In this paper, a practical Multidisciplinary Design Optimization (MDO) system for an aircraft design is developed. The MDO system is based on the integration of computational fluid dynamics (CFD) codes and the NASTRAN based aeroelastic-structural interface code. Kriging model is employed to save the computational time of objective function evaluation in Multi-Objective Genetic Algorithm (MOGA). As a result of optimization, several nondominated solutions, indicating the trade-off among the drag, the structural weight, the drag divergence and the pitching moment, have been found.
A new approach, Multi-Objective Design Exploration (MODE), is presented to address multidisciplinary design optimization (MDO) problems using computational fluid dynamics-computational structural dynamics (CFD-CSD) coupling. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists of the Kriging Model, adaptive range multi objective genetic algorithms, analysis of variance and a self-organizing map. The main emphasis of this approach is visual data mining. An MDO system using high-fidelity simulation codes, a Navier-Stokes solver and NASTRAN has been developed and applied to a regional-jet wing design. Because the optimization system becomes very expensive computationally, only brief exploration of the design space has been performed. However, visual data mining results demonstrate that design knowledge can produce a good design even after brief design exploration.
A large-scale, real-world application of Evolutionary Multi-Objective Optimization is reported. The Multidisciplinary Design Optimization among aerodynamics, structures, and aeroelasticity of the wing of a transonic regional jet aircraft was performed using highfidelity evaluation models. Euler and Navier-Stokes solvers were employed for aerodynamic evaluation. The commercial software NASTRAN was coupled with a Computational Fluid Dynamics solver for the structural and aeroelastic evaluations. Adaptive Range MultiObjective Genetic Algorithm was employed as an optimizer. The objective functions were minimizations of block fuel and maximum takeoff weight in addition to drag divergence between transonic and subsonic flight conditions. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. Moreover, all solutions evaluated during the evolution were analyzed using a Self-Organizing Map as a Data Mining technique to extract key features of the design space. One of the key features found by Data Mining was the non-gull wing geometry, although the present MDO results showed the reverse-gull wings as non-dominated solutions. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance and to achieve 3.6 percent improvement in the block fuel compared to the original geometry designed in the conventional manner.
A large-scale, real-world application of evolutionary multi-objective optimization is reported. The multidisciplinary design optimization among aerodynamics, structures, and aeroelasticity of the wing of a transonic regional-jet aircraft was performed using high-fidelity evaluation models. Euler and Navier-Stokes solvers were employed for aerodynamic evaluation. The commercial software NASTRAN was coupled with a computational fluid dynamics solver for the structural and aeroelastic evaluations. An adaptive range multi-objective genetic algorithm was employed as an optimizer. The objective functions were minimizations of block fuel and maximum takeoff weight in addition to drag divergence between transonic and subsonic flight conditions. As a result, nine nondominated solutions were generated and used for tradeoff analysis among three objectives. Moreover, all solutions evaluated during the evolution were analyzed using a self-organizing map as a data mining technique to extract key features of the design space. One of the key features found by data mining was the nongull wing geometry, although the present multidisciplinary design optimization results showed the inverted gull wings as nondominated solutions. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance and to achieve 3.6% improvement in the block fuel compared to the original geometry designed in the conventional manner.
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