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.

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.

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.

Experimental Design for Computer System Platform Course

The computer system platform course is a comprehensive platform course formed by modifying and optimizing the content of the relevant basic courses of computer major to meet the requirement for basic computer knowledge in digital media technology major.The scope and extent of the course content make its experimental design more challenging.By considering the scope of contents,the difficulty of experiments,and the rationality of intrinsic logic,the experiments covering from operating system to computer network are designed.The relevance and continuity of the experiments are ensured,so as to achieve the goals of promoting students’understanding of key concepts,improving their hands-on ability,cultivating their interest,and guiding their self-learning within the limited class time.

Application of Uniform Experimental Design in Optimizing Excitation Parameters for Magnetic Frequency Mixing Measurements

Excitation parameter preferences are key factors a ecting the performance of magnetic frequency mixing detection.A uniform experimental design method was used to analyze this influence.Using fuzzy theory,a comprehensive model is established for evaluating the e ect of magnetic frequency mixing.A polynomial is selected as the regression function to express explicitly the correlation between the excitation parameters and the frequency-mixing e ect.The excitation parameters were then optimized using genetic algorithm.Magnetic frequency mixing experiments were conducted to measure the surface hardness of some ferromagnetic materials.Frequency mixing is further enhanced under the optimal settings,resulting in an improvement in the measurement sensitivity.The results of this study support the application of the magnetic frequency mixing technique in non-destructive testing.

Experimental Design of Measuring Soil-Water Characteristic Curve of Unsaturated Soil Using Bayesian Approach

Soil-water characteristic curve (SWCC) is significant to estimate the site-specific unsaturated soil properties (such as unsaturated shear strength and coefficient of permeability) for geotechnical analyses involving unsaturated soils. Determining SWCC can be achieved by fitting data points obtained according to the prescribed experimental scheme, which is specified by the number of measuring points and their corresponding values of the control variable. The number of measuring points is limited since direct measurement of SWCC is often costly and time-consuming. Based on the limited number of measuring points, the estimated SWCC is unavoidably associated with uncertainties, which depends on measurement data obtained from the prescribed experimental scheme. Therefore, it is essential to plan the experimental scheme so as to reduce the uncertainty in the estimated SWCC. This study presented a Bayesian approach, called OBEDO, for probabilistic experimental design optimization of measuring SWCC based on the prior knowledge and information of testing apparatus. The uncertainty in estimated SWCC is quantified and the optimal experimental scheme with the maximum expected utility is determined by Subset Simulation optimization (SSO) in candidate experimental scheme space. The proposed approach is illustrated using an experimental design example given prior knowledge and the information of testing apparatus and is verified based on a set of real loess SWCC data, which were used to generate random experimental schemes to mimic the arbitrary arrangement of measuring points during SWCC testing in practice. Results show that the arbitrary arrangement of measuring points of SWCC testing is hardly superior to the optimal scheme obtained from OBEDO in terms of the expected utility. The proposed OBEDO approach provides a rational tool to optimize the arrangement of measuring points of SWCC test so as to obtain SWCC measurement data with relatively high expected utility for uncertainty reduction.

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.

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.

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.

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.

Ideological and Political Teaching Case Design and Research Based on the Course Simulation Electronic Technology Experiment

As critical measures to educate and cultivate people’s morality,“Ideology and Politics of Course”has gradually become the focus in actual undergraduate education research in universities.Targeting the single ideological and political education mode and students’lack of interest in experimental courses,this paper,through various links,including pre-class preview,in-class teaching,and after-class data analysis,takes the common emitter single tube amplifier experiment as an example to organically integrate the excavated ideological and political elements with practical operation and carry out teaching case design and practice;realize the improvement of students’learning initiative and problem-analysis ability;and achieve the purpose of cultivating people throughout the whole process and in a comprehensive direction.

Optimization of PCR Reaction System for Random Single-strand DNA Pool in SELEX Technology

[Objective] This study aimed to optimize the PCR amplification conditions for random ssDNA pool in SELEX technology. [Method] L16（45） orthogonal experimental design was adopted for optimization of five important factors affecting PCR reaction system for random single-stranded DNA pool including Mg2＋ concentration, dNTP concentration, amount of Taq DNA polymerase, primer concentration and amount of random single-stranded DNA pool at four levels. Meanwhile, the annealing temperature and number of PCR reaction cycles were optimized to establish the optimal reaction system and PCR procedure. [Result] The optimal combination of PCR reaction system for random ssDNA pool was obtained, with a total system volume of 20 μl containing 2.0 μl of 10 × Buffer, 0.5 ng of random ssDNA pool, 2.5 mmol/L Mg2＋, 0.25 mmol/L dNTP Mixture, 0.6 μmol/L upstream and downstream primers and 1.5 U of Taq DNA polymerase; the optimal annealing temperature was 68 ℃ and the optimal number of cycles was 12. Under the above conditions, clear and stable bands with high specificity for random ssDNA pool were amplified. [Conclusion] This study laid the foundation for selection of parameters with higher specificity in SELEX technology.

Application of quality by design in the current drug development

Quality by Test was the only way to guarantee quality of drug products before FDA launched current Good Manufacturing Practice. To clearly understand the manufacture processes, FDA generalized Quality by Design(QbD) in the field of pharmacy, which is based on the thorough understanding of how materials and process parameters affect the quality profile of final products. The application of QbD in drug formulation and process design is based on a good understanding of the sources of variability and the manufacture process. In this paper,the basic knowledge of QbD, the elements of QbD, steps and tools for QbD implementation in pharmaceutics field, including risk assessment, design of experiment, and process analytical technology(PAT), are introduced briefly. Moreover, the concrete applications of QbD in various pharmaceutical related unit operations are summarized and presented.

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.

Applications of quality by design(QbD) and its tools in drug delivery

Quality by Test (Qb T) was the only way to guarantee the quality of drug products before FDA launches current Good Manufacturing Practice (c GMP)[1], which is an approach without clear understanding of the processes. In order to solve this problem,FDA generalized Quality by Design (QbD) in the field of pharmacy (2)In pharmaceutical industry, Qb D brings cost-efficiency and simplicity of manufacturing process into reality.