• Proceedings of the KSME Conference
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• 2007.05a
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• pp.360-365
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• 2007

This work is concerned with the topology optimization of three-dimensional cooling fins or heat sinks. Motivated by earlier success of the Internal Element Connectivity Method (I-ECP) method in two-dimensional problems, the extension of I-ECP to three-dimensional problems is carried out. The main efforts were made to maintain the numerical trouble-free characteristics of I-ECP for full three-dimensional problems; a serious numerical problem appearing in thermal topology optimization is erroneous temperature undershooting. The effectiveness of the present implementation was checked through the design optimization of three-dimensional fins.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.115-125
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• 2021

Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the "curse of dimensionality" when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.

• Architectural research
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• v.11 no.1
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• pp.15-23
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• 2009

An application of the constrained adaptive topology optimization (CATO) algorithm is described for three-dimensional topology optimization of engineering structures. The enhanced assumed strain lower order solid finite element (FE) is used to evaluate the values of objective and constraint functions required in optimization process. The strain energy (SE) terms such as elastic and modal SEs are employed as the objective function to be minimized and the initial volume of structures is introduced as the constraint function. The SIMP model is adopted to facilitate the material redistribution and also to produce clearer and more distinct structural topologies. The linearly weighted objective function is introduced to consider both static and dynamic characteristics of structures. Several numerical tests are tackled and it is used to investigate the performance of the proposed three-dimensional topology optimization process. From numerical results, it is found to be that the CATO algorithm is easy to implement and extremely applicable to produce the reasonable optimum topologies for three dimensional optimization problems.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.313-321
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• 2004

In this paper we study the problem of three-dimensional layout optimization on the simplified rotating vessel of satellite. The layout optimization model with behavioral constraints is established and some effective and convenient conditions of performance optimization are presented. Moreover, we prove that the performance objective function is locally Lipschitz continuous and the results on the relations between the local optimal solution and the global optimal solution are derived.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.283-290
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• 2008

This paper is motivated by the problem of fitting a group of cuboids into a simplified rotating vessel of the artificial satellite. Here we introduce a combinatorial optimization model which reduces the three-dimensional layout problem with behavioral constraints to a finite enumeration scheme. Moreover, a global combinatorial optimization algorithm is described in detail, which is an improved graph-theoretic heuristic.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.67-70
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• 1991

When analyzing field or optimizing the shape of electrode in three dimensional space by using the surface charge method, we need to divide finely the surface of electrode into surface element like triangle or rectangle. In this case, there exist any variables in field analysis or field optimization. In particular, smoothness on the surface of optimized shape is not good. Recently, A paper is published where introducing NURB curve to field analysis and field optimization about two dimensional space model and axial symmetric three dimensional space model results in reduced variables, enhenced accuracy and improved smoothness. NURB curve has some useful properties like continuity, controllability and locality. Therefore in this paper, in order to improve the demerits of the established optimization method for three dimensional space models, the NURB surface that has same properties in common with NURB curve is used to analyze and optimize simple model.

• Journal of Ocean Engineering and Technology
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• v.16 no.3
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• pp.28-33
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• 2002

Optimization of marine propellers of constant pitch is studied, with the help of the infinite dimensional optimization (Jang and Kinoshita, 2000a), which is based on the Hilbert space theory. As a numerical example, the MAU type propeller is considered and used as he initial guess for the optimization method. The numerical computations for an optimal marine propeller are performed for the constant pitch distribution. In addition, a new optimization is suggested with the constraint of constant pitch during optimization.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.130-135
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• 2008

An aerodynamic optimization design process of multistage axial turbine is presented in this article: first, applying quasi-three dimensional(Q3D) design methods to conduct preliminary design and then adopting modern optimization design methods to implement multistage local optimization. Quasi-three dimensional(Q3D) design methods, which mainly refer to S2 flow surface direct problem calculation, adopt the S2 flow surface direct problem calculation program of Harbin Institute of Technology. Multistage local optimization adopts the software of Numeca/Design3D, which jointly adopts genetic algorithm and artificial neural network. The major principle of the methodology is that the successive design evaluation is performed by using an artificial neural network instead of a flow solver and the genetic algorithms may be used in an efficient way. Flow computation applies three-dimensional viscosity Navier Stokes(N-S) equation solver. Such optimization process has three features: (i) local optimization based on aerodynamic performance of every cascade; (ii) several times of optimizations being performed to every cascade; and (iii) alternate use of coarse grid and fine grid. Such process was applied to optimize a three-stage axial turbine. During the optimization, blade shape and meridional channel were respectively optimized. Through optimization, the total efficiency increased 1.3% and total power increased 2.4% while total flow rate only slightly changed. Therefore, the total performance was improved and the design objective was achieved. The preliminary design makes use of quasi-three dimensional(Q3D) design methods to achieve most reasonable parameter distribution so as to preliminarily enhance total performance. Then total performance will be further improved by adopting multistage local optimization design. Thus the design objective will be successfully achieved without huge expenditure of manpower and calculation time. Therefore, such optimization design process may be efficiently applied to the aerodynamic design optimization of multistage axial turbine.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.275-280
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• 2004

Shape optimization was performed to obtain the precise shape of cutouts including the internal shape of cutouts in laminated composite plates by three dimensional modeling using solid element. The volume control of the growth-strain method was implemented and the distributed parameter chosen as Tsai-Hill fracture index for shape optimization. The volume control of the growth-strain method makes Tsai-Hill failure index at each element uniform in laminated composites under the initial volume. Then shapes optimized by Tsai-Hill failure index were compared with those of the initial shapes for the various load conditions and cutouts. The following conclusions were obtained in this study. (1) It was found that growth-strain method was applied efficiently to shape optimization of three dimensional cutouts in a laminate composite, (2) The optimal shapes of the various load conditions and cutouts were obtained, (3) The maximum Tsal-Hill failure index was reduced up to 67% when shape optimization was peformed under the initial volume by volume control of growth-strain method.

• Smart Structures and Systems
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• v.18 no.3
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• pp.425-448
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• 2016

In this paper, a new Pigeon Colony Algorithm (PCA) based on the features of a pigeon colony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution; the flying process is designed to search for the local and global optimum and improve the global worst solution; and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.