Sonic-boom generated sound field under a wavy air–water interface: Analyses for incident N waves

Cheng, H. K.; Lee, C. J.; Edwards, J. Riley

doi:10.1121/1.2035590

Cited by 1 publication

(1 citation statement)

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“…For a complete presentation of these advanced features, interested readers must refer to several experimental [35,36], numerical, and theoretical studies [37][38][39] on these issues. Furthermore, aircraft design for sonic boom minimization may include interactions with the water surface [40,41] during flight over the oceans. These effects have to be considered in the subsequent stages of design.…”

Section: Discussionmentioning

confidence: 99%

Sonic Boom Minimization Using Inverse Design and Probabilistic Acoustic Propagation

Rallabhandi

Mavris

2006

Journal of Aircraft

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Using improved linearized tools that operate on unstructured watertight geometries, the accuracy and efficacy of aerodynamic shape optimization in conceptual design stages can be greatly improved. The conventional area distribution method for minimizing sonic boom is theoretically extended by adding additional parameters so that the near-field signature is more accurately represented. The problem of F-function parameters' estimation is reformulated as a gradient-based optimization problem and solved. Sonic boom propagation is carried out in a probabilistic fashion using parametric atmospheric models and statistical techniques. A bilevel pseudoinverse optimization is performed using coarse-grained parallel genetic algorithms to design aircraft that meet low sonic boom requirements under atmospheric uncertainty. The optimization analysis is split into two cycles with multiple conflicting objectives. Results are presented and discussed. Nomenclature A = area of ray tube, ft 2 A e = equivalent area, ft 2 A h = area of ray tube at altitude h, ft 2 AD 2 = Anderson-Darling test statistic a = local speed of sound, ft 2 a h = local speed of sound at altitude h, ft=s B, B 1 , B 2 , B 3 = rise slopes in F-function b 1 = input layer bias vector b 2 = output layer bias vector CV = critical value in Anderson-Darling test F = F-function F 0 = cumulative distribution function of the assumed distribution GW = gross weight of the aircraft, lb h = cruise altitude, ft H, C, D, , y r = parameters associated with the F-function l = aircraft length, ft M h = Mach number at cruise altitude h M z = Mach number at altitude z below cruise altitude N = statistical sample size p = local pressure, psf pr=pf = ratio of rear-to-front shock strength S = slope of balancing line in F-function S 1 = heuristic in Anderson-Darling test U = freestream velocity, ft=s V = hidden layer network weights W = output layer network weights x, y, = dummy axial locations x k = vector of known variables y f = bluntness parameter y r = intersection of the F-function and the rear balancing line z = vertical distance below cruise altitude = dummy variables for equation simplification y = nonlinear advance of acoustic rays = M 2 1 p = local density, slugs=ft 3 h = local density at altitude h, slugs=ft 3 = 1.2

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