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.

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).

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 design of vegetation in residential district with numerical simulation and field experiment

Vegetation plays a key role in improving wind environment of residential districts,and is helpful for creating a comfortable and beautiful living environment.The optimal design of vegetation for wind environment improvement in winter was investigated by carrying out field experiments in Heqingyuan residential area in Beijing,and after that,numerical simulation with SPOTE(simulation platform for outdoor thermal environment) experiments for outdoor thermal environment of vegetation was adopted for comparison.The conclusions were summarized as follows:1) By comparing the experimental data with simulation results,it could be concluded that the wind field simulated was consistent with the actual wind field,and the flow distribution impacted by vegetation could be accurately reflected;2) The wind velocity with vegetation was lower than that without vegetation,and the wind velocity was reduced by 46%;3) By adjusting arrangement and types of vegetation in the regions with excessively large wind velocity,the pedestrian-level wind velocity could be obviously improved through the simulation and comparison.

Optimization Design and Comprehensive Evaluation of Screw Contact of Space Battery Based on ANSYS

In order to improve the safety of the battery of satellite side mounting,and prevent the screw from producing excess due to frequent assembly and disassembly,the YS-20 material replacement and structure optimization design of the screw body are carried out under the premise of not changing the original tooling.The double⁃shear test of YS-20 bar is carried out,and the ANSYS optimization design module is used to design 7×7×6,a total of 294,calculation cases of D1,D2,T,the three important dimension parameters of screw structure.The actual bearing state of screw composite structure is accurately simulated by using asymmetric contact model.Three comprehensive evaluations are established,and the calculation examples satisfying the conditions are evaluated comprehensively.The final results are T=12.2 mm,D1=16 mm,D2=2 mm.The stress verification and contact analysis are carried out for the final scheme and the bearing state and contact state optimized screw structure are obtained.

Parameters optimization design of quenching and partitioning for best combination between strength and ductility using orthogonal experimental design

Quenching and partitioning(Q&P)which provides a balance between toughness and strength is a promising heat treatment in iron and steel industry.However,there are three parameters(quenching temperature,partitioning temperature and partitioning time)which affect the properties dramatically.As a result,it remains a challenge to get the best parameters in a low-cost way for Q&P process.Here,the orthogonal experimental design combined with a local optimization was adopted to optimize the quenching and partitioning parameters of 65Si2MnWA steel.By using this method,the combination between strength and ductility was optimized;meanwhile,the number of experiments was reduced significantly.When treated by quenching at 180℃followed by partitioning at 330℃for 20 min,the steel reached the best combination between strength and ductility.In detail,the product of ultimate tensile strength and reduction in area was 1.36 times(from 64.9 to 88.8 GPa%)that treated by quenching and tempering.In addition,owing to the grain refinement,the strength and ductility increase simultaneously.Specifically,the reduction in area increased by 27.4%(from 35.8%to 45.6%)coupled with a little improvement in ultimate tensile strength(7.4%).

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.

Optimization design of drilling string by screw coal miner based on ant colony algorithm

It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat- egy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re- search screw coal mine machine.

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.

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.

Experimental Design as a Tool for the Development of Citric Acid Production by Yarrowia lipolytica Using Glycerol as a Feedstock

Due to the scarcity of fossil fuels in the world, there is increasing interest in the commercial production of biodiesel, which leads to obtaining large amounts of glycerol as a byproduct. If not disposed of properly, glycerol can generate environmental impact. One of the promises, the application of the crude glycerol is the production of citric acid by microbial fermentation. Citric acid is industrially produced by a submerged fermentation process with Aspergillus niger, using sucrose as carbon source, but due to increased demand for citric acid, alternative processes using renewable sources or waste materials as substrates and the cultivation of yeast strains are being studied. The aim of the study was to determine the best culture condition for maximum citric acid synthesis and lower isocitric acid production from crude glycerol through experimental design tool. For this purpose, the yeast strain Yarrowia lipolytica IMUFRJ-50682 was cultivated in nitrogen-limited glycerol-based media. Therefore, glycerol and yeast extract concentrations and agitation speed were evaluated as independent variables. With pure glycerol, the highest citric acid production achieved was 16.5 g/L with an isocitric acid production of 7.7% （in relation to citric acid）. With crude glycerol, citric acid production reduced to 6.7 g/L because of higher biomass yield. Therefore, an increase in the initial carbon to nitrogen molar ratio from 714 to 1,561 was necessary to increase citric acid production to 9.2 g/L, reducing isocitric acid production and to achieve a yield of 0.41 g of citric acid per glycerol consumed. In this condition, less nitrogen source was used, reducing production costs.

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.

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.

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.

Analysis and Trajectory Design for Supersonic Target

To seek an inexpensive supersonic target,characteristics of various targets are analyzed,its tactical and technical specifications are drafted,and a refitting scheme using big-caliber supersonic rockets is put forward. An optimization model to meet tactical and technical requirements is built,in which the speed and the trajectory angle at the end of active phase are used as the design variables and the horizontal flight distance of the target is used as the objective function. The simulated results show that the method is reasonable and feasible.

Objective array design for three-dimensional temperature and salinity observation:Application to the South China Sea

In this study,a moored array optimization tool(MAOT)was developed and applied to the South China Sea(SCS)with a focus on three-dimensional temperature and salinity observations.Application of the MAOT involves two steps:(1)deriving a set of optimal arrays that are independent of each other for different variables at different depths based on an empirical orthogonal function method,and(2)consolidating these arrays using a K-center clustering algorithm.Compared with the assumed initial array consisting of 17 mooring sites located on a 3°×3°horizontal grid,the consolidated array improved the observing ability for three-dimensional temperature and salinity in the SCS with optimization efficiencies of 19.03%and 21.38%,respectively.Experiments with an increased number of moored sites showed that the most cost-effective option is a total of 20 moorings,improving the observing ability with optimization efficiencies up to 26.54%for temperature and 27.25%for salinity.The design of an objective array relies on the ocean phenomenon of interest and its spatial and temporal scales.In this study,we focus on basin-scale variations in temperature and salinity in the SCS,and thus our consolidated array may not well resolve mesoscale processes.The MAOT can be extended to include other variables and multi-scale variability and can be applied to other regions.

Control-Relevant Identification Test Design for Open-Loop Experiment

An optimal experiment design (DED) with respect to the use of designing model-base controller was studied. The mean squared error at the setpoint is chosen as the performance criterion. Simple design formulas are derived based on the asymptotic theory. The signal is used for the open loop experiment. The design constraint is the power of the process signal or the process input signal. The results give guideline for identification application.

A Robust Optimization Approach Considering the Robustness of Design Objectives and Constraints

The problem of robust design is treated as a multi-objective optimization issue in which the performance mean and variation are optimized and minimized respectively, while maintaining the feasibility of design constraints under uncertainty. To effectively address this issue in robust design, this paper presents a novel robust optimization approach which integrates multi-objective optimization concepts with Taguchi’s crossed arrays techniques. In this approach, Pareto-optimal robust design solution sets are obtained with the aid of design of experiment set-ups, which utilize the results of Analysis of Variance to quantify relative dominance and significance of design variables. A beam design problem is used to illustrate the effectiveness of the proposed approach.