• The Transactions of the Korean Institute of Power Electronics
• /
• v.25 no.3
• /
• pp.227-234
• /
• 2020

This paper presents the methods for the modeling and parameter optimization of the electric vehicle battery. The state variables of the battery are defined, and the test methods for battery parameters are presented. The state-space equation, which consists of four state variables, and the output equation, which is a combination of to-be-determined parameters, are shown. The parameter optimization method is the key point of this study. The least square of the modeling error can be used as an initial value of the multivariable function. It is equivalent to find the minimum value of the error function to obtain optimal parameters from multivariable function. The SIMULINK model is presented, and the 10-hour full operational range test results are shown to verify the performance of the model. The modeling error for 25 degrees is approximately 1% for full operational ranges. The comments to enhance modeling accuracy are shown in the conclusion.

• Structural Engineering and Mechanics
• /
• v.73 no.4
• /
• pp.463-479
• /
• 2020

This paper is aimed to address a simultaneous optimization of the size, shape, and topology of steel lattice towers through a combination of the radial basis function (RBF) neural networks and the artificial bee colony (ABC) metaheuristic algorithm to reduce the computational time because mere metaheuristic optimization algorithms require much time for calculations. To verify the results, use has been made of the CIGRE Tower and a 132 kV transmission towers as numerical examples both based on the design requirements of the ASCE10-97, and the size, shape, and topology have been optimized (in both cases) once by the RBF neural network and once by the MSTOWER analyzer. A comparison of the results shows that the neural network-based method has been able to yield acceptable results through much less computational time.

• Structural Engineering and Mechanics
• /
• v.70 no.5
• /
• pp.513-524
• /
• 2019

This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.31 no.8
• /
• pp.663-671
• /
• 2007

In this work, shape optimization of a wire-wrapped fuel assembly in a liquid metal reactor has been carried out by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial basis neural network method, a well known surrogate modeling technique for optimization. Sequential Quadratic Programming is used to search the optimal point from the constructed surrogate. Two geometric design variables are selected for the optimization and design space is sampled using Latin Hypercube Sampling. The optimization problem has been defined as a maximization of the objective function, which is as a linear combination of heat transfer and friction loss related terms with a weighing factor. The objective function value is more sensitive to the ratio of the wire spacer diameter to the fuel rod diameter than to the ratio of the wire wrap pitch to the fuel rod diameter. The optimal values of the design variables are obtained by varying the weighting factor.

• Journal of Computational Design and Engineering
• /
• v.1 no.4
• /
• pp.256-265
• /
• 2014

Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using non-dominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.3
• /
• pp.1301-1318
• /
• 2017

In order to utilize the licensed channel of cognitive radio (CR) when the primary user (PU) is detected busy, a benefit-exchange access mode based on cooperative communication is proposed to allow secondary user (SU) to access the busy channel through giving assistance to PU's communication in exchange for some transmission bandwidth. A holistic joint optimization problem is formulated to maximize the total throughput of CR system through jointly optimizing the parameters of cooperative spectrum sensing (CSS), including the local sensing time, the pre-configured sensing decision threshold, the forward power of cooperative communication, and the bandwidth and transmission power allocated to SUs in benefit-exchange access mode and traditional access mode, respectively. To solve this complex problem, a combination of bi-level optimization, interior-point optimization and exhaustive optimization is proposed. Simulation results show that, compared with the tradition throughput maximizing model (TTMM), the proposed holistic joint optimization model (HJOM) can make use of the channel effectively even if PU is busy, and the total throughput of CR obtains a considerable improvement by HJOM.

• Steel and Composite Structures
• /
• v.27 no.3
• /
• pp.297-310
• /
• 2018

A simple and efficient numerical optimization approach for the lightweight optimal design of composite laminated beams is presented in this paper. The proposed procedure is a combination between the finite element method (FEM) and a global optimization algorithm developed recently, namely Jaya. In the present procedure, the advantages of FEM and Jaya are exploited, where FEM is used to analyze the behavior of beam, and Jaya is modified and applied to solve formed optimization problems. In the optimization problems, the objective aims to minimize the overall weight of beam; and fiber volume fractions, thicknesses and fiber orientation angles of layers are selected as design variables. The constraints include the restriction on the first fundamental frequency and the boundaries of design variables. Several numerical examples with different design scenarios are executed. The influence of the design variable types and the boundary conditions of beam on the optimal results is investigated. Moreover, the performance of Jaya is compared with that of the well-known methods, viz. differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The obtained results reveal that the proposed approach is efficient and provides better solutions than those acquired by the compared methods.

• Journal of the Society of Naval Architects of Korea
• /
• v.34 no.1
• /
• pp.93-101
• /
• 1997

Generally the traditional optimization methods have possibilities not only to give a different optimum value according to their starting point, but also to get to local optima. On the other hand, Genetic Algorithm (GA) has an ability of robust global search. In this paper, a new optimization method - the combination of traditional optimization method and genetic algorithm - is presented so as to overcome the above disadvantage of traditional methods. In order to increase the efficiency of the hybrid optimization method, a knowledge-based reasoning is adopted in the part of mathematical modeling, algorithm selection, and process control. The validation of the developed knowledge-based hybrid optimization method was examined and verified applying the method to nonlinear mathematical models.

• Steel and Composite Structures
• /
• v.44 no.5
• /
• pp.603-617
• /
• 2022

Structural design, in general, is developed through trial and error technique which is guided by standards criteria and based on the intuition and experience of the engineer, a context that leads to structural over-dimensioning, with uneconomic solutions. Aiming to find the optimal design, structural optimization methods have been developed to find a balance between cost, structural safety, and material performance. These methods have become a great opportunity in the steel structural engineering domain since they have as their main purpose is weight minimization, a factor directly correlated to the real cost of the structure. Assuming an objective function of minimum weight with stress and displacement constraints provided by Brazilian standards, the present research proposes the sizing optimization and combined approach of sizing and shape optimization, through a software developed to implement the Simulated Annealing metaheuristic algorithm. Therefore, two steel plane frame layouts, each admitting four typical truss geometries, were proposed in order to expose the difference between the optimal solutions. The assessment of the optimal solutions indicates a notable weight reduction, especially in sizing and shape optimization combination, in which the quantity of design variables is increased along with the search space, improving the efficiency of the optimal solutions achieved.

• 한국전산유체공학회:학술대회논문집
• /
• 2009.04a
• /
• pp.57-65
• /
• 2009

An efficient and high-fidelity design approach for wing-body shape optimization is presented. Depending on the size of design space and the number of design of variable, aerodynamic shape optimization process is carried out via different optimization strategies at each design stage. In the first stage, global optimization techniques are applied to planform design with a few geometric design variables. In the second stage, local optimization techniques are used for wing surface design with a lot of design variables to maintain a sufficient design space with a high DOF (Degree of Freedom) geometric change. For global optimization, Kriging method in conjunction with Genetic Algorithm (GA) is used. Asearching algorithm of EI (Expected Improvement) points is introduced to enhance the quality of global optimization for the wing-planform design. For local optimization, a discrete adjoint method is adopted. By the successive combination of global and local optimization techniques, drag minimization is performed for a multi-body aircraft configuration while maintaining the baseline lift and the wing weight at the same time. Through the design process, performances of the test models are remarkably improved in comparison with the single stage design approach. The performance of the proposed design framework including wing planform design variables can be efficiently evaluated by the drag decomposition method, which can examine the improvement of various drag components, such as induced drag, wave drag, viscous drag and profile drag.