D-optimal Method Research Articles

Ignition characteristics of semiconductor bridge based on lead styphnate and lead azide charges under capacitor discharge conditions

Eight different sizes of semiconductor bridges and three kinds of charges, namely normal-lead styphnate (N-LS), lead azide (LA) and the double stacked charge (N-LS/LA), were selected to study the ignition characteristics of semiconductor bridge igniters under capacitor discharge conditions. The Neyer D-optimal Method was utilized in the experiments to explore the critical ignition voltages of the igniters. The relationships among the ignition voltage, current, resistance, and ignition time as well as other parameters of these igniters were analyzed. The results obtained were as follows: 1) The critical ignition voltage is positively proportional with the bridge area for the igniters possessing identical charge and ignition mechanism. 2) The ignition time and critical ignition voltage are related to the charges. The relationship of ignition time and charge following the short to long ranking is LA, N-LS/LA, and N-LS, but the relationship of critical ignition voltage and charge follows the opposite ranking. 3) The electroexploding ignition mechanism brings about shorter ignition time than electrothermal ignition mechanism for the igniters with identical charge, but the electroexploding ignition mechanism is accompanied by higher critical ignition voltage than the other.

Evaluation of Biopolymer-Based Emulsion for Delivering Conjugated Linoleic Acid (CLA) as a Functional Ingredient in Beverages

The experiment was designed to determine the most suitable formula for conjugated linoleic acid (CLA) oil-in-water-beverage emulsion using D-optimal design and response surface method. The influence of the main emulsion ingredients of Arabic gum (AG), xanthan gum (XG), and CLA oil was investigated on the beverage emulsion characteristics. The studied responses were mainly surface-weighted mean diameter (D32), specific surface area (SSA) of droplets, heat stability (HS), surface tension, and pH. Based on the regression analysis, 10% w/w AG, 3.5% w/w CLA, and 0.3% w/w XG were introduced as the optimum formula and at this formulation, D32, SSA, HS, surface tension, and pH were 2.8963 µm, 2.0790 m2/mL, 99.5756%, 43.65 mN/m, and 2.8637, respectively. The physicochemical and the oxidative stability of the selected emulsion were also evaluated over 49 days of storage. The results generally revealed that this beverage emulsion was stable during storage and it is possible to produce a CLA oil-in-water emulsion in order to fortify beverages with CLA as the functional lipid ingredient.

Preparation, Optimization, and Screening of the Effect of Processing Variables on Agar Nanospheres Loaded with Bupropion HCl by a D-Optimal Design

Bupropion is an atypical antidepressant drug. Fluctuating in its serum levels following oral administration of immediate release dosage forms leads to occasional seizure. The aim of the present work was designing of sustained release bupropion HCl nanospheres suited for pulmonary delivery. Agar nanospheres were prepared by transferring the w/o emulsion to solid in oil (s/o) suspension. Calcium chloride was used as cross-linking agent and hydroxypropyl β-cyclodextrin (HPβCD) was used as permeability enhancer. A response surface D-optimal design was used for optimization of nanospheres. Independent factors included in the design were calcium chloride percent, speed of homogenization, agar percent, and HPβCD percent. Optimum condition was predicted to be achieved when the calcium chloride was set at 7.19%, homogenization speed at 8500 rpm, agar content at 2%, and HPβCD at 0.12%. The optimized nanoparticles showed particle size of 587 nm, zeta potential of −30.9 mV, drug loading efficiency of 38.6%, and release efficiency of 51% until 5 h. The nanospheres showed high degree of bioadhesiveness. D-optimal response surface method is a satisfactory design to optimize the fabrication of bupropion HCl loaded agar nanospheres and these nanospheres can be successively exploited to deliver bupropion in a controlled manner for a sufficiently extended period.

Improving Measurement Precision of Hierarchical Latent Traits Using Adaptive Testing

Many latent traits in social sciences display a hierarchical structure, such as intelligence, cognitive ability, or personality. Usually a second-order factor is linearly related to a group of first-order factors (also called domain abilities in cognitive ability measures), and the first-order factors directly govern the actual item responses. Because only a subtest of items is used to measure each domain, the lack of sufficient reliability becomes the primary impediment for generating and reporting domain abilities. In recent years, several item response theory (IRT) models have been proposed to account for hierarchical factor structures, and these models are also shown to alleviate the low reliability issue by using in-test collateral information to improve measurement precision. This article advocates using adaptive item selection together with a higher order IRT model to further increase the reliability of hierarchical latent trait estimation. Two item selection algorithms are proposed—the constrained D-optimal method and the sequencing domain method. Both are shown to yield improved measurement precision as compared to the unidimensional item selection (by treating each dimension separately). The improvement is more prominent when the test length is short and when the correlation between dimensions is high (e.g., higher than .64). Moreover, two reliability indices for hierarchical latent traits are discussed and their use for quantifying the reliability of hierarchical traits measured by adaptive testing is demonstrated.

Closed-loop non-parametric model identification of synchronous generator using NARX polynomials

SUMMARY System identification can be carried out by perturbing the system input(s) and processing the recorded input(s) and output(s) of the system. In synchronous generators, if the governor is not in action, one of the inputs (the mechanical torque) is not available for measurement, and the experiments cannot be carried out. In the published literature, the input perturbation is mostly carried out through the excitation system, and the mechanical torque is assumed to be constant during the experiment. This would affect the accuracy of the results. In this paper, various multivariable closed-loop identification methods (direct and indirect and linear and nonlinear) are used to obtain an accurate and comprehensive model for the synchronous generator. To simplify model structure, the orthogonal least squares with D-optimality method is used to remove unnecessary terms. A comparison of the performance of various techniques shows that closed-loop nonlinear identification using nonlinear auto regressive with exogenous input polynomials is very effective, and a simple nonlinear model for the synchronous generator can be identified successfully using multivariable closed-loop input and output data. Copyright © 2014 John Wiley & Sons, Ltd.

Modeling and optimization of turning duplex stainless steels

The attractive combination of high mechanical strength, good corrosion resistance and relatively low cost has contributed to making duplex stainless steels (DSSs) one of the fastest growing groups of stainless steels. As the importance of DSSs is increasing, practical information about their successful machining is expected to be crucial. To address this industrial need, standard EN 1.4462 and super EN 1.4410 DSSs are machined under constant cutting speed multi-pass facing operations. A systematic approach which employs different modeling and optimization tools under a three phase investigation scheme has been adopted. In phase I, the effect of design variables such as cutting parameters, cutting fluids and axial length of cuts are investigated using the D-Optimal method. The mathematical models for performance characteristics such as; percentage increase in radial cutting force (%Fr), effective cutting power (Pe), maximum tool flank wear (VBmax) and chip volume ratio (R) are developed using response surface methodology (RSM). The adequacy of derived models for each cutting scenario is checked using analysis of variance (ANOVA). Parametric meta-heuristic optimization using Cuckoo search (CS) algorithm is then performed to determine the optimum design variable set for each performance. In the phase II, comprehensive experiment-based production cost and production rate models are developed. To overcome the conflict between the desire of minimizing the production cost and maximizing the production rate, compromise solutions are suggested using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The alternatives are ranked according to their relative closeness to the ideal solution. In the phase III, expert systems based on fuzzy rule modeling approach are adopted to derive measures of machining operational sustainability called operational sustainability index (OSI). Artificial neural network (ANN) based models are developed to study the effect of design variables on computed OSIs. Cuckoo search neural network systems (CSNNS) are finally utilized to constrainedly optimize the cutting process per each cutting scenario. The most appropriate cutting setup to ensure successful turning of standard EN 1.4462 and super EN 1.4410 for each scenario is selected in accordance with conditions which give the maximum OSI.

Hexagonal honeycomb cell optimisation by way of meta-model techniques

This paper presents the result of an optimisation study by linear, quadratic, Kriging and radial basis meta-models in order to augment the crashworthiness characteristic of cellular structures. Thin-walled cellular structures (honeycomb) have the ability to absorb impact energy during crashing, thus it is important to enhance the crushing efficiency and optimise the structural reliability. The optimisation carried out in this study is aimed at maximising the energy absorption characteristics using meta-models while considering some limitation on the maximum force as a constraint. Achieving these characteristics is an important factor in crashworthiness analysis, which minimises the damage in dynamic performance. The objective of using various meta-models is to qualify the meta-model in crashworthiness analysis using different point selection schemes and different number of points. The optimisation is performed in two stages; through experimental design methods in which a set of sampling points is selected from design space and polynomial fitting in order to optimise the objective. It is concluded that D-optimal best suits response surface method for model approximation. Kriging performed best by space filling and the best point selection scheme for radial basis surrogate is latin hypercube design. The results show that for optimising the crashworthiness characteristics of honeycomb, Kriging and quadratic response surface (RS) are best, in terms of accuracy and robustness point of view and the radial basis neural network would be the second best. During this optimisation, the RS was combined with detailed geometrically simplified finite element model of honeycomb cell using ANSYS/LS-DYNA, LS-DYNA and LS-opt packages. Approximated functions combined with the finite element analysis were an effective tool to optimise highly non-linear impact problems. This has led to the development of validated algorithm that enabled the development of the optimised solutions.

Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments

The use of synthetic cutting fluids is nowadays limited due to the adverse effects such as health of the operator, environmental and economic reasons. In order to diminish these negative effects, bio-based cutting fluids are used. Bio-based or vegetable based cutting fluid is considered environmentally friendly and sustainable. Vegetable based cutting fluids have some characteristics such as biodegradability, less toxicity and renewability. This study deals with the assessment of vegetable based cutting fluids for end milling. For this purpose, effects of cutting fluid types are investigated as a function of three milling factors (cutting speed, depth of cut and feed rate) on process responses (specific energy, tool life and surface roughness). D-optimal method is conducted to develop mathematical models for process responses. Variance analysis shows that specific energy, surface roughness and tool life can be predicted effectively with quadratic models. Furthermore, mono and multi objective optimization studies are conducted using specific energy, surface roughness and tool life as primary or secondary objectives. From results of the optimization, various conclusions are drawn about effects of the milling parameters and the cutting fluid types on the milling performance criteria. The most appropriate cutting fluid during milling of AISI 304 material is selected in accordance with energy, tool life and surface roughness.

순차적 실험계획법을 적용한 다층관 벨로우즈 형상 최적설계

Because of their high flexibility and durability, multilayer bellows are manufactured for use in commercial vehicles, while single-layer bellows are manufactured for use in passenger vehicles. A study based on the finite element method (FEM) and shape optimization for the single-layer bellows has been actively performed; however, until now, a study based on the FEM has rarely been performed for the multilayer bellows with gaps between the layers. This paper presents a finite-element modeling scheme for the multilayer bellows to improve simulation reliability during the evaluation of stress and flexibility. For performing shape optimization for the multilayer bellows, DOE (design of experiment) and the Kriging metamodel followed by the D-optimal method are used.

Design, Analysis, and Simulated Testing of a Surrogate Nitinol Fatigue Specimen

Endurance limit analysis and testing require an accurate determination of the strain environment a device experiences in its life cycle (crimp strains, mean strain, and strain amplitudes) as well as the strain capability of the processed device. Surrogate fatigue specimens that represent a critical portion of a device can be designed through finite element analysis to experience the requisite strain life cycle, and in addition, can be efficiently tested to failure in a controlled strain environment. The surrogate should be processed in a similar manner as the parent device to ensure accurate material micro-structural properties as well as surface finish characteristics. In this article, we present the design, analysis, and simulated testing of a fatigue test specimen to determine a fatigue endurance limit to be used in the design assessment of a medical device. The Neyer D-Optimal sensitivity test method was used in a simulation to construct a design envelope.

Kinetic Parameter Determination for Enzyme Hydrolysis of Fish Protein Residue Using D-optimal Design

The objective of this work was to compare the quality of parameter estimation from experiments with protein hydrolysis of fish muscle carried out following a traditional versus D-optimal design of experiments. Traditional design was done from hydrolysis experiments using the rapid titration method, from which a large number of data points was obtained and made available for parameter estimation. D-optimal design is recommended under conditions when it would not be possible to use rapid titration with analytical tools. Such conditions would make it necessary to reduce the number of samples that would need to be taken as well as the error and variance in the estimated parameters needed for the kinetic model. Results have shown that D-optimal and traditional designs of experiments obtained accurate and reliable estimates of kinetic model parameters, with a great difference in the number of data points between both types of design: 18 points in traditional and 3 points in D-optimal design. Nevertheless, a few number of observations in D-optimal design impacted negatively the confidence interval because of a low degree of freedom. Confidence intervals ranging from 7.8% to 87% of the parameters values were obtained with D-optimal design method in contrast with traditional design where confidence intervals ranged from 6.2% to 15% of the parameters values. It is estimated that combining the advantages of both types of design will result in an optimal experimental design in terms of reliability, accuracy, and costs saving of analysis. This would be done by determining the number of observations required to achieve an appropriate confidence interval (about six or more experimental points) and, then, a selection of the best experimental points of a curve of hydrolysis by D-optimal design.

The Performance of Melamine – Urea – Formaldehyde Resin with Palm Kernel as Filler

Palm kernel meal (PKM) which is the by-products of palm oil industry consists of considerable amount of protein that showed its great potential to to be used as a filler of melamine-urea-formaldehyde (MUF) resin based wood adhesive. Besides, due to the high price of melamine, it is attempted to reduce the content of melamine in MUF resins to as low as possible. So, in this study, PKM was used as filler in MUF resin based wood adhesive to study its effects toward the performance of the adhesive. In practical, the shear strength performance tests are done by using automated bonding evaluation system (ABES). However, ABES is a destructive test that only produces one point of data per test. So, in order to make the evaluation of the prediction data more convenience, a response surface methodology D-optimal method was approached. From the results, it has shown that increasing in temperature and pressing time is not always true in obtaining higher shear strength performance for MUF resin with PKM as filler. Smaller the particle size of PKM filler in MUF resin, shorter time the high shear strength performance can be achieved. Also, the relationships of temperature, pressing time and particle size of PKM to obtain optimum and maximum shear strength were studied.

Optimal operation strategy development for fuel cell hybrid vehicle

An overall simulation model for fuel cell hybrid vehicle (FCHV) power train in parallel configuration using MATLAB/Simulink pro- gramming is constructed in this study. The model runs on power control strategy by using logic-threshold approach, achieved by the hybrid control unit (HCU) and fuel cell stack number. Using accelerator and decelerator pedal positions deduced from the driving sched- ule as the primary input, the simulation implements power flow and distribution under different vehicle operating modes. The HCU con- trol strategy also incorporates regenerative braking and recharge for battery capacity recovery. Using the D-optimality method for ex- periment points selection and sequential quadratic programming (SQP) algorithm for obtaining the optimal operational parameters, three control threshold variables of HCU and optimal stack cell number are selected for hydrogen fuel economy under certain driving cycles. The proposed method provides optimized configurations of the FCHV model and the fuel cell stack, which has the capability in satisfy- ing drive power request while satisfying vehicle driving schedule and battery state of charge (SOC) recovery with lower fuel consump- tion.

Developing a statistical model to predict hydrogen production by a mixed anaerobic mesophilic culture

Optimizing the hydrogen (H 2) yield at several initial pH conditions in a mixed batch anaerobic mesophilic culture fed with glucose and linoleic acid (LA) was performed using a three factor three level Box–Benkhen design (BBD). Based on the BBD approach, a statistical model was developed to predict the H 2 yield. The variables considered for the experimental design were the LA concentration, the initial pH and the number of times glucose was added to the culture. The D-optimality method predicted a maximum H 2 yield of 3.49 mol H 2 mol glucose −1 for cultures fed 1.9 g l −1 LA, maintained at an initial pH of 5.15 and received 1.79 glucose additions. The response outcome (H 2 yield of 3.38 ± 0.22 mol mol glucose −1) at the nearest setting of the experimental factors (2.0 g l −1 LA, an initial pH of 5.0 and two glucose additions) was 3.3% less than the predicted maximum value. The model provides a useful approach for predicting H 2 production when H 2 consumers are inhibited in mixed batch anaerobic cultures.

Modeling and control strategy development for fuel cell hybrid vehicles

Using MATLAB/Simulink, we constructed a comprehensive simulation model for the fuel cell hybrid vehicle (FCHV) power train in parallel with a power control strategy that uses a logic threshold approach implemented with a hybrid control unit (HCU). The simulation implements power flow and power distribution under different vehicle operating modes using the accelerator and decelerator pedal positions deduced from the driving schedule as primary inputs. The HCU control strategy also incorporates regenerative braking and recharging for recovery of battery capacity. Using the D-optimality method for selection of the optimal experiment values, three control threshold variables for the HCU are selected to maximize the hydrogen fuel economy under certain driving cycles. The proposed method provides the optimal configuration of the FCHV model, which has the capability of achieving the requested drive power while also meeting the vehicle driving schedule and recovery needs of the state of charge (SOC) battery, with lower fuel consumption levels.

Design of experiment characterization of microneedle fabrication processes based on dry silicon etching

This paper reports on the characterization of dry etching-based processes for the fabrication of silicon microneedles using a design of experiment (DoE) approach. The possibility of using such microneedles as protruding microelectrodes able to electroporate adherently growing cells and record intracellular potentials motivates the systematic analysis of the influence of etching parameters on the needle shape. Two processes are characterized: a fully isotropic etch process and a three-step etching approach. In the first case, the shape of the microneedles is defined by a single etch step. For the stepped method, the structures are realized using the following sequence: a first, isotropic step defines the tip; this is followed by anisotropic etching that increases the height of the needle; a final isotropic procedure thins the microneedle and sharpens its tip. From the various process parameters tested, it is concluded that the isotropic fabrication is influenced mostly by four process parameters, whereas six parameters dominantly govern the outcome of the stepped etching technique. The dependence of the needle shape on the etch mask diameter is also investigated. Microneedles with diameters down to the sub-micrometer range and heights below 10 µm are obtained. The experimental design is performed using the D-optimal method. The resulting geometry, i.e. heights, diameters and radii of curvature measured at different positions, is extracted from scanning electron micrographs of needle cross-sections obtained from cuts by focused ion beam. The process parameters are used as inputs and the geometry features of the microneedles as outputs for the analysis of the process.

Multiobjective Optimization of Staggered Elliptical Pin-Fin Arrays

This work presents a numerical procedure for multiple objectives to optimize staggered elliptic-shaped short pin-fin arrays. The multiobjective problem is to achieve an acceptable compromise between augmentation of turbulent heat transfer and reduction in friction loss. Four nondimensional variables, pin-fin height-to-channel height ratio, major axis length-to-channel height ratio, minor-axis length-to-channel height ratio, and pin-fin pitch-to-channel height ratio are chosen as design variables. The D-optimal method is used to determine the training points. The response surface method is used to approximate the Pareto optimal front with Reynolds-averaged Navier-Stokes analysis of the flow and heat transfer using the shear stress transport (SST) turbulence model. The Pareto-optimal solutions are obtained using a combination of an evolutionary algorithm and a local search.

Optimal design of high temperature vacuum furnace using response surface method

A new method using the response surface method and optimization technique has been developed instead of the original method based on trial and error. In order to construct a response surface, thermal analysis was performed under the condition of using the calculated thermal conductivity of the insulator in a previous study. In order to set up the response surface, the D-Optimal method was used in the process of selecting experimental points. Using a weighting factor, an optimization study was carried out under the condition of satisfying user requirements. Finally, the merits and drawbacks of the new method were described by comparing with the optimal design method based on the thermal analysis database which was developed in a previous study. The optimal results show that the developed method can be used to design an energy efficient, low manufacturing cost, high temperature vacuum furnace with avoiding unnecessary iterative manufacturing, and anticipating the performance before manufacturing.

Modeling the effects of cutting parameters in MQL-employed finish hard-milling process using D-optimal method

Abstract In any finish milling process the minimization of surface roughness is the prime objective, and if the workpiece belongs to the category of ultra-hard steels then the objective of maximizing tool life also gains considerable importance. In this research work, the effects of four parameters, namely, hardened steel's microstructure, workpiece inclination angle, cutting speed, and radial depth of cut were studied upon tool life and surface roughness (in directions of feed and pick-feed). The milling was performed under environment of minimum quantity of lubrication (MQL), using coated carbide ball-nose end mills. The quantification of the aforementioned effects was done using a new response surface methodology known as the D-optimal method. For tool life, workpiece material was found as the most influential parameter followed by the rotational speed of tool. High values of tool's rotational speed proved unfavorable for tool life but favorable for surface finish. In addition, the effects of workpiece inclination angle and radial depth of cut were analyzed upon effective cutting speed and cusp height and, subsequently, upon surface roughness. SEM and EDS analyses of the worn-out tools were carried out in order to study the effects of different levels of parameters selected upon the severity of different tool damage modes. The major tool damage mechanisms detected were notch wear, adhesion, and chipping. The severity of chipping was relatively smaller as compared to that of adhesion and of notch wear because of reduced effective cutting speeds and feed rate employed.

Design Optimization of Internal Cooling Passage with V-shaped Ribs

This work presents an optimization procedure for design of a square channel with V-shaped square ribs extruded on both walls to enhance turbulent heat transfer. The response surface-based optimization is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis. The shear stress transport (SST) model is used as a turbulence closure. Computational results for average heat transfer coefficient show good agreement with experimental data at several different Reynolds numbers. Rib pitch-to-rib height ratio, rib height-to-channel height ratio, and the attack angle of the rib are chosen as design variables. The objective function is defined as a linear combination of heat transfer and friction loss-related terms with a weighting factor. D-optimal method is used to determine the training points as a mean of design of experiment. Optimal values of the design variables have been obtained in a range of the weighting factor.