Research on experimental design method of engine system based on D-optimal design

The mixture designs are represented in terms of variables (xi , i = 1 , · , q ) called by components. The components display an experimental region described in a simplex space, where each variable has values between 0 and 1, portraying the specification of proportions. With these characteristics, the practice of the designs both in several industrial and in pharmaceutical sectors becomes justifiable. The issue is that defining a structure with N experimental points becomes complex since we can generate infinite configurations. Therefore, we used D-optimal mixture designs, which, in summary, guarantee more accurate confidence regions for parameter estimates as we develop these designs conditioned to a statistical criterion. For this reason, we have undertaken this approach to fulfill the aim of this study. We propose a modification in the simulated annealing algorithm combined with the Monte Carlo procedure in the generation of D-optimal mixture designs classified both by the presence and by the absence of bias in the proportion specifications. We conclude that the proposed modification in the simulated annealing algorithm provides the search with generation of D-optimal designs, supporting the control of the distribution of experimental points either near or far from the edges of the observed region limited by the simplex space. The optimal strategies generated in a biased way are less sensitive in relation to the number of components.

An experimental study on the development of self-compacting alkali-activated slag concrete mixes under ambient curing

Transient dynamic modeling and validation of an organic Rankine cycle waste heat recovery system for heavy duty diesel engine applications

BackgroundAn accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first.ResultsThe authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks.ConclusionsSimulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/.

Optimization of the derivatization reaction and the solid-phase microextraction conditions using a D-optimal design and three-way calibration in the determination of non-steroidal anti-inflammatory drugs in bovine milk by gas chromatography–mass spectrometry

Nowadays, computer experiments are often used for evaluation of functions with random inputs. Instead of accessing a complex function and calculating the probability distribution function of the result, a set of computer simulations may be processed and the information concerning the properties of the resulting variable/random vector can be estimated. The quality of such an estimate depends on the number of simulations and also on the distribution of points in the sampling space. Since the time required for the calculation is proportional to the number of the design points, it is desirable to reduce this number while keeping the quality of the design. This can be achieved by appropriate selection of the design points (specific combinations of values of input variables out of the permissible range of values given by the probability distribution of each variable).This paper describes several frequently used methods for generation and optimization of the designs and compares them using several basic functions with known resulting random vector as a measure of quality. It also contains the description of the shortcomings identified for some of the combinations and proposes possible solution for those imperfections.

This study investigates the application of D-optimal mixture experimental design in optimization of O/W cosmetic emulsions. Cetearyl glucoside was used as a natural, biodegradable non-ionic emulsifier in the relatively low concentration (1%), and the mixture of co-emulsifiers (stearic acid, cetyl alcohol, stearyl alcohol and glyceryl stearate) was used to stabilize the formulations. To determine the optimal composition of co-emulsifiers mixture, D-optimal mixture experimental design was used. Prepared emulsions were characterized with rheological measurements, centrifugation test, specific conductivity and pH value measurements. All prepared samples appeared as white and homogenous creams, except for one homogenous and viscous lotion co-stabilized by stearic acid alone. Centrifugation testing revealed some phase separation only in the case of sample co-stabilized using glyceryl stearate alone. The obtained pH values indicated that all samples expressed mild acid value acceptable for cosmetic preparations. Specific conductivity values are attributed to the multiple phases O/W emulsions with high percentages of fixed water. Results of the rheological measurements have shown that the investigated samples exhibited non-Newtonian thixotropic behaviour. To determine the influence of each of the co-emulsifiers on emulsions properties, the obtained results were evaluated by the means of statistical analysis (ANOVA test). On the basis of comparison of statistical parameters for each of the studied responses, mixture reduced quadratic model was selected over the linear model implying that interactions between co-emulsifiers play the significant role in overall influence of co-emulsifiers on emulsions properties. Glyceryl stearate was found to be the dominant co-emulsifier affecting emulsions properties. Interactions between the glyceryl stearate and other co-emulsifiers were also found to significantly influence emulsions properties. These findings are especially important as they can be used for development of the product that meets users' requirements, as represented in the study.

Which active learning methods can we expect to yield good performance in learning binary and multi-category logistic regression classifiers? Addressing this question is a natural first step in providing robust solutions for active learning across a wide variety of exponential models including maximum entropy, generalized linear, log-linear, and conditional random field models. For the logistic regression model we re-derive the variance reduction method known in experimental design circles as `A-optimality.' We then run comparisons against different variations of the most widely used heuristic schemes: query by committee and uncertainty sampling, to discover which methods work best for different classes of problems and why. We find that among the strategies tested, the experimental design methods are most likely to match or beat a random sample baseline. The heuristic alternatives produced mixed results, with an uncertainty sampling variant called margin sampling and a derivative method called QBB-MM providing the most promising performance at very low computational cost. Computational running times of the experimental design methods were a bottleneck to the evaluations. Meanwhile, evaluation of the heuristic methods lead to an accumulation of negative results. We explore alternative evaluation design parameters to test whether these negative results are merely an artifact of settings where experimental design methods can be applied. The results demonstrate a need for improved active learning methods that will provide reliable performance at a reasonable computational cost.

A Predictive Model for Weld Properties in AA-7075-FSW: A Heterogeneous AMIS-Ensemble Machine Learning Approach

A general nonlinear controller design method for multivariable nonminimum-phase systems is presented that utilizes a class of synthetic outputs which are statically equivalent to the process outputs. A systematic procedure for the selection of particular synthetic outputs, which lead to prescribed transmission zeros, is outlined. The selected synthetic outputs are then used to construct a model-state feedback controller, which is guaranteed to induce zero steady-state error to the original plant outputs. The proposed control scheme is illustrated in a simulation case study for a multivariable nonisothermal CSTR, where a series/parallel van de Vusse reaction is taking place.

From hexafluorotitanate waste to TiO2 powder: Characterization and evaluation of the influence of synthesis parameters by the experimental design method

A calibration method of discrete element contact model parameters for bulk materials based on experimental design method

Modeling and optimizing of factors affecting erosion–corrosion of AA6063–(TiC/Al 2O 3) hybrid composites by experimental design method

An improved genetic algorithm (IGA) is presented to solve the mixed-discrete-continuous design optimization problems. The IGA approach combines the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions. The presented IGA approach is effectively applied to solve one structural and five mechanical engineering problems. The computational results show that the presented IGA approach can obtain better solutions than both the GA-based and the particle-swarm-optimizer-based methods reported recently.

Experimental design methods are tools for conducting informative, time‐ and cost‐effective experiments. Used during product development, these methods can contribute to building quality into products as well as shortening the development cycle time. These techniques make it possible to study the effect of many factors (parameters) simultaneously, to select the factor combination that results in both improved quality and reduced cost, and hence allow for the development of reliable and robust products of high quality. In addition, these methods provide a systematic approach for problem solving during the product development process. This article provides a non‐technical discussion of the role of experimentation and the advantage of using experimental design during product development. Different experimental design methods and examples of their application during product development will also be presented.