Open Access
Marotta, Taylor Ries
Graduate Program:
Aerospace Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
August 04, 2011
Committee Members:
  • Kenneth Steven Brentner, Thesis Advisor
  • Philip John Morris, Thesis Advisor
  • Boeing
  • airframe noise
  • landing gear
  • acoustics
  • aerospace
  • trailing edge
  • aeroacoustics
  • aircraft
  • noise
  • nosie prediction
  • prediction
  • Gulfstream
The aerocoustic prediction program LGMAP has been enhanced and new comparisons have been made with experiments. The LGMAP method models landing gear with separate acoustic elements. These acoustic elements are assembled into groups that model specific assemblies of the landing gear such as the strut, wheel, and trailing edges. The upgrades to the program include corrections involving the cylinder acoustic element’s scaling, non-dimensional lift and drag spectrums, and incident velocity vector. A new wheel model based on the Fink method was created in order to improve the directivity of the original LGMAP wheel model. This model differs from the Fink method, which models the landing gear’s wheels as one bogey, because each individual wheel’s location and size is specified by the user. A parameter has been created to model the flow effects the wheels induce on each other. The cylinder coefficients were recalibrated because of the changes made to the cylinder acoustic element. The calibration case was the QFF 6.3% scaled Boeing 777 main landing gear. For validation, landing gear models built by different users were compared and the importance of an appropriate and accurate model was discovered. Hoses and cables extremely close to large structures such as the oleo should not be represented in the LGMAP landing gear geometry in order to prevent an overprediction in high frequency noise. A blind test of LGMAP’s capabilities was completed using an experimental case of a full scale Boeing 737 main landing gear. The Guo and Fink landing gear prediction methods, both included in NASA’s ANOPP program, were compared against LGMAP’s prediction method. The results from LGMAP produced underpredictions in high frequency noise and the directivity did match that from experimental data. The discrepancies between the experimental data and the LGMAP prediction could be the lack of resources that were used to build the landing gear geometry. The Guo method produces a more accurate prediction than LGMAP, but it still requires experimental data to complete its prediction. LGMAP fared better against the Fink method because of LGMAP’s strut prediction model. The capabilities of LGMAP as a noise source locator compared with experimental phased array measurements. The high fidelity features of the landing gear such as hoses and wires were dominant in both predicted and measured results. LGMAP’s wheel model over predicts for all sideline observer cases. Based on the phased array measurements, an update of the wheel directivity model is advised.