Experimental Design Method Research Articles

A new test method for simulating wear failure of hydraulic pump slipper pair under high-speed and high-pressure conditions

In practical engineering, it is very difficult to obtain data on the slipper wear of hydraulic pumps, especially under high-speed, high-pressure conditions, which limits the development of fault diagnosis technology for hydraulic pumps. At present, a test method that can accurately simulate the operating state of the slipper pair under high-speed and high-pressure conditions does not exist. The reliable load-bearing design of the slipper pair is difficult to carry out effectivetest verification, which limits the development of high-speed and high-pressure piston pumps. Therefore, an experimental design method was proposed to directly simulate the high-speed, high-pressure friction state of the slipper pair based on the change law of reprinting residual pressing force.

Effect of Bajakah Tea Extract (Spatholobus littoralis Hassk) on High Density Lipoprotein, Triglyceride and Total Cholesterol Levels in Male Wistar Rats

Hyperlipidemia is the high concentrations of low-density lipoprotein, cholesterol, and triglycerides in the blood. Furthermore, hyperlipidemia has a risk factor for cardiovascular complications through atherosclerosis that triggers coronary heart disease (CHD). Spatholobus littoralis Hassk known as Bajakah tea is a plant with high antioxidant content and acts as an HMG-CoA reductase inhibitor to suppress lipid metabolism disorders, increase HDL levels, and reduce triglycerides and total cholesterol. Experimental and posttest control group design methods were used to obtain 24 male Wistar rats. Furthermore, the experimental animals were randomly divided into 4 groups, namely K1 (normal rats), K2 (hyperlipidemia rats), K3 (hyperlipidemic rats + pirated tea extract dose 9 mg/200gramBW/day), and K4 (hyperlipidemic rats + simvastatin dose 0.18 mg/200 gBW/day). The study was conducted at the Center for Food and Nutrition Studies Laboratory, Gajah Mada University, Yogyakarta. Data levels of HDL, triglycerides, and total cholesterol were analyzed using the One Way Anova test and LSD post hoc to determine between groups. The results showed that an increase in triglyceride and total cholesterol and a decrease in HDL levels in the K2 group compared to the K1. Triglyceride and total cholesterol decreased in groups K3 and K4 compared to K2, and HDL levels increased in groups K3 and K4 compared to K2. Meanwhile, the results of the analysis obtained p value <0.05. This study concluded that the administration of Bajakah tea extract at a dose of 9 mg/200gram BW/day for 14 days increase HDL, reduce triglyceride and total cholesterol levels (p<0,05).

Optimizing construction parameters for fractured horizontal wells in shale oil

Shale oil is mainly extracted by fracturing. However, it is difficult to determine the optimum construction parameters to obtain maximum productivity. In this paper, a fuzzy comprehensive production evaluation model for fractured shale oil horizontal wells based on random forest algorithm and coordinated principal component analysis is proposed. The fracturing parameters of the target wells are optimized by combining this model with an orthogonal experimental design. The random forest algorithm was used to calculate the importance of data sample factors. The main controlling factors of the production of fractured horizontal wells in shale oil were obtained. To reduce the noise of the sample data, principal component analysis was used to reduce the dimensions of the main control factors. Furthermore, the random forest algorithm was used to determine the weight of the principal components after reducing the dimensionality. The membership function of the main control factors after reducing dimensionality was established by combining the fuzzy statistics and assignment methods. In addition, the membership matrix of the effect prediction of fractured horizontal wells in shale oil was determined. The fuzzy comprehensive evaluation method is used to score and evaluate the effect of fractured horizontal wells. Combined with the orthogonal experimental design method, the optimized parameter design of a fractured horizontal well considering the comprehensive action of multiple parameters is realized. After construction according to the optimized parameters, production following fracturing increases significantly. This verifies the rationality of the optimization method that is proposed in this paper.

An optimal experimental design strategy for improving parameter estimation in stochastic models

Systematic model-based design of experiment is essential to maximise the information from an experimental campaign. This technique is even more important to design experiments in systems described by stochastic models where the information quantity is characterised by intrinsic uncertainty, which has a significant impact on the experimental design for yielding informative data for precisely estimating model parameters. In this work, a new method for stochastic model-based design of experiments (SMBDoE) is presented to simultaneously identify the optimal operating conditions and the allocation of sampling points in time. The optimal experiment is identified by two sampling strategies selecting sampling intervals based on the average and the uncertainty of Fisher information. Seed coating is used as a case study to illustrate the feasibility of the method in identifying optimal coating conditions and sampling strategy in an industrial application.

Investigation of RBFNN Based on Improved PSO Optimization Algorithm for Performance and Emissions Prediction of a High‐Pressure Common‐Rail Diesel Engine

AbstractThe purpose of this study is improve calibration efficiency and obtain accurate diesel engine operating parameters, achieving improved diesel engine emissions and fuel efficiency. A PSO‐RBF (particle swarm optimization‐radial basis function) diesel engine performance prediction model combining an improved PSO (particle swarm optimization algorithm and an RBF neural network is proposed. A space‐filling experimental design method for diesel engine performance prediction is proposed based on the actual operating conditions of diesel engines. Training data are collected at the bench to build the RBF prediction model. An optimization PSO search method is proposed to improve the PSO optimization capability. An improved PSO algorithm is used to optimize the model and improve prediction accuracy. Then the BSFC (diesel brake‐specific fuel consumption), NOx ((Nitrogen Oxid), CO (Carbon Monoxide), and HC (Hydrocarbon) prediction models are constructed. Results show that the PSO‐RBF can find the global solution with good prediction accuracy and generalization ability during small amounts of data. The PSO‐RBF model fitting degrees of BSFC, NOx, CO, and HC are 0.9952, 0.9910, 0.9820, and 0.9870 respectively. Mean relative errors are 3.02%, 2.78%, 1.39%, and 2.01% respectively. Mean absolute percentage errors are 1.58%, 3.26%, 3.69%, and 2.96% respectively. The optimized model R2 (Model determination coefficient) is improved by 0.065, 0.102, 0.10, and 0.085, respectively.

Comparative Design Improvement of the Growing Rod for the Scoliosis Treatment Considering the Mechanical Complications

In this study, the focus is on an implant used in the treatment of early-onset scoliosis called magnetically controlled growing rods (MCGR). The primary goal of the study is to address and propose solutions for the mechanical problems reported in the literature concerning MCGR. The problems of the MCGR are mainly due to excessive stress and mechanical bearing problems. Therefore, an MCGR removed from a patient is teardown and geometrically modeled. Then, eleven design parameters are determined on the MCGR for the mechanical problems experienced and these are evaluated by mechanical analysis over 14 control points. In this study, analysis processes are carried out with L12 orthogonal array for eleven design parameters and 2 levels using Taguchi’s experimental design method (DoE). With the obtained data by analyzing the experiments in L12, the fitness functions depending on the design parameters are created for 14 control points. Since the problem is multi-objective, a non-dominated sorting genetic algorithm (NSGA II) and multi-objective particle swarm optimization (MOPSO) are used to minimize stress and displacement in existing mechanical problems using fitness functions. The obtained design models from NSGA II and MOPSO are analyzed and evaluated in comparison with the existing mechanical model obtained through pre-optimization teardown study of MCGR.

The Effect of Physical Environment Factors on Human Cognitive Performance Through EEG Signals

Human activities that require cognitive performance is doing cognitive computer-based tasks. One of the important cognitive performances in people when doing some active thinking tasks is attention. This study focuses on measuring cognitive performance through cognitive tasks in the form of a Stroop Color Word Test with a neurophysiological approach, namely EEG, related to the effects of physical environment factors: room temperature. The temperature variations that will be studied refer to the concepts of slightly warm, neutral, and slightly cool with 18 °C representing slightly cool, 24 °C representing neutral, and 30 °C representing slightly warm. This study used an experimental design method and was conducted on 12 subjects. This study showed that room temperature treatment had a significant effect on TRT (sig. 0.00), but not on accuracy which it refers to cognitive performance to do the task correctly. To explain of the beta EEG signal, it was found that the highest power spectrum value occurred when the respondent was subjected to room temperature treatment of 24 °C. While in the ERP P3 measurement, it was found that the amplitude (µV) of P3 was best at 24 °C.

Effect of Road Transport Operations on the Environmental Quality of Air and Associated Health Implications in Ondo State, Nigeria.

Transport movement along road corridors and freight movement generates on-street business for some individuals. Operations of fossil powered automobiles contribute to poor quality of air in the environment, using survey research and experimental design methods. Traffic survey of vehicles were carried out to determine the volume and composition of vehicles on selected roads in Ondo State. Altair 5X analyser was used to collect and analyse air pollutants of CO, SO2 and NO2, while, questionnaires were administered to randomly selected respondents for data collection on effect of air-borne transport pollutants on human health. The study revealed that about 4562 to 729 pcu/hr traverse through different road corridors in Ondo state, while about 13.690 to 2.190 ppm of CO, 0.020 to 0.006 ppm of SO2 and about 0.068 to 0.011 ppm of NO2 were produced. The study showed that the level of CO, SO2 and NO2 produced at some corridors exceeds the average minimum acceptable standard. Findings from respondents who subsist and do business along the selected corridors showed that 11.70% of them have high levels of cough which suggest a serious sign of danger as 4% is the allowable level for cough in a community, 9.10% have high throat irritation, 10.79% have nasal discharge and 4.45% have Asthma. The study recommended that vehicles plying on urban road in Ondo state should undergo regular check to meet up with recommended emission standard and that proactive measures taken to reduce over dependence on fossil fuel powered automobile.

Post-marketing effectiveness evaluation of traditional Chinese medicine in paradigm based on "proving efficacy, conforming standard, and exploring mechanism":a case study of Danhong Injection

At present, new concepts, new technologies, and new methods are emerging in the field of medical research, breaking through the inherent thinking patterns and research models, and promoting the transformation of the research paradigm of traditional Chinese medicine(TCM). This paper gave a case study of clinical research in Danhong Injection in the treatment of chronic stable angina, and based on the background of the study, index evaluation model, experimental design method, blind implementation of placebo, data management system, and exploration of clinical efficacy mechanism of traditional Chinese medicine compounds under the framework of modular pharmacology, the scientific idea of &quot;proving efficacy, conforming standard, and exploring mechanism&quot; was used as the guideline to discuss the research model of reevaluation of the effectiveness of post-marketing TCM varieties. This paper drew a target network map of Danhong Injection in the treatment of chronic stable angina for the first time, which was composed of targeted functional modules. By combining evidence-based clinical research with modular pharmacology framework, changes in the pharmacolo-gical mechanism were finally associated with changes in clinical efficacy, and the advantages of phenotypic correlation of efficacy were explored. This study is expected to provide references for the post-marketing effectiveness evaluation and new ideas for the phenotypic pharmacological mechanism study of multi-target TCM compounds and precise treatment, thereby promoting the innovative development of TCM.

Application of a first-order method to estimate the failure probability of component subjected to thermal transients for optimization of design parameters

The Japan Atomic Energy Agency has been developing “Advanced Reactor Knowledge- and AI-aided Design Integration Approach through the whole plant lifecycle (ARKADIA)” to offer the best solutions for challenges in the design and operation of nuclear plants. A part of ARKADIA for design study, which included design optimization of components, is named as ARKADIA-Design. In the development of ARKADIA-Design, we have been developing a process to automatically optimize design parameters of structural components subjected to various kinds of loads, including thermal transients. In this paper, we propose a simplified procedure to estimate the failure probability of components subjected to thermal transients for design optimization. An objective function of this optimization is defined on the basis of failure probability of the components, because failure probability can be commonly used as an indicator of component integrity for various mechanisms, and it helps future introductions of a risk-informed performance-based approach to component design. To enable the necessary number of estimations for design optimization with practical calculation time, we aimed to reduce the number of analyses required for one estimation. For this purpose, we adopted the first-order second-moment (FOSM) method as the estimation method for failure probability in the process of optimization. An orthogonal table in the experiment design method is utilized to define the conditions of the analyses for evaluation of the mean and variance of thermal transient stress, which are used as inputs in the FOSM method. The superposition of ramp responses is also utilized to evaluate the time history of thermal transient stress instead of finite element analysis. The proposed procedure was applied in a demonstration study to optimize the thickness of a cylindrical vessel subjected to thermal transients derived from shutdown. We confirmed that the procedure can evaluate the failure probability depending on the cylinder thickness with practical calculation time.

Combination of DOE and PIV methods for multi-criteria decision making

When performing the multi-criteria decision making to choose the best solution, if some solutions are removed from the list of solutions or some solutions are added to the list of solutions, the decision making must be re-performed from the beginning. This study proposes a new method to remove this limitation. The combination of the DOE (Design Of Experimental) method and PIV (Proximity Indexed Value) method is proposed in this paper. This combination is used to build the relationship between the scores of the solutions and the criteria. When the list of solution to be ranked has been removed or have been added some solutions, the ranking of some solutions only needs to use this relationship without having to recalculate from the beginning. Four different examples were applied to evaluate the effectiveness of the proposed method. The obtained results show that the proposed method ensures the required accuracy as well as its outstanding advantages. The limitations of the proposed method that need to be overcome are also pointed out at the end of this paper

Ideological and Political Education in College Experimental Teaching

Experimental teaching in colleges and universities plays an important role in the whole training process of students. However, the integration of ideological and political elements in experimental teaching in many colleges and universities is very scarce. This paper combs the common ideological and political elements in experimental teaching, including safety awareness and responsibility awareness; Innovative thinking and scientific research accomplishment; Professional quality, craftsmanship spirit; Sense of overall situation and team spirit. Through innovative experimental teaching design and experimental teaching methods, experimental teachers properly, flexibly and silently integrate ideological and political elements into experimental teaching, making experimental teaching an important practice place of ideological and political education.

Multiple response optimization of sizing machine settings and sized yarn tensile properties using advanced method of experimental design

On a loom, the warp yarns experience exceptionally high mechanical stresses and strains, as well as significant abrasion from friction. As a result, significant yarn breakages cause loom stoppages and, hence, production and quality interruptions. To mitigate this problem, the warp yarns should be treated with a thin, strong film of an appropriate adhesive. The aim of this study is therefore, to investigate the effect of sizing machine parameters on tensile properties of the yarn and to optimize the sized yarn tensile properties. Design expert 11 software with Box-Behnken design has been employed to design the research, analyze the results of experiments, and optimize the responses. In this research, three factors, each at three levels (wet zone yarn tension (A) (340, 380, and 420 N), squeezing roller pressure (B) (13, 15, and 17 N), and sizing machine speed (C) (30, 49, and 68 m/min), as well as three response variables (gain strength, loss elongation, and stretch), are considered. 15 samples of sized yarn have been produced with various combinations of the given factors, and the values of the response variables are measured for each sample. It has been observed that wet zone yarn tension and squeezing roller pressure have a significant effect on gain strength, stretch, and loss elongation. According to the regression analysis, gain strength has a negative relationship with wet zone yarn tension and a positive relationship with squeezing roller pressure. In the other case, both wet-zone yarn tension and squeezing roller pressure have a positive and significant effect on loss elongation and yarn stretch. At 340 N wet zone yarn tension, 13 N squeezing roller pressure, and 51 m/min sizing machine speed, the optimum values of gain strength (32.7 cN/tex), loss elongation (18.5%), and stretch (1.4%) were obtained. The methodologies employed in this study revealed the empirical relationship between the factors and the responses, the interaction effects of the factors on the responses, and multiple response optimizations. The optimized result of this study can be applied to the warp yarn sizing and can improve the tensile properties of the yarn and the loom’s efficiency significantly.

Numerical Study of the Effect of Angle and Location of the First and Second Steps on the Performance of Planing Hull and Their Optimization Using the Taguchi Statistical Method

Providing steps at the bottom of a high‐speed craft could decrease the resistance and enhance the longitudinal stability by dividing a single pressure hump into several ones and extending the hydrodynamic pressure towards aft of the vessel. In the present paper, the effects of the fore and aft steps’ locations and angles on double‐stepped hulls are investigated systematically via a numerical approach. The parent model investigated in this study is a model of a high‐speed recreational craft called Cougar, with a 1 : 5 scale. The three‐dimensional analysis of the model is conducted via the finite volume method, and the free surface is tracked using the volume of the fluid technique. The computational domain is discretized by the overset technique, and the calculations are validated via experimental data. Overall, 27 numerical tests designed by the Taguchi experiment design method are simulated, and the resistance, trim, and rise‐up values are calculated at 8 to 10 m/s speeds. The findings support the notion that the resistance increases by increasing the distance between the fore step and vessel stern. In addition, increasing the distance between the aft step and vessel stern and the increase of the angle of the fore step leads to reducing the vessel resistance. However, the results showed that finding a sensible relationship between the angle of the aft step and resistance requires more investigation. The findings revealed that the interactional effects between the angles of the aft and fore steps are the most considerable among all studied interactional effects. Among the all studied configurations, the lowest resistance was experienced by the model with an aft step at 25% of the vessel’s length to the stern, having an angle of 150°, and a fore step at 40% of the vessel’s length to the stern, having an angle of 180°.

城市生态学自然模拟实验研究进展

PDF HTML阅读 XML下载 导出引用 引用提醒 城市生态学自然模拟实验研究进展 DOI: 10.5846/stxb202111033092 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家生态网络平台专题服务（能力建设）项目 Research progress of natural manipulative experiment in urban ecology Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:自然模拟实验，通过适当控制影响因素研究自然状态下生态系统变化，已经越来越受到生态学家的欢迎。新兴的快速发展的城市生态学越来越多的采用自然模拟实验方法。研究从城市生态学的实验设计方法（如人工梯度、自然梯度和城市建设实验）、环境因子（如空气污染、热岛、灯光和地表硬化）生态效应和生态系统（如社区、绿地、绿化屋顶和集水区）响应等方面，分析了国内外大量城市生态学自然模拟实验的研究案例，并总结了城市生态学自然模拟实验的特点，指出了城市生态学模拟实验应该考虑的主要问题，并提出未来主要发展方向：（1）多种环境因子复合影响模拟实验研究，（2）生态系统结构和功能响应模拟实验研究，（3）近自然的长期模拟实验研究，（4）大范围的联网模拟实验研究和（5）面向城市生态恢复和建设的模拟实验研究。希望研究能够为推进我国城市生态学的实验研究起到抛砖引玉作用。 Abstract:The natural manipulative experiment, by controlling influencing factors appropriately to study changes in the ecosystem under natural conditions, has become more and more popular with ecologists. This article, which focused on the experimental design methods of urban ecology (such as artificial gradients, natural gradients and urban construction experiments), environmental factors (such as air pollution, heat islands, nightlight and surface hardening) and the response of urban ecosystems (such as communities, green spaces, green roofs and watersheds), reviewed a great number of cases using natural manipulative experiments in urban ecology at home and abroad. It also summarized the characteristics of the natural manipulative experiment of urban ecology, pointed out that the main problems which should be considered in the urban ecology manipulative experiment, and proposed the main development direction in the future:(1) Manipulative experiments on the compound impact of multiple environmental factors; (2) Manipulative experiments on the response of the ecosystem structure and function; (3) Long-term near-natural manipulative experiments; (4) Large-scale network of manipulative experiments; (5) Manipulative experiments on the urban ecological restoration and construction. We hope that this review can play a role in advancing the experimental research of urban ecology in China. 参考文献 相似文献 引证文献

Bayesian D-Optimal Design of Experiments with Quantitative and Qualitative Responses

Systems with both quantitative and qualitative responses are widely encountered in many applications. Design of experiment methods are needed when experiments are conducted to study such systems. Classic experimental design methods are unsuitable here because they often focus on one type of response. In this paper, we develop a Bayesian D-optimal design method for experiments with one continuous and one binary response. Both noninformative and conjugate informative prior distributions on the unknown parameters are considered. The proposed design criterion has meaningful interpretations regarding the D-optimality for the models for both types of responses. An efficient point-exchange search algorithm is developed to construct the local D-optimal designs for given parameter values. Global D-optimal designs are obtained by accumulating the frequencies of the design points in local D-optimal designs, where the parameters are sampled from the prior distributions. The performances of the proposed methods are evaluated through two examples.

Experimental characterization of pure copper heat sinks fabricated by powder metallurgy

In this study, porous copper plates were produced to be used as heat sinks for cooling purposes. Copper powders were used in the production process and powder metallurgy steps were followed. The relationship between pressure, sintering temperature, time and powder size affecting the porosity level and volume was revealed using the Taguchi experimental design method. Copper powders of 5 ?m and 50 ?m sizes were used in the experiments, and the powders were compressed in the mold at two different pressures, 50 and 60 bar, and sintered at 700?C and 900?C temperatures for 30 and 60 minutes. The L8 orthogonal array was chosen by targeting the surface with the highest porosity in the Taguchi method. The effect ratios of each design parameter on porosity were determined with the analysis of variance (ANOVA). The microstructure characterization, thermal and mechanical properties of the produced samples were analyzed, comparatively.

Experimental and statistical analysis on the effects of fouling and effective parameters on performance of heat exchanger

Increasing the performance of heat exchangers is one of the main objectives of researchers and engineers in the industry. In this paper an experimental work is carried out aiming at modeling the efficiency of shell and tube heat exchanger under the influence of the most contributing factors on performance as fouling resistance, the inlet temperatures and the flow rate of the fluids. An experimental design method of type 31.24 was used for the studies, requiring 48 tests with three repeats. The following factors are taken into account: the inlet temperature and flow rate of crude oil fluid and TEG fluid on two levels, and the fouling resistance on three levels. Regression analysis was produced a mathematical model for the efficiency based on the influential variables. The response surface and contour plots were allowed the selected from the optimum and predicted the behaviors of parameters. The effectiveness decreases considerably with the increase in fouling resistance and the decrease of inlet temperatures and flow rates.

Box–Behnken experimental design method application in pretreatment of olive oil mill wastewater by ultrafiltration

Box–Behnken experimental design method application in pretreatment of olive oil mill wastewater by ultrafiltration

Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning.

The impressive performance exhibited by modern machine learning models hinges on the ability to train such models on a very large amounts of labeled data. However, since access to large volumes of labeled data is often limited or expensive, it is desirable to alleviate this bottleneck by carefully curating the training set. Optimal experimental design is a well-established paradigm for selecting data point to be labeled so to maximally inform the learning process. Unfortunately, classical theory on optimal experimental design focuses on selecting examples in order to learn underparameterized (and thus, non-interpolative) models, while modern machine learning models such as deep neural networks are overparameterized, and oftentimes are trained to be interpolative. As such, classical experimental design methods are not applicable in many modern learning setups. Indeed, the predictive performance of underparameterized models tends to be variance dominated, so classical experimental design focuses on variance reduction, while the predictive performance of overparameterized models can also be, as is shown in this paper, bias dominated or of mixed nature. In this paper we propose a design strategy that is well suited for overparameterized regression and interpolation, and we demonstrate the applicability of our method in the context of deep learning by proposing a new algorithm for single shot deep active learning.