Reduced Order Modeling Methods for Aviation Noise Estimation

A key enabler for sustainable growth of aviation is the mitigation adverse environmental effects. One area concern community noise exposure at large hub airports serving growing population centers. Traditionally, computed using contours around airports, which requires knowledge a dataset pertaining to air traffic operations airport interest. Due underlying variability in real-world aircraft operations, numerous assumptions need be made adversely affect accuracy model. Reduced-Order Modeling (ROM) methods provide new framework retention number these parameters, thus improving model speed and accuracy. In this work, proper orthogonal decomposition conjunction with response surface methodology-based surrogate used create rapid assessment Validation performed against results obtained from design tool quantitative error metrics visual contour comparisons. Obtained are encouraging motivate further work other ROM methods. based models expand solution space strategies can evaluated, therefore lead novel solutions cannot found traditional modeling