Sonic Boom Minimization Using Inverse Design and Probabilistic Acoustic Propagation

Abstract

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

Integration of Multifidelity Multidisciplinary Computer Codes for Design and Analysis of Supersonic Aircraft

“…[1][2][3][4][5][6][7][8][9][10][11][12][13] There are also many examples of conceptual aircraft design reports where the authors described going "deep" in a particular discipline, focusing on a single cruise point low-boom and/or low-drag design in their process. [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Many of these are often byproducts of tool and method development and the testing of optimization algorithms and/or schemes. There are fewer instances focused on supersonic design for low-boom concepts with shape optimization tied to overall vehicle performance.…”

Airworthiness and Conceptual Design Optimization Considerations of the Environmental Impact Assessment of a Supersonic Business Jet Aircraft

“…One of the design features for sonic boom minimization is the blunt nose, which creates a strong detached shock, so that the secondary shocks are weak and do not overtake and enhance the front shock. This way, the produced far field pressure becomes much weaker in comparison to the case of a sharp nose, in which the front detached shock wave coalesces with the stronger secondary shock waves [22].…”

Lobe Balancing Design Method to Create Frozen Sonic Booms Using Aircraft Components

“…This quote is often used to justify the existence of atmospheric freezing [10,15,[37][38][39][40][41]. Hayes refers to his 1968 paper [42], which gives credit for this idea to Busemann [43].…”