정신규 교수

The Kriging-based genetic algorithm is applied to aerodynamic design problems. The Kriging model, one of the response surface models, represents a relationship between the objective function (output) and design variables (input) using stochastic process. The kriging model drastically reduces the computational time required for objective function evaluation in the optimization (optimum searching) process. ‘Expected improvement (EI)’ is used as a criterion to select additional sample points. This

Analysis of variance (ANOVA) and self-organizing map (SOM) were applied to data mining for aerodynamic design space. These methods make it possible to identify the effect of each design variable on objective functions. ANOVA shows the information quantitatively, while SOM shows it qualitatively. Furthermore, ANOVA can show the effects of interaction between design variables on objective functions and SOM can visualize the trade-offs among objective functions. This information will be helpful for

In this study, a surrogate model is applied to multi-objective aerodynamic optimization design. For the balanced exploration and exploitation with the surrogate model, objective functions are converted to the Expected Improvements (EI) and these values are directly used as fitness values in the multi-objective optimization. Among the non-dominated solutions about EIs, additional sample points for the update of the Kriging model are selected. The present method is applied to a transonic airfoil d

A sophisticated GA/PSO-hybrid algorithm for application to real-world optimization problems was proposed. The configurations of the two consisting methods, GA and PSO, were investigated to enhance the diversity of the former and the fast convergence of the latter simultaneously. The new hybrid algorithm was applied to two test function problems, and the results indicated that the search ability was improved by suitable tuning of the configurations. In addition, the new hybrid algorithm showed ro

Diesel engine combustion chamber which reduces exhaust emission has been designed using CFD analysis and optimization techniques. In order to save computational time for design, the Kriging model, one of the response surface models, is adopted here. For a robust exploration, both the estimated function value of the model and its uncertainty are considered at the same time. In the present problem, the k-means method is used to limit the number of additional sample points to a reasonable level. Am

One of the difficulties in multi-disciplinary design optimization lies in the complicated interactions between large numbers of objective functions, design variables, and constraints. This difficulty often leads to an unsuitable formulation of design problems. Data mining is often used to address these challenges. Data mining provides insight into the design of complicated systems. The information obtained from data mining can be used to support (a) formulation of design problems, (b) decision m

Genetic Algorithms (GAs) generally maintain diverse solutions of good quality in multi-objective problems, while Particle Swarm Optimization (PSO) shows rapid convergence to the optimum solution. Previous studies indicated that search abilities can be improved by simply coupling these two algorithms; GA compensates for the low diversity of PSO, while PSO compensates for the high computational costs of GA. In this study, the configurations of the two methods when used in a fully coupled hybrid al

A high-efficiency design exploration framework for hull form has been developed. The framework consists of multiobjective shape optimization and design knowledge extraction. In multiobjective shape optimization, a multiobjective genetic algorithm (MOGA) using the response surface methodology is introduced to achieve efficient design space exploration. As a response surface methodology, the Kriging model, which was developed in the field of spatial statistics and geostatistics, is applied. A new

In this paper, Kriging model is applied to a constrained multi-objective optimization problem. In order to balance the local and global search in the Kriging model, the criterion 'expected improvement (EI)' is adopted. Probability of satisfying the constraints is calculated in the Kriging model to impose the constraint effect into EI. Search region of the design space is modified during the optimization by investigating the distribution of the design variables. Functional analysis of variance (A

A practical inverse design method for supersonic airfoils/wings has been developed. The method is based on Takanashi's iterative residual-correction concept. A geometry that materializes a specified pressure distribution is sought by solving an integrodifferential form of the linearized small perturbation (LSP) equation. The integration is limited to the Mach forecone from the point of interest. Several design results are presented Nomenclature

The kriging-based genetic algorithm is applied to aerodynamic design problems. The kriging model is a response surface model that represents a relationship between objective function (output) and design variables (input) using a stochastic process. The kriging model drastically reduces the computational time required for objective function evaluation in the optimization (optimum searching) process. Expected improvement is used as a criterion to select additional sample points. This makes it poss