• Title/Summary/Keyword: Multi-Phase Optimization

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Multi-Phase Optimization of Quill Type Machine Structures(1) (Static Compliance Analysis & Multi-Objective Function Optimization) (퀼형 공작기계구조물의 다단계 최적화(1) (정강성 해석 및 다목적함수 최적화))

  • Lee, Yeong-U;Seong, Hwal-Gyeong
    • Journal of the Korean Society for Precision Engineering
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    • v.18 no.11
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    • pp.155-160
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    • 2001
  • To achieve high precision cutting as well as production capability in the machine tool, it is needed to develop excellent rigidity statically, dynamically and thermally as well. In order to predict the qualitative behavior of a machine tool, simultaneous analysis of mechanics and heat transfer is required. Generally, machine tool designers have solved designing problems based on partial estimation of the specified rigidity. This study clears the inter-relationship between therm, and propose multi-phase optimization of machine tool structure using a genetic algorithm. The multi-phase solution method is consists of a series of mechanical design problem. At this first phase of static design problem, multi-objective optimization for the purpose of minimization of the total weight and static compliance minimization is solved using the Pareto Genetic Algorithm.

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Topology optimization of variable thickness Reissner-Mindlin plate using multiple in-plane bi-directional functionally graded materials

  • Nam G. Luu;Thanh T. Banh;Dongkyu Lee
    • Steel and Composite Structures
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    • v.48 no.5
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    • pp.583-597
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    • 2023
  • This paper introduces a novel approach to multi-material topology optimization (MTO) targeting in-plane bi-directional functionally graded (IBFG) non-uniform thickness Reissner-Mindlin plates, employing an alternative active phase approach. The mathematical formulation integrates a first shear deformation theory (FSDT) to address compliance minimization as the objective function. Through an alternating active-phase algorithm in conjunction with the block Gauss-Seidel method, the study transforms a multi-phase topology optimization challenge with multi-volume fraction constraints into multiple binary phase sub-problems, each with a single volume fraction constraint. The investigation focuses on IBFG materials that incorporate adequate local bulk and shear moduli to enhance the precision of material interactions. Furthermore, the well-established mixed interpolation of tensorial components 4-node elements (MITC4) is harnessed to tackle shear-locking issues inherent in thin plate models. The study meticulously presents detailed mathematical formulations for IBFG plates in the MTO framework, underscored by numerous numerical examples demonstrating the method's efficiency and reliability.

Multi-Objective Optimal Design of a NEMA Design D Three-phase Induction Machine Utilizing Gaussian-MOPSO Algorithm

  • Zhang, Dianhai;Ren, Ziyan;Koh, Chang-Seop
    • Journal of Electrical Engineering and Technology
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    • v.9 no.1
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    • pp.184-189
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    • 2014
  • This paper presents a multi-objective optimization approach to design rotor slot geometry of three-phase squirrel cage induction machine to achieve NEMA design D torque-speed (T-S) characteristics with high efficiency. The multi-objective Particle Swarm Optimization (MOPSO) algorithm combined with the adaptive response surface method and Latin hypercube sampling strategy is applied to obtain the Pareto optimal designs. In order to demonstrate the validity of the suggested optimal algorithm, an application to rotor slot design of three-phase induction motor is presented.

Time-Varying Two-Phase Optimization and its Application to neural Network Learning (시변 2상 최적화 및 이의 신경회로망 학습에의 응용)

  • Myeong, Hyeon;Kim, Jong-Hwan
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.7
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    • pp.179-189
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    • 1994
  • A two-phase neural network finds exact feasible solutions for a constrained optimization programming problem. The time-varying programming neural network is a modified steepest-gradient algorithm which solves time-varying optimization problems. In this paper, we propose a time-varying two-phase optimization neural network which incorporates the merits of the two-phase neural network and the time-varying neural network. The proposed algorithm is applied to system identification and function approximation using a multi-layer perceptron. Particularly training of a multi-layer perceptrion is regarded as a time-varying optimization problem. Our algorithm can also be applied to the case where the weights are constrained. Simulation results prove the proposed algorithm is efficient for solving various optimization problems.

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The Correlation Parameters and the Optimization of a PN Sequence Phase for Variable Spreading Gain (VSG) Multi-Rate DS/CDMA System (멀티레이트 서비스를 지원하는 VSG-DS/C음 시스템에서의 PN 시퀀스 상관 파라미터 특성과 최적화)

  • 이연우;김응배;차균현
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.25 no.1A
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    • pp.10-17
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    • 2000
  • In this paper, we evaluate the correlation properties and the optimization of PN sequence phase for multi-media DS/CDMA system with variable spreading gain (VSG) scheme. In multi-media multi-rate DS/CDMA systems, the optimization of PN sequence phase is not a tractable problem, since the sequences should be optimized against both sequences of the same length and other sequences with different length. Hence, we verify the correlation properties of PN sequence phase in multi-rate system environment and furthermore, we propose the new phase criterion, MIN-AIP (minimum-average interference parameter), to minimize the bit error rate (BER). As the results of performance evaluations, it is shown that the performance of MIN-AIP criteria gives the best results.

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An Optimized PI Controller Design for Three Phase PFC Converters Based on Multi-Objective Chaotic Particle Swarm Optimization

  • Guo, Xin;Ren, Hai-Peng;Liu, Ding
    • Journal of Power Electronics
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    • v.16 no.2
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    • pp.610-620
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    • 2016
  • The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.

Improvement of the Phase Section Method for Multi-material Topology Optimization (다중 물질 위상최적설계를 위한 페이즈섹션 설계법 개선)

  • Kang, Min-sung;Kim, Cheolwoong;Yoo, Jeonghoon
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.35 no.2
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    • pp.65-71
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    • 2022
  • Recently, multi-material structural topology optimization is more critical because it provides reasonable solution to weight reduction challenges and can as well provide effective conceptual design. For conventional multi-material topology optimization (MMTO), the number of design variable increases when the number of candidate materials increases, and accordingly, a significant increase in computational time occurs. Therefore, MMTO with a single design variable, such as the phase section method (PSM) was proposed. This research is focused on improving the PSM, considering three major limitations: the composition ratio does not represent the area or volume ratio, design variables are not sufficiently concentrated to target values, and certain materials are created less than they are required. To address such limitations, the redefined composition ratio and adjusted parameters for better convergence are proposed. The validation of proposed modifications is verified via two- and three-dimensional numerical examples.

Multi-material topology optimization of Reissner-Mindlin plates using MITC4

  • Banh, Thien Thanh;Lee, Dongkyu
    • Steel and Composite Structures
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    • v.27 no.1
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    • pp.27-33
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    • 2018
  • In this study, a mixed-interpolated tensorial component 4 nodes method (MITC4) is treated as a numerical analysis model for topology optimization using multiple materials assigned within Reissner-Mindlin plates. Multi-material optimal topology and shape are produced as alternative plate retrofit designs to provide reasonable material assignments based on stress distributions. Element density distribution contours of mixing multiple material densities are linked to Solid Isotropic Material with Penalization (SIMP) as a design model. Mathematical formulation of multi-material topology optimization problem solving minimum compliance is an alternating active-phase algorithm with the Gauss-Seidel version as an optimization model of optimality criteria. Numerical examples illustrate the reliability and accuracy of the present design method for multi-material topology optimization with Reissner-Mindlin plates using MITC4 elements and steel materials.

Topology optimization for thin plate on elastic foundations by using multi-material

  • Banh, Thien Thanh;Shin, Soomi;Lee, Dongkyu
    • Steel and Composite Structures
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    • v.27 no.2
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    • pp.177-184
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    • 2018
  • This study contributes to evaluate multiphase topology optimization design of plate-like structures on elastic foundations by using classic plate theory. Multi-material optimal topology and shape are produced as an alternative to provide reasonable material assignments based on stress distributions. Multi-material topology optimization problem is solved through an alternative active-phase algorithm with Gauss-Seidel version as an optimization model of optimality criteria. Stiffness and adjoint sensitivity formulations linked to thin plate potential strain energy are derived in terms of multiphase design variables and Winkler-Pasternak parameters considering elastic foundation to apply to the current topology optimization. Numerical examples verify efficiency and diversity of the present topology optimization method of elastic thin plates depending on multiple materials and Winkler-Pasternak parameters with the same amount of volume fraction and total structural volume.

Hybrid artificial bee colony-grey wolf algorithm for multi-objective engine optimization of converted plug-in hybrid electric vehicle

  • Gujarathi, Pritam K.;Shah, Varsha A.;Lokhande, Makarand M.
    • Advances in Energy Research
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    • v.7 no.1
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    • pp.35-52
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    • 2020
  • The paper proposes a hybrid approach of artificial bee colony (ABC) and grey wolf optimizer (GWO) algorithm for multi-objective and multidimensional engine optimization of a converted plug-in hybrid electric vehicle. The proposed strategy is used to optimize all emissions along with brake specific fuel consumption (FC) for converted parallel operated diesel plug-in hybrid electric vehicle (PHEV). All emissions particulate matter (PM), nitrogen oxide (NOx), carbon monoxide (CO) and hydrocarbon (HC) are considered as optimization parameters with weighted factors. 70 hp engine data of NOx, PM, HC, CO and FC obtained from Oak Ridge National Laboratory is used for the study. The algorithm is initialized with feasible solutions followed by the employee bee phase of artificial bee colony algorithm to provide exploitation. Onlooker and scout bee phase is replaced by GWO algorithm to provide exploration. MATLAB program is used for simulation. Hybrid ABC-GWO algorithm developed is tested extensively for various values of speeds and torque. The optimization performance and its environmental impact are discussed in detail. The optimization results obtained are verified by real data engine maps. It is also compared with modified ABC and GWO algorithm for checking the effectiveness of proposed algorithm. Hybrid ABC-GWO offers combine benefits of ABC and GWO by reducing computational load and complexity with less computation time providing a balance of exploitation and exploration and passes repeatability towards use for real-time optimization.