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RISS 인기검색어
- 검색키워드
- 저자 : Huachao Dong (검색결과 6 건)
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Huachao Dong,Baowei Song,Peng Wang,Shuai Huang 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.5
In this paper, a novel kriging-based algorithm for global optimization of computationally expensive black-box functions is presented. This algorithm utilizes a multi-start approach to find all of the local optimal values of the surrogate model and performs searches withinthe neighboring area around these local optimal positions. Compared with traditional surrogate-based global optimization method, thisalgorithm provides another kind of balance between exploitation and exploration on kriging-based model. In addition, a new search strategyis proposed and coupled into this optimization process. The local search strategy employs a kind of improved “Minimizing the predictor”method, which dynamically adjusts search direction and radius until finds the optimal value. Furthermore, the global search strategyutilizes the advantage of kriging-based model in predicting unexplored regions to guarantee the reliability of the algorithm. Finally,experiments on 13 test functions with six algorithms are set up and the results show that the proposed algorithm is very promising.
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Kriging-based multi-fidelity optimization via information fusion with uncertainty
Chengshan Li,Peng Wang,Huachao Dong 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.1
In this paper, a Multi-fidelity optimization method via information fusion with uncertainty (MFOIFU) is proposed. MFOIFU combines prediction uncertainty of kriging and model uncertainty, aiming at reducing computational cost of optimization and guaranteeing reliability of the optima. Firstly, the uncertainty of Low-fidelity (LF) and High-fidelity (HF) models is confirmed, respectively. After that, the optimal estimation theory of Kalman filter is employed to fuse information from LF and HF models. Then, the fused model is optimized and a distinctive updating strategy is presented to supplement feasible solutions. The newly introduced MFOIFU is verified through eight benchmark examples. Results showed that MFOIFU has some advantages over the Single high-fidelity optimization (SHO) method and some of the well-established multi-fidelity methods on computational expense and optimization efficiency. Finally, the MFOIFU method is successfully applied to the shell structure design of an Autonomous underwater vehicle (AUV).
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Multi-objective optimization design for the multi-bubble pressure cabin in BWB underwater glider
Yanru He,Baowei Song,Huachao Dong 대한조선학회 2018 International Journal of Naval Architecture and Oc Vol.10 No.4
In this paper, multi-objective optimization of a multi-bubble pressure cabin in the underwater glider with Blended-Wing-Body (BWB) is carried out using Kriging and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Two objective functions are considered: buoyancyweight ratio and internal volume. Multi-bubble pressure cabin has a strong compressive capacity, and makes full use of the fuselage space. Parametric modeling of the multi-bubble pressure cabin structure is automatic generated using UG secondary development. Finite Element Analysis (FEA) is employed to study the structural performance using the commercial software ANSYS. The weight of the primary structure is determined from the volume of the Finite Element Structure (FES). The stress limit is taken into account as the constraint condition. Finally, Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) method is used to find some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. The best solution is compared with the initial design results to prove the efficiency and applicability of this optimization method.
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Multi-objective optimization design for the multi-bubble pressure cabin in BWB underwater glider
He, Yanru,Song, Baowei,Dong, Huachao The Society of Naval Architects of Korea 2018 International Journal of Naval Architecture and Oc Vol.10 No.4
In this paper, multi-objective optimization of a multi-bubble pressure cabin in the underwater glider with Blended-Wing-Body (BWB) is carried out using Kriging and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Two objective functions are considered: buoyancy-weight ratio and internal volume. Multi-bubble pressure cabin has a strong compressive capacity, and makes full use of the fuselage space. Parametric modeling of the multi-bubble pressure cabin structure is automatic generated using UG secondary development. Finite Element Analysis (FEA) is employed to study the structural performance using the commercial software ANSYS. The weight of the primary structure is determined from the volume of the Finite Element Structure (FES). The stress limit is taken into account as the constraint condition. Finally, Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) method is used to find some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. The best solution is compared with the initial design results to prove the efficiency and applicability of this optimization method.
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Li, Chengshan,Wang, Peng,Li, Tianbo,Dong, Huachao The Society of Naval Architects of Korea 2020 International Journal of Naval Architecture and Oc Vol.12 No.-
Shape design optimization for Blended-wing-body Underwater Gliders (BWBUGs) is usually computationally expensive. In our previous work, a simplified shape optimization (SSO) strategy is proposed to alleviate the computational burden, which optimizes some of the Sectional Airfoils (SAs) instead of optimizing the 3-D shape of the BWBUG directly. Test results show that SSO can obtain a good result at a much smaller computational cost when three SAs are adopted. In this paper, the performance of SSO is investigated with a different number of SAs selected from the BWBUG, and the results are compared with that of the Direct Shape Optimization (DSO) strategy. Results indicate that SSO tends to perform better with more SAs or even outperforms the DSO strategy in some cases, and the amount of saved computational cost also increases when more SAs are adopted, which provides some reference significance and enlarges the applicability range of SSO.
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Li, Chengshan,Wang, Peng,Li, Tianbo,Dong, Huachao The Society of Naval Architects of Korea 2020 International Journal of Naval Architecture and Oc Vol.12 No.1
Shape design optimization for Blended-wing-body Underwater Gliders (BWBUGs) is usually computationally expensive. In our previous work, a simplified shape optimization (SSO) strategy is proposed to alleviate the computational burden, which optimizes some of the Sectional Airfoils (SAs) instead of optimizing the 3-D shape of the BWBUG directly. Test results show that SSO can obtain a good result at a much smaller computational cost when three SAs are adopted. In this paper, the performance of SSO is investigated with a different number of SAs selected from the BWBUG, and the results are compared with that of the Direct Shape Optimization (DSO) strategy. Results indicate that SSO tends to perform better with more SAs or even outperforms the DSO strategy in some cases, and the amount of saved computational cost also increases when more SAs are adopted, which provides some reference significance and enlarges the applicability range of SSO.
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