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成長ひずみ法による平等強さ形状の解析(定常振動問題への適用)畔上, 秀幸, Azegami, Hideyuki, 荻原, 忠, Ogihara, Tadashi, 高見, 昭康, Takami, Akiyasu 03 1900 (has links)
No description available.
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座屈に対する形状最適化畔上, 秀幸, Azegami, Hideyuki, 須貝, 康弘, Sugai, Yasuhiro, 下田, 昌利, Shimoda, Masatoshi 07 1900 (has links)
No description available.
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汎用FEMコードを利用した領域最適化問題の数値解析法(力法によるアプローチ)下田, 昌利, Shimoda, Masatoshi, 呉, 志強, Wu, Zhi Chang, 畔上, 秀幸, Azegami, Hideyuki, 桜井, 俊明, Sakurai, Toshiaki 10 1900 (has links)
No description available.
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粘性流れ場領域最適化問題の解法(力法によるアプローチ)片峯, 英次, Katamine, Eiji, 畔上, 秀幸, Azegami, Hideyuki 11 1900 (has links)
No description available.
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粘性流れ場の領域最適化解析(対流項を考慮した場合)片峯, 英次, Katamine, Eiji, 畔上, 秀幸, Azegami, Hideyuki 05 1900 (has links)
No description available.
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逆変分原理に基礎をおく成長ひずみ法 (最大剛性形状解析へのアプローチ)畔上, 秀幸, Azegami, Hideyuki, 高見, 昭康, Takami, Akiyasu 10 1900 (has links)
No description available.
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A Proposal of a Shape-Optimization Method Using a Constitutive Equation of Growth (In the Case of a Static Elastic Body)Azegami, Hideyuki 01 1900 (has links)
No description available.
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動吸振器を用いた非線形回転軸系の制振石田, 幸男, ISHIDA, Yukio, 井上, 剛志, INOUE, Tsuyoshi 07 1900 (has links)
No description available.
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Development of optimization methods to solve computationally expensive problemsIsaacs, Amitay, Engineering & Information Technology, Australian Defence Force Academy, UNSW January 2009 (has links)
Evolutionary algorithms (EAs) are population based heuristic optimization methods used to solve single and multi-objective optimization problems. They can simultaneously search multiple regions to find global optimum solutions. As EAs do not require gradient information for the search, they can be applied to optimization problems involving functions of real, integer, or discrete variables. One of the drawbacks of EAs is that they require evaluations of numerous candidate solutions for convergence. Most real life engineering design optimization problems involve highly nonlinear objective and constraint functions arising out of computationally expensive simulations. For such problems, the computation cost of optimization using EAs can become quite prohibitive. This has stimulated the research into improving the efficiency of EAs reported herein. In this thesis, two major improvements are suggested for EAs. The first improvement is the use of spatial surrogate models to replace the expensive simulations for the evaluation of candidate solutions, and other is a novel constraint handling technique. These modifications to EAs are tested on a number of numerical benchmarks and engineering examples using a fixed number of evaluations and the results are compared with basic EA. addition, the spatial surrogates are used in the truss design application. A generic framework for using spatial surrogate modeling, is proposed. Multiple types of surrogate models are used for better approximation performance and a prediction accuracy based validation is used to ensure that the approximations do not misguide the evolutionary search. Two EAs are proposed using spatial surrogate models for evaluation and evolution. For numerical benchmarks, the spatial surrogate assisted EAs obtain significantly better (even orders of magnitude better) results than EA and on an average 5-20% improvements in the objective value are observed for engineering examples. Most EAs use constraint handling schemes that prefer feasible solutions over infeasible solutions. In the proposed infeasibility driven evolutionary algorithm (IDEA), a few infeasible solutions are maintained in the population to augment the evolutionary search through the infeasible regions along with the feasible regions to accelerate convergence. The studies on single and multi-objective test problems demonstrate the faster convergence of IDEA over EA. In addition, the infeasible solutions in the population can be used for trade-off studies. Finally, discrete structures optimization (DSO) algorithm is proposed for sizing and topology optimization of trusses. In DSO, topology optimization and sizing optimization are separated to speed up the search for the optimum design. The optimum topology is identified using strain energy based material removal procedure. The topology optimization process correctly identifies the optimum topology for 2-D and 3-D trusses using less than 200 function evaluations. The sizing optimization is performed later to find the optimum cross-sectional areas of structural elements. In surrogate assisted DSO (SDSO), spatial surrogates are used to accelerate the sizing optimization. The truss designs obtained using SDSO are very close (within 7% of the weight) to the best reported in the literature using only a fraction of the function evaluations (less than 7%).
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Two essays on costly contemplation and efficient resource allocation subject to priorities /Ergin, Haluk Ihsan. January 2003 (has links) (PDF)
NJ, Univ., Dep. of Econmics, Diss.--Princeton, 2003. / Kopie, ersch. im Verl. UMI, Ann Arbor, Mich. - Enth. 2 Beitr.
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