• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.7
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• pp.535-540
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• 2019

The coupling between different models for MDO (Multi-disciplinary Optimization) greatly increases the complexity of the computational framework, while at the same time increasing CPU time and memory usage. To overcome these difficulties, POD (Proper Orthogonal Decomposition) and RBF (Radial Basis Function) are used to solve the optimization problem of determining the thickness of composites and sandwich cores when composite sandwich structures are used as aircraft wing skin materials. POD and RBF are used to construct surrogate models for the wing shape and the load data. Optimization is performed using the objective function and constraint function values which are obtained from the surrogate models.

• Korean Journal of Computational Design and Engineering
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• v.16 no.3
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• pp.236-245
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• 2011

Many researchers have recently studied multi-level formulation strategies to solve the MDO problems and they basically distributed the coupling compatibilities across all disciplines, while single-level formulations concentrate all the controls at the system-level. In addition, approximation techniques became remedies for computationally expensive analyses and simulations. This paper studies comparisons of the MDO methods with respect to computing performance considering both conventional sequential and modem distributed/parallel processing environments. The comparisons show Individual Disciplinary Feasible (IDF) formulation is the most efficient for sequential processing and IDF with approximation (IDFa) is the most efficient for parallel processing. Results incorporating to popular design examples show this finding. The author suggests design engineers should firstly choose IDF formulation to solve MDO problems because of its simplicity of implementation and not-bad performance. A single drawback of IDF is requiring more memory for local design variables and coupling variables. Adding cheap memories can save engineers valuable time and effort for complicated multi-level formulations and let them free out of no solution headache of Multi-Disciplinary Analysis (MDA) of the Multi-Disciplinary Feasible (MDF) formulation.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.1
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• pp.30-35
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• 2017

The major parts of a vacuum cleaner are molded. The vacuum cleaner works in multi-load conditions. Therefore, the designer needs to optimize the structure and injection molding conditions simultaneously. Here, the main factor of design is the rib shape and thickness. The greater the rib thickness, the greater the stiffness of the structure. However, it causes an increase in weight. On the other hand, the lower the rib thickness, the greater the increase in the injection pressure. However, the weight will be reduced. Therefore, the designer needs to optimize the rib shape and thickness for structure stiffness and injection molding. In order to solve this problem, we propose an optimization method using D.O.E and a response surface model, which is a multi-objective optimization method using the multi-objective genetic algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.358-359
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• 2013

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.2
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• pp.179-188
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• 2000

Multi-disciplinary optimization design concept can provide a solution to many engineering problems. In the field of structural analysis, much development of size or topology optimization has been achieved in the application of research. This paper demonstrates an optimum design of a multi-layer cylindrical tube which behaves thermoelastically. A multi-layer cylindrical tube that has several different material properties at each layer is optimized within allowable stress and temperature range when mechanical and thermal loads are applied simultaneously. When thermal loads are applied to a multi-layer tube, stress phenomena become complicated due to each layer's thermal expansion and the layer thicknesses. Factors like temperature; stress; and material thermal thicknesses of each tube layer are very difficult undertaking. To analyze these problems using an efficient and precise method, the optimization theories are adopted to perform thermoelastic finite element analysis.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.1
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• pp.1-7
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• 2006

This paper presents an ASF (Aluminum Space Frame) BIW optimal design, which minimizes the weight and satisfies multi-disciplinary constraints such as the static stiffness, vibration characteristics, low-speed crash, high-speed crash and occupant protection. As only one cycle CPU time for all the analyses is 12 hours, the ASF design having 11-design variable is a large scaled problem. In this study, ISCD-II and conservative least square fitting method is used for efficient RSM modeling. Then, ALM method is used to solve the approximate optimization problem. The approximate optimum is sequentially added to remodel the RSM. The proposed optimization method used only 20 analyses to solve the 11-design variable design problem. Also, the optimal design can reduce the] $15\%$ of total weight while satisfying all of the multi-disciplinary design constraints.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.339-344
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• 2001

In recent engineering, the designer has become more and more dependent on the computer simulations such as FEM (Finite Element Method) and BEM (Boundary Element Method). In order to optimize such implicit models more efficiently and reliably, the meta-modeling technique has been developed for solving such a complex problems combined with the DACE (Design and Analysis of Computer Experiments). It is widely used for exploring the engineer's design space and for building meta-models in order to facilitate an effective solution of multi-objective and multi-disciplinary optimization problems. Optimization of a train suspension is performed according to the minimization of forty-six responses that represent ten ride comforts, twelve derailment quotients, twelve unloading ratios, and twelve stabilities by using the Kriging meta-model of a train suspension. After each Kriging meta-model is constructed, multi-objective optimal solutions are achieved by using a nonlinear programming method called SQP (Sequential Quadratic Programming).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.381-384
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• 2000

Multi-disciplinary optimization design concept can provide a solution to many engineering problems. In the field of structural analysis, much development of size or topology optimization has been achieved in the application of research. This paper demonstrates an optimum design of a multi-layer cylindrical tube which behaves thermoelastically. A multi-layer cylindrical tube that has several different material properties at each layer is optimized within allowable stress and temperature range when mechanical and thermal loads are applied simultaneously. To analyze these problems using an efficient and precise method, the optimization theories are adopted to perform thermoelastic finite element analysis.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.3
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• pp.229-237
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• 2008

In this research, a MDO(multi-disciplinary design optimization) framework, which integrates aerodynamic and structural analysis to design an aircraft wing, is constructed. Whole optimization process is automated by a parametric-modeling approach. A CFD mesh is generated automatically from parametric modeling of CATIA and Gridgen followed by automatic flow analysis using Fluent. Finite element mesh is generated automatically by parametric method of MSC.Patran PCL. Aerodynamic load is transferred to Finite element model by the volume spline method. RSM(Response Surface Method) is applied for optimization, which helps to achieve global optimum. As the design problem to test the current MDO framework, a wing weight minimization with constraints of lift-drag ratio and deflection of the wing is selected. Aspect ratio, taper ratio and sweepback angle are defined as design variables. The optimization result demonstrates the successful construction of the MDO framework.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.6
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• pp.864-870
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• 2003

In recent engineering, the designer has become more and more dependent on the computer simulations such as FEM(Finite Element Method) and BEM(Boundary Element Method). In order to optimize such implicit models more efficiently and reliably, the meta -modeling technique has been developed for solving such a complex problems combined with the DACE(Design and Analysis of Computer Experiments). It is widely used for exploring the engineer's design space and for building approximation models in order to facilitate an effective solution of multi-objective and multi-disciplinary optimization problems. Optimization of a train suspension is performed according to the minimization of forty -six responses that represent ten ride comforts, twelve derailment quotients, twelve unloading ratios, and twelve stabilities by using the Kriging model of a train suspension. After each Kriging model is constructed, multi -objective optimal solutions are achieved by using a nonlinear programming method called SQP(Sequential Quadratic Programming).