• Steel and Composite Structures
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• 제44권2호
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• pp.211-224
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• 2022

Multi-objective optimization was conducted to obtain the optimal configuration of a composite sandwich structure with honeycomb-foam hybrid core subjected to underwater shock waves, which can fulfill the demand for light weight and energy efficient design of structures against underwater blast. Effects of structural parameters on the dynamic response of the sandwich structures subjected to underwater shock waves were analyzed numerically, from which the correlations of different parameters to the dynamic response were determined. Multi-objective optimization of the structure subjected to underwater shock waves of which the initial pressure is 30 MPa was conducted based on surrogate modelling method and genetic algorithm. Moreover, optimization results of the sandwich structure subjected to underwater shock waves with different initial pressures were compared. The research can guide the optimal design of composite sandwich structures subjected to underwater shock waves.

• International Journal of Fluid Machinery and Systems
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• 제9권4호
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• pp.313-324
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• 2016

Surrogate model based shape optimization methodology to enhance performance of a centrifugal pump has been implemented in this work. Design variables, such as blade number and blade angles defining the pump impeller blade shape were selected and a three-level full factorial design approach was used for efficiency enhancement. A three-dimensional simulation using Reynolds-averaged Navier Stokes (RANS) equations for the performance analysis was carried out after designing the geometries of the impellers at the design points. Standard $k-{\varepsilon}$ turbulence model was used for steady incompressible flow simulations. The optimized impeller incurred lower losses by shifting the trailing edge towards the impeller pressure side. It is observed that the surrogates are problem dependent and most accurate surrogate does not deliver the best design always.

• 대한기계학회논문집A
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• 제41권9호
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• pp.817-823
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• 2017

기존에는 연비를 고려한 적응형 순항 제어 알고리즘 개발과 연비 등의 성능을 고려한 적응형 순항 제어 시스템 개발 연구가 수행되었지만, 제어 파라미터를 포함한 차량 파라미터의 적응형 순항 제어에 대한 최적설계 연구는 미흡한 편이다. 이에 본 논문에서는 연비, 추종성, 승차감, 안전거리를 고려한 차량 및 제어 파라미터 최적설계를 수행하고자 한다. 이를 위해 차량 거동의 성능 측정 방법을 제안하고 적응형 순항 제어 시스템을 구축하였다. 그리고 성능에 주요한 영향을 미치는 차량 파라미터를 선별하여 이를 토대로 순차적 실험계획을 통해 크리깅 대체모델을 구축하였고, 연비를 최대화하며 목표 성능을 만족하는 크리깅 대체모델 기반 최적설계를 수행하였다.

• Nuclear Engineering and Technology
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• 제49권2호
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• pp.434-441
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• 2017

Containment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, from a simulation-based perspective, using an integrated severe accident code, THALES2/KICHE. The effectiveness of the containment-venting strategies needs to be verified via numerical simulations based on various settings of the venting conditions. The number of iterations, however, needs to be controlled to avoid cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using a Gaussian process regression, a surrogate model of the "black-box" code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. Compared with the case of pure random searches, the number of code queries is largely reduced for the optimum finding. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents.

• 한국항공우주학회지
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• 제42권2호
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• pp.108-118
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• 2014

근사모델을 이용한 최적설계 문제에서는 설계변수의 수가 증가함에 따라 근사모델의 정확도를 확보하기 위한 계산 횟수가 급격히 증가한다. 이를 해결하기 위해 저정확도 모델을 바탕으로 고정확도 모델로 보정하는 Variable-Fidelity Modeling을 이용하였다. 본 논문에서 Variable-Fidelity Model로는 계층적 크리깅 모델을 이용하였으며, 다목적 유전자 알고리즘과 결합하여 최적화 프레임워크를 제안하였다. 이 방법의 유용성을 검증하기 위하여 천음속 영역에 대한 익형 최적 설계를 하였다. 설계변수로는 PARSEC의 파라메터를 이용하였으며, 서로 다른 격자수를 가지는 경우 그리고 서로 다른 정확도를 가지는 해석자를 이용한 경우에 관하여 해석을 수행하였다. 검증을 위해 단일 정확도 모델에 대한 최적화 결과와 비교하였다. 모든 경우에 관하여 파레토 라인이 유사하게 나오는 것을 확인 할 수 있었으며, 계산시간은 계층적 크리깅 모델을 이용한 Variable-Fidelity Model이 단일 정확도 모델에 비하여 훨씬 줄어들었다. 이를 바탕으로 본 논문의 방법이 단일 정확도를 가지는 모델에 대한 최적화 방법과 유사한 정확도를 가지며 더욱 효율적임을 확인 할 수 있다.

• International Journal of Aeronautical and Space Sciences
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• 제17권1호
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• pp.29-36
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• 2016

Tensairity girder is a light weight inflatable fabric structural concept which can be used in road emergency transportation. It uses low pressure air to stabilize compression elements against buckling. With the purpose of obtaining the comprehensive target of minimum deflection and weight under ultimate load, the cross-section and the inner pressure of tensairity girder was optimized in this paper. The Variable Complexity Modeling (VCM) method was used in this paper combining the Kriging approximate method with the Finite Element Analysis (FEA) method, which was implemented by ABAQUS. In the Kriging method, the sample points of the surrogate model were outlined by Design of Experiment (DOE) technique based on Optimal Latin Hypercube. The optimization framework was constructed in iSIGHT with a global optimization method, Multi-Island Genetic Algorithm (MIGA), followed by a local optimization method, Sequential Quadratic Program (SQP). The result of the optimization gives a prominent conceptual design of the tensairity girder, which approves the solution architecture of VCM is feasible and efficient. Furthermore, a useful trend of sensitivity between optimization variables and responses was performed to guide future design. It was proved that the inner pressure is the key parameter to balance the maximum Von Mises stress and deflection on tensairity girder, and the parameters of cross section impact the mass of tensairity girder obviously.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회B
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• pp.2544-2547
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• 2008

A micro channel heat sink has been studied and optimized for mixed pressure driven and electroosmotic flows through three-dimensional numerical analysis. The effects of ionic concentration represented by zeta potential and Debye thickness are studied with the various steps of externally applied electric potential. Optimization of the micro channel heat sink has been performed considering two design variables related to the micro channel width, depth and fin width. The surrogate-based optimization is performed using a search algorithm taking overall thermal resistance as objective function. The thermal resistance is found to be more sensitive to channel width-to-depth ratio than fin width-to-depth of channel ratio.

• 한국산학기술학회논문지
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• 제22권5호
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• pp.128-135
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• 2021

박판관은 충돌안전을 위한 에너지 흡수장치 중의 하나로써 가벼우면서도 에너지 흡수 효율이 좋아 널리 이용되어 왔다. 그러나 일반적인 박판관은 충돌 초기 피크하중(IPCF: Initial Peak Crushing Force)이 높게 유발될 뿐 아니라 반복적으로 나타나는 피크하중으로 인하여 충돌에너지 흡수장치로써의 안정성이 다소 떨어진다는 단점이 있다. 충돌 초기 피크하중을 감소시키고 충돌에너지 흡수장치의 안정성을 키우기 위하여 박판 파형관이 도입되었다. 파형관의 성능은 기하 형상에 큰 영향을 받기 때문에 최적설계 기법을 적용하여 파형관의 성능을 최적화할 수 있다. 본 논문에서는 충돌안전도 해석에 기반한 형상 최적설계를 수행하기 위하여 적응 근사모델(adaptive surrogate model)을 활용한 최적설계 기법을 활용하였다. 파형의 진폭 및 파장 뿐 아니라 형상의 곡률 변화를 설계 변수로써 고려하였다. 형상 설계 매개변수화를 수행하기 위하여 몰핑(morphing) 방법을 채택하였다. 수치 예제를 통해서 적응 근사모델에 기반한 최적설계 결과와 기존 근사모델에 기반한 최적설계 결과를 비교하고 적응 근사모델에 기반한 최적설계 방법이 좀 더 효율적인 결과를 도출함을 보였다.

• Management Science and Financial Engineering
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• 제18권2호
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• pp.13-17
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• 2012

Set covering has widely been accepted as a staple tool for feature selection in data mining. We present a generalized version of this classical combinatorial optimization model to make it better suited for the purpose and propose a surrogate relaxation-based procedure for its meta-heuristic solution. Mathematically and also numerically with experiments on 25 set covering instances, we demonstrate the utility of the proposed model and the proposed solution method.

• Journal of Electrical Engineering and Technology
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• 제9권5호
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• pp.1544-1550
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• 2014

In this paper, an adaptive optimization strategy utilizing Kriging model and genetic algorithm is proposed for the optimal design of electromagnetic devices. The ordinary Kriging assisted by the spherical covariance model is used to construct surrogate models. In order to improve the computational efficiency, the adaptive uniform sampling strategy is applied to generate sampling points in design space. Through several iterations and gradual refinement process, the global optimal point can be found by genetic algorithm. The proposed algorithm is validated by application to the optimal design of a switched reluctance motor, where the stator pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.