Low-boom design method by numerical optimization

Making, Yoshikazu; Watanuki, Tadaharu; Kubota, Hirotoshi; Aoyama, Takashi; Iwamiya, Toshiyuki

doi:10.2514/6.1998-2246

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…In order to reduce ΔA E to nearly zero, it is necessary to use a gradient-based optimization which uses gradient information of object functions. 30) Notice that the genetic algorithm is a gradient-free method. An advantage of the gradient-based method is the ability to find a local optimum quickly.…”

Section: Optimizationmentioning

confidence: 99%

Multi-Point Optimization Study of Hydrogen-Fueled, Low-Boom Supersonic Transport

Yuhara

Rinoie

2012

AEROSPACE TECHNOLOGY JAPAN

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Many projects have made efforts to realize commercial supersonic transports (SSTs). These trials have revealed many barriers to the successful completion of various necessary project goals related to supersonic cruise efficiency, sonic boom annoyance, economic viability and also the development of solutions for avoiding environmental problems. The shift of aircraft fuel from kerosene to liquid hydrogen will come in the near future. The purpose of this study is to discuss the feasibility of a hydrogen-fueled, low-boom SST designed to carry 50 passengers for a range of 3,500 nm at a Mach 1.6 cruise speed using a method which considers the effects of hydrogen and incorporates the sonic boom minimization theory. A multi-point optimization using a genetic algorithm is conducted. The objective functions are to maximize range, minimize takeoff weight, WTO, and to minimize the difference of equivalent areas, ΔA E , which is a metric about sonic booms. The design variables were the fuselage fineness ratio, cruise lift coefficient and wing area, which are fundamental for aircraft performance. The results show that there are trade-off relationships among them. By analyzing the higher-ranked populations that have lower ΔA E , the promising range of a wing area, a cruise C L , and an altitude for the reduction of ΔA E are revealed.

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Section: Optimizationmentioning

confidence: 99%

Multi-Point Optimization Study of Hydrogen-Fueled, Low-Boom Supersonic Transport

Yuhara

Rinoie

2012

AEROSPACE TECHNOLOGY JAPAN

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“…Several papers have demonstrated the capability to perform design optimization of a low-boom supersonic aircraft but with only a limited number of design variables due to the high computational cost associated with off-body pressure sensitivity calculation [1][2][3][4]. One exception to this by Li and Rallabhandi [5] demonstrated an inverse design optimization consisting of 120 variables and using volume equivalent area sensitivity with a tool called BOSS [6].…”

Section: Introductionmentioning

confidence: 99%

Using CFD Surface Solutions to Shape Sonic Boom Signatures Propagated from Off-Body Pressure

Ordaz

2013

31st AIAA Applied Aerodynamics Conference

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The conceptual design of a low-boom and low-drag supersonic aircraft remains a challenge despite significant progress in recent years. Inverse design using reversed equivalent area and adjoint methods have been demonstrated to be effective in shaping the ground signature propagated from computational fluid dynamics (CFD) off-body pressure distributions. However, there is still a need to reduce the computational cost in the early stages of design to obtain a baseline that is feasible for low-boom shaping, and in the search for a robust low-boom design over the entire sonic boom footprint. The proposed design method addresses the need to reduce the computational cost for robust low-boom design by using surface pressure distributions from CFD solutions to shape sonic boom ground signatures propagated from CFD off-body pressure. Nomenclature Acronyms BOSS= boom optimization using smoothest shape (a computer code for low-boom design) CFD = computational fluid dynamics SymbolsA e = equivalent area A Mach e,baseline = classical equivalent area based on Mach tangent cutting planes for the baseline configuration A Mach e,design = classical equivalent area based on Mach tangent cutting planes for the design configuration A e,mixed = reversed equivalent area prediction for the design configuration A reversed e,baseline = reversed equivalent area for the design configuration dp/p = off-body pressure distribution

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“…Choi, Alonso, and Kroo [5] used a multi-fidelity approach to correct the low-fidelity linearized panel aerodynamics code with high-fidelity CFD solutions at the near-field locations to improve the computational efficiency for numerical optimization. Other researchers have opted to exclusively use the more costly high-fidelity off-body CFD calculations for simple configurations, such as wing-body configurations [6].…”

Section: Off-bodymentioning

confidence: 99%

Adaptive Aft Signature Shaping of a Low-Boom Supersonic Aircraft Using Off-Body Pressures

Ordaz

2012

50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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The design and optimization of a low-boom supersonic aircraft using the state-of-theart off-body aerodynamics and sonic boom analysis has long been a challenging problem. The focus of this paper is to demonstrate an effective geometry parameterization scheme and a numerical optimization approach for the aft shaping of a low-boom supersonic aircraft using off-body pressure calculations. A gradient-based numerical optimization algorithm that models the objective and constraints as response surface equations is used to drive the aft ground signature toward a ramp shape. The design objective is the minimization of the variation between the ground signature and the target signature subject to several geometric and signature constraints. The target signature is computed by using a least-squares regression of the aft portion of the ground signature. The parameterization and the deformation of the geometry is performed with a NASA inhouse shaping tool. The optimization algorithm uses the shaping tool to drive the geometric deformation of a horizontal tail with a parameterization scheme that consists of seven camber design variables and an additional design variable that describes the spanwise location of the midspan section. The demonstration cases show that numerical optimization using the state-of-the-art off-body aerodynamic calculations is not only feasible and repeatable but also allows the exploration of complex design spaces for which a knowledge-based design method becomes less effective. Nomenclature Acronyms BOSS= boom optimization using smoothest shape (a computer code for low-boom design) CFD = computational fluid dynamics DFO = derivative-free optimization DOE = design of experiments GBO = gradient-based optimization PLdB = perceived loudness in decibels psf = pounds per square foot Symbols C L = coefficient of lift dp/p = near-field pressure waveform X e = equivalent length

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Low-boom design method by numerical optimization

Cited by 4 publications

References 4 publications

Multi-Point Optimization Study of Hydrogen-Fueled, Low-Boom Supersonic Transport

Multi-Point Optimization Study of Hydrogen-Fueled, Low-Boom Supersonic Transport

Using CFD Surface Solutions to Shape Sonic Boom Signatures Propagated from Off-Body Pressure

Adaptive Aft Signature Shaping of a Low-Boom Supersonic Aircraft Using Off-Body Pressures

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