• Title/Summary/Keyword: coefficient optimization algorithm

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A study on the Evaluation of Heat Transfer Coefficient by Optimization Algorithm (최적화 기법을 활용한 열전달계수의 측정)

  • Kim, J.T.;Lim, C.H.;Choi, J.K.
    • Transactions of Materials Processing
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    • v.15 no.9 s.90
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    • pp.679-685
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    • 2006
  • New method for evaluation of heat transfer coefficient is proposed. In general, many researchers have been studied about inverse problem in order to calculate the heat transfer coefficient on three-dimensional heat conduction problem. But they can get the time-dependent heat transfer coefficient only through inverse problem. In order to acquire temperature-dependent heat transfer coefficient, it requires much time for numerous repetitive calculation and inconvenient manual modification. In order to solve these problems, we are using the SQP(Sequential Quadratic Programming) as an optimization algorithm. When the temperature history is given by experiment, the optimization algorithm can evaluate the temperature-dependent heat transfer coefficient with automatic repetitive calculation until difference between calculated temperature history and experimental ones is minimized. Finally, temperature-dependent heat transfer coefficient evaluated by developed program can used on various heat transfer problem.

An Optimization Method Based on Hybrid Genetic Algorithm for Scramjet Forebody/Inlet Design

  • Zhou, Jianxing;Piao, Ying;Cao, Zhisong;Qi, Xingming;Zhu, Jianhong
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2008.03a
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    • pp.469-475
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    • 2008
  • The design of a scramjet inlet is a process to search global optimization results among those factors influencing the geometry of scramjet in their ranges for some requirements. An optimization algorithm of hybrid genetic algorithm based on genetic algorithm and simplex algorithm was established for this purpose. With the sample provided by a uniform method, the compressive angles which also are wedge angles of the inlet were chosen as the inlet design variables, and the drag coefficient, total pressure recovery coefficient, pressure rising ratio and the combination of these three variables are designed specifically as different optimization objects. The contrasts of these four optimization results show that the hybrid genetic algorithm developed in this paper can capably implement the optimization process effectively for the inlet design and demonstrate some good adaptability.

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Optimization of Carr's Automotive Aerodynamic Underbody Drag Coefficient Using Genetic Algorithm (유전 알고리즘을 이용한 Carr의 차량 하체 공력계수 최적화)

  • Kim, Ki Hyuk;Lee, Tea Sup
    • Proceeding of EDISON Challenge
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    • 2015.03a
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    • pp.518-520
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    • 2015
  • Automotive aerodynamic drag coefficient is important variable for vehicle's driving performance and fuel economy. In this research, we applied genetic algorithm to determine the geometrical figure which can optimize Carr's automotive aerodynamic underbody coefficient. And it's verified by previous research.

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AERODYNAMIC DESIGN OPTIMIZATION OF UAV ROTOR BLADES USING A GENETIC ALGORITHM AND ARTIFICIAL NEURAL NETWORKS (유전 알고리즘과 인공 신경망 기법을 이용한 무인항공기 로터 블레이드 공력 최적설계)

  • Lee, H.M.;Ryu, J.K.;Ahn, S.J.;Kwon, O.J.
    • Journal of computational fluids engineering
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    • v.19 no.3
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    • pp.29-36
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    • 2014
  • In the present study, an aerodynamic design optimization of UAV rotor blades was conducted using a genetic algorithm(GA) coupled with computational fluid dynamics(CFD). To reduce computational cost in making databases, a function approximation was applied using artificial neural networks(ANN) based on a radial basis function network. Three dimensional Reynolds-Averaged Navier-Stokes(RANS) solver was used to solve the flow around UAV rotor blades. Design directions were specified to maximize thrust coefficient maintaining torque coefficient and minimize torque coefficient maintaining thrust coefficient. Design variables such as twist angle, thickness and chord length were adopted to perform a planform optimization. As a result of an optimization regarding to maximizing thrust coefficient, thrust coefficient was increased about 4.5% than base configuration. In case of an optimization minimizing torque coefficient, torque coefficient was decreased about 7.4% comparing with base configuration.

The Variable Amplitude Coefficient Fireworks Algorithm with Uniform Local Search Operator

  • Li, Lixian;Lee, Jaewan
    • Journal of Internet Computing and Services
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    • v.21 no.3
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    • pp.21-28
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    • 2020
  • Fireworks Algorithm (FWA) is a relatively novel swarm-based metaheuristic algorithm for global optimization. To solve the low-efficient local searching problem and convergence of the FWA, this paper presents a Variable Amplitude Coefficient Fireworks Algorithm with Uniform Local Search Operator (namely VACUFWA). Firstly, the explosive amplitude is used to adjust improving the convergence speed dynamically. Secondly, Uniform Local Search (ULS) enhances exploitation capability of the FWA. Finally, the ULS and Variable Amplitude Coefficient operator are used in the VACUFWA. The comprehensive experiment carried out on 13 benchmark functions. Its results indicate that the performance of VACUFWA is significantly improved compared with the FWA, Differential Evolution, and Particle Swarm Optimization.

Parameter Optimization of QUAL2K Using Influence Coefficient Algorithm and Genetic Algorithm (영향계수법과 유전알고리즘을 이용한 QUAL2K 모형의 매개변수 최적화)

  • Cho, Jae-Heon;Lee, Chang-Hun
    • Journal of Environmental Impact Assessment
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    • v.18 no.2
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    • pp.99-109
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    • 2009
  • In general, manual calibration is commonly used for the stream water quality modelling. Because the manual calibration depends upon the subjectivity and experience of the researcher, it has a problem with the objectivity of the modelling. Thus, the interest about the automatic calibration by the optimization technique is deeply increased. In this study, Influence coefficient algorithm and Genetic algorithm are introduced to develop an automatic calibration model for the QUAL2K that are the latest version of the QUAL2E. Genetic algorithm, used in this study, is very simple and easy to understand but also applicable to any complicated mathematical problem, and it can find out the global optimum solution effectively. The developed automatic calibration model is applied to the Gangneung Namdaecheon. The calibration results about the 11 water quality variables show the good correspondence between the calculated and observed water quality values.

Metaheuristic Optimization Techniques for an Electromagnetic Multilayer Radome Design

  • Nguyen, Trung Kien;Lee, In-Gon;Kwon, Obum;Kim, Yoon-Jae;Hong, Ic-Pyo
    • Journal of electromagnetic engineering and science
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    • v.19 no.1
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    • pp.31-36
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    • 2019
  • In this study, an effective method for designing an electromagnetic multilayer radome is introduced. This method is achieved by using ant colony optimization for a continuous domain in the transmission coefficient maximization with stability for a wide angle of incidence in both perpendicular and parallel polarizations in specific X- and Ku-bands. To obtain the optimized parameter for a C-sandwich radome, particle swarm optimization algorithm is operated to give a clear comparison on the effectiveness of ant colony optimization for a continuous domain. The qualification of an optimized multilayer radome is also compared with an effective solid radome type in transmitted power stability and presented in this research.

Topology Optimization of a Brake Pad to Avoid the Brake Moan Noise Using Genetic Algorithm (Brake Moan Noise 소피를 위한 Brake Pad 위상최적화의 GA적용)

  • 한상훈;윤덕현;이종수;유정훈
    • Transactions of the Korean Society of Automotive Engineers
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    • v.10 no.4
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    • pp.216-222
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    • 2002
  • Brake Moan is a laud and strong noise occurring at any vehicle speed over 2 mph as a low frequency in below 600Hz. In this study, we targeted to shift the unstable mode that causes the brake moan from the moats frequency range to sufficiently higher frequency range to avoid the moan phenomenon. We simulated the finite element model and found out the nodes in which the brake moan occurs the most and we regarded the boundary and its relationship between the brake pad and the rotor as a spring coefficient k. With the binary set of the spring coefficient k, we finally used genetic algorithm (GA) to get the optimal topology of the brake pad and its shape to avoid the brake moan. The final result remarkably shows that genetic algorithm can be used in topology optimization procedures requiring complex eigenvalue problems.

Optimization study of a clustering algorithm for cosmic-ray muon scattering tomography used in fast inspection

  • Hou, Linjun;Huo, Yonggang;Zuo, Wenming;Yao, Qingxu;Yang, Jianqing;Zhang, Quanhu
    • Nuclear Engineering and Technology
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    • v.53 no.1
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    • pp.208-215
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    • 2021
  • Cosmic-ray muon scattering tomography (MST) technology is a new radiation imaging technology with unique advantages. As the performance of its image reconstruction algorithm has a crucial influence on the imaging quality, researches on this algorithm are of great significance to the development and application of this technology. In this paper, a fast inspection algorithm based on clustering analysis for the identification of the existence of nuclear materials is studied and optimized. Firstly, the principles of MST technology and a binned clustering algorithm were introduced, and then several simulation experiments were carried out using Geant4 toolkit to test the effects of exposure time, algorithm parameter, the size and structure of object on the performance of the algorithm. Based on these, we proposed two optimization methods for the clustering algorithm: the optimization of vertical distance coefficient and the displacement of sub-volumes. Finally, several sets of experiments were designed to validate the optimization effect, and the results showed that these two optimization methods could significantly enhance the distinguishing ability of the algorithm for different materials, help to obtain more details in practical applications, and was therefore of great importance to the development and application of the MST technology.

Vibration Ride Quality Optimization of a Suspension Seat System Using Genetic Algorithm (유전자 알고리즘을 이용한 SUSPENSION SEAT SYSTEM의 진동 승차감 최적화)

  • Park, S.K.;Choi, Y.H.;Choi, H.O.;Bae, B.T.
    • Proceedings of the KSME Conference
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    • 2001.06b
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    • pp.584-589
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    • 2001
  • This paper presents the dynamic parameter design optimization of a suspension seat system using the genetic algorithm. At first, an equivalent 1-D.O.F. mass-spring-damper model of a suspension seat system was constructed for the purpose of its vibration analysis. Vertical vibration response and transmissibility of the equivalent model due to base excitations, which are defined in the ISO's seat vibration test codes, were computed. Furthermore, seat vibration test, that is ISO's damping test, was carried out in order to investigate the validity of the equivalent suspension seat model. Both analytical and experimental results showed good agreement each other. For the design optimization, the acceleration transmissibility of the suspension seat model was adopted as an object function. A simple genetic algorithm was used to search the optimum values of the design variables, suspension stiffness and damping coefficient. Finally, vibration ride performance test results showed that the optimum suspension parameters gives the lowest vibration transmissibility. Accordingly the genetic algorithm and the equivalent suspension seat modelling can be successfully adopted in the vibration ride quality optimization of a suspension seat system.

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