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A higher harmonic control (HHC) investigation was conducted on a full-scale, isolated XV-15 rotor in helicopter modein the NASA Ames 80by 120-Foot Wind Tunnel to independently control noise and vihration. The higher harmonic blade pitch was generated using swashplate oscillations. The radiated hlade-vortex interaction (RVI) noise footprint was measured on 11 plillie I~r~~r r l h the rotor uith an act~artir tra\rrpr.'li.st results showed lhi~t IIHC is highly efYecliv~ in reducin(: I!VI naoise, arhirvinv il I2 dl! redurlinn in nrak n18isu level within lhe noiw fntttnrint. Nt~ire r c d u r t i t~~~ wit11 IIIIC renlains eITecliv~cvrn with perturbations in rotor trim and flight conditions. Blade pressure feedback was demonstrated to be a viahle method for closed-loop noise control. Some noise reduction mas achieved with no increase in vibratory huh loads. Increases in control loads due to HHC generally limited further noise reduction. The vibration controller achieved about 50 percent reduction in vibratory huh loads with control loads limiting the HHC amplitude.

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A BVI BVISPL C r MIMO MIS0 PE R RBF v VLi , a, I* r T a Scsi Kottapnlli Cahit Kitaplioglu Ar,,r]fNASA Roro~rafr Di~,isio,i NASA Allies Research Ce~zrei; Mofferr Field, CAResults from a neural network study of the noise data from a full-scale XV-15 tilt-rotor are presented. Specifically, this database was acquired during the 1998 NASA Ames 80-by 120-foot wind tunnel test to estahlish the blade-vortex-interaction noisesignature. The present study has threeobjectives: 1) Toconduet anenral-net\vork-based quality assessment of tltenoise data; 2) To obtain neural network representations of the noise data and to demonstrate their sensitivity to test conditions; 3) To obtain neural-network-based noise predictions. Overall, neural networks are successfully used to assess the quality of the noise data and to represent the complete database as well as to predict tilt-rotor noise using the minimal amount of input data. As major findings, the data quality is found to he acceptable, and accurate neural network representations are obtained for the test-condition-sensitivity cases.Notation lator disc area, nR2, m2 blade vortex interaction blade-vortex-interaction sound pressure level, 30th to 150th rot?! harmonics, dB rotor thrust coefficient, thrustlp~~',, multiple-input, multiple-output multiple-input, single-output neural network processing element rotor radius, m radial-basis function wind tunnel airspeed, mls blade tip speed, nR, mls rotor shaft angle, positive nose up, deg lntur advance ratio, Vcos a,/(QR) rotor solidity ratio mtor rotation speed, radlsec

No abstract