Optimization of a structure with contact conditions using equivalent loads

Sang-Il Yi,이현아,박경진 대한기계학회 2011 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.25 No.3

Engineering structures consist of various components, and the components interact with each other through contact. Engineers tend to consider the interaction in analysis and design. Interactions of the components have nonlinearity because of the friction force and boundary conditions. Nonlinear analysis has been developed to accommodate the contact condition. However, structural optimization using nonlinear analysis is fairly expensive, and sensitivity information is difficult to calculate. Therefore, an efficient optimization method using nonlinear analysis is needed to consider the contact condition in design. Nonlinear Response Optimization using Equivalent Loads (NROEL) has been proposed for nonlinear response structural optimization. The method was originally developed for optimization problems considering geometric/material nonlinearities. The method is modified to consider the contact nonlinearity in this research. Equivalent loads are defined as the loads for linear analysis, which generate the same response field as that of nonlinear analysis. A nonlinear response optimization problem is converted to linear response optimization with equivalent loads. The modified NROEL is verified through three examples with contact conditions. Three structural examples using the finite element method are demonstrated. They are shape optimization with stress constraints, size optimization with stress/displacement constraints and topology optimization. Reasonable results are obtained in the optimization process.

A Review of Parameters for Improving the Performance of Particle Swarm Optimization

보안공학연구지원센터(IJHIT) 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.4

Particle swarm optimization (PSO) is an artificial intelligence (AI) technique that can be used to find approximate solutions to extremely difficult or impossible numeric maximization and minimization problems. Particle swarm optimization is an optimization method. It is an optimization algorithm, which is based on swarm intelligence. Optimization problems are widely used in different fields of science and technology. Sometimes such problems can be complex due to its practical nature. Particle swarm optimization (PSO) is a stochastic algorithm used for optimization. It is a very good technique for the optimization problems. But still there is a drawback that it gets stuck in local minima. To improve the performance of PSO, the researchers have proposed some variants of PSO. Some researchers try to improve it by improving the initialization of swarm. Some of them introduced new parameters like constriction coefficient and inertia weight. Some define different methods of the inertia weight to improve performance of PSO and some of them work on the global and local best. This paper transplants some of the parameters used to enhance the performance of Particle Swarm Optimization technique.

Topology Optimization of the Decking Unit in the Aluminum Bass Boat and Strength Verification using the FEM-program

Kwang-Cheol Seo,Jin Gwak,Joo-Shin Park 해양환경안전학회 2018 해양환경안전학회지 Vol.24 No.3

The objective of this paper is to optimize the cross-section of aluminum decking units used in the bass boats under operating conditions, and to verify the optimized model from the results via by ANSYS software. Aluminum decking unit is needed to endure specific loading while leisure activity and sailing. For a stiffer and more cost-neutral aluminum decking unit, optimization is often considered in the naval and marine industries. This optimization of the aluminum decking unit is performed using the ANSYS program, which is based on the topology optimization method. The generation of finite element models and stress evaluations are conducted using the ANSYS Multiphysics module, which is based on the Finite Element Method (FEM). Through such a series of studies, it was possible to determine the most suitable case for satisfying the structural strength found among the phase-optimized aluminum deck units in bass boats. From these optimization results, CASE 1 shows the best solution in comparison with the other cases for this optimization. By linking the topology optimization with the structural strength analysis, the optimal solution can be found in a relatively short amount of time, and these procedures are expected to be applicable to many fields of engineering.

Evaluation of Topology Optimization Objectives in IP Networks

Y. Sinan Hanay,Shinichi Arakawa,Masayuki Murata 한국통신학회 2019 Journal of communications and networks Vol.21 No.4

In the past, various optimization objective functions havebeen proposed to help in network optimization, especially for usein traffic engineering (TE) and topology optimization. This varietyof optimization objectives resulted in the emergence of algorithmstargeting different objectives. However, the role of the objectivefunction has been largely overlooked. Because, the choiceof a particular objective function was not justified in most of thecases. Some researchers criticized this arbitrary selection of objectivefunctions. Even though some researchers intuitively suggestusing a specific objective, only few work tackled with the problemof evaluating the objectives. In this paper, we evaluate various networkoptimization objectives on topology optimization. Previously,a study analyzed the efficiency of some routing optimization objectivesusing linear programming (LP) by linear relaxation. However,some of the objective functions are nonlinear, and such a linear relaxationdoes not treat each objective equally.The difficulty arisesdue to the fact that optimization algorithms are objective functiontailored heuristics. To achieve fairness, we compare and analyzedifferent traffic optimization objectives for topology optimizationusing neural networks which are used to model nonlinear relations. By using neural networks, we strive to avoid any unfairness, suchas obviating linear approximation. Also, our work suggests whichfeatures are meaningful for machine learning in network optimization. Our method partially agrees with the previous work, and weconclude that delay is the best performing optimization objective.

Multi-Objective Particle Swarm Optimization: An Introduction

Vipin Kumar,Sonajharia Minz 한국산학기술학회 2014 SmartCR Vol.4 No.5

In the real world, reconciling a choice between multiple conflicting objectives is a common problem. Solutions to a multi-objective problem are those that have the best possible negotiation given the objectives. An evolutionary algorithm called Particle swarm optimization is used to find a solution from the solution space. It is a population-based optimization technique that is effective, efficient, and easy to implement. Changes in the particle swarm optimization technique are required in order to get solutions to a multi-objective optimization problem. Therefore, this paper provides the proper concept of particle swarm optimization and the multi-objective optimization problem in order to build a basic background with which to conduct multi-objective particle swarm optimization. Then, we discuss multi-objective particle swarm optimization concepts. Multi-objective particle swarm optimization techniques and some of the most important future research directions are also included.

A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems

Hasan Koyuncu,Rahime Ceylan 한국CDE학회 2019 Journal of computational design and engineering Vol.6 No.2

In the literature, most studies focus on designing new methods inspired by biological processes, however hybridization of methods and hybridization way should be examined carefully to generate more suitable optimization methods. In this study, we handle Particle Swarm Optimization (PSO) and an efficient oper-ator of Artificial Bee Colony Optimization (ABC) to design an efficient technique for continuous function optimization. In PSO, velocity and position concepts guide particles to achieve convergence. At this point, variable and stable parameters are ineffective for regenerating awkward particles that cannot improve their personal best position (Pbest). Thus, the need for external intervention is inevitable once a useful par-ticle becomes an awkward one. In ABC, the scout bee phase acts as external intervention by sustaining the resurgence of incapable individuals. With the addition of a scout bee phase to standard PSO, Scout Particle Swarm Optimization (ScPSO) is formed which eliminates the most important handicap of PSO. Consequently, a robust optimization algorithm is obtained. ScPSO is tested on constrained optimization problems and optimum parameter values are obtained for the general use of ScPSO. To evaluate the performance, ScPSO is compared with Genetic Algorithm (GA), with variants of the PSO and ABC methods, and with hybrid approaches based on PSO and ABC algorithms on numerical function optimization. As seen in the results, ScPSO results in better optimal solutions than other approaches. In addition, its convergence is superior to a basic optimization method, to the variants of PSO and ABC algorithms, and to the hybrid approaches on different numerical benchmark functions. According to the results, the Total Statistical Success (TSS) value of ScPSO ranks first (5) in comparison with PSO variants; the second best TSS (2) belongs to CLPSO and SP-PSO techniques. In a comparison with ABC variants, the best TSS value (6) is obtained by ScPSO, while TSS of BitABC is 2. In comparison with hybrid techniques, ScPSO obtains the best Total Average Rank (TAR) as 1.375, and TSS of ScPSO ranks first (6) again. The fitness values obtained by ScPSO are generally more satisfactory than the values obtained by other methods. Consequently, ScPSO achieve promising gains over other optimization methods; in parallel with this result, its usage can be extended to different working disciplines.

Generalized equivalent uniform dose-based biological optimization in hippocampus-sparing whole-brain radiation therapy

Won Young Jin,Lee Eungman,Cho Sam Ju,Kim Kyung Su 한국물리학회 2021 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.79 No.12

We applied and evaluated a generalized equivalent uniform dose (gEUD)-based optimization method to hippocampus-sparing whole-brain radiation therapy (HS-WBRT) using volumetric modulated arc therapy (VMAT). Thirteen patients treated with WBRT were enrolled. To investigate the optimal value of the parameter “a” of the hippocampus for optimizing gEUD, we changed value from 1 to 40. Using the determined parameter “a” value for the hippocampus, dose-volume (DV)-based optimization and DV-based gEUD optimization (DV-gEUD) were used to generate HS-WBRT plans for 13 patients. According to the gEUD optimization, the plans for organ at risk (OAR) and the target plus OAR were denoted as DV-Upper gEUD optimization and DV-Upper+Target gEUD optimization. We analyzed and compared the dose-volume histogram (DVH) of each generated plan. The mean dosage ( Dmean) of the hippocampus was lowest when the parameter “a” of the hippocampus was 20, with acceptable compromise of the D100% and the Dmax of the hippocampus. The hippocampus D100%, Dmean, and Dmax were reduced to 1.78% (p = 0.014), 2.40% (p = 0.236), and 2.81% (p = 0.207), respectively, when the DV-Upper gEUD optimization was used compared to DV-base optimization and reduced to 4.76% (p = 0.001), 5.84% (p = 0.001), and 6.91% (p = 0.000), respectively, when using the DV-Upper+Target gEUD optimization. Comparing the DV-Upper gEUD and the Upper+Target gEUD optimization, these values were further reduced to 2.99% (p = 0.022), 3.50% (p = 0.059), and 4.18% (p = 0.049), respectively, when the Upper+Target gEUD optimization was used. The DV-gEUD optimization plan showed a better dosimetric profile compared to the DV-based optimization. For HS-WBRT treatment plans using gEUD-based optimization, we recommend that the parameter “a” of the hippocampus be set at 20 and that the gEUD of the OARs and the planning target volume (PTV) be optimized at the same time.

Mono and multi-objective optimization techniques applied to a large range of industrial test casesusing Metamodel assisted Evolutionary Algorithms

Lionel FOURMENT,Richard DUCLOUX,Stephane MARIE,Mohsen EJDAY,Dominique MONNEREAU,Thomas MASSE,Pierre MONTMITONNET 한국소성가공학회 2010 기타자료 Vol.2010 No.6

The use of material processing numerical simulation allows a strategy of trial and error to improve virtual processes without incurring material costs or interrupting production and therefore save a lot of money, but it requires user time to analyze the results, adjust the operating conditions and restart the simulation. Automatic optimization is the perfect complement to simulation. Evolutionary Algorithm coupled with metamodelling makes it possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. Ten industrial partners have been selected to cover the different area of the mechanical forging industry and provide different examples of the forming simulation tools. It aims to demonstrate that it is possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. The large computational time is handled by a metamodel approach. It allows interpolating the objective function on the entire parameter space by only knowing the exact function values at a reduced number of “master points”. Two algorithms are used: an evolution strategy combined with a Kriging metamodel and a genetic algorithm combined with a Meshless Finite Difference Method. The later approach is extended to multi-objective optimization. The set of solutions, which corresponds to the best possible compromises between the different objectives, is then computed in the same way. The population based approach allows using the parallel capabilities of the utilized computer with a high efficiency. An optimization module, fully embedded within the Forge2009 IHM, makes possible to cover all the defined examples, and the use of new multi-core hardware to compute several simulations at the same time reduces the needed time dramatically. The presented examples demonstrate the method versatility. They include billet shape optimization of a common rail, the cogging of a bar and a wire drawing problem.

Topology optimization of compressor bracket

장정우,이영신 대한기계학회 2008 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.22 No.9

Topology optimization is very useful engineering technique especially at the concept design stage. It is common habit to design depending on the designer's experience at the early stage of product development. Structural analysis methodology of compressor bracket was verified on the static and dynamic loading condition with 2 bracket samples for the topology optimization base model. Topology optimization is able to produce reliable and satisfactory results with the verified structural model. Base bracket model for the topology optimization was modeled considering the interference with the adjacent vehicle parts. Objective function was to minimize combined compliance and the constraint was the first natural frequency over 250 Hz. Multiple load cases such as normal mode calculation and gravity load conditions with 3-axis direction were also applied for the optimization, expecting an even stress distribution and vibration durability performance. Commercial structural optimization code such as optistruct of Altair Engineering was used for the structural topology optimization. Optimization was converged after 14 iterations with the satisfaction of natural frequency constraint. New bracket shape was produced with the CATIA based on the topology optimization result. The new bracket from topology optimization result was compared with the traditional concept model and topology optimization base model under 4 load cases. 14 % 1’st natural frequency of new bracket with only 4 % mass increment increased compared to the concept model. 31 % mass decreased compared to the base model without the increment of stress under gravity load cases. It was analyzed thata new bracket would not fail during a vibration durability test, and these results were verified with a fabricated real sample under the durability condition.

Multi-objective optimization using evolutionary algorithm for axial flow pump impeller

H.S. Park,Fu-qing Miao (사)한국CDE학회 2013 한국CAD/CAM학회 국제학술발표 논문집 Vol.2010 No.8

Evolutionary algorithms (EA) are a unique and attractive approach to the real world multiobjective optimization design such as a axial flow pump impeller optimization design problem. In this paper, multi-objective evolutionary algorithm to the axial flow pump impeller optimization design is presented. In axial flow pump design process, in order to get high performance pump, designers usually try to increase the efficiency (η) and decrease the required NPSH (NPSHr) simultaneously. In this paper, multi-objective optimization of axial flow pump based on modified evolutionary algorithm Particle Swarm Optimization (MPSO) is performed. At first, the NPSHr and η in a set of axial flow pump are numerically investigated using commercial software ANSYS with the design variables concerning hub angle βh, chord angle βc, cascade solidity of chord σc, maximum thickness of blade H. And then, using the Group Method of Data Handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to design variables are obtained. Finally, multi objective optimization based on modified Particle Swarm Optimization (MPSO) approach is used for Pareto based optimization. The result shows that an optimal solution of the axial flow pump impeller was obtained: NPSHr was decreased by 11.68% and efficiency was increased by 4.24% simultaneously. It means this optimization is feasible.