Design of Low-boom Supersonic Business Jet with Evolutionary Algorithms Using Adaptive Unstructured Mesh

Choi, Seongim; Chung, Hyoung Seog; Alonso, Juan J.

doi:10.2514/6.2004-1758

Cited by 20 publications

(26 citation statements)

References 23 publications

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“…We start from a high plateau with an existing prototype of a general Toolbox for Optimization, which has been successfully applied to other multi-disciplinary realms [16], and a number of proven tools for aircraft design [1,2,[7][8][9]. In this work we sketch some of the principal components necessary to carry on this type of task.…”

Section: The Problem Considered and The Various Modules Employed For mentioning

confidence: 99%

See 1 more Smart Citation

Aircraft design optimization

Alonso

LeGresley

Pereyra

2009

Mathematics and Computers in Simulation

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Section: The Problem Considered and The Various Modules Employed For mentioning

confidence: 99%

“…In high-dimensional spaces the available techniques are problematic at their best and one often must resort to surrogate models, divide and conquer techniques and parallel computing, in order to even have a chance to solve the problem in a reasonable time [2,7,14,15].…”

Section: Introductionmentioning

confidence: 99%

Aircraft design optimization

Alonso

LeGresley

Pereyra

2009

Mathematics and Computers in Simulation

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“…[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.…”

Section: Introductionmentioning

confidence: 99%

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

Geiselhart

Ozoroski

Fenbert

et al. 2011

49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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This paper documents the development of a conceptual level integrated process for design and analysis of efficient and environmentally acceptable supersonic aircraft. To overcome the technical challenges to achieve this goal, a conceptual design capability which provides users with the ability to examine the integrated solution between all disciplines and facilitates the application of multidiscipline design, analysis, and optimization on a scale greater than previously achieved, is needed. The described capability is both an interactive design environment as well as a high powered optimization system with a unique blend of low, mixed and high-fidelity engineering tools combined together in the software integration framework, ModelCenter. The various modules are described and capabilities of the system are demonstrated. The current limitations and proposed future enhancements are also discussed. = equivalent area C L = lift coefficient dp/p = (the calculated pressure -the ambient pressure)/(the ambient pressure) EXTR = extraction ratio FPR = fan pressure ratio L/D = lift to drag ratio nmi = nautical miles OPR = overall pressure ratio OML = outer mold line SFC = specific fuel consumption T 3 = compressor exit temperature, °R T 4 = combustor exit temperature, °R TTR = throttle ratio V app = approach velocity, kts V jet = jet velocity, ft/s

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“…Choi et al [11] used the NSGA-II [22] in the solution of a multidisciplinary supersonic business jet design. In this case, the disciplines involved were (i) aerodynamics and, (ii) aeroacoustics.…”

Section: Use Of a Gaussian Process Or Krigingmentioning

confidence: 99%

A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

Santana‐Quintero

Montano

Coello

2010

Evolutionary Learning and Optimization

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Abstract. Evolutionary algorithms have been very popular for solving multiobjective optimization problems, mainly because of their ease of use, and their wide applicability. However, multi-objective evolutionary algorithms (MOEAs) tend to consume an important number of objective function evaluations, in order to achieve a reasonably good approximation of the Pareto front. This is a major concern when attempting to use MOEAs for real-world applications, since we can normally afford only a fairly limited number of fitness function evaluations in such cases. Despite these concerns, relatively few efforts have been reported in the literature to reduce the computational cost of MOEAs. It has been until relatively recently, that researchers have developed techniques to achieve an effective reduction of fitness function evaluations by exploiting knowledge acquired during the search. In this chapter, we analyze different proposals currently available in the specialized literature to deal with expensive functions in evolutionary multi-objective optimization. Additionally, we review some real-world applications of these methods, which can be seen as case studies in which such techniques led to a substantial reduction in the computational cost of the MOEA adopted. Finally, we also indicate some of the potential paths for future research in this area.

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Design of Low-boom Supersonic Business Jet with Evolutionary Algorithms Using Adaptive Unstructured Mesh

Cited by 20 publications

References 23 publications

Aircraft design optimization

Aircraft design optimization

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

A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

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