The efficient global optimization method is a global optimization technique based on the stochastic kriging model to efficiently search the global optimum in a design space. Efficient global optimization selects the next sample point in the view of the probability that a global minimum is located. To present the probability, the efficient global optimization method introduces the expected improvement function. The mean and variance at the untried point provided from the kriging model are used to calculate the expected improvement function. Efficient global optimization selects the maximum expected improvement point as the next sample point. After validating the efficient global optimization method by several test functions, we applied it to a diffusing S-duct shape design problem which needs a computationally expensive turbulent computational fluid dynamics analysis. The design objective is to improve the total pressure recovery of the S-duct. The improved S-duct shape was searched globally through the efficient global optimization method. Our results confirmed that the efficient global optimization method can efficiently provide a meaningful engineering result in the S-duct shape design.

Aerodynamic analysis(DATCOM) and radar cross section(RCS) analysis(POFACETS) were integrated for the air-to-surface missile concept design using a design framework. The missile geometry was defined based on the CAD(CATIA) for synchronizing the manufacturing with design processes. Aero/RCS analyses were linked with the CAD process under the ModelCenter framework in order to receive the geometry data automatically. The missile design baseline configuration was selected from ROC(requirement of capability). Then the RCS minimization was performed subject to thelargerthebetter constraint of the missile lift-to-drag ratio. This study demonstrated that various design strategies can be performed efficiently about many missile configurations using this design framework in the missile conceptual design phase.

In this study, RBDO(Reliability Based Design Optimization) was performed for a supersonic double-wedge inlet. By considering uncertainty of design with given design space, the pressure recovery was transformed into the probabilistic constraint while the inlet drag was considered as a deterministic objective function. To save computational analysis cost and to search good design space, Latin-Hypercube design of experiment and the Kriging model were incorporated and then RBDO was performed. Monte-Carlo simulation was performed to verify the accuracy of AFORM(Advanced First Order Reliability Method). It was found that AFORM result agreed very well with the Monte-Carlo simulation result. The system reliability was guaranteed by considering uncertainty of the design variables. In case of considering diverse uncertainty of system design, RBDO was found to be useful.

The optimal design of a supersonic inlet is generally performed with expensive computer analysis such as the computational fluid dynamics. Thus, the feasibility criterion was incorporated into efficient global optimization (EGO) in order to efficiently perform optimal design of a supersonic inlet in this study. The strategy of EGO incorporating feasibility criterion is to improve computational efficiency by skipping unessential updation of the Kriging surrogate model of constraint function. The surrogate model is not updated when a new sample is expected to be feasible by feasibility criterion. The efficiency of EGO incorporating feasibility criterion was demonstrated by the test functions. Then, this method was applied to the optimal design of a supersonic inlet. The objective function is the coefficient of cowl drag. The constraint functions are the pressure recovery and mass flowrate. As a result, the EGO incorporating feasibility criterion for the design of a supersonic inlet was effectively performed with lower computational cost.