noise emitted from an optimum hovering rotor through experimental and multifidelity computati...
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This work illustrates the effect of a rotor blade's boundary layer on the broadband laminar boundary layer vortex shedding (LBL-VS) self-noise emitted from an optimum hovering rotor through experimental and multifidelity computational studies. Blade surface roughness effects associated with different manufacturing techniques and the effect of adding a boundary layer trip were shown to decrease LBL-VS noise by upwards of 30 dB at the frequency of maximum emission with a slight penalty in aerodynamic performance when compared with smooth rotor blades. Low-fidelity 2-D viscous flow analysis verified the presence of laminar separation bubbles on the rotor blades, which are responsible for LBL-VS noise. Three high-fidelity lattice-Boltzmann simulations were conducted with different wall-functions to predict the boundary layer character correspondent to their experimental counterpart and the resultant presence or absence of LBL-VS noise. Excellent aerodynamic and aeroacoustic agreement was seen between the lattice-Boltzmann simulations and the experimental data for the cases with surface roughness and the boundary layer trip. The broadband noise results from the simulation with fully turbulent wall-functions diverged from the experimental results above 5 kHz. The transitional wall-function simulation, which emulated the smooth experimental blades, underpredicted thrust by 14% and broadband noise by a minimum of 10 dB with an accurately predicted broadband noise trend.
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摘要 :
Advanced Air Mobility is a vision for a safe, accessible, and sustainable aviation system to transport people and cargo between places not served by traditional aviation. With this emerging transportation industry, there is motiva...
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Advanced Air Mobility is a vision for a safe, accessible, and sustainable aviation system to transport people and cargo between places not served by traditional aviation. With this emerging transportation industry, there is motivation to characterize the noise of vehicles to determine their potential impacts on the community. An experimental testing campaign was conducted on a representative model of a small unmanned aircraft system in the NASA Langley Low Speed Aeroacoustic Wind Tunnel as a continuation of a previous testing campaign. The goals of the current test are to identify sources of interactional noise as well as to test custom-designed rotors and noise reduction devices. The tested noise reduction methods involve increasing the vertical distances between the rotors and the vehicle airframe as well as between the forward and aft rotor disk planes. These methods are intended to reduce rotor-airframe interaction noise in hover and fore-aft rotor wake ingestion noise in forward flight. A phased microphone array is also utilized to identify the locations of prominent noise generation for the different vehicle configurations in forward flight. Elevation of the rotors from the vehicle airframe yielded nearly 8 dBA overall noise reduction in forward flight, while yielding up to 4 dB reduction in overall tonal levels for one of the rotors in hover.
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This work illustrates the use of artificial neural network modeling in the aerodynamic and aeroacoustic characterization of optimum hovering rotors over a broad range of design and operating conditions. Design of Experiments was u...
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This work illustrates the use of artificial neural network modeling in the aerodynamic and aeroacoustic characterization of optimum hovering rotors over a broad range of design and operating conditions. Design of Experiments was used to create input feature spaces over eight input factors: the number of rotor blades, rotor radius, rotor rotation rate, design thrust condition, collective pitch, airfoil camber, the location of maximum camber, and the airfoil thickness. A low-fidelity tool chain was then used at the discrete data points defined by the designed input feature spaces to analytically design optimum hovering rotors and simulate aerodynamic and aeroacoustic quantities. This allowed for the generation of data sets over which to train and test the artificial neural network prediction models. Prediction models were trained over the data sets for the actual thrust generated by the rotor, power loading, tonal thickness and loading noise at the fundamental blade passage frequency, and broadband self-noise at seventeen one-third octave bands between 1 kHz and 40 kHz. These prediction models were validated by testing over data previously unseen by the models to quantify their capability for generalization to new data within the design feature space. The models were then used to study the effect each input feature had on the aeroacoustics and aerodynamics of optimum hovering rotors, and physical insights were gained to further explain the effect of each input. This characterization study showed that tonal noise and power loading were most sensitive to the number of rotor blades and the rotor rotation rate and that broadband noise was most sensitive to collective pitch and the design thrust condition.
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