A Mixed-Variable Experimental Design Method

Mixed-variable problems are inevitable in engineering. However, few researches pay attention to discrete variables. This paper proposed a mixed-variable experimental design method (ODCD): first, the design variables were divided into discrete variables and continuous variables;then, the DVD method was employed for handling discrete variables, the LHD method was applied for continuous variables, and finally, a Columnwise-Pairwise Algorithm was used for the overall optimization of the design matrix. Experimental results demonstrated that the ODCD method outperforms in terms of the sample space coverage performance.

A Spider Monkey Optimization Algorithm Combining Opposition-Based Learning and Orthogonal Experimental Design

As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant.Thus,this paper focuses on how to reconstruct SMO to improve its performance,and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design(SMO^(3))is developed.A position updatingmethod based on the historical optimal domain and particle swarmfor Local Leader Phase(LLP)andGlobal Leader Phase(GLP)is presented to improve the diversity of the population of SMO.Moreover,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values.Also,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time.Furthermore,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization problems.The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems.Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.

The Study of Optimizing Reservoir Model Using Experimental Design in Stochastic Models

In response to the main problems in commonly used model selection methods,a method was proposed to apply the concept of experimental design to the optimization of uncertain reservoir models.Firstly,based on the actual situation of the oil field,the uncertain variables were determined that affect the geological reserves of the model and their possible range of variation,and experimental design was used to determine the modeling plan.Then,multiple geological models were established and reserves were calculated,and multiple regression was performed between uncertain variables and the corresponding geological reserves of the model.Finally,Monte Carlo simulation technology was applied to determine the parameters of the P10,P50,and P90 models for probabilistic reserves,and P10,P50,and P90 models were established.This method is not only more objective and time-saving in the application process,but also can determine the main geological variables that affect geological reserves,providing a new idea for evaluating the uncertainty of geological reserves.

Optimal Experiment Design for the Identification of the Interfacial Heat Transfer Coefficient in Sand Casting

The interfacial heat transfer coefficient(IHTC)is one of the main input parameters required by casting simulation software.It plays an important role in the accurate modeling of the solidification process.However,its value is not easily identifiable by means of experimental methods requiring temperature measurements during the solidification process itself.For these reasons,an optimal experiment design was performed in this study to determine the optimal position for the temperature measurement and the optimal thickness of the rectangular cast iron part.This parameter was identified using an inverse technique.In particular,two different algorithms were used:Levenberg Marquard(LM)and Monte Carlo(MC).A numerical model of the solidification process was associated with the optimization algorithm.The temperature was measured at different positions from the mould/metal interface at d=0 mm(mould/metal interface),30 mm,60 mm and 90 mm.the thicknesses of the cast part were:L1=40 mm,60 mm and 80 mm.A comparative study on the IHTC identification was then carried out by varying the initial value of the IHTC between 500 Wm^(-2)K^(-1) and 1050 Wm^(-2)K^(-1).Results showed that the MC algorithm used for estimating the IHTC gives the best results,and the optimal position was at d=30 mm,the position closest to the mould/metal interface,for the lowest thickness L1=40 mm.

Cavitation Optimization for a Centrifugal Pump Impeller by Using Orthogonal Design of Experiment

Cavitation is one of the most important performance of centrifugal pumps. However, the current optimization works of centrifugal pump are mostly focusing on hydraulic efficiency only, which may result in poor cavitation performance. Therefore, it is necessary to find an appropriate solution to improve cavitation performance with acceptable efficiency. In this paper, to improve the cavitation performance of a centrifugal pump with a vaned diffuser, the influence of impeller geometric parameters on the cavitation of the pump is investigated using the orthogonal design of experiment （DOE） based on computational fluid dynamics. The impeller inlet diameter D1, inlet incidence angle Aft, and blade wrap angle ~0 are selected as the main impeller geometric parameters and the orthogonal experiment of L9（3＂3） is performed. Three-dimensional steady simulations for cavitation are conducted by using constant gas mass fraction model with second-order upwind, and the predicated cavitation performance is validated by laboratory experiment. The optimization results are obtained by the range analysis method to improve cavitation performance without obvious decreasing the efficiency of the centrifugal pump. The internal flow of the pump is analyzed in order to identify the flow behavior that can affect cavitation performance. The results show that D1 has the greatest influence on the pump cavitation and the final optimized impeller provides better flow distribution at blade leading edge. The final optimized impeller accomplishes better cavitation and hydraulic performance and the NPSHR decreases by 0.63m compared with the original one. The presented work supplies a feasible route in engineering practice to optimize a centrifugal pump impeller for better cavitation performance.

Optimal Design and Dynamic Simulation of Mountain Bike with Rear Suspension

This paper investigates the dynamic design methodology of mountain bikes with rear suspension. Firstly, a multi-rigid body dynamic model of rider and mountain bike coupled system is constructed. The rider model includes 19 skeletons, 18 joints and 118 main muscles. Secondly, to validate the feasibility of the model, an experiment test is designed to reflect the real cycling status. Finally, aiming at enhancing the performance of the rider vibration comfort, the scale parameters of rear suspension are optimized with computer simulation and uniform design. The mathematical model in the vibration performance and the design variables is constructed with regression analysis. The result shows that when the length of side link is 90 mm, the length of connected rod is 336.115 1 mm and the included angle between absorber and side link is 60°, the mountain bike has better vibration comfort. This study and relevant conclusions are of practical importance to the design of the mountain bike＇s rear suspension system.

Investigation of the optimum differential gear ratio for real driving cycles by experiment design and genetic algorithm

Experiment statistical method and genetic algorithms based optimization method are used to obtain the optimum differential gear ratio for heavy truck that provides best fuel consumption when changing the working condition that affects its torque and speed range. The aim of the study is to obtain the optimum differential gear ratio with fast and accurate optimization calculation without affecting drivability characteristics of the vehicle according to certain driving cycles that represent the new working conditions of the truck. The study is carried on a mining dump truck YT3621 with 9 for- ward shift manual transmission. Two loading conditions, no load and 40 t, and four on road real driving cycles have been discussed. The truck powertrain is modeled using GT-drive, and DOE -post processing tool of the GT-suite is used for DOE analysis and genetic algorithm optimization.

Optimal Building Frame Column Design Based on the Genetic Algorithm

Building structure is like the skeleton of the building,it bears the effects of various forces and forms a supporting system,which is the material basis on which the building depends.Hence building structure design is a vital part in architecture design,architects often explore novel applications of their technologies for building structure innovation.However,such searches relied on experiences,expertise or gut feeling.In this paper,a new design method for the optimal building frame column design based on the genetic algorithm is proposed.First of all,in order to construct the optimal model of the building frame column,building units are divided into three categories in general:building bottom,main building and building roof.Secondly,the genetic algorithm is introduced to optimize the building frame column.In the meantime,a PGA-Skeleton based concurrent genetic algorithm design plan is proposed to improve the optimization efficiency of the genetic algorithm.Finally,effectiveness of the mentioned algorithm is verified through the simulation experiment.

Numerical-Experimental Updating Identification of Elastic Behavior of a Composite Plate Using New Multi-Objective Optimization Procedure

This work focuses on the updating-based identification of the three-dimensional orthotropic elastic behavior of a thin carbon fiber reinforced plastic multilayer composite plate. This consists in identifying the engineering constants that minimize the relative deviations between the first eight experimental and three-dimensional finite element frequencies of the vibrating free plate. For this purpose, a multi-objective optimization procedure is applied;it exploits a Particle Swarm Optimizer algorithm (PSO) that is coupled to a metamodeling by the new response surfaces method procedure (NRSMP);the latter is based on numerical design experiments. The conducted sensitivity analyses indicate that the four engineering constants of the two-dimensional elasticity are the most influent.

Discovering optimal weights in weighted‑scoring stock‑picking models: a mixture design approach

Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings.First,it cannot effectively identify the connection between the weights of stock-picking concepts and portfolio performances.Second,it cannot provide stock-picking concepts’optimal combination of weights.Third,it cannot meet various investor preferences.Thus,this study employs a mixture experimental design to determine the weights of stock-picking concepts,collect portfolio performance data,and construct performance prediction models based on the weights of stock-picking concepts.Furthermore,these performance prediction models and optimization techniques are employed to discover stock-picking concepts’optimal combination of weights that meet investor preferences.The samples consist of stocks listed on the Taiwan stock market.The modeling and testing periods were 1997–2008 and 2009–2015,respectively.Empirical evidence showed(1)that our methodology is robust in predicting performance accurately,(2)that it can identify significant interactions between stock-picking concepts’weights,and(3)that which their optimal combination should be.This combination of weights can form stock portfolios with the best performances that can meet investor preferences.Thus,our methodology can fill the three drawbacks of the classical weighted-scoring approach.

Optimal Designs Technique for Locating the Optimum of a Second Order Response Function

A more efficient method of locating the optimum of a second order response function was of interest in this work. In order to do this, the principles of optimal designs of experiment is invoked and used for this purpose. At the end, it was discovered that the noticeable pitfall in response surface methodology (RSM) was circumvented by this method as the step length was obtained by taking the derivative of the response function rather than doing so by intuition or trial and error as is the case in RSM. A numerical illustration shows that this method is suitable for obtaining the desired optimizer in just one move which compares favourably with other known methods such as Newton-Raphson method which requires more than one iteration to reach the optimizer.

Multi-species particle swarms optimization based on orthogonal learning and its application for optimal design of a butterfly-shaped patch antenna

A new multi-species particle swarm optimization with a two-level hierarchical topology and the orthogonal learning strategy(OMSPSO) is proposed, which enhances the global search ability of particles and increases their convergence rates. The numerical results on 10 benchmark functions demonstrated the effectiveness of our proposed algorithm. Then, the proposed algorithm is presented to design a butterfly-shaped microstrip patch antenna. Combined with the HFSS solver, a butterfly-shaped patch antenna with a bandwidth of about 40.1% is designed by using the proposed OMSPSO. The return loss of the butterfly-shaped antenna is greater than 10 d B between 4.15 and 6.36 GHz. The antenna can serve simultaneously for the high-speed wireless computer networks(5.15–5.35 GHz) and the RFID systems(5.8 GHz).

基于“仿真—半实物仿真—实物”三阶段开发流程的DAB变换器实验探索与研究

该文以双有源桥DC-DC变换器为对象,详细分析了变换器在三重移相调制下的两种数学模型,又基于这两种数学模型提出了对应的电感电流优化控制策略,搭建了包含MATLAB/Simulink仿真平台、StarSim半实物仿真平台和实物平台的三阶段实验平台,在平台中通过实验验证了优化控制策略的可行性和有效性。所设计的三阶段开发流程能够加深学生对电力电子领域相关知识的理解,逐步提升学生的理论分析能力、仿真验证能力和实践操作能力。

架空裸导线涂覆机器人作业关键因素分析及其参数优化

为了提高电网带电作业的安全可靠性,针对架空裸导线不停电绝缘化处理问题,结合一种自动涂覆绝缘胶的机器人装置,通过分析该装置注胶系统的注胶桶收敛角、胶管内径、注胶工作参数对涂覆质量的影响,开展仿真研究,得到注胶系统压力场、速度场的分布规律。利用多目标遗传算法求解涂覆作业关键技术参数的优化问题,以较大的胶管出口压力、出胶速度与较优的胶体流动稳定性作为提高注胶系统涂覆质量的优化目标,得到涂覆机器人注胶系统的最优结构及工作参数组合。试验结果表明:以LGJ-120/20规格导线为例,注胶系统的注胶桶收敛角约为23°、注胶压力约为1.925×10~5 Pa和管径为12 mm时,可以达到较好的涂覆质量,满足电网带电作业安全可靠的要求。

基于多项式代理模型的车顶绝缘子背风侧沿面压力优化研究

高速气流下,车顶绝缘子背风侧出现低气压会使得闪络电压降低,影响列车安全运行,为了改善车顶绝缘子背风侧的低气压现象,使用代理模型对车顶绝缘子结构参数进行优化。在车顶绝缘子结构参数上选取了伞裙上、下倾角以及伞裙间距作为因子,以绝缘子背风侧沿面压力的最小值作为响应,采用全因子试验设计方法获取样本数据,并对因子的主效应和交互效应进行分析,在此基础上建立多项式代理模型,进一步得到最佳的结构参数,最后进行闪络试验验证其有效性。结果表明:伞裙上、下倾角的主效应与伞裙间距相比更加显著,各因子之间没有显著的交互效应;最佳参数:伞裙上、下倾角分别为8°和0°,伞裙间距为45mm,背风侧沿面压力最小值提高了25.62%,同时压力平均值提高了42.47%;在100m/s的风速条件下,车顶绝缘子的闪络电压提高了16.80%。研究结果可以为车顶绝缘子结构设计提供参考。

基于正交试验的硅基MEMS齿形后坐保险机构参数敏感性

为了对硅基MEMS后坐保险机构进行优化设计和性能研究,基于正交试验与有限元仿真软件相结合的方法,选择齿高、齿宽、弹簧线宽、闭锁梁宽、锁钩间距、初始距离这6个设计参数,对后坐保险机构在正常发射和勤务跌落载荷下的位移和应力的影响进行参数敏感性分析。通过极差分析与方差分析,定量地给出了后坐保险机构各参数对结构在不同冲击载荷下的影响。结果表明,齿高的影响最显著,是影响后坐保险机构位移与应力响应的最主要因素;初始距离、弹簧线宽、齿宽是影响勤务跌落最大位移的次要因素;其他因素均对正常发射与勤务跌落载荷下位移和应力不显示显著性。在相对少的试验次数中定量得出不同设计参数对硅基后坐保险机构的影响大小,对多参数双目标的硅基后坐保险机构优化提供了一定的参考。

基于RF-GWO的水利工程地质渗透系数智能反演分析

地质渗透系数是准确分析水利工程渗流的关键参数。针对传统反演方法计算效率低、精度差的问题,采用有限元正演模型和正交试验设计构建渗透系数反演样本集,建立了基于随机森林(RF)算法的渗流计算代理模型;在此基础上,引入灰狼优化(GWO)算法,提出了基于RF-GWO的渗透系数智能反演方法,并以Z抽水蓄能电站为研究案例进行了验证。结果表明:RF模型对各钻孔水位预测结果均接近实测值,性能优于CART和BP模型;GWO可搜寻到地质最佳渗透系数,钻孔水位反演结果合理,相对误差最大为0.42%,精度满足工程要求,计算的天然渗流场分布形态也符合一般山体渗流场分布规律。建立的反演模型能够快速准确地推断工程区地层渗透系数,具有实际工程应用价值。

可重构智能表面辅助通信系统的非线性优化及实验设计

针对可重构智能表面辅助的通信系统,提出了一种智能反射表面开关选择和功率分配的联合设计方法,以改变电磁波传播环境、提高信号传输效率、减少功率消耗。首先,在保证接收端数据传输速率达到一定门限值等多个约束条件下,建立以系统能量效率最大化为目标的多维变量联合非线性优化问题,其中涉及的优化变量包括:智能反射表面开关选择、相位调整和功率分配。在开关选择方面,采用贪婪算法选择开启起主要作用的元器件,并关掉起次要作用的智能反射表面。在功率分配方面,通过求解凸优化问题得到功率分配的闭式解。基于此,设计出一种高传输速率、低功率消耗的迭代优化算法。最后,设计仿真教学实验,通过MATLAB软件,验证了所提出的联合优化算法通过改变电磁波传播环境,有效地提高了信号传输的速率和能量效率;并分析了实验中导致算法性能发生变化的机理,这也有利于帮助学生提高科研创新能力和工程实践能力。