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multiobjective design optimization of the performance for the cavity flameholder in supersonic flows

AEROSPACE SCIENCE AND TECHNOLOGY 2013年第1期30卷 246-254页

作者： Huang, Wei Liu, Jun Yan, Li Jin, Liang Natl Univ Def Technol Sci & Technol Scramjet Lab Changsha 410073 Hunan Peoples R China

The wall-mounted cavity makes a great difference to the flameholding mechanism in the supersonic flow, and it has been widely employed in the design process of the scramjet flowpath. However, the cavity would bring additional drag force to the engine, and the temperature within the cavity is very high. Therefore, both the drag force and the temperature should be minimized in the design process of the cavity flameholder. In the current study, the non-dominated sorting genetic algorithm (NSGA II) coupled with the Kriging surrogate model has been employed to optimize the nonreacting flow field around the cavity flameholder in the supersonic freestream with a Mach number of 3.0, and the test cases have been selected by the orthogonal table. At the same time, the numerical results have been compared with the experimental data obtained by Gruber et al., and four grid scales have been utilized to perform the grid independency analysis as well. The obtained results show that the wall static pressure profiles predicted by the numerical approaches show very reasonable agreement with the experimental data, and the static pressure along the floor face of the cavity is underpredicted. This may be induced by the inaccuracy of the turbulence model. The Pareto front for the multiobjective design optimization results is obtained, and there must be a compromise between the drag force and the area-weighted average temperature. The optimized drag force increases with the increase of the length-to-depth ratio and with the decrease of the swept angle, and the optimized area-weighted average temperature increases with the decrease of the length-to-depth ratio and with the increase of the swept angle. The drag force of the acoustic cavity is the smallest in the range considered in the current study. However, it would generate the strongest pressure oscillation. (C) 2013 Elsevier Masson SAS. All rights reserved.

关键词： Aerospace propulsion system Cavity flameholder multiobjective design optimization Supersonic flow Drag force Area-weighted average temperature

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multiobjective design optimization of brushless permanent magnet motor using 3D equivalent magnetic circuit network method

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2004年第2期14卷 1910-1913页

作者： Chun, YD Wakao, S Kim, TH Jang, KB Lee, J Waseda Univ Adv Res Inst Sci & Engn Shinjuku Ku Tokyo 1698555 Japan Waseda Univ Dept Elect Engn & Biosci Shinjuku Ku Tokyo 1698555 Japan Hanyang Univ Div Elect & Comp Engn Seoul 133791 South Korea

In this paper, we discuss multiobjective design optimization of brushless permanent magnet motor (BLPMM) solved by genetic algorithm (GA) and 3D equivalent magnetic circuit network (EMCN) method. In the multiobjective optimization (MO) problem, we choose the decrease of cogging torque and the increase of torque as objectives. The airgap length, teeth width and magnetization angle of permanent magnet (PM) are also selected for the design variables respectively. From the results, our approach method enabled us to efficiently obtain diverse Pareto optimal (PO) solutions from the practical point of view. The experimental results are shown to confirm the validity of the optimization results.

关键词： brushless permanent magnet motor genetic algorithm multiobjective design optimization 3D equivalent magnetic circuit network method

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multiobjective design optimization of brushless permanent magnet motor using 3D equivalent magnetic circuit network method

Multiobjective design optimization of brushless permanent ma...

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18th International Conference on Magnet Technology

关键词： brushless permanent magnet motor genetic algorithm multiobjective design optimization 3D equivalent magnetic circuit network method

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multiobjective design optimization of a Biconcave Mobile-Bearing Lumbar Total Artificial Disk Considering Spinal Kinematics, Facet Joint Loading, and Metal-on-Polyethylene Contact Mechanics

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JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME 2020年第4期142卷 6-6页

作者： Zhou, Chaochao Willing, Ryan SUNY Binghamton Dept Mech Engn Binghamton NY 13902 USA Western Univ Dept Mech & Mat Engn Thompson Engn BldgRoom TEB 363 London ON N6A 5B9 Canada

Total disk arthroplasty (TDA) using an artificial disk (AD) is an attractive surgical technique for the treatment of spinal disorders, since it can maintain or restore spinal motion (unlike interbody fusion). However, adverse surgical outcomes of contemporary lumbar TDAs have been reported. We previously proposed a new mobile-bearing AD design concept featuring a biconcave ultrahigh-molecular-weight polyethylene (UHMWPE) mobile core. The objective of this study was to develop an artificial neural network (NN) based multiobjective optimization framework to refine the biconcave-core AD design considering multiple TDA performance metrics, simultaneously. We hypothesized that there is a tradeoff relationship between the performance metrics in terms of range of motion (ROM), facet joint force (FJF), and polyethylene contact pressure (PCP). By searching the resulting three-dimensional (3D) Pareto frontier after multiobjective optimization, it was found that there was a "best-tradeoff" AD design, which could balance all the three metrics, without excessively sacrificing each metric. However, for each single-objective optimum AD design, only one metric was optimal, and distinct sacrifices were observed in the other two metrics. For a commercially available biconvex-core AD design, the metrics were even worse than the poorest outcomes of the single-objective optimum AD designs. Therefore, multiobjective design optimization could be useful for achieving native lumbar segment biomechanics and minimal PCPs, as well as for improving the existing lumbar motion-preserving surgical treatments.

关键词： total disk arthroplasty total artificial disk biconcave mobile-bearing lumbar spinal segment multiobjective design optimization finite element analysis artificial neural network

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multiobjective design optimization of a Cantilevered Ramp Injector Using the Surrogate-Assisted Evolutionary Algorithm

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JOURNAL OF AEROSPACE ENGINEERING 2015年第5期28卷 04014138-04014138页

作者： Huang, Wei Li, Shi-bin Yan, Li Tan, Jian-guo Natl Univ Def Technol Sci & Technol Scramjet Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Sci & Technol Scramjet Lab Changsha 410073 Hunan Peoples R China

The mixing efficiency of the fuel and the supersonic freestream has a great impact on the operation of the scramjet engine because of the very short residence time. In the current study, the cantilevered ramp injector was used to promote mixing of the fuel and the supersonic flow, and the injector's configuration was optimized by the surrogate-assisted evolutionary algorithm-namely, the nondominated sorting genetic algorithm coupled with the Kriging surrogate model. In the optimization process, the swept angle, the ramp angle, the compression angle, the length of the step, and the diameter of the injector were considered as the design variables, and the mixing efficiency, the total pressure recovery efficiency, and the drag force were taken as the objective functions. Finally, the Pareto front for the multiobjective design optimization results was obtained, and the relationship between the objective functions was explored. The results show that optimized drag force increases with increased mixing efficiency and total pressure recovery efficiency, and that there must be a compromise between objective functions. The larger swept angle promotes the mixing of the fuel and the supersonic airstream, and the smaller diameter of the injector is beneficial for reducing the drag force and improving both mixing efficiency and total pressure recovery efficiency. (C) 2014 American Society of Civil Engineers.

关键词： Aerospace propulsion system Cantilevered ramp injector multiobjective design optimization Kriging model Supersonic mixing Evolutionary algorithm

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Quantifying the competing relationship between durability and kinematics of total knee replacements using multiobjective design optimization and validated computational models

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JOURNAL OF BIOMECHANICS 2012年第1期45卷 141-147页

作者： Willing, Ryan Kim, Il Yong Queens Univ Dept Mech & Mat Engn Kingston ON K7L 3N6 Canada

Durability and kinematics are two critical factors which must be considered during total knee replacement (TKR) implant design. It is hypothesized, however, that there exists a competing relationship between these two performance measures, such that improvement of one requires sacrifice with respect to the other. No previous studies have used rigorous and systematic methods to quantify this relationship. During this study, multiobjective design optimization (MOO) using the adaptive weighted sum (AWS) method is used to determine a set of Pareto-optimal implant designs considering durability and kinematics simultaneously. Previously validated numerical simulations and a parametric modeller are used in conjunction with the AWS method in order to generate a durability-versus-kinematics Pareto curve. In terms of kinematics, a design optimized for kinematics alone outperformed a design optimized for durability by 61.8%. In terms of durability, the design optimized for durability outperformed the kinematics-optimized design by 70.6%. Considering the entire Pareto curve, a balanced (1:1) trade-off could be obtained when equal weighting was placed on both performance measures;however improvement of one performance measure required greater sacrifices with respect to the other when the weighting was extremized. For the first time, the competing relationship between durability and kinematics was confirmed and quantified using optimization methods. This information can aid future developments in TKR design and can be expanded to other total joint replacement designs. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Total knee replacement Implant durability Kinematics design optimization Adaptive weighted-sum method multiobjective design optimization Computational biomechanics

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An Efficient multiobjective design optimization Method for a PMSLM Based on an Extreme Learning Machine

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2019年第2期66卷 1001-1011页

作者： Song, Juncai Dong, Fei Zhao, Jiwen Wang, Hui He, Zhongyan Wang, Lijun Anhui Univ Sch Elect & Informat Engn Hefei 230601 Anhui Peoples R China Anhui Univ Sch Elect Engn & Automat Hefei 230601 Anhui Peoples R China

This paper focuses on the multiobjective design optimization of the permanent magnet synchronous linear motors (PMSLMs), which are applied to a high-precision laser engraving machine. A novel efficient multiobjective design optimization method for a PMSLM is proposed to achieve optimal performances as indicated by high average thrust, low thrust ripple, and low total harmonic distortion at different running speeds. First, based on the finite-element analysis (FEA) data, a regression machine learning algorithm, called an extreme learning machine (ELM), is introduced to solve the calculation modeling problem by mapping out the nonlinear and complex relationship between input structural factors and output motor performances. Comparative simulation experiments conducted using the traditional analytical modeling method and another machine learning modeling method, i.e., support vector machine, confirm the superiority of the ELM. Then, a new bionic intelligent optimization algorithm, called the gray wolf optimizer algorithm, is used to search the best optimization performances and structural parameters by performing iteration optimization calculation for multiobjective functions. Finally, FEA and prototype motor experiments prove the effectiveness and validity of the proposed method.

关键词： Extreme learning machine (ELM) finite-element analysis (FEA) gray wolf optimizer algorithm (GWOA) multiobjective design optimization permanent magnet synchronous linear motors (PMSLMs) support vector machine (SVM)

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Reliability-Based multiobjective design optimization of Reinforced Concrete Bridges Considering Corrosion Effect

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING 2017年第3期3卷

作者： Sajedi, Siavash Huang, Qindan Gandomi, Amir H. Kiani, Behnam Univ Akron Dept Civil Engn ASEC 210 Akron OH 44325 USA Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA

Chloride-induced corrosion is known as the dominant cause of premature damage in reinforced concrete (RC) bridges in the United States. However, the current corrosion management strategies do not suggest an optimum design procedure for RC bridges in corrosive environments considering both reliability and cost. In this paper, a module based on a reliability-based multiobjective design optimization (RB-MODO) technique using a nondominated sorting genetic algorithm is proposed for the optimum design of RC bridge beams considering corrosion. The procedure simultaneously maximizes the reliability of the structure and minimizes the material costs, given a design service life. As an illustration, the developed procedure is used for flexural design of interior T beams of a RC bridge with and without considering corrosion effect subjected to various design constraints and service lives. Three types of materials are used in the design process: normal strength concrete with black steel rebars, normal strength concrete with epoxy-coated rebars, and high-performance concrete with black steel rebars. Lastly, the optimum design strategy is selected among the considered materials based on the Pareto front results obtained from the proposed RB-MODO procedure. (C) 2016 American Society of Civil Engineers.

关键词： Reliability analysis multiobjective design optimization Nondominated sorting algorithm Reinforced bridge Corrosion

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multiobjective optimization design of axial flux permanent magnet motor

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2007年第1-4期25卷 613-619页

作者： Chun, Yon-Do Koo, Dae-Hyun Cho, Yun-Hyun Korea Electrotechnol Res Inst Mechatron Res Grp Changwon Si 641120 South Korea Dong A Univ Dept Elect Engn Pusan 604714 South Korea

The multiobjective design optimization generally includes the conflicting objectives and the use of conventional design optimization for MO problem does not so good approach to obtain an effective optimal solution. In this article, genetic algorithm is used to solve such optimal multiobjective design of axial flux permanent magnet (AFPM) motor. In order to effectively obtain a set of Pareto optimal solutions, ranking method is applied. The objective functions are decrease of cogging torque, increase of torque respectively. The airgap length and teeth angle are selected for the design variables. Some experimental results are compared with the simulation ones for the validity.

关键词： multiobjective design optimization axial flux motor permanent magnet genetic algorithm

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multiobjective optimization design of axial flux permanent magnet motor

Multiobjective optimization design of axial flux permanent m...

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12th International Symposium on Interdisciplinary Electromagnetic, Mechanic and Biomedical Problems

作者： Chun, Yon-Do Koo, Dae-Hyun Cho, Yun-Hyun Korea Electrotechnol Res Inst Mechatron Res Grp Changwon Si 641120 South Korea Dong A Univ Dept Elect Engn Pusan 604714 South Korea

关键词： multiobjective design optimization axial flux motor permanent magnet genetic algorithm

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