Mutiobjective Optimization Using Approximation Model-Based Genetic Algorithms

Chung, Hyoung Seog; Alonso, Juan J.

doi:10.2514/6.2004-4325

Cited by 63 publications

(38 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[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

View full text Add to dashboard Cite

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

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Concerning MMOGA algorithm, the output responses are improved, adjusted with higher level variables, and presented in two-dimensional forms. Kriging algorithm [21][22][23][24]] is a precise multi-dimensional interpolation using a simple polynomial function. Its efficiency can be maximized based on the capability of estimating the errors and modifying the preliminary population.…”

Section: Moga and Mmogamentioning

confidence: 99%

Detecting structural damage in Timoshenko beams based on optimization via simulation (OVS)

Farokhzad¹,

Mohebi²,

Amiri³

et al. 2016

J. vibroeng.

View full text Add to dashboard Cite

Abstract. Considering easy determination of natural frequency in structures leads researchers to focus on detecting the damage through the dynamic parameters using combination of various artificial intelligence algorithms. The main contribution of this research is to detect damage in structures (including its depth and location) for the deep beams with Timoshenko behavior using optimization via simulation (OVS). This method is established based on the first three natural frequencies of the deep or semi-deep beams. The finite element method (FEM) is conducted to obtain essential inputs parameters for OVS. The exact location and depth of the structural damage are determined, using combination of multi-objective optimization algorithms, multi-objective genetic algorithm (MOGA), and modified multi-objective genetic algorithm (MMOGA). This research also remarkably concerns about detecting the location of the defect in the beams with several cracks. In order to verify the results obtained from numerical analysis, several experimental specimens are presented. The dynamics parameters of the beams are experimentally identified using modal hammer. The responses obtained from the numerical method, proposed in this research, are also compared with the results obtained from previous studies. Practically, a beam with real dimensions is examined for different boundary conditions. In addition, the results obtained from MOGA and MMOGA are compared with the other researchers' achievements. Finally, it was observed that the proposed method, (OVS), can be satisfyingly determined the exact location and depth of damage with the high accuracy.Keywords: first three natural frequency, multi-objective genetic algorithm (MOGA), modified multi-objective genetic algorithm (MMOGA), optimization via simulation (OVS), and Timoshenko beam.

show abstract

“…Global optimisation method may provide the global optimum value within the specified design space. For example, GA originated from the theory of natural evolution is widely used as a global optimisation tool [18][19][20] but it is generally costly in imitating an accurate evolutional process. Particularly for 3-D aerodynamic design problems with a lot of design variables, it requires an enormous amount of computational time in evaluating experimental data at each design point.…”

Section: Aerodynamic Shape Optimisationmentioning

confidence: 99%

Computational Elements for High-fidelity Aerodynamic Analysis and Design Optimisation

Kim

2010

DSJ

View full text Add to dashboard Cite

The study reviews the role of computational fluid dynamics (CFD) in aerodynamic shape optimisation, and discusses some of the efficient design methodologies. The article in the first part, numerical schemes required for high-fidelity aerodynamic flow analysis are discussed. To accurately resolve high-speed flow physics, high-fidelity shock-stable schemes as well as intelligent limiting strategy mimicking multi-dimensional flow physics are essential. Exploiting these numerical schemes, some applications for 3-D internal/external flow analyses were carried out with various grid systems which enable the treatment of complex geometries. In the second part, depending on the number of design variables and the way to obtain sensitivities or design points, several global and local optimisation methods for aerodynamic shape optimisation are discussed. To avoid the problem that solutions of gradient-based optimisation method, (GBOM) are often trapped in local optimum, remedy by combining GBOM with global optimum strategy, such as surrogate models and genetic algorithm (GA) has been examined. As an efficient grid deformation tool, grid deformation technique using NURBS function is discussed. Lastly, some 3-D examples for aerodynamic shape optimisation works based on the proposed design methodology are presented.

show abstract

Mutiobjective Optimization Using Approximation Model-Based Genetic Algorithms

Cited by 63 publications

References 7 publications

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

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

Detecting structural damage in Timoshenko beams based on optimization via simulation (OVS)

Computational Elements for High-fidelity Aerodynamic Analysis and Design Optimisation

Contact Info

Product

Resources

About