Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model.

PubMed

Casey, F P; Baird, D; Feng, Q; Gutenkunst, R N; Waterfall, J J; Myers, C R; Brown, K S; Cerione, R A; Sethna, J P

2007-05-01

We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor signalling, trafficking and down-regulation. The model incorporates the role of a recently discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine exchange factor (GEF), Cool-1 (beta -Pix) and the Rho family G protein Cdc42. The complex has been suggested to be important in disrupting receptor down-regulation. We demonstrate that the model interactions can accurately reproduce the experimental observations, that they can be used to make predictions with accompanying uncertainties, and that we can apply ideas of optimal experimental design to suggest new experiments that reduce the uncertainty on unmeasurable components of the system.

Optimal experimental design for parameter estimation of a cell signaling model.

PubMed

Bandara, Samuel; Schlöder, Johannes P; Eils, Roland; Bock, Hans Georg; Meyer, Tobias

2009-11-01

Differential equation models that describe the dynamic changes of biochemical signaling states are important tools to understand cellular behavior. An essential task in building such representations is to infer the affinities, rate constants, and other parameters of a model from actual measurement data. However, intuitive measurement protocols often fail to generate data that restrict the range of possible parameter values. Here we utilized a numerical method to iteratively design optimal live-cell fluorescence microscopy experiments in order to reveal pharmacological and kinetic parameters of a phosphatidylinositol 3,4,5-trisphosphate (PIP(3)) second messenger signaling process that is deregulated in many tumors. The experimental approach included the activation of endogenous phosphoinositide 3-kinase (PI3K) by chemically induced recruitment of a regulatory peptide, reversible inhibition of PI3K using a kinase inhibitor, and monitoring of the PI3K-mediated production of PIP(3) lipids using the pleckstrin homology (PH) domain of Akt. We found that an intuitively planned and established experimental protocol did not yield data from which relevant parameters could be inferred. Starting from a set of poorly defined model parameters derived from the intuitively planned experiment, we calculated concentration-time profiles for both the inducing and the inhibitory compound that would minimize the predicted uncertainty of parameter estimates. Two cycles of optimization and experimentation were sufficient to narrowly confine the model parameters, with the mean variance of estimates dropping more than sixty-fold. Thus, optimal experimental design proved to be a powerful strategy to minimize the number of experiments needed to infer biological parameters from a cell signaling assay.

Optimal design of disc-type magneto-rheological brake for mid-sized motorcycle: experimental evaluation

NASA Astrophysics Data System (ADS)

Sohn, Jung Woo; Jeon, Juncheol; Nguyen, Quoc Hung; Choi, Seung-Bok

2015-08-01

In this paper, a disc-type magneto-rheological (MR) brake is designed for a mid-sized motorcycle and its performance is experimentally evaluated. The proposed MR brake consists of an outer housing, a rotating disc immersed in MR fluid, and a copper wire coiled around a bobbin to generate a magnetic field. The structural configuration of the MR brake is first presented with consideration of the installation space for the conventional hydraulic brake of a mid-sized motorcycle. The design parameters of the proposed MR brake are optimized to satisfy design requirements such as the braking torque, total mass of the MR brake, and cruising temperature caused by the magnetic-field friction of the MR fluid. In the optimization procedure, the braking torque is calculated based on the Herschel-Bulkley rheological model, which predicts MR fluid behavior well at high shear rate. An optimization tool based on finite element analysis is used to obtain the optimized dimensions of the MR brake. After manufacturing the MR brake, mechanical performances regarding the response time, braking torque and cruising temperature are experimentally evaluated.

Experimental Investigation of a Point Design Optimized Arrow Wing HSCT Configuration

NASA Technical Reports Server (NTRS)

Narducci, Robert P.; Sundaram, P.; Agrawal, Shreekant; Cheung, S.; Arslan, A. E.; Martin, G. L.

1999-01-01

The M2.4-7A Arrow Wing HSCT configuration was optimized for straight and level cruise at a Mach number of 2.4 and a lift coefficient of 0.10. A quasi-Newton optimization scheme maximized the lift-to-drag ratio (by minimizing drag-to-lift) using Euler solutions from FL067 to estimate the lift and drag forces. A 1.675% wind-tunnel model of the Opt5 HSCT configuration was built to validate the design methodology. Experimental data gathered at the NASA Langley Unitary Plan Wind Tunnel (UPWT) section #2 facility verified CFL3D Euler and Navier-Stokes predictions of the Opt5 performance at the design point. In turn, CFL3D confirmed the improvement in the lift-to-drag ratio obtained during the optimization, thus validating the design procedure. A data base at off-design conditions was obtained during three wind-tunnel tests. The entry into NASA Langley UPWT section #2 obtained data at a free stream Mach number, M(sub infinity), of 2.55 as well as the design Mach number, M(sub infinity)=2.4. Data from a Mach number range of 1.8 to 2.4 was taken at UPWT section #1. Transonic and low supersonic Mach numbers, M(sub infinity)=0.6 to 1.2, was gathered at the NASA Langley 16 ft. Transonic Wind Tunnel (TWT). In addition to good agreement between CFD and experimental data, highlights from the wind-tunnel tests include a trip dot study suggesting a linear relationship between trip dot drag and Mach number, an aeroelastic study that measured the outboard wing deflection and twist, and a flap scheduling study that identifies the possibility of only one leading-edge and trailing-edge flap setting for transonic cruise and another for low supersonic acceleration.

A new experimental design method to optimize formulations focusing on a lubricant for hydrophilic matrix tablets.

PubMed

Choi, Du Hyung; Shin, Sangmun; Khoa Viet Truong, Nguyen; Jeong, Seong Hoon

2012-09-01

A robust experimental design method was developed with the well-established response surface methodology and time series modeling to facilitate the formulation development process with magnesium stearate incorporated into hydrophilic matrix tablets. Two directional analyses and a time-oriented model were utilized to optimize the experimental responses. Evaluations of tablet gelation and drug release were conducted with two factors x₁ and x₂: one was a formulation factor (the amount of magnesium stearate) and the other was a processing factor (mixing time), respectively. Moreover, different batch sizes (100 and 500 tablet batches) were also evaluated to investigate an effect of batch size. The selected input control factors were arranged in a mixture simplex lattice design with 13 experimental runs. The obtained optimal settings of magnesium stearate for gelation were 0.46 g, 2.76 min (mixing time) for a 100 tablet batch and 1.54 g, 6.51 min for a 500 tablet batch. The optimal settings for drug release were 0.33 g, 7.99 min for a 100 tablet batch and 1.54 g, 6.51 min for a 500 tablet batch. The exact ratio and mixing time of magnesium stearate could be formulated according to the resulting hydrophilic matrix tablet properties. The newly designed experimental method provided very useful information for characterizing significant factors and hence to obtain optimum formulations allowing for a systematic and reliable experimental design method.

Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models.

PubMed

Van Daele, Timothy; Gernaey, Krist V; Ringborg, Rolf H; Börner, Tim; Heintz, Søren; Van Hauwermeiren, Daan; Grey, Carl; Krühne, Ulrich; Adlercreutz, Patrick; Nopens, Ingmar

2017-09-01

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1278-1293, 2017. © 2017 American Institute of Chemical Engineers.

Demonstration of decomposition and optimization in the design of experimental space systems

NASA Technical Reports Server (NTRS)

Padula, Sharon; Sandridge, Chris A.; Haftka, Raphael T.; Walsh, Joanne L.

1989-01-01

Effective design strategies for a class of systems which may be termed Experimental Space Systems (ESS) are needed. These systems, which include large space antenna and observatories, space platforms, earth satellites and deep space explorers, have special characteristics which make them particularly difficult to design. It is argued here that these same characteristics encourage the use of advanced computer-aided optimization and planning techniques. The broad goal of this research is to develop optimization strategies for the design of ESS. These strategics would account for the possibly conflicting requirements of mission life, safety, scientific payoffs, initial system cost, launch limitations and maintenance costs. The strategies must also preserve the coupling between disciplines or between subsystems. Here, the specific purpose is to describe a computer-aided planning and scheduling technique. This technique provides the designer with a way to map the flow of data between multidisciplinary analyses. The technique is important because it enables the designer to decompose the system design problem into a number of smaller subproblems. The planning and scheduling technique is demonstrated by its application to a specific preliminary design problem.

Optimal rates for phylogenetic inference and experimental design in the era of genome-scale datasets.

PubMed

Dornburg, Alex; Su, Zhuo; Townsend, Jeffrey P

2018-06-25

With the rise of genome- scale datasets there has been a call for increased data scrutiny and careful selection of loci appropriate for attempting the resolution of a phylogenetic problem. Such loci are desired to maximize phylogenetic information content while minimizing the risk of homoplasy. Theory posits the existence of characters that evolve under such an optimum rate, and efforts to determine optimal rates of inference have been a cornerstone of phylogenetic experimental design for over two decades. However, both theoretical and empirical investigations of optimal rates have varied dramatically in their conclusions: spanning no relationship to a tight relationship between the rate of change and phylogenetic utility. Here we synthesize these apparently contradictory views, demonstrating both empirical and theoretical conditions under which each is correct. We find that optimal rates of characters-not genes-are generally robust to most experimental design decisions. Moreover, consideration of site rate heterogeneity within a given locus is critical to accurate predictions of utility. Factors such as taxon sampling or the targeted number of characters providing support for a topology are additionally critical to the predictions of phylogenetic utility based on the rate of character change. Further, optimality of rates and predictions of phylogenetic utility are not equivalent, demonstrating the need for further development of comprehensive theory of phylogenetic experimental design.

Time-oriented experimental design method to optimize hydrophilic matrix formulations with gelation kinetics and drug release profiles.

PubMed

Shin, Sangmun; Choi, Du Hyung; Truong, Nguyen Khoa Viet; Kim, Nam Ah; Chu, Kyung Rok; Jeong, Seong Hoon

2011-04-04

A new experimental design methodology was developed by integrating the response surface methodology and the time series modeling. The major purposes were to identify significant factors in determining swelling and release rate from matrix tablets and their relative factor levels for optimizing the experimental responses. Properties of tablet swelling and drug release were assessed with ten factors and two default factors, a hydrophilic model drug (terazosin) and magnesium stearate, and compared with target values. The selected input control factors were arranged in a mixture simplex lattice design with 21 experimental runs. The obtained optimal settings for gelation were PEO, LH-11, Syloid, and Pharmacoat with weight ratios of 215.33 (88.50%), 5.68 (2.33%), 19.27 (7.92%), and 3.04 (1.25%), respectively. The optimal settings for drug release were PEO and citric acid with weight ratios of 191.99 (78.91%) and 51.32 (21.09%), respectively. Based on the results of matrix swelling and drug release, the optimal solutions, target values, and validation experiment results over time were similar and showed consistent patterns with very small biases. The experimental design methodology could be a very promising experimental design method to obtain maximum information with limited time and resources. It could also be very useful in formulation studies by providing a systematic and reliable screening method to characterize significant factors in the sustained release matrix tablet. Copyright © 2011 Elsevier B.V. All rights reserved.

OPTIMIZING THE PRECISION OF TOXICITY THRESHOLD ESTIMATION USING A TWO-STAGE EXPERIMENTAL DESIGN

EPA Science Inventory

An important consideration for risk assessment is the existence of a threshold, i.e., the highest toxicant dose where the response is not distinguishable from background. We have developed methodology for finding an experimental design that optimizes the precision of threshold mo...

Factorial experimental design intended for the optimization of the alumina purification conditions

NASA Astrophysics Data System (ADS)

Brahmi, Mounaouer; Ba, Mohamedou; Hidri, Yassine; Hassen, Abdennaceur

2018-04-01

The objective of this study was to determine the optimal conditions by using the experimental design methodology for the removal of some impurities associated with the alumina. So, three alumina qualities of different origins were investigated under the same conditions. The application of full-factorial designs on the samples of different qualities of alumina has followed the removal rates of the sodium oxide. However, a factorial experimental design was developed to describe the elimination of sodium oxide associated with the alumina. The experimental results showed that chemical analyze followed by XRF prior treatment of the samples, provided a primary idea concerning these prevailing impurities. Therefore, it appeared that the sodium oxide constituted the largest amount among all impurities. After the application of experimental design, analysis of the effectors different factors and their interactions showed that to have a better result, we should reduce the alumina quantity investigated and by against increase the stirring time for the first two samples, whereas, it was necessary to increase the alumina quantity in the case of the third sample. To expand and improve this research, we should take into account all existing impurities, since we found during this investigation that the levels of partial impurities increased after the treatment.

Robust experimental design for optimizing the microbial inhibitor test for penicillin detection in milk.

PubMed

Nagel, O G; Molina, M P; Basílico, J C; Zapata, M L; Althaus, R L

2009-06-01

To use experimental design techniques and a multiple logistic regression model to optimize a microbiological inhibition test with dichotomous response for the detection of Penicillin G in milk. A 2(3) x 2(2) robust experimental design with two replications was used. The effects of three control factors (V: culture medium volume, S: spore concentration of Geobacillus stearothermophilus, I: indicator concentration), two noise factors (Dt: diffusion time, Ip: incubation period) and their interactions were studied. The V, S, Dt, Ip factors and V x S, V x Ip, S x Ip interactions showed significant effects. The use of 100 microl culture medium volume, 2 x 10(5) spores ml(-1), 60 min diffusion time and 3 h incubation period is recommended. In these elaboration conditions, the penicillin detection limit was of 3.9 microg l(-1), similar to the maximum residue limit (MRL). Of the two noise factors studied, the incubation period can be controlled by means of the culture medium volume and spore concentration. We were able to optimize bioassays of dichotomous response using an experimental design and logistic regression model for the detection of residues at the level of MRL, aiding in the avoidance of health problems in the consumer.

Fast Synthesis of Gibbsite Nanoplates and Process Optimization using Box-Behnken Experimental Design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xin; Zhang, Xianwen; Graham, Trent R.

Developing the ability to synthesize compositionally and morphologically well-defined gibbsite particles at the nanoscale with high yield is an ongoing need that has not yet achieved the level of rational design. Here we report optimization of a clean inorganic synthesis route based on statistical experimental design examining the influence of Al(OH)3 gel precursor concentration, pH, and aging time at temperature. At 80 oC, the optimum synthesis conditions of gel concentration at 0.5 M, pH at 9.2, and time at 72 h maximized the reaction yield up to ~87%. The resulting gibbsite product is composed of highly uniform euhedral hexagonal nanoplatesmore » within a basal plane diameter range of 200-400 nm. The independent roles of key system variables in the growth mechanism are considered. On the basis of these optimized experimental conditions, the synthesis procedure, which is both cost-effective and environmentally friendly, has the potential for mass production scale-up of high quality gibbsite material for various fundamental research and industrial applications.« less

Model-Based Optimal Experimental Design for Complex Physical Systems

DTIC Science & Technology

2015-12-03

for public release. magnitude reduction in estimator error required to make solving the exact optimal design problem tractable. Instead of using a naive...for designing a sequence of experiments uses suboptimal approaches: batch design that has no feedback, or greedy ( myopic ) design that optimally...approved for public release. Equation 1 is difficult to solve directly, but can be expressed in an equivalent form using the principle of dynamic programming

A new multiresponse optimization approach in combination with a D-Optimal experimental design for the determination of biogenic amines in fish by HPLC-FLD.

PubMed

Herrero, A; Sanllorente, S; Reguera, C; Ortiz, M C; Sarabia, L A

2016-11-16

A new strategy to approach multiresponse optimization in conjunction to a D-optimal design for simultaneously optimizing a large number of experimental factors is proposed. The procedure is applied to the determination of biogenic amines (histamine, putrescine, cadaverine, tyramine, tryptamine, 2-phenylethylamine, spermine and spermidine) in swordfish by HPLC-FLD after extraction with an acid and subsequent derivatization with dansyl chloride. Firstly, the extraction from a solid matrix and the derivatization of the extract are optimized. Ten experimental factors involved in both stages are studied, seven of them at two levels and the remaining at three levels; the use of a D-optimal design leads to optimize the ten experimental variables, significantly reducing by a factor of 67 the experimental effort needed but guaranteeing the quality of the estimates. A model with 19 coefficients, which includes those corresponding to the main effects and two possible interactions, is fitted to the peak area of each amine. Then, the validated models are used to predict the response (peak area) of the 3456 experiments of the complete factorial design. The variability among peak areas ranges from 13.5 for 2-phenylethylamine to 122.5 for spermine, which shows, to a certain extent, the high and different effect of the pretreatment on the responses. Then the percentiles are calculated from the peak areas of each amine. As the experimental conditions are in conflict, the optimal solution for the multiresponse optimization is chosen from among those which have all the responses greater than a certain percentile for all the amines. The developed procedure reaches decision limits down to 2.5 μg L -1 for cadaverine or 497 μg L -1 for histamine in solvent and 0.07 mg kg -1 and 14.81 mg kg -1 in fish (probability of false positive equal to 0.05), respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

Optimal experimental design for assessment of enzyme kinetics in a drug discovery screening environment.

PubMed

Sjögren, Erik; Nyberg, Joakim; Magnusson, Mats O; Lennernäs, Hans; Hooker, Andrew; Bredberg, Ulf

2011-05-01

A penalized expectation of determinant (ED)-optimal design with a discrete parameter distribution was used to find an optimal experimental design for assessment of enzyme kinetics in a screening environment. A data set for enzyme kinetic data (V(max) and K(m)) was collected from previously reported studies, and every V(max)/K(m) pair (n = 76) was taken to represent a unique drug compound. The design was restricted to 15 samples, an incubation time of up to 40 min, and starting concentrations (C(0)) for the incubation between 0.01 and 100 μM. The optimization was performed by finding the sample times and C(0) returning the lowest uncertainty (S.E.) of the model parameter estimates. Individual optimal designs, one general optimal design and one, for laboratory practice suitable, pragmatic optimal design (OD) were obtained. In addition, a standard design (STD-D), representing a commonly applied approach for metabolic stability investigations, was constructed. Simulations were performed for OD and STD-D by using the Michaelis-Menten (MM) equation, and enzyme kinetic parameters were estimated with both MM and a monoexponential decay. OD generated a better result (relative standard error) for 99% of the compounds and an equal or better result [(root mean square error (RMSE)] for 78% of the compounds in estimation of metabolic intrinsic clearance. Furthermore, high-quality estimates (RMSE < 30%) of both V(max) and K(m) could be obtained for a considerable number (26%) of the investigated compounds by using the suggested OD. The results presented in this study demonstrate that the output could generally be improved compared with that obtained from the standard approaches used today.

Doehlert experimental design applied to optimization of light emitting textile structures

NASA Astrophysics Data System (ADS)

Oguz, Yesim; Cochrane, Cedric; Koncar, Vladan; Mordon, Serge R.

2016-07-01

A light emitting fabric (LEF) has been developed for photodynamic therapy (PDT) for the treatment of dermatologic diseases such as Actinic Keratosis (AK). A successful PDT requires homogenous and reproducible light with controlled power and wavelength on the treated skin area. Due to the shape of the human body, traditional PDT with external light sources is unable to deliver homogenous light everywhere on the skin (head vertex, hand, etc.). For better light delivery homogeneity, plastic optical fibers (POFs) have been woven in textile in order to emit laterally the injected light. The previous studies confirmed that the light power could be locally controlled by modifying the radius of POF macro-bendings within the textile structure. The objective of this study is to optimize the distribution of macro-bendings over the LEF surface in order to increase the light intensity (mW/cm2), and to guarantee the best possible light deliver homogeneity over the LEF which are often contradictory. Fifteen experiments have been carried out with Doehlert experimental design involving Response Surface Methodology (RSM). The proposed models are fitted to the experimental data to enable the optimal set up of the warp yarns tensions.

Optimization Method of a Low Cost, High Performance Ceramic Proppant by Orthogonal Experimental Design

NASA Astrophysics Data System (ADS)

Zhou, Y.; Tian, Y. M.; Wang, K. Y.; Li, G.; Zou, X. W.; Chai, Y. S.

2017-09-01

This study focused on optimization method of a ceramic proppant material with both low cost and high performance that met the requirements of Chinese Petroleum and Gas Industry Standard (SY/T 5108-2006). The orthogonal experimental design of L9(34) was employed to study the significance sequence of three factors, including weight ratio of white clay to bauxite, dolomite content and sintering temperature. For the crush resistance, both the range analysis and variance analysis reflected the optimally experimental condition was weight ratio of white clay to bauxite=3/7, dolomite content=3 wt.%, temperature=1350°C. For the bulk density, the most important factor was the sintering temperature, followed by the dolomite content, and then the ratio of white clay to bauxite.

Optimization and evaluation of clarithromycin floating tablets using experimental mixture design.

PubMed

Uğurlu, Timucin; Karaçiçek, Uğur; Rayaman, Erkan

2014-01-01

The purpose of the study was to prepare and evaluate clarithromycin (CLA) floating tablets using experimental mixture design for treatment of Helicobacter pylori provided by prolonged gastric residence time and controlled plasma level. Ten different formulations were generated based on different molecular weight of hypromellose (HPMC K100, K4M, K15M) by using simplex lattice design (a sub-class of mixture design) with Minitab 16 software. Sodium bicarbonate and anhydrous citric acid were used as gas generating agents. Tablets were prepared by wet granulation technique. All of the process variables were fixed. Results of cumulative drug release at 8th h (CDR 8th) were statistically analyzed to get optimized formulation (OF). Optimized formulation, which gave floating lag time lower than 15 s and total floating time more than 10 h, was analyzed and compared with target for CDR 8th (80%). A good agreement was shown between predicted and actual values of CDR 8th with a variation lower than 1%. The activity of clarithromycin contained optimizedformula against H. pylori were quantified using well diffusion agar assay. Diameters of inhibition zones vs. log10 clarithromycin concentrations were plotted in order to obtain a standard curve and clarithromycin activity.

Application of D-optimal experimental design method to optimize the formulation of O/W cosmetic emulsions.

PubMed

Djuris, J; Vasiljevic, D; Jokic, S; Ibric, S

2014-02-01

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

Optimal design and experimental analyses of a new micro-vibration control payload-platform

NASA Astrophysics Data System (ADS)

Sun, Xiaoqing; Yang, Bintang; Zhao, Long; Sun, Xiaofen

2016-07-01

This paper presents a new payload-platform, for precision devices, which possesses the capability of isolating the complex space micro-vibration in low frequency range below 5 Hz. The novel payload-platform equipped with smart material actuators is investigated and designed through optimization strategy based on the minimum energy loss rate, for the aim of achieving high drive efficiency and reducing the effect of the magnetic circuit nonlinearity. Then, the dynamic model of the driving element is established by using the Lagrange method and the performance of the designed payload-platform is further discussed through the combination of the controlled auto regressive moving average (CARMA) model with modified generalized prediction control (MGPC) algorithm. Finally, an experimental prototype is developed and tested. The experimental results demonstrate that the payload-platform has an impressive potential of micro-vibration isolation.

Reliability-based structural optimization: A proposed analytical-experimental study

NASA Technical Reports Server (NTRS)

Stroud, W. Jefferson; Nikolaidis, Efstratios

1993-01-01

An analytical and experimental study for assessing the potential of reliability-based structural optimization is proposed and described. In the study, competing designs obtained by deterministic and reliability-based optimization are compared. The experimental portion of the study is practical because the structure selected is a modular, actively and passively controlled truss that consists of many identical members, and because the competing designs are compared in terms of their dynamic performance and are not destroyed if failure occurs. The analytical portion of this study is illustrated on a 10-bar truss example. In the illustrative example, it is shown that reliability-based optimization can yield a design that is superior to an alternative design obtained by deterministic optimization. These analytical results provide motivation for the proposed study, which is underway.

Optimal Experimental Design for Model Discrimination

ERIC Educational Resources Information Center

Myung, Jay I.; Pitt, Mark A.

2009-01-01

Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it…

Online optimal experimental re-design in robotic parallel fed-batch cultivation facilities.

PubMed

Cruz Bournazou, M N; Barz, T; Nickel, D B; Lopez Cárdenas, D C; Glauche, F; Knepper, A; Neubauer, P

2017-03-01

We present an integrated framework for the online optimal experimental re-design applied to parallel nonlinear dynamic processes that aims to precisely estimate the parameter set of macro kinetic growth models with minimal experimental effort. This provides a systematic solution for rapid validation of a specific model to new strains, mutants, or products. In biosciences, this is especially important as model identification is a long and laborious process which is continuing to limit the use of mathematical modeling in this field. The strength of this approach is demonstrated by fitting a macro-kinetic differential equation model for Escherichia coli fed-batch processes after 6 h of cultivation. The system includes two fully-automated liquid handling robots; one containing eight mini-bioreactors and another used for automated at-line analyses, which allows for the immediate use of the available data in the modeling environment. As a result, the experiment can be continually re-designed while the cultivations are running using the information generated by periodical parameter estimations. The advantages of an online re-computation of the optimal experiment are proven by a 50-fold lower average coefficient of variation on the parameter estimates compared to the sequential method (4.83% instead of 235.86%). The success obtained in such a complex system is a further step towards a more efficient computer aided bioprocess development. Biotechnol. Bioeng. 2017;114: 610-619. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design.

PubMed

Sanchez, Gaëtan; Lecaignard, Françoise; Otman, Anatole; Maby, Emmanuel; Mattout, Jérémie

2016-01-01

The relatively young field of Brain-Computer Interfaces has promoted the use of electrophysiology and neuroimaging in real-time. In the meantime, cognitive neuroscience studies, which make extensive use of functional exploration techniques, have evolved toward model-based experiments and fine hypothesis testing protocols. Although these two developments are mostly unrelated, we argue that, brought together, they may trigger an important shift in the way experimental paradigms are being designed, which should prove fruitful to both endeavors. This change simply consists in using real-time neuroimaging in order to optimize advanced neurocognitive hypothesis testing. We refer to this new approach as the instantiation of an Active SAmpling Protocol (ASAP). As opposed to classical (static) experimental protocols, ASAP implements online model comparison, enabling the optimization of design parameters (e.g., stimuli) during the course of data acquisition. This follows the well-known principle of sequential hypothesis testing. What is radically new, however, is our ability to perform online processing of the huge amount of complex data that brain imaging techniques provide. This is all the more relevant at a time when physiological and psychological processes are beginning to be approached using more realistic, generative models which may be difficult to tease apart empirically. Based upon Bayesian inference, ASAP proposes a generic and principled way to optimize experimental design adaptively. In this perspective paper, we summarize the main steps in ASAP. Using synthetic data we illustrate its superiority in selecting the right perceptual model compared to a classical design. Finally, we briefly discuss its future potential for basic and clinical neuroscience as well as some remaining challenges.

Optimal experimental design for placement of boreholes

NASA Astrophysics Data System (ADS)

Padalkina, Kateryna; Bücker, H. Martin; Seidler, Ralf; Rath, Volker; Marquart, Gabriele; Niederau, Jan; Herty, Michael

2014-05-01

Drilling for deep resources is an expensive endeavor. Among the many problems finding the optimal drilling location for boreholes is one of the challenging questions. We contribute to this discussion by using a simulation based assessment of possible future borehole locations. We study the problem of finding a new borehole location in a given geothermal reservoir in terms of a numerical optimization problem. In a geothermal reservoir the temporal and spatial distribution of temperature and hydraulic pressure may be simulated using the coupled differential equations for heat transport and mass and momentum conservation for Darcy flow. Within this model the permeability and thermal conductivity are dependent on the geological layers present in the subsurface model of the reservoir. In general, those values involve some uncertainty making it difficult to predict actual heat source in the ground. Within optimal experimental the question is which location and to which depth to drill the borehole in order to estimate conductivity and permeability with minimal uncertainty. We introduce a measure for computing the uncertainty based on simulations of the coupled differential equations. The measure is based on the Fisher information matrix of temperature data obtained through the simulations. We assume that the temperature data is available within the full borehole. A minimization of the measure representing the uncertainty in the unknown permeability and conductivity parameters is performed to determine the optimal borehole location. We present the theoretical framework as well as numerical results for several 2d subsurface models including up to six geological layers. Also, the effect of unknown layers on the introduced measure is studied. Finally, to obtain a more realistic estimate of optimal borehole locations, we couple the optimization to a cost model for deep drilling problems.

Multi-objective optimization design and experimental investigation of centrifugal fan performance

NASA Astrophysics Data System (ADS)

Zhang, Lei; Wang, Songling; Hu, Chenxing; Zhang, Qian

2013-11-01

Current studies of fan performance optimization mainly focus on two aspects: one is to improve the blade profile, and another is only to consider the influence of single impeller structural parameter on fan performance. However, there are few studies on the comprehensive effect of the key parameters such as blade number, exit stagger angle of blade and the impeller outlet width on the fan performance. The G4-73 backward centrifugal fan widely used in power plants is selected as the research object. Based on orthogonal design and BP neural network, a model for predicting the centrifugal fan performance parameters is established, and the maximum relative errors of the total pressure and efficiency are 0.974% and 0.333%, respectively. Multi-objective optimization of total pressure and efficiency of the fan is conducted with genetic algorithm, and the optimum combination of impeller structural parameters is proposed. The optimized parameters of blade number, exit stagger angle of blade and the impeller outlet width are seperately 14, 43.9°, and 21 cm. The experiments on centrifugal fan performance and noise are conducted before and after the installation of the new impeller. The experimental results show that with the new impeller, the total pressure of fan increases significantly in total range of the flow rate, and the fan efficiency is improved when the relative flow is above 75%, also the high efficiency area is broadened. Additionally, in 65% -100% relative flow, the fan noise is reduced. Under the design operating condition, total pressure and efficiency of the fan are improved by 6.91% and 0.5%, respectively. This research sheds light on the considering of comprehensive effect of impeller structrual parameters on fan performance, and a new impeller can be designed to satisfy the engineering demand such as energy-saving, noise reduction or solving air pressure insufficiency for power plants.

Application of mixture experimental design in the formulation and optimization of matrix tablets containing carbomer and hydroxy-propylmethylcellulose.

PubMed

Petrovic, Aleksandra; Cvetkovic, Nebojsa; Ibric, Svetlana; Trajkovic, Svetlana; Djuric, Zorica; Popadic, Dragica; Popovic, Radmila

2009-12-01

Using mixture experimental design, the effect of carbomer (Carbopol((R)) 971P NF) and hydroxypropylmethylcellulose (Methocel((R)) K100M or Methocel((R)) K4M) combination on the release profile and on the mechanism of drug liberation from matrix tablet was investigated. The numerical optimization procedure was also applied to establish and obtain formulation with desired drug release. The amount of TP released, release rate and mechanism varied with carbomer ratio in total matrix and HPMC viscosity. Increasing carbomer fractions led to a decrease in drug release. Anomalous diffusion was found in all matrices containing carbomer, while Case - II transport was predominant for tablet based on HPMC only. The predicted and obtained profiles for optimized formulations showed similarity. Those results indicate that Simplex Lattice Mixture experimental design and numerical optimization procedure can be applied during development to obtain sustained release matrix formulation with desired release profile.

Ceramic processing: Experimental design and optimization

NASA Technical Reports Server (NTRS)

Weiser, Martin W.; Lauben, David N.; Madrid, Philip

1992-01-01

The objectives of this paper are to: (1) gain insight into the processing of ceramics and how green processing can affect the properties of ceramics; (2) investigate the technique of slip casting; (3) learn how heat treatment and temperature contribute to density, strength, and effects of under and over firing to ceramic properties; (4) experience some of the problems inherent in testing brittle materials and learn about the statistical nature of the strength of ceramics; (5) investigate orthogonal arrays as tools to examine the effect of many experimental parameters using a minimum number of experiments; (6) recognize appropriate uses for clay based ceramics; and (7) measure several different properties important to ceramic use and optimize them for a given application.

A Surrogate Approach to the Experimental Optimization of Multielement Airfoils

NASA Technical Reports Server (NTRS)

Otto, John C.; Landman, Drew; Patera, Anthony T.

1996-01-01

The incorporation of experimental test data into the optimization process is accomplished through the use of Bayesian-validated surrogates. In the surrogate approach, a surrogate for the experiment (e.g., a response surface) serves in the optimization process. The validation step of the framework provides a qualitative assessment of the surrogate quality, and bounds the surrogate-for-experiment error on designs "near" surrogate-predicted optimal designs. The utility of the framework is demonstrated through its application to the experimental selection of the trailing edge ap position to achieve a design lift coefficient for a three-element airfoil.

Optimization of the intravenous glucose tolerance test in T2DM patients using optimal experimental design.

PubMed

Silber, Hanna E; Nyberg, Joakim; Hooker, Andrew C; Karlsson, Mats O

2009-06-01

Intravenous glucose tolerance test (IVGTT) provocations are informative, but complex and laborious, for studying the glucose-insulin system. The objective of this study was to evaluate, through optimal design methodology, the possibilities of more informative and/or less laborious study design of the insulin modified IVGTT in type 2 diabetic patients. A previously developed model for glucose and insulin regulation was implemented in the optimal design software PopED 2.0. The following aspects of the study design of the insulin modified IVGTT were evaluated; (1) glucose dose, (2) insulin infusion, (3) combination of (1) and (2), (4) sampling times, (5) exclusion of labeled glucose. Constraints were incorporated to avoid prolonged hyper- and/or hypoglycemia and a reduced design was used to decrease run times. Design efficiency was calculated as a measure of the improvement with an optimal design compared to the basic design. The results showed that the design of the insulin modified IVGTT could be substantially improved by the use of an optimized design compared to the standard design and that it was possible to use a reduced number of samples. Optimization of sample times gave the largest improvement followed by insulin dose. The results further showed that it was possible to reduce the total sample time with only a minor loss in efficiency. Simulations confirmed the predictions from PopED. The predicted uncertainty of parameter estimates (CV) was low in all tested cases, despite the reduction in the number of samples/subject. The best design had a predicted average CV of parameter estimates of 19.5%. We conclude that improvement can be made to the design of the insulin modified IVGTT and that the most important design factor was the placement of sample times followed by the use of an optimal insulin dose. This paper illustrates how complex provocation experiments can be improved by sequential modeling and optimal design.

Separation of 20 coumarin derivatives using the capillary electrophoresis method optimized by a series of Doehlert experimental designs.

PubMed

Woźniakiewicz, Michał; Gładysz, Marta; Nowak, Paweł M; Kędzior, Justyna; Kościelniak, Paweł

2017-05-15

The aim of this study was to develop the first CE-based method enabling separation of 20 structurally similar coumarin derivatives. To facilitate method optimization a series of three consequent Doehlert experimental designs with the response surface methodology was employed, using number of peaks and the adjusted time of analysis as the selected responses. Initially, three variables were examined: buffer pH, ionic strength and temperature (No. 1 Doehlert design). The optimal conditions provided only partial separation, on that account, several buffer additives were examined at the next step: organic cosolvents and cyclodextrin (No. 2 Doehlert design). The optimal cyclodextrin type was also selected experimentally. The most promising results were obtained for the buffers fortified with methanol, acetonitrile and heptakis(2,3,6-tri-O-methyl)-β-cyclodextrin. Since these additives may potentially affect acid-base equilibrium and ionization state of analytes, the third Doehlert design (No. 3) was used to reconcile concentration of these additives with optimal pH. Ultimately, the total separation of all 20 compounds was achieved using the borate buffer at basic pH 9.5 in the presence of 10mM cyclodextrin, 9% (v/v) acetonitrile and 36% (v/v) methanol. Identity of all compounds was confirmed using the in-lab build UV-VIS spectra library. The developed method succeeded in identification of coumarin derivatives in three real samples. It demonstrates a huge resolving power of CE assisted by addition of cyclodextrins and organic cosolvents. Our unique optimization approach, based on the three Doehlert designs, seems to be prospective for future applications of this technique. Copyright © 2017 Elsevier B.V. All rights reserved.

Experimental design and optimization of raloxifene hydrochloride loaded nanotransfersomes for transdermal application.

PubMed

Mahmood, Syed; Taher, Muhammad; Mandal, Uttam Kumar

2014-01-01

Raloxifene hydrochloride, a highly effective drug for the treatment of invasive breast cancer and osteoporosis in post-menopausal women, shows poor oral bioavailability of 2%. The aim of this study was to develop, statistically optimize, and characterize raloxifene hydrochloride-loaded transfersomes for transdermal delivery, in order to overcome the poor bioavailability issue with the drug. A response surface methodology experimental design was applied for the optimization of transfersomes, using Box-Behnken experimental design. Phospholipon(®) 90G, sodium deoxycholate, and sonication time, each at three levels, were selected as independent variables, while entrapment efficiency, vesicle size, and transdermal flux were identified as dependent variables. The formulation was characterized by surface morphology and shape, particle size, and zeta potential. Ex vivo transdermal flux was determined using a Hanson diffusion cell assembly, with rat skin as a barrier medium. Transfersomes from the optimized formulation were found to have spherical, unilamellar structures, with a homogeneous distribution and low polydispersity index (0.08). They had a particle size of 134±9 nM, with an entrapment efficiency of 91.00%±4.90%, and transdermal flux of 6.5±1.1 μg/cm(2)/hour. Raloxifene hydrochloride-loaded transfersomes proved significantly superior in terms of amount of drug permeated and deposited in the skin, with enhancement ratios of 6.25±1.50 and 9.25±2.40, respectively, when compared with drug-loaded conventional liposomes, and an ethanolic phosphate buffer saline. Differential scanning calorimetry study revealed a greater change in skin structure, compared with a control sample, during the ex vivo drug diffusion study. Further, confocal laser scanning microscopy proved an enhanced permeation of coumarin-6-loaded transfersomes, to a depth of approximately160 μM, as compared with rigid liposomes. These ex vivo findings proved that a raloxifene hydrochloride

Experimental design and optimization of raloxifene hydrochloride loaded nanotransfersomes for transdermal application

PubMed Central

Mahmood, Syed; Taher, Muhammad; Mandal, Uttam Kumar

2014-01-01

Raloxifene hydrochloride, a highly effective drug for the treatment of invasive breast cancer and osteoporosis in post-menopausal women, shows poor oral bioavailability of 2%. The aim of this study was to develop, statistically optimize, and characterize raloxifene hydrochloride-loaded transfersomes for transdermal delivery, in order to overcome the poor bioavailability issue with the drug. A response surface methodology experimental design was applied for the optimization of transfersomes, using Box-Behnken experimental design. Phospholipon® 90G, sodium deoxycholate, and sonication time, each at three levels, were selected as independent variables, while entrapment efficiency, vesicle size, and transdermal flux were identified as dependent variables. The formulation was characterized by surface morphology and shape, particle size, and zeta potential. Ex vivo transdermal flux was determined using a Hanson diffusion cell assembly, with rat skin as a barrier medium. Transfersomes from the optimized formulation were found to have spherical, unilamellar structures, with a homogeneous distribution and low polydispersity index (0.08). They had a particle size of 134±9 nM, with an entrapment efficiency of 91.00%±4.90%, and transdermal flux of 6.5±1.1 μg/cm2/hour. Raloxifene hydrochloride-loaded transfersomes proved significantly superior in terms of amount of drug permeated and deposited in the skin, with enhancement ratios of 6.25±1.50 and 9.25±2.40, respectively, when compared with drug-loaded conventional liposomes, and an ethanolic phosphate buffer saline. Differential scanning calorimetry study revealed a greater change in skin structure, compared with a control sample, during the ex vivo drug diffusion study. Further, confocal laser scanning microscopy proved an enhanced permeation of coumarin-6-loaded transfersomes, to a depth of approximately160 μM, as compared with rigid liposomes. These ex vivo findings proved that a raloxifene hydrochloride

A novel experimental design method to optimize hydrophilic matrix formulations with drug release profiles and mechanical properties.

PubMed

Choi, Du Hyung; Lim, Jun Yeul; Shin, Sangmun; Choi, Won Jun; Jeong, Seong Hoon; Lee, Sangkil

2014-10-01

To investigate the effects of hydrophilic polymers on the matrix system, an experimental design method was developed to integrate response surface methodology and the time series modeling. Moreover, the relationships among polymers on the matrix system were studied with the evaluation of physical properties including water uptake, mass loss, diffusion, and gelling index. A mixture simplex lattice design was proposed while considering eight input control factors: Polyethylene glycol 6000 (x1 ), polyethylene oxide (PEO) N-10 (x2 ), PEO 301 (x3 ), PEO coagulant (x4 ), PEO 303 (x5 ), hydroxypropyl methylcellulose (HPMC) 100SR (x6 ), HPMC 4000SR (x7 ), and HPMC 10(5) SR (x8 ). With the modeling, optimal formulations were obtained depending on the four types of targets. The optimal formulations showed the four significant factors (x1 , x2 , x3 , and x8 ) and other four input factors (x4 , x5 , x6 , and x7 ) were not significant based on drug release profiles. Moreover, the optimization results were analyzed with estimated values, targets values, absolute biases, and relative biases based on observed times for the drug release rates with four different targets. The result showed that optimal solutions and target values had consistent patterns with small biases. On the basis of the physical properties of the optimal solutions, the type and ratio of the hydrophilic polymer and the relationships between polymers significantly influenced the physical properties of the system and drug release. This experimental design method is very useful in formulating a matrix system with optimal drug release. Moreover, it can distinctly confirm the relationships between excipients and the effects on the system with extensive and intensive evaluations. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Framework for the rapid optimization of soluble protein expression in Escherichia coli combining microscale experiments and statistical experimental design.

PubMed

Islam, R S; Tisi, D; Levy, M S; Lye, G J

2007-01-01

A major bottleneck in drug discovery is the production of soluble human recombinant protein in sufficient quantities for analysis. This problem is compounded by the complex relationship between protein yield and the large number of variables which affect it. Here, we describe a generic framework for the rapid identification and optimization of factors affecting soluble protein yield in microwell plate fermentations as a prelude to the predictive and reliable scaleup of optimized culture conditions. Recombinant expression of firefly luciferase in Escherichia coli was used as a model system. Two rounds of statistical design of experiments (DoE) were employed to first screen (D-optimal design) and then optimize (central composite face design) the yield of soluble protein. Biological variables from the initial screening experiments included medium type and growth and induction conditions. To provide insight into the impact of the engineering environment on cell growth and expression, plate geometry, shaking speed, and liquid fill volume were included as factors since these strongly influence oxygen transfer into the wells. Compared to standard reference conditions, both the screening and optimization designs gave up to 3-fold increases in the soluble protein yield, i.e., a 9-fold increase overall. In general the highest protein yields were obtained when cells were induced at a relatively low biomass concentration and then allowed to grow slowly up to a high final biomass concentration, >8 g.L-1. Consideration and analysis of the model results showed 6 of the original 10 variables to be important at the screening stage and 3 after optimization. The latter included the microwell plate shaking speeds pre- and postinduction, indicating the importance of oxygen transfer into the microwells and identifying this as a critical parameter for subsequent scale translation studies. The optimization process, also known as response surface methodology (RSM), predicted there to be a

An Integrated Optimization Design Method Based on Surrogate Modeling Applied to Diverging Duct Design

NASA Astrophysics Data System (ADS)

Hanan, Lu; Qiushi, Li; Shaobin, Li

2016-12-01

This paper presents an integrated optimization design method in which uniform design, response surface methodology and genetic algorithm are used in combination. In detail, uniform design is used to select the experimental sampling points in the experimental domain and the system performance is evaluated by means of computational fluid dynamics to construct a database. After that, response surface methodology is employed to generate a surrogate mathematical model relating the optimization objective and the design variables. Subsequently, genetic algorithm is adopted and applied to the surrogate model to acquire the optimal solution in the case of satisfying some constraints. The method has been applied to the optimization design of an axisymmetric diverging duct, dealing with three design variables including one qualitative variable and two quantitative variables. The method of modeling and optimization design performs well in improving the duct aerodynamic performance and can be also applied to wider fields of mechanical design and seen as a useful tool for engineering designers, by reducing the design time and computation consumption.

Probabilistic Finite Element Analysis & Design Optimization for Structural Designs

NASA Astrophysics Data System (ADS)

Deivanayagam, Arumugam

This study focuses on implementing probabilistic nature of material properties (Kevlar® 49) to the existing deterministic finite element analysis (FEA) of fabric based engine containment system through Monte Carlo simulations (MCS) and implementation of probabilistic analysis in engineering designs through Reliability Based Design Optimization (RBDO). First, the emphasis is on experimental data analysis focusing on probabilistic distribution models which characterize the randomness associated with the experimental data. The material properties of Kevlar® 49 are modeled using experimental data analysis and implemented along with an existing spiral modeling scheme (SMS) and user defined constitutive model (UMAT) for fabric based engine containment simulations in LS-DYNA. MCS of the model are performed to observe the failure pattern and exit velocities of the models. Then the solutions are compared with NASA experimental tests and deterministic results. MCS with probabilistic material data give a good prospective on results rather than a single deterministic simulation results. The next part of research is to implement the probabilistic material properties in engineering designs. The main aim of structural design is to obtain optimal solutions. In any case, in a deterministic optimization problem even though the structures are cost effective, it becomes highly unreliable if the uncertainty that may be associated with the system (material properties, loading etc.) is not represented or considered in the solution process. Reliable and optimal solution can be obtained by performing reliability optimization along with the deterministic optimization, which is RBDO. In RBDO problem formulation, in addition to structural performance constraints, reliability constraints are also considered. This part of research starts with introduction to reliability analysis such as first order reliability analysis, second order reliability analysis followed by simulation technique that

Optimal design of isotope labeling experiments.

PubMed

Yang, Hong; Mandy, Dominic E; Libourel, Igor G L

2014-01-01

Stable isotope labeling experiments (ILE) constitute a powerful methodology for estimating metabolic fluxes. An optimal label design for such an experiment is necessary to maximize the precision with which fluxes can be determined. But often, precision gained in the determination of one flux comes at the expense of the precision of other fluxes, and an appropriate label design therefore foremost depends on the question the investigator wants to address. One could liken ILE to shadows that metabolism casts on products. Optimal label design is the placement of the lamp; creating clear shadows for some parts of metabolism and obscuring others.An optimal isotope label design is influenced by: (1) the network structure; (2) the true flux values; (3) the available label measurements; and, (4) commercially available substrates. The first two aspects are dictated by nature and constrain any optimal design. The second two aspects are suitable design parameters. To create an optimal label design, an explicit optimization criterion needs to be formulated. This usually is a property of the flux covariance matrix, which can be augmented by weighting label substrate cost. An optimal design is found by using such a criterion as an objective function for an optimizer. This chapter uses a simple elementary metabolite units (EMU) representation of the TCA cycle to illustrate the process of experimental design of isotope labeled substrates.

Experimental design, modeling and optimization of polyplex formation between DNA oligonucleotides and branched polyethylenimine.

PubMed

Clima, Lilia; Ursu, Elena L; Cojocaru, Corneliu; Rotaru, Alexandru; Barboiu, Mihail; Pinteala, Mariana

2015-09-28

The complexes formed by DNA and polycations have received great attention owing to their potential application in gene therapy. In this study, the binding efficiency between double-stranded oligonucleotides (dsDNA) and branched polyethylenimine (B-PEI) has been quantified by processing of the images captured from the gel electrophoresis assays. The central composite experimental design has been employed to investigate the effects of controllable factors on the binding efficiency. On the basis of experimental data and the response surface methodology, a multivariate regression model has been constructed and statistically validated. The model has enabled us to predict the binding efficiency depending on experimental factors, such as concentrations of dsDNA and B-PEI as well as the initial pH of solution. The optimization of the binding process has been performed using simplex and gradient methods. The optimal conditions determined for polyplex formation have yielded a maximal binding efficiency close to 100%. In order to reveal the mechanism of complex formation at the atomic-scale, a molecular dynamic simulation has been carried out. According to the computation results, B-PEI amine hydrogen atoms have interacted with oxygen atoms from dsDNA phosphate groups. These interactions have led to the formation of hydrogen bonds between macromolecules, stabilizing the polyplex structure.

Experimental design methods for bioengineering applications.

PubMed

Keskin Gündoğdu, Tuğba; Deniz, İrem; Çalışkan, Gülizar; Şahin, Erdem Sefa; Azbar, Nuri

2016-01-01

Experimental design is a form of process analysis in which certain factors are selected to obtain the desired responses of interest. It may also be used for the determination of the effects of various independent factors on a dependent factor. The bioengineering discipline includes many different areas of scientific interest, and each study area is affected and governed by many different factors. Briefly analyzing the important factors and selecting an experimental design for optimization are very effective tools for the design of any bioprocess under question. This review summarizes experimental design methods that can be used to investigate various factors relating to bioengineering processes. The experimental methods generally used in bioengineering are as follows: full factorial design, fractional factorial design, Plackett-Burman design, Taguchi design, Box-Behnken design and central composite design. These design methods are briefly introduced, and then the application of these design methods to study different bioengineering processes is analyzed.

Experimental Validation of an Integrated Controls-Structures Design Methodology

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Gupta, Sandeep; Elliot, Kenny B.; Walz, Joseph E.

1996-01-01

The first experimental validation of an integrated controls-structures design methodology for a class of large order, flexible space structures is described. Integrated redesign of the controls-structures-interaction evolutionary model, a laboratory testbed at NASA Langley, was described earlier. The redesigned structure was fabricated, assembled in the laboratory, and experimentally tested against the original structure. Experimental results indicate that the structure redesigned using the integrated design methodology requires significantly less average control power than the nominal structure with control-optimized designs, while maintaining the required line-of-sight pointing performance. Thus, the superiority of the integrated design methodology over the conventional design approach is experimentally demonstrated. Furthermore, amenability of the integrated design structure to other control strategies is evaluated, both analytically and experimentally. Using Linear-Quadratic-Guassian optimal dissipative controllers, it is observed that the redesigned structure leads to significantly improved performance with alternate controllers as well.

PopED lite: An optimal design software for preclinical pharmacokinetic and pharmacodynamic studies.

PubMed

Aoki, Yasunori; Sundqvist, Monika; Hooker, Andrew C; Gennemark, Peter

2016-04-01

Optimal experimental design approaches are seldom used in preclinical drug discovery. The objective is to develop an optimal design software tool specifically designed for preclinical applications in order to increase the efficiency of drug discovery in vivo studies. Several realistic experimental design case studies were collected and many preclinical experimental teams were consulted to determine the design goal of the software tool. The tool obtains an optimized experimental design by solving a constrained optimization problem, where each experimental design is evaluated using some function of the Fisher Information Matrix. The software was implemented in C++ using the Qt framework to assure a responsive user-software interaction through a rich graphical user interface, and at the same time, achieving the desired computational speed. In addition, a discrete global optimization algorithm was developed and implemented. The software design goals were simplicity, speed and intuition. Based on these design goals, we have developed the publicly available software PopED lite (http://www.bluetree.me/PopED_lite). Optimization computation was on average, over 14 test problems, 30 times faster in PopED lite compared to an already existing optimal design software tool. PopED lite is now used in real drug discovery projects and a few of these case studies are presented in this paper. PopED lite is designed to be simple, fast and intuitive. Simple, to give many users access to basic optimal design calculations. Fast, to fit a short design-execution cycle and allow interactive experimental design (test one design, discuss proposed design, test another design, etc). Intuitive, so that the input to and output from the software tool can easily be understood by users without knowledge of the theory of optimal design. In this way, PopED lite is highly useful in practice and complements existing tools. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Optimal design of high temperature metalized thin-film polymer capacitors: A combined numerical and experimental method

NASA Astrophysics Data System (ADS)

Wang, Zhuo; Li, Qi; Trinh, Wei; Lu, Qianli; Cho, Heejin; Wang, Qing; Chen, Lei

2017-07-01

The objective of this paper is to design and optimize the high temperature metalized thin-film polymer capacitor by a combined computational and experimental method. A finite-element based thermal model is developed to incorporate Joule heating and anisotropic heat conduction arising from anisotropic geometric structures of the capacitor. The anisotropic thermal conductivity and temperature dependent electrical conductivity required by the thermal model are measured from the experiments. The polymer represented by thermally crosslinking benzocyclobutene (BCB) in the presence of boron nitride nanosheets (BNNSs) is selected for high temperature capacitor design based on the results of highest internal temperature (HIT) and the time to achieve thermal equilibrium. The c-BCB/BNNS-based capacitor aiming at the operating temperature of 250 °C is geometrically optimized with respect to its shape and volume. "Safe line" plot is also presented to reveal the influence of the cooling strength on capacitor geometry design.

Identification of vehicle suspension parameters by design optimization

NASA Astrophysics Data System (ADS)

Tey, J. Y.; Ramli, R.; Kheng, C. W.; Chong, S. Y.; Abidin, M. A. Z.

2014-05-01

The design of a vehicle suspension system through simulation requires accurate representation of the design parameters. These parameters are usually difficult to measure or sometimes unavailable. This article proposes an efficient approach to identify the unknown parameters through optimization based on experimental results, where the covariance matrix adaptation-evolutionary strategy (CMA-es) is utilized to improve the simulation and experimental results against the kinematic and compliance tests. This speeds up the design and development cycle by recovering all the unknown data with respect to a set of kinematic measurements through a single optimization process. A case study employing a McPherson strut suspension system is modelled in a multi-body dynamic system. Three kinematic and compliance tests are examined, namely, vertical parallel wheel travel, opposite wheel travel and single wheel travel. The problem is formulated as a multi-objective optimization problem with 40 objectives and 49 design parameters. A hierarchical clustering method based on global sensitivity analysis is used to reduce the number of objectives to 30 by grouping correlated objectives together. Then, a dynamic summation of rank value is used as pseudo-objective functions to reformulate the multi-objective optimization to a single-objective optimization problem. The optimized results show a significant improvement in the correlation between the simulated model and the experimental model. Once accurate representation of the vehicle suspension model is achieved, further analysis, such as ride and handling performances, can be implemented for further optimization.

Optimal experimental designs for fMRI when the model matrix is uncertain.

PubMed

Kao, Ming-Hung; Zhou, Lin

2017-07-15

This study concerns optimal designs for functional magnetic resonance imaging (fMRI) experiments when the model matrix of the statistical model depends on both the selected stimulus sequence (fMRI design), and the subject's uncertain feedback (e.g. answer) to each mental stimulus (e.g. question) presented to her/him. While practically important, this design issue is challenging. This mainly is because that the information matrix cannot be fully determined at the design stage, making it difficult to evaluate the quality of the selected designs. To tackle this challenging issue, we propose an easy-to-use optimality criterion for evaluating the quality of designs, and an efficient approach for obtaining designs optimizing this criterion. Compared with a previously proposed method, our approach requires a much less computing time to achieve designs with high statistical efficiencies. Copyright © 2017 Elsevier Inc. All rights reserved.

Achieving optimal SERS through enhanced experimental design

PubMed Central

Fisk, Heidi; Westley, Chloe; Turner, Nicholas J.

2016-01-01

One of the current limitations surrounding surface‐enhanced Raman scattering (SERS) is the perceived lack of reproducibility. SERS is indeed challenging, and for analyte detection, it is vital that the analyte interacts with the metal surface. However, as this is analyte dependent, there is not a single set of SERS conditions that are universal. This means that experimental optimisation for optimum SERS response is vital. Most researchers optimise one factor at a time, where a single parameter is altered first before going onto optimise the next. This is a very inefficient way of searching the experimental landscape. In this review, we explore the use of more powerful multivariate approaches to SERS experimental optimisation based on design of experiments and evolutionary computational methods. We particularly focus on colloidal‐based SERS rather than thin film preparations as a result of their popularity. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd. PMID:27587905

Achieving optimal SERS through enhanced experimental design.

PubMed

Fisk, Heidi; Westley, Chloe; Turner, Nicholas J; Goodacre, Royston

2016-01-01

One of the current limitations surrounding surface-enhanced Raman scattering (SERS) is the perceived lack of reproducibility. SERS is indeed challenging, and for analyte detection, it is vital that the analyte interacts with the metal surface. However, as this is analyte dependent, there is not a single set of SERS conditions that are universal. This means that experimental optimisation for optimum SERS response is vital. Most researchers optimise one factor at a time, where a single parameter is altered first before going onto optimise the next. This is a very inefficient way of searching the experimental landscape. In this review, we explore the use of more powerful multivariate approaches to SERS experimental optimisation based on design of experiments and evolutionary computational methods. We particularly focus on colloidal-based SERS rather than thin film preparations as a result of their popularity. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd.

Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design.

PubMed

Bertocci, Francesco; Fort, Ada; Vignoli, Valerio; Mugnaini, Marco; Berni, Rossella

2017-06-10

Eight different types of nanostructured perovskites based on YCoO 3 with different chemical compositions are prepared as gas sensor materials, and they are studied with two target gases NO 2 and CO. Moreover, a statistical approach is adopted to optimize their performance. The innovative contribution is carried out through a split-plot design planning and modeling, also involving random effects, for studying Metal Oxide Semiconductors (MOX) sensors in a robust design context. The statistical results prove the validity of the proposed approach; in fact, for each material type, the variation of the electrical resistance achieves a satisfactory optimized value conditional to the working temperature and by controlling for the gas concentration variability. Just to mention some results, the sensing material YCo 0 . 9 Pd 0 . 1 O 3 (Mt1) achieved excellent solutions during the optimization procedure. In particular, Mt1 resulted in being useful and feasible for the detection of both gases, with optimal response equal to +10.23% and working temperature at 312 ∘ C for CO (284 ppm, from design) and response equal to -14.17% at 185 ∘ C for NO 2 (16 ppm, from design). Analogously, for NO 2 (16 ppm, from design), the material type YCo 0 . 9 O 2 . 85 + 1 % Pd (Mt8) allows for optimizing the response value at - 15 . 39 % with a working temperature at 181 . 0 ∘ C, whereas for YCo 0 . 95 Pd 0 . 05 O 3 (Mt3), the best response value is achieved at - 15 . 40 % with the temperature equal to 204 ∘ C.

Optimal designs for copula models

PubMed Central

Perrone, E.; Müller, W.G.

2016-01-01

Copula modelling has in the past decade become a standard tool in many areas of applied statistics. However, a largely neglected aspect concerns the design of related experiments. Particularly the issue of whether the estimation of copula parameters can be enhanced by optimizing experimental conditions and how robust all the parameter estimates for the model are with respect to the type of copula employed. In this paper an equivalence theorem for (bivariate) copula models is provided that allows formulation of efficient design algorithms and quick checks of whether designs are optimal or at least efficient. Some examples illustrate that in practical situations considerable gains in design efficiency can be achieved. A natural comparison between different copula models with respect to design efficiency is provided as well. PMID:27453616

Vitamin B12 production from crude glycerol by Propionibacterium freudenreichii ssp. shermanii: optimization of medium composition through statistical experimental designs.

PubMed

Kośmider, Alicja; Białas, Wojciech; Kubiak, Piotr; Drożdżyńska, Agnieszka; Czaczyk, Katarzyna

2012-02-01

A two-step statistical experimental design was employed to optimize the medium for vitamin B(12) production from crude glycerol by Propionibacterium freudenreichii ssp. shermanii. In the first step, using Plackett-Burman design, five of 13 tested medium components (calcium pantothenate, NaH(2)PO(4)·2H(2)O, casein hydrolysate, glycerol and FeSO(4)·7H(2)O) were identified as factors having significant influence on vitamin production. In the second step, a central composite design was used to optimize levels of medium components selected in the first step. Valid statistical models describing the influence of significant factors on vitamin B(12) production were established for each optimization phase. The optimized medium provided a 93% increase in final vitamin concentration compared to the original medium. Copyright © 2011 Elsevier Ltd. All rights reserved.

Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design

PubMed Central

Bertocci, Francesco; Fort, Ada; Vignoli, Valerio; Mugnaini, Marco; Berni, Rossella

2017-01-01

Eight different types of nanostructured perovskites based on YCoO3 with different chemical compositions are prepared as gas sensor materials, and they are studied with two target gases NO2 and CO. Moreover, a statistical approach is adopted to optimize their performance. The innovative contribution is carried out through a split-plot design planning and modeling, also involving random effects, for studying Metal Oxide Semiconductors (MOX) sensors in a robust design context. The statistical results prove the validity of the proposed approach; in fact, for each material type, the variation of the electrical resistance achieves a satisfactory optimized value conditional to the working temperature and by controlling for the gas concentration variability. Just to mention some results, the sensing material YCo0.9Pd0.1O3 (Mt1) achieved excellent solutions during the optimization procedure. In particular, Mt1 resulted in being useful and feasible for the detection of both gases, with optimal response equal to +10.23% and working temperature at 312∘C for CO (284 ppm, from design) and response equal to −14.17% at 185∘C for NO2 (16 ppm, from design). Analogously, for NO2 (16 ppm, from design), the material type YCo0.9O2.85+1%Pd (Mt8) allows for optimizing the response value at −15.39% with a working temperature at 181.0∘C, whereas for YCo0.95Pd0.05O3 (Mt3), the best response value is achieved at −15.40% with the temperature equal to 204∘C. PMID:28604587

Experimental design in chemistry: A tutorial.

PubMed

Leardi, Riccardo

2009-10-12

In this tutorial the main concepts and applications of experimental design in chemistry will be explained. Unfortunately, nowadays experimental design is not as known and applied as it should be, and many papers can be found in which the "optimization" of a procedure is performed one variable at a time. Goal of this paper is to show the real advantages in terms of reduced experimental effort and of increased quality of information that can be obtained if this approach is followed. To do that, three real examples will be shown. Rather than on the mathematical aspects, this paper will focus on the mental attitude required by experimental design. The readers being interested to deepen their knowledge of the mathematical and algorithmical part can find very good books and tutorials in the references [G.E.P. Box, W.G. Hunter, J.S. Hunter, Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, John Wiley & Sons, New York, 1978; R. Brereton, Chemometrics: Data Analysis for the Laboratory and Chemical Plant, John Wiley & Sons, New York, 1978; R. Carlson, J.E. Carlson, Design and Optimization in Organic Synthesis: Second Revised and Enlarged Edition, in: Data Handling in Science and Technology, vol. 24, Elsevier, Amsterdam, 2005; J.A. Cornell, Experiments with Mixtures: Designs, Models and the Analysis of Mixture Data, in: Series in Probability and Statistics, John Wiley & Sons, New York, 1991; R.E. Bruns, I.S. Scarminio, B. de Barros Neto, Statistical Design-Chemometrics, in: Data Handling in Science and Technology, vol. 25, Elsevier, Amsterdam, 2006; D.C. Montgomery, Design and Analysis of Experiments, 7th edition, John Wiley & Sons, Inc., 2009; T. Lundstedt, E. Seifert, L. Abramo, B. Thelin, A. Nyström, J. Pettersen, R. Bergman, Chemolab 42 (1998) 3; Y. Vander Heyden, LC-GC Europe 19 (9) (2006) 469].

Global Design Optimization for Fluid Machinery Applications

NASA Technical Reports Server (NTRS)

Shyy, Wei; Papila, Nilay; Tucker, Kevin; Vaidyanathan, Raj; Griffin, Lisa

2000-01-01

Recent experiences in utilizing the global optimization methodology, based on polynomial and neural network techniques for fluid machinery design are summarized. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. Another advantage is that these methods do not need to calculate the sensitivity of each design variable locally. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables and methods for predicting the model performance. Examples of applications selected from rocket propulsion components including a supersonic turbine and an injector element and a turbulent flow diffuser are used to illustrate the usefulness of the global optimization method.

A Tutorial on Adaptive Design Optimization

PubMed Central

Myung, Jay I.; Cavagnaro, Daniel R.; Pitt, Mark A.

2013-01-01

Experimentation is ubiquitous in the field of psychology and fundamental to the advancement of its science, and one of the biggest challenges for researchers is designing experiments that can conclusively discriminate the theoretical hypotheses or models under investigation. The recognition of this challenge has led to the development of sophisticated statistical methods that aid in the design of experiments and that are within the reach of everyday experimental scientists. This tutorial paper introduces the reader to an implementable experimentation methodology, dubbed Adaptive Design Optimization, that can help scientists to conduct “smart” experiments that are maximally informative and highly efficient, which in turn should accelerate scientific discovery in psychology and beyond. PMID:23997275

Optimization of microwave-assisted extraction of analgesic and anti-inflammatory drugs from human plasma and urine using response surface experimental designs.

PubMed

Fernández, Purificación; Fernández, Ana M; Bermejo, Ana M; Lorenzo, Rosa A; Carro, Antonia M

2013-04-01

The performance of microwave-assisted extraction and HPLC with photodiode array detection method for determination of six analgesic and anti-inflammatory drugs from plasma and urine, is described, optimized, and validated. Several parameters affecting the extraction technique were optimized using experimental designs. A four-factor (temperature, phosphate buffer pH 4.0 volume, extraction solvent volume, and time) hybrid experimental design was used for extraction optimization in plasma, and three-factor (temperature, extraction solvent volume, and time) Doehlert design was chosen to extraction optimization in urine. The use of desirability functions revealed the optimal extraction conditions as follows: 67°C, 4 mL phosphate buffer pH 4.0, 12 mL of ethyl acetate and 9 min, for plasma and the same volume of buffer and ethyl acetate, 115°C and 4 min for urine. Limits of detection ranged from 4 to 45 ng/mL in plasma and from 8 to 85 ng/mL in urine. The reproducibility evaluated at two concentration levels was less than 6.5% for both specimens. The recoveries were from 89 to 99% for plasma and from 83 to 99% for urine. The proposed method was successfully applied in plasma and urine samples obtained from analgesic users. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimal Objective-Based Experimental Design for Uncertain Dynamical Gene Networks with Experimental Error.

PubMed

Mohsenizadeh, Daniel N; Dehghannasiri, Roozbeh; Dougherty, Edward R

2018-01-01

In systems biology, network models are often used to study interactions among cellular components, a salient aim being to develop drugs and therapeutic mechanisms to change the dynamical behavior of the network to avoid undesirable phenotypes. Owing to limited knowledge, model uncertainty is commonplace and network dynamics can be updated in different ways, thereby giving multiple dynamic trajectories, that is, dynamics uncertainty. In this manuscript, we propose an experimental design method that can effectively reduce the dynamics uncertainty and improve performance in an interaction-based network. Both dynamics uncertainty and experimental error are quantified with respect to the modeling objective, herein, therapeutic intervention. The aim of experimental design is to select among a set of candidate experiments the experiment whose outcome, when applied to the network model, maximally reduces the dynamics uncertainty pertinent to the intervention objective.

Three-dimensional shape optimization of a cemented hip stem and experimental validations.

PubMed

Higa, Masaru; Tanino, Hiromasa; Nishimura, Ikuya; Mitamura, Yoshinori; Matsuno, Takeo; Ito, Hiroshi

2015-03-01

This study proposes novel optimized stem geometry with low stress values in the cement using a finite element (FE) analysis combined with an optimization procedure and experimental measurements of cement stress in vitro. We first optimized an existing stem geometry using a three-dimensional FE analysis combined with a shape optimization technique. One of the most important factors in the cemented stem design is to reduce stress in the cement. Hence, in the optimization study, we minimized the largest tensile principal stress in the cement mantle under a physiological loading condition by changing the stem geometry. As the next step, the optimized stem and the existing stem were manufactured to validate the usefulness of the numerical models and the results of the optimization in vitro. In the experimental study, strain gauges were embedded in the cement mantle to measure the strain in the cement mantle adjacent to the stems. The overall trend of the experimental study was in good agreement with the results of the numerical study, and we were able to reduce the largest stress by more than 50% in both shape optimization and strain gauge measurements. Thus, we could validate the usefulness of the numerical models and the results of the optimization using the experimental models. The optimization employed in this study is a useful approach for developing new stem designs.

Molecular identification of potential denitrifying bacteria and use of D-optimal mixture experimental design for the optimization of denitrification process.

PubMed

Ben Taheur, Fadia; Fdhila, Kais; Elabed, Hamouda; Bouguerra, Amel; Kouidhi, Bochra; Bakhrouf, Amina; Chaieb, Kamel

2016-04-01

Three bacterial strains (TE1, TD3 and FB2) were isolated from date palm (degla), pistachio and barley. The presence of nitrate reductase (narG) and nitrite reductase (nirS and nirK) genes in the selected strains was detected by PCR technique. Molecular identification based on 16S rDNA sequencing method was applied to identify positive strains. In addition, the D-optimal mixture experimental design was used to optimize the optimal formulation of probiotic bacteria for denitrification process. Strains harboring denitrification genes were identified as: TE1, Agrococcus sp LN828197; TD3, Cronobacter sakazakii LN828198 and FB2, Pedicoccus pentosaceus LN828199. PCR results revealed that all strains carried the nirS gene. However only C. sakazakii LN828198 and Agrococcus sp LN828197 harbored the nirK and the narG genes respectively. Moreover, the studied bacteria were able to form biofilm on abiotic surfaces with different degree. Process optimization showed that the most significant reduction of nitrate was 100% with 14.98% of COD consumption and 5.57 mg/l nitrite accumulation. Meanwhile, the response values were optimized and showed that the most optimal combination was 78.79% of C. sakazakii LN828198 (curve value), 21.21% of P. pentosaceus LN828199 (curve value) and absence (0%) of Agrococcus sp LN828197 (curve value). Copyright © 2016 Elsevier Ltd. All rights reserved.

Formulation and optimization by experimental design of eco-friendly emulsions based on d-limonene.

PubMed

Pérez-Mosqueda, Luis M; Trujillo-Cayado, Luis A; Carrillo, Francisco; Ramírez, Pablo; Muñoz, José

2015-04-01

d-Limonene is a natural occurring solvent that can replace more pollutant chemicals in agrochemical formulations. In the present work, a comprehensive study of the influence of dispersed phase mass fraction, ϕ, and of the surfactant/oil ratio, R, on the emulsion stability and droplet size distribution of d-limonene-in-water emulsions stabilized by a non-ionic triblock copolymer surfactant has been carried out. An experimental full factorial design 3(2) was conducted in order to optimize the emulsion formulation. The independent variables, ϕ and R were studied in the range 10-50 wt% and 0.02-0.1, respectively. The emulsions studied were mainly destabilized by both creaming and Ostwald ripening. Therefore, initial droplet size and an overall destabilization parameter, the so-called turbiscan stability index, were used as dependent variables. The optimal formulation, comprising minimum droplet size and maximum stability was achieved at ϕ=50 wt%; R=0.062. Furthermore, the surface response methodology allowed us to obtain the formulation yielding sub-micron emulsions by using a single step rotor/stator homogenizer process instead of most commonly used two-step emulsification methods. In addition, the optimal formulation was further improved against Ostwald ripening by adding silicone oil to the dispersed phase. The combination of these experimental findings allowed us to gain a deeper insight into the stability of these emulsions, which can be applied to the rational development of new formulations with potential application in agrochemical formulations. Copyright © 2015 Elsevier B.V. All rights reserved.

Bayesian cross-entropy methodology for optimal design of validation experiments

NASA Astrophysics Data System (ADS)

Jiang, X.; Mahadevan, S.

2006-07-01

An important concern in the design of validation experiments is how to incorporate the mathematical model in the design in order to allow conclusive comparisons of model prediction with experimental output in model assessment. The classical experimental design methods are more suitable for phenomena discovery and may result in a subjective, expensive, time-consuming and ineffective design that may adversely impact these comparisons. In this paper, an integrated Bayesian cross-entropy methodology is proposed to perform the optimal design of validation experiments incorporating the computational model. The expected cross entropy, an information-theoretic distance between the distributions of model prediction and experimental observation, is defined as a utility function to measure the similarity of two distributions. A simulated annealing algorithm is used to find optimal values of input variables through minimizing or maximizing the expected cross entropy. The measured data after testing with the optimum input values are used to update the distribution of the experimental output using Bayes theorem. The procedure is repeated to adaptively design the required number of experiments for model assessment, each time ensuring that the experiment provides effective comparison for validation. The methodology is illustrated for the optimal design of validation experiments for a three-leg bolted joint structure and a composite helicopter rotor hub component.

Optimization of glibenclamide tablet composition through the combined use of differential scanning calorimetry and D-optimal mixture experimental design.

PubMed

Mura, P; Furlanetto, S; Cirri, M; Maestrelli, F; Marras, A M; Pinzauti, S

2005-02-07

A systematic analysis of the influence of different proportions of excipients on the stability of a solid dosage form was carried out. In particular, a d-optimal mixture experimental design was applied for the evaluation of glibenclamide compatibility in tablet formulations, consisting of four classic excipients (natrosol as binding agent, stearic acid as lubricant, sorbitol as diluent and cross-linked polyvinylpyrrolidone as disintegrant). The goal was to find the mixture component proportions which correspond to the optimal drug melting parameters, i.e. its maximum stability, using differential scanning calorimetry (DSC) to quickly obtain information about possible interactions among the formulation components. The absolute value of the difference between the melting peak temperature of pure drug endotherm and that in each analysed mixture and the absolute value of the difference between the enthalpy of the pure glibenclamide melting peak and that of its melting peak in the different analyzed mixtures, were chosen as indexes of the drug-excipient interaction degree.

Hybrid computational and experimental approach for the study and optimization of mechanical components

NASA Astrophysics Data System (ADS)

Furlong, Cosme; Pryputniewicz, Ryszard J.

1998-05-01

Increased demands on the performance and efficiency of mechanical components impose challenges on their engineering design and optimization, especially when new and more demanding applications must be developed in relatively short periods of time while satisfying design objectives, as well as cost and manufacturability. In addition, reliability and durability must be taken into consideration. As a consequence, effective quantitative methodologies, computational and experimental, should be applied in the study and optimization of mechanical components. Computational investigations enable parametric studies and the determination of critical engineering design conditions, while experimental investigations, especially those using optical techniques, provide qualitative and quantitative information on the actual response of the structure of interest to the applied load and boundary conditions. We discuss a hybrid experimental and computational approach for investigation and optimization of mechanical components. The approach is based on analytical, computational, and experimental resolutions methodologies in the form of computational, noninvasive optical techniques, and fringe prediction analysis tools. Practical application of the hybrid approach is illustrated with representative examples that demonstrate the viability of the approach as an effective engineering tool for analysis and optimization.

Optimal experimental designs for estimating Henry's law constants via the method of phase ratio variation.

PubMed

Kapelner, Adam; Krieger, Abba; Blanford, William J

2016-10-14

When measuring Henry's law constants (k H ) using the phase ratio variation (PRV) method via headspace gas chromatography (G C ), the value of k H of the compound under investigation is calculated from the ratio of the slope to the intercept of a linear regression of the inverse G C response versus the ratio of gas to liquid volumes of a series of vials drawn from the same parent solution. Thus, an experimenter collects measurements consisting of the independent variable (the gas/liquid volume ratio) and dependent variable (the G C -1 peak area). A review of the literature found that the common design is a simple uniform spacing of liquid volumes. We present an optimal experimental design which estimates k H with minimum error and provides multiple means for building confidence intervals for such estimates. We illustrate performance improvements of our design with an example measuring the k H for Naphthalene in aqueous solution as well as simulations on previous studies. Our designs are most applicable after a trial run defines the linear G C response and the linear phase ratio to the G C -1 region (where the PRV method is suitable) after which a practitioner can collect measurements in bulk. The designs can be easily computed using our open source software optDesignSlopeInt, an R package on CRAN. Copyright © 2016 Elsevier B.V. All rights reserved.

Optimization of critical quality attributes in continuous twin-screw wet granulation via design space validated with pilot scale experimental data.

PubMed

Liu, Huolong; Galbraith, S C; Ricart, Brendon; Stanton, Courtney; Smith-Goettler, Brandye; Verdi, Luke; O'Connor, Thomas; Lee, Sau; Yoon, Seongkyu

2017-06-15

In this study, the influence of key process variables (screw speed, throughput and liquid to solid (L/S) ratio) of a continuous twin screw wet granulation (TSWG) was investigated using a central composite face-centered (CCF) experimental design method. Regression models were developed to predict the process responses (motor torque, granule residence time), granule properties (size distribution, volume average diameter, yield, relative width, flowability) and tablet properties (tensile strength). The effects of the three key process variables were analyzed via contour and interaction plots. The experimental results have demonstrated that all the process responses, granule properties and tablet properties are influenced by changing the screw speed, throughput and L/S ratio. The TSWG process was optimized to produce granules with specific volume average diameter of 150μm and the yield of 95% based on the developed regression models. A design space (DS) was built based on volume average granule diameter between 90 and 200μm and the granule yield larger than 75% with a failure probability analysis using Monte Carlo simulations. Validation experiments successfully validated the robustness and accuracy of the DS generated using the CCF experimental design in optimizing a continuous TSWG process. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimization and modeling of laccase production by Trametes versicolor in a bioreactor using statistical experimental design.

PubMed

Tavares, A P M; Coelho, M A Z; Agapito, M S M; Coutinho, J A P; Xavier, A M R B

2006-09-01

Experimental design and response surface methodologies were applied to optimize laccase production by Trametes versicolor in a bioreactor. The effects of three factors, initial glucose concentration (0 and 9 g/L), agitation (100 and 180 rpm), and pH (3.0 and 5.0), were evaluated to identify the significant effects and its interactions in the laccase production. The pH of the medium was found to be the most important factor, followed by initial glucose concentration and the interaction of both factors. Agitation did not seem to play an important role in laccase production, nor did the interaction agitation x medium pH and agitation x initial glucose concentration. Response surface analysis showed that an initial glucose concentration of 11 g/L and pH controlled at 5.2 were the optimal conditions for laccase production by T. versicolor. Under these conditions, the predicted value for laccase activity was >10,000 U/L, which is in good agreement with the laccase activity obtained experimentally (11,403 U/L). In addition, a mathematical model for the bioprocess was developed. It is shown that it provides a good description of the experimental profile observed, and that it is capable of predicting biomass growth based on secondary process variables.

Experimental design approach to the process parameter optimization for laser welding of martensitic stainless steels in a constrained overlap configuration

NASA Astrophysics Data System (ADS)

Khan, M. M. A.; Romoli, L.; Fiaschi, M.; Dini, G.; Sarri, F.

2011-02-01

This paper presents an experimental design approach to process parameter optimization for the laser welding of martensitic AISI 416 and AISI 440FSe stainless steels in a constrained overlap configuration in which outer shell was 0.55 mm thick. To determine the optimal laser-welding parameters, a set of mathematical models were developed relating welding parameters to each of the weld characteristics. These were validated both statistically and experimentally. The quality criteria set for the weld to determine optimal parameters were the minimization of weld width and the maximization of weld penetration depth, resistance length and shearing force. Laser power and welding speed in the range 855-930 W and 4.50-4.65 m/min, respectively, with a fiber diameter of 300 μm were identified as the optimal set of process parameters. However, the laser power and welding speed can be reduced to 800-840 W and increased to 4.75-5.37 m/min, respectively, to obtain stronger and better welds.

Optimizing Experimental Designs Relative to Costs and Effect Sizes.

ERIC Educational Resources Information Center

Headrick, Todd C.; Zumbo, Bruno D.

A general model is derived for the purpose of efficiently allocating integral numbers of units in multi-level designs given prespecified power levels. The derivation of the model is based on a constrained optimization problem that maximizes a general form of a ratio of expected mean squares subject to a budget constraint. This model provides more…

Optimal Experimental Design of Borehole Locations for Bayesian Inference of Past Ice Sheet Surface Temperatures

NASA Astrophysics Data System (ADS)

Davis, A. D.; Huan, X.; Heimbach, P.; Marzouk, Y.

2017-12-01

Borehole data are essential for calibrating ice sheet models. However, field expeditions for acquiring borehole data are often time-consuming, expensive, and dangerous. It is thus essential to plan the best sampling locations that maximize the value of data while minimizing costs and risks. We present an uncertainty quantification (UQ) workflow based on rigorous probability framework to achieve these objectives. First, we employ an optimal experimental design (OED) procedure to compute borehole locations that yield the highest expected information gain. We take into account practical considerations of location accessibility (e.g., proximity to research sites, terrain, and ice velocity may affect feasibility of drilling) and robustness (e.g., real-time constraints such as weather may force researchers to drill at sub-optimal locations near those originally planned), by incorporating a penalty reflecting accessibility as well as sensitivity to deviations from the optimal locations. Next, we extract vertical temperature profiles from these boreholes and formulate a Bayesian inverse problem to reconstruct past surface temperatures. Using a model of temperature advection/diffusion, the top boundary condition (corresponding to surface temperatures) is calibrated via efficient Markov chain Monte Carlo (MCMC). The overall procedure can then be iterated to choose new optimal borehole locations for the next expeditions.Through this work, we demonstrate powerful UQ methods for designing experiments, calibrating models, making predictions, and assessing sensitivity--all performed under an uncertain environment. We develop a theoretical framework as well as practical software within an intuitive workflow, and illustrate their usefulness for combining data and models for environmental and climate research.

Using Central Composite Experimental Design to Optimize the Degradation of Tylosin from Aqueous Solution by Photo-Fenton Reaction

PubMed Central

Sarrai, Abd Elaziz; Hanini, Salah; Merzouk, Nachida Kasbadji; Tassalit, Djilali; Szabó, Tibor; Hernádi, Klára; Nagy, László

2016-01-01

The feasibility of the application of the Photo-Fenton process in the treatment of aqueous solution contaminated by Tylosin antibiotic was evaluated. The Response Surface Methodology (RSM) based on Central Composite Design (CCD) was used to evaluate and optimize the effect of hydrogen peroxide, ferrous ion concentration and initial pH as independent variables on the total organic carbon (TOC) removal as the response function. The interaction effects and optimal parameters were obtained by using MODDE software. The significance of the independent variables and their interactions was tested by means of analysis of variance (ANOVA) with a 95% confidence level. Results show that the concentration of the ferrous ion and pH were the main parameters affecting TOC removal, while peroxide concentration had a slight effect on the reaction. The optimum operating conditions to achieve maximum TOC removal were determined. The model prediction for maximum TOC removal was compared to the experimental result at optimal operating conditions. A good agreement between the model prediction and experimental results confirms the soundness of the developed model. PMID:28773551

Optimization of poorly compactable drug tablets manufactured by direct compression using the mixture experimental design.

PubMed

Martinello, Tiago; Kaneko, Telma Mary; Velasco, Maria Valéria Robles; Taqueda, Maria Elena Santos; Consiglieri, Vladi O

2006-09-28

The poor flowability and bad compressibility characteristics of paracetamol are well known. As a result, the production of paracetamol tablets is almost exclusively by wet granulation, a disadvantageous method when compared to direct compression. The development of a new tablet formulation is still based on a large number of experiments and often relies merely on the experience of the analyst. The purpose of this study was to apply experimental design methodology (DOE) to the development and optimization of tablet formulations containing high amounts of paracetamol (more than 70%) and manufactured by direct compression. Nineteen formulations, screened by DOE methodology, were produced with different proportions of Microcel 102, Kollydon VA 64, Flowlac, Kollydon CL 30, PEG 4000, Aerosil, and magnesium stearate. Tablet properties, except friability, were in accordance with the USP 28th ed. requirements. These results were used to generate plots for optimization, mainly for friability. The physical-chemical data found from the optimized formulation were very close to those from the regression analysis, demonstrating that the mixture project is a great tool for the research and development of new formulations.

Experimental Design for Parameter Estimation of Gene Regulatory Networks

PubMed Central

Timmer, Jens

2012-01-01

Systems biology aims for building quantitative models to address unresolved issues in molecular biology. In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated. As the validity of models built for GRNs depends crucially on the kinetic rates, various methods have been developed to estimate these parameters from experimental data. For this purpose, it is favorable to choose the experimental conditions yielding maximal information. However, existing experimental design principles often rely on unfulfilled mathematical assumptions or become computationally demanding with growing model complexity. To solve this problem, we combined advanced methods for parameter and uncertainty estimation with experimental design considerations. As a showcase, we optimized three simulated GRNs in one of the challenges from the Dialogue for Reverse Engineering Assessment and Methods (DREAM). This article presents our approach, which was awarded the best performing procedure at the DREAM6 Estimation of Model Parameters challenge. For fast and reliable parameter estimation, local deterministic optimization of the likelihood was applied. We analyzed identifiability and precision of the estimates by calculating the profile likelihood. Furthermore, the profiles provided a way to uncover a selection of most informative experiments, from which the optimal one was chosen using additional criteria at every step of the design process. In conclusion, we provide a strategy for optimal experimental design and show its successful application on three highly nonlinear dynamic models. Although presented in the context of the GRNs to be inferred for the DREAM6 challenge, the approach is generic and applicable to most types of quantitative models in systems biology and other disciplines. PMID:22815723

Experimental designs for a Benign Paroxysmal Positional Vertigo model

PubMed Central

2013-01-01

Background The pathology of the Benign Paroxysmal Positional Vertigo (BPPV) is detected by a clinician through maneuvers consisting of a series of consecutive head turns that trigger the symptoms of vertigo in patient. A statistical model based on a new maneuver has been developed in order to calculate the volume of endolymph displaced after the maneuver. Methods A simplification of the Navier‐Stokes problem from the fluids theory has been used to construct the model. In addition, the same cubic splines that are commonly used in kinematic control of robots were used to obtain an appropriate description of the different maneuvers. Then experimental designs were computed to obtain an optimal estimate of the model. Results D‐optimal and c‐optimal designs of experiments have been calculated. These experiments consist of a series of specific head turns of duration Δt and angle α that should be performed by the clinician on the patient. The experimental designs obtained indicate the duration and angle of the maneuver to be performed as well as the corresponding proportion of replicates. Thus, in the D‐optimal design for 100 experiments, the maneuver consisting of a positive 30° pitch from the upright position, followed by a positive 30° roll, both with a duration of one and a half seconds is repeated 47 times. Then the maneuver with 60° /6° pitch/roll during half a second is repeated 16 times and the maneuver 90° /90° pitch/roll during half a second is repeated 37 times. Other designs with significant differences are computed and compared. Conclusions A biomechanical model was derived to provide a quantitative basis for the detection of BPPV. The robustness study for the D‐optimal design, with respect to the choice of the nominal values of the parameters, shows high efficiencies for small variations and provides a guide to the researcher. Furthermore, c‐optimal designs give valuable assistance to check how efficient the D‐optimal design is for the

Experimental broadband absorption enhancement in silicon nanohole structures with optimized complex unit cells.

PubMed

Lin, Chenxi; Martínez, Luis Javier; Povinelli, Michelle L

2013-09-09

We design silicon membranes with nanohole structures with optimized complex unit cells that maximize broadband absorption. We fabricate the optimized design and measure the optical absorption. We demonstrate an experimental broadband absorption about 3.5 times higher than an equally-thick thin film.

Optimizing laboratory animal stress paradigms: The H-H* experimental design.

PubMed

McCarty, Richard

2017-01-01

Major advances in behavioral neuroscience have been facilitated by the development of consistent and highly reproducible experimental paradigms that have been widely adopted. In contrast, many different experimental approaches have been employed to expose laboratory mice and rats to acute versus chronic intermittent stress. An argument is advanced in this review that more consistent approaches to the design of chronic intermittent stress experiments would provide greater reproducibility of results across laboratories and greater reliability relating to various neural, endocrine, immune, genetic, and behavioral adaptations. As an example, the H-H* experimental design incorporates control, homotypic (H), and heterotypic (H*) groups and allows for comparisons across groups, where each animal is exposed to the same stressor, but that stressor has vastly different biological and behavioral effects depending upon each animal's prior stress history. Implementation of the H-H* experimental paradigm makes possible a delineation of transcriptional changes and neural, endocrine, and immune pathways that are activated in precisely defined stressor contexts. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparison of optimal design methods in inverse problems

NASA Astrophysics Data System (ADS)

Banks, H. T.; Holm, K.; Kappel, F.

2011-07-01

Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric-based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher information matrix. A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criterion with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model (Banks H T and Tran H T 2009 Mathematical and Experimental Modeling of Physical and Biological Processes (Boca Raton, FL: Chapman and Hall/CRC)), the standard harmonic oscillator model (Banks H T and Tran H T 2009) and a popular glucose regulation model (Bergman R N, Ider Y Z, Bowden C R and Cobelli C 1979 Am. J. Physiol. 236 E667-77 De Gaetano A and Arino O 2000 J. Math. Biol. 40 136-68 Toffolo G, Bergman R N, Finegood D T, Bowden C R and Cobelli C 1980 Diabetes 29 979-90).

A numerical identifiability test for state-space models--application to optimal experimental design.

PubMed

Hidalgo, M E; Ayesa, E

2001-01-01

This paper describes a mathematical tool for identifiability analysis, easily applicable to high order non-linear systems modelled in state-space and implementable in simulators with a time-discrete approach. This procedure also permits a rigorous analysis of the expected estimation errors (average and maximum) in calibration experiments. The methodology is based on the recursive numerical evaluation of the information matrix during the simulation of a calibration experiment and in the setting-up of a group of information parameters based on geometric interpretations of this matrix. As an example of the utility of the proposed test, the paper presents its application to an optimal experimental design of ASM Model No. 1 calibration, in order to estimate the maximum specific growth rate microH and the concentration of heterotrophic biomass XBH.

Experimental design and response surface modelling for optimization of vat dye from water by nano zero valent iron (NZVI).

PubMed

Arabi, Simin; Sohrabi, Mahmoud Reza

2013-01-01

In this study, NZVI particles was prepared and studied for the removal of vat green 1 dye from aqueous solution. A four-factor central composite design (CCD) combined with response surface modeling (RSM) to evaluate the combined effects of variables as well as optimization was employed for maximizing the dye removal by prepared NZVI based on 30 different experimental data obtained in a batch study. Four independent variables, viz. NZVI dose (0.1-0.9 g/L), pH (1.5-9.5), contact time (20-100 s), and initial dye concentration (10-50 mg/L) were transform to coded values and quadratic model was built to predict the responses. The significant of independent variables and their interactions were tested by the analysis of variance (ANOVA). Adequacy of the model was tested by the correlation between experimental and predicted values of the response and enumeration of prediction errors. The ANOVA results indicated that the proposed model can be used to navigate the design space. Optimization of the variables for maximum adsorption of dye by NZVI particles was performed using quadratic model. The predicted maximum adsorption efficiency (96.97%) under the optimum conditions of the process variables (NZVI dose 0.5 g/L, pH 4, contact time 60 s, and initial dye concentration 30 mg/L) was very close to the experimental value (96.16%) determined in batch experiment. In the optimization, R2 and R2adj correlation coefficients for the model were evaluated as 0.95 and 0.90, respectively.

Efficient experimental design for uncertainty reduction in gene regulatory networks.

PubMed

Dehghannasiri, Roozbeh; Yoon, Byung-Jun; Dougherty, Edward R

2015-01-01

An 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. The 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. Simulation 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/.

Iterative optimization method for design of quantitative magnetization transfer imaging experiments.

PubMed

Levesque, Ives R; Sled, John G; Pike, G Bruce

2011-09-01

Quantitative magnetization transfer imaging (QMTI) using spoiled gradient echo sequences with pulsed off-resonance saturation can be a time-consuming technique. A method is presented for selection of an optimum experimental design for quantitative magnetization transfer imaging based on the iterative reduction of a discrete sampling of the Z-spectrum. The applicability of the technique is demonstrated for human brain white matter imaging at 1.5 T and 3 T, and optimal designs are produced to target specific model parameters. The optimal number of measurements and the signal-to-noise ratio required for stable parameter estimation are also investigated. In vivo imaging results demonstrate that this optimal design approach substantially improves parameter map quality. The iterative method presented here provides an advantage over free form optimal design methods, in that pragmatic design constraints are readily incorporated. In particular, the presented method avoids clustering and repeated measures in the final experimental design, an attractive feature for the purpose of magnetization transfer model validation. The iterative optimal design technique is general and can be applied to any method of quantitative magnetization transfer imaging. Copyright © 2011 Wiley-Liss, Inc.

Experimental Optimization of a Free-to-Rotate Wing for Small UAS

NASA Technical Reports Server (NTRS)

Logan, Michael J.; DeLoach, Richard; Copeland, Tiwana; Vo, Steven

2014-01-01

This paper discusses an experimental investigation conducted to optimize a free-to-rotate wing for use on a small unmanned aircraft system (UAS). Although free-to-rotate wings have been used for decades on various small UAS and small manned aircraft, little is known about how to optimize these unusual wings for a specific application. The paper discusses some of the design rationale of the basic wing. In addition, three main parameters were selected for "optimization", wing camber, wing pivot location, and wing center of gravity (c.g.) location. A small apparatus was constructed to enable some simple experimental analysis of these parameters. A design-of-experiment series of tests were first conducted to discern which of the main optimization parameters were most likely to have the greatest impact on the outputs of interest, namely, some measure of "stability", some measure of the lift being generated at the neutral position, and how quickly the wing "recovers" from an upset. A second set of tests were conducted to develop a response-surface numerical representation of these outputs as functions of the three primary inputs. The response surface numerical representations are then used to develop an "optimum" within the trade space investigated. The results of the optimization are then tested experimentally to validate the predictions.

Global Design Optimization for Aerodynamics and Rocket Propulsion Components

NASA Technical Reports Server (NTRS)

Shyy, Wei; Papila, Nilay; Vaidyanathan, Rajkumar; Tucker, Kevin; Turner, James E. (Technical Monitor)

2000-01-01

Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design

A new optimal sliding mode controller design using scalar sign function.

PubMed

Singla, Mithun; Shieh, Leang-San; Song, Gangbing; Xie, Linbo; Zhang, Yongpeng

2014-03-01

This paper presents a new optimal sliding mode controller using the scalar sign function method. A smooth, continuous-time scalar sign function is used to replace the discontinuous switching function in the design of a sliding mode controller. The proposed sliding mode controller is designed using an optimal Linear Quadratic Regulator (LQR) approach. The sliding surface of the system is designed using stable eigenvectors and the scalar sign function. Controller simulations are compared with another existing optimal sliding mode controller. To test the effectiveness of the proposed controller, the controller is implemented on an aluminum beam with piezoceramic sensor and actuator for vibration control. This paper includes the control design and stability analysis of the new optimal sliding mode controller, followed by simulation and experimental results. The simulation and experimental results show that the proposed approach is very effective. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Actinobacteria consortium as an efficient biotechnological tool for mixed polluted soil reclamation: Experimental factorial design for bioremediation process optimization.

PubMed

Aparicio, Juan Daniel; Raimondo, Enzo Emanuel; Gil, Raúl Andrés; Benimeli, Claudia Susana; Polti, Marta Alejandra

2018-01-15

The objective of the present work was to establish optimal biological and physicochemical parameters in order to remove simultaneously lindane and Cr(VI) at high and/or low pollutants concentrations from the soil by an actinobacteria consortium formed by Streptomyces sp. M7, MC1, A5, and Amycolatopsis tucumanensis AB0. Also, the final aim was to treat real soils contaminated with Cr(VI) and/or lindane from the Northwest of Argentina employing the optimal biological and physicochemical conditions. In this sense, after determining the optimal inoculum concentration (2gkg -1 ), an experimental design model with four factors (temperature, moisture, initial concentration of Cr(VI) and lindane) was employed for predicting the system behavior during bioremediation process. According to response optimizer, the optimal moisture level was 30% for all bioremediation processes. However, the optimal temperature was different for each situation: for low initial concentrations of both pollutants, the optimal temperature was 25°C; for low initial concentrations of Cr(VI) and high initial concentrations of lindane, the optimal temperature was 30°C; and for high initial concentrations of Cr(VI), the optimal temperature was 35°C. In order to confirm the model adequacy and the validity of the optimization procedure, experiments were performed in six real contaminated soils samples. The defined actinobacteria consortium reduced the contaminants concentrations in five of the six samples, by working at laboratory scale and employing the optimal conditions obtained through the factorial design. Copyright © 2017 Elsevier B.V. All rights reserved.

Bioremediation of chlorpyrifos contaminated soil by two phase bioslurry reactor: Processes evaluation and optimization by Taguchi's design of experimental (DOE) methodology.

PubMed

Pant, Apourv; Rai, J P N

2018-04-15

Two phase bioreactor was constructed, designed and developed to evaluate the chlorpyrifos remediation. Six biotic and abiotic factors (substrate-loading rate, slurry phase pH, slurry phase dissolved oxygen (DO), soil water ratio, temperature and soil micro flora load) were evaluated by design of experimental (DOE) methodology employing Taguchi's orthogonal array (OA). The selected six factors were considered at two levels L-8 array (2^7, 15 experiments) in the experimental design. The optimum operating conditions obtained from the methodology showed enhanced chlorpyrifos degradation from 283.86µg/g to 955.364µg/g by overall 70.34% of enhancement. In the present study, with the help of few well defined experimental parameters a mathematical model was constructed to understand the complex bioremediation process and optimize the approximate parameters upto great accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

Robust Bayesian Experimental Design for Conceptual Model Discrimination

NASA Astrophysics Data System (ADS)

Pham, H. V.; Tsai, F. T. C.

2015-12-01

A robust Bayesian optimal experimental design under uncertainty is presented to provide firm information for model discrimination, given the least number of pumping wells and observation wells. Firm information is the maximum information of a system can be guaranteed from an experimental design. The design is based on the Box-Hill expected entropy decrease (EED) before and after the experiment design and the Bayesian model averaging (BMA) framework. A max-min programming is introduced to choose the robust design that maximizes the minimal Box-Hill EED subject to that the highest expected posterior model probability satisfies a desired probability threshold. The EED is calculated by the Gauss-Hermite quadrature. The BMA method is used to predict future observations and to quantify future observation uncertainty arising from conceptual and parametric uncertainties in calculating EED. Monte Carlo approach is adopted to quantify the uncertainty in the posterior model probabilities. The optimal experimental design is tested by a synthetic 5-layer anisotropic confined aquifer. Nine conceptual groundwater models are constructed due to uncertain geological architecture and boundary condition. High-performance computing is used to enumerate all possible design solutions in order to identify the most plausible groundwater model. Results highlight the impacts of scedasticity in future observation data as well as uncertainty sources on potential pumping and observation locations.

Efficient experimental design for uncertainty reduction in gene regulatory networks

PubMed Central

2015-01-01

Background An 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. Results The 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. Conclusions Simulation 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/. PMID:26423515

A More Rigorous Quasi-Experimental Alternative to the One-Group Pretest-Posttest Design.

ERIC Educational Resources Information Center

Johnson, Craig W.

1986-01-01

A simple quasi-experimental design is described which may have utility in a variety of applied and laboratory research settings where ordinarily the one-group pretest-posttest pre-experimental design might otherwise be the procedure of choice. The design approaches the internal validity of true experimental designs while optimizing external…

Optimization of scaffold design for bone tissue engineering: A computational and experimental study.

PubMed

Dias, Marta R; Guedes, José M; Flanagan, Colleen L; Hollister, Scott J; Fernandes, Paulo R

2014-04-01

In bone tissue engineering, the scaffold has not only to allow the diffusion of cells, nutrients and oxygen but also provide adequate mechanical support. One way to ensure the scaffold has the right properties is to use computational tools to design such a scaffold coupled with additive manufacturing to build the scaffolds to the resulting optimized design specifications. In this study a topology optimization algorithm is proposed as a technique to design scaffolds that meet specific requirements for mass transport and mechanical load bearing. Several micro-structures obtained computationally are presented. Designed scaffolds were then built using selective laser sintering and the actual features of the fabricated scaffolds were measured and compared to the designed values. It was possible to obtain scaffolds with an internal geometry that reasonably matched the computational design (within 14% of porosity target, 40% for strut size and 55% for throat size in the building direction and 15% for strut size and 17% for throat size perpendicular to the building direction). These results support the use of these kind of computational algorithms to design optimized scaffolds with specific target properties and confirm the value of these techniques for bone tissue engineering. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Sequential experimental design based generalised ANOVA

NASA Astrophysics Data System (ADS)

Chakraborty, Souvik; Chowdhury, Rajib

2016-07-01

Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover, generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability.

Experimental design to optimize an Haemophilus influenzae type b conjugate vaccine made with hydrazide-derivatized tetanus toxoid.

PubMed

Laferriere, Craig; Ravenscroft, Neil; Wilson, Seanette; Combrink, Jill; Gordon, Lizelle; Petre, Jean

2011-10-01

The introduction of type b Haemophilus influenzae conjugate vaccines into routine vaccination schedules has significantly reduced the burden of this disease; however, widespread use in developing countries is constrained by vaccine costs, and there is a need for a simple and high-yielding manufacturing process. The vaccine is composed of purified capsular polysaccharide conjugated to an immunogenic carrier protein. To improve the yield and rate of the reductive amination conjugation reaction used to make this vaccine, some of the carboxyl groups of the carrier protein, tetanus toxoid, were modified to hydrazides, which are more reactive than the ε -amine of lysine. Other reaction parameters, including the ratio of the reactants, the size of the polysaccharide, the temperature and the salt concentration, were also investigated. Experimental design was used to minimize the number of experiments required to optimize all these parameters to obtain conjugate in high yield with target characteristics. It was found that increasing the reactant ratio and decreasing the size of the polysaccharide increased the polysaccharide:protein mass ratio in the product. Temperature and salt concentration did not improve this ratio. These results are consistent with a diffusion controlled rate limiting step in the conjugation reaction. Excessive modification of tetanus toxoid with hydrazide was correlated with reduced yield and lower free polysaccharide. This was attributed to a greater tendency for precipitation, possibly due to changes in the isoelectric point. Experimental design and multiple regression helped identify key parameters to control and thereby optimize this conjugation reaction.

Optimization of primaquine diphosphate tablet formulation for controlled drug release using the mixture experimental design.

PubMed

Duque, Marcelo Dutra; Kreidel, Rogério Nepomuceno; Taqueda, Maria Elena Santos; Baby, André Rolim; Kaneko, Telma Mary; Velasco, Maria Valéria Robles; Consiglieri, Vladi Olga

2013-01-01

A tablet formulation based on hydrophilic matrix with a controlled drug release was developed, and the effect of polymer concentrations on the release of primaquine diphosphate was evaluated. To achieve this purpose, a 20-run, four-factor with multiple constraints on the proportions of the components was employed to obtain tablet compositions. Drug release was determined by an in vitro dissolution study in phosphate buffer solution at pH 6.8. The polynomial fitted functions described the behavior of the mixture on simplex coordinate systems to study the effects of each factor (polymer) on tablet characteristics. Based on the response surface methodology, a tablet composition was optimized with the purpose of obtaining a primaquine diphosphate release closer to a zero order kinetic. This formulation released 85.22% of the drug for 8 h and its kinetic was studied regarding to Korsmeyer-Peppas model, (Adj-R(2) = 0.99295) which has confirmed that both diffusion and erosion were related to the mechanism of the drug release. The data from the optimized formulation were very close to the predictions from statistical analysis, demonstrating that mixture experimental design could be used to optimize primaquine diphosphate dissolution from hidroxypropylmethyl cellulose and polyethylene glycol matrix tablets.

Structural Optimization of a Force Balance Using a Computational Experiment Design

NASA Technical Reports Server (NTRS)

Parker, P. A.; DeLoach, R.

2002-01-01

This paper proposes a new approach to force balance structural optimization featuring a computational experiment design. Currently, this multi-dimensional design process requires the designer to perform a simplification by executing parameter studies on a small subset of design variables. This one-factor-at-a-time approach varies a single variable while holding all others at a constant level. Consequently, subtle interactions among the design variables, which can be exploited to achieve the design objectives, are undetected. The proposed method combines Modern Design of Experiments techniques to direct the exploration of the multi-dimensional design space, and a finite element analysis code to generate the experimental data. To efficiently search for an optimum combination of design variables and minimize the computational resources, a sequential design strategy was employed. Experimental results from the optimization of a non-traditional force balance measurement section are presented. An approach to overcome the unique problems associated with the simultaneous optimization of multiple response criteria is described. A quantitative single-point design procedure that reflects the designer's subjective impression of the relative importance of various design objectives, and a graphical multi-response optimization procedure that provides further insights into available tradeoffs among competing design objectives are illustrated. The proposed method enhances the intuition and experience of the designer by providing new perspectives on the relationships between the design variables and the competing design objectives providing a systematic foundation for advancements in structural design.

Optimization of Xylanase Production from Penicillium sp.WX-Z1 by a Two-Step Statistical Strategy: Plackett-Burman and Box-Behnken Experimental Design

PubMed Central

Cui, Fengjie; Zhao, Liming

2012-01-01

The objective of the study was to optimize the nutrition sources in a culture medium for the production of xylanase from Penicillium sp.WX-Z1 using Plackett-Burman design and Box-Behnken design. The Plackett-Burman multifactorial design was first employed to screen the important nutrient sources in the medium for xylanase production by Penicillium sp.WX-Z1 and subsequent use of the response surface methodology (RSM) was further optimized for xylanase production by Box-Behnken design. The important nutrient sources in the culture medium, identified by the initial screening method of Placket-Burman, were wheat bran, yeast extract, NaNO3, MgSO4, and CaCl2. The optimal amounts (in g/L) for maximum production of xylanase were: wheat bran, 32.8; yeast extract, 1.02; NaNO3, 12.71; MgSO4, 0.96; and CaCl2, 1.04. Using this statistical experimental design, the xylanase production under optimal condition reached 46.50 U/mL and an increase in xylanase activity of 1.34-fold was obtained compared with the original medium for fermentation carried out in a 30-L bioreactor. PMID:22949884

Optimization of Xylanase production from Penicillium sp.WX-Z1 by a two-step statistical strategy: Plackett-Burman and Box-Behnken experimental design.

PubMed

Cui, Fengjie; Zhao, Liming

2012-01-01

The objective of the study was to optimize the nutrition sources in a culture medium for the production of xylanase from Penicillium sp.WX-Z1 using Plackett-Burman design and Box-Behnken design. The Plackett-Burman multifactorial design was first employed to screen the important nutrient sources in the medium for xylanase production by Penicillium sp.WX-Z1 and subsequent use of the response surface methodology (RSM) was further optimized for xylanase production by Box-Behnken design. The important nutrient sources in the culture medium, identified by the initial screening method of Placket-Burman, were wheat bran, yeast extract, NaNO(3), MgSO(4), and CaCl(2). The optimal amounts (in g/L) for maximum production of xylanase were: wheat bran, 32.8; yeast extract, 1.02; NaNO(3), 12.71; MgSO(4), 0.96; and CaCl(2), 1.04. Using this statistical experimental design, the xylanase production under optimal condition reached 46.50 U/mL and an increase in xylanase activity of 1.34-fold was obtained compared with the original medium for fermentation carried out in a 30-L bioreactor.

Convergence in parameters and predictions using computational experimental design.

PubMed

Hagen, David R; White, Jacob K; Tidor, Bruce

2013-08-06

Typically, biological models fitted to experimental data suffer from significant parameter uncertainty, which can lead to inaccurate or uncertain predictions. One school of thought holds that accurate estimation of the true parameters of a biological system is inherently problematic. Recent work, however, suggests that optimal experimental design techniques can select sets of experiments whose members probe complementary aspects of a biochemical network that together can account for its full behaviour. Here, we implemented an experimental design approach for selecting sets of experiments that constrain parameter uncertainty. We demonstrated with a model of the epidermal growth factor-nerve growth factor pathway that, after synthetically performing a handful of optimal experiments, the uncertainty in all 48 parameters converged below 10 per cent. Furthermore, the fitted parameters converged to their true values with a small error consistent with the residual uncertainty. When untested experimental conditions were simulated with the fitted models, the predicted species concentrations converged to their true values with errors that were consistent with the residual uncertainty. This paper suggests that accurate parameter estimation is achievable with complementary experiments specifically designed for the task, and that the resulting parametrized models are capable of accurate predictions.

Optimal Experiment Design for Thermal Characterization of Functionally Graded Materials

NASA Technical Reports Server (NTRS)

Cole, Kevin D.

2003-01-01

The purpose of the project was to investigate methods to accurately verify that designed , materials meet thermal specifications. The project involved heat transfer calculations and optimization studies, and no laboratory experiments were performed. One part of the research involved study of materials in which conduction heat transfer predominates. Results include techniques to choose among several experimental designs, and protocols for determining the optimum experimental conditions for determination of thermal properties. Metal foam materials were also studied in which both conduction and radiation heat transfer are present. Results of this work include procedures to optimize the design of experiments to accurately measure both conductive and radiative thermal properties. Detailed results in the form of three journal papers have been appended to this report.

Optimal Design of an Automotive Exhaust Thermoelectric Generator

NASA Astrophysics Data System (ADS)

Fagehi, Hassan; Attar, Alaa; Lee, Hosung

2018-07-01

The consumption of energy continues to increase at an exponential rate, especially in terms of conventional automobiles. Approximately 40% of the applied fuel into a vehicle is lost as waste exhausted to the environment. The desire for improved fuel efficiency by recovering the exhaust waste heat in automobiles has become an important subject. A thermoelectric generator (TEG) has the potential to convert exhaust waste heat into electricity as long as it is improving fuel economy. The remarkable amount of research being conducted on TEGs indicates that this technology will have a bright future in terms of power generation. The current study discusses the optimal design of the automotive exhaust TEG. An experimental study has been conducted to verify the model that used the ideal (standard) equations along with effective material properties. The model is reasonably verified by experimental work, mainly due to the utilization of the effective material properties. Hence, the thermoelectric module that was used in the experiment was optimized by using a developed optimal design theory (dimensionless analysis technique).

Optimal Design of an Automotive Exhaust Thermoelectric Generator

NASA Astrophysics Data System (ADS)

Fagehi, Hassan; Attar, Alaa; Lee, Hosung

2018-04-01

The consumption of energy continues to increase at an exponential rate, especially in terms of conventional automobiles. Approximately 40% of the applied fuel into a vehicle is lost as waste exhausted to the environment. The desire for improved fuel efficiency by recovering the exhaust waste heat in automobiles has become an important subject. A thermoelectric generator (TEG) has the potential to convert exhaust waste heat into electricity as long as it is improving fuel economy. The remarkable amount of research being conducted on TEGs indicates that this technology will have a bright future in terms of power generation. The current study discusses the optimal design of the automotive exhaust TEG. An experimental study has been conducted to verify the model that used the ideal (standard) equations along with effective material properties. The model is reasonably verified by experimental work, mainly due to the utilization of the effective material properties. Hence, the thermoelectric module that was used in the experiment was optimized by using a developed optimal design theory (dimensionless analysis technique).

Sequential experimental design based generalised ANOVA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chakraborty, Souvik, E-mail: csouvik41@gmail.com; Chowdhury, Rajib, E-mail: rajibfce@iitr.ac.in

Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover,more » generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability.« less

IsoDesign: a software for optimizing the design of 13C-metabolic flux analysis experiments.

PubMed

Millard, Pierre; Sokol, Serguei; Letisse, Fabien; Portais, Jean-Charles

2014-01-01

The growing demand for (13) C-metabolic flux analysis ((13) C-MFA) in the field of metabolic engineering and systems biology is driving the need to rationalize expensive and time-consuming (13) C-labeling experiments. Experimental design is a key step in improving both the number of fluxes that can be calculated from a set of isotopic data and the precision of flux values. We present IsoDesign, a software that enables these parameters to be maximized by optimizing the isotopic composition of the label input. It can be applied to (13) C-MFA investigations using a broad panel of analytical tools (MS, MS/MS, (1) H NMR, (13) C NMR, etc.) individually or in combination. It includes a visualization module to intuitively select the optimal label input depending on the biological question to be addressed. Applications of IsoDesign are described, with an example of the entire (13) C-MFA workflow from the experimental design to the flux map including important practical considerations. IsoDesign makes the experimental design of (13) C-MFA experiments more accessible to a wider biological community. IsoDesign is distributed under an open source license at http://metasys.insa-toulouse.fr/software/isodes/ © 2013 Wiley Periodicals, Inc.

Limit of detection of 15{sub N} by gas-chromatography atomic emission detection: Optimization using an experimental design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deruaz, D.; Bannier, A.; Pionchon, C.

1995-08-01

This paper deals with the optimal conditions for the detection of {sup 15}N determined using a four-factor experimental design from [2{sup 13}C,-1,3 {sup 15}N] caffeine measured with an atomic emission detector (AED) coupled to gas chromatography (GC). Owing to the capability of a photodiodes array, AED can simultaneously detect several elements using their specific emission lines within a wavelength range of 50 nm. So, the emissions of {sup 15}N and {sup 14}N are simultaneously detected at 420.17 nm and 421.46 nm respectively. Four independent experimental factors were tested (1) helium flow rate (plasma gas); (2) methane pressure (reactant gas); (3)more » oxygen pressure; (4) hydrogen pressure. It has been shown that these four gases had a significant influence on the analytical response of {sup 15}N. The linearity of the detection was determined using {sup 15}N amounts ranging from 1.52 pg to 19 ng under the optimal conditions obtained from the experimental design. The limit of detection was studied using different methods. The limits of detection of {sup 15}N was 1.9 pg/s according to the IUPAC method (International-Union of Pure and Applied Chemistry). The method proposed by Quimby and Sullivan gave a value of 2.3 pg/s and that of Oppenheimer gave a limit of 29 pg/s. For each determination, and internal standard: 1-isobutyl-3.7 dimethylxanthine was used. The results clearly demonstrate that GC AED is sensitive and selective enough to detect and measure {sup 15}N-labelled molecules after gas chromatographic separation.« less

Optimization of cold-adapted lysozyme production from the psychrophilic yeast Debaryomyces hansenii using statistical experimental methods.

PubMed

Wang, Quanfu; Hou, Yanhua; Yan, Peisheng

2012-06-01

Statistical experimental designs were employed to optimize culture conditions for cold-adapted lysozyme production of a psychrophilic yeast Debaryomyces hansenii. In the first step of optimization using Plackett-Burman design (PBD), peptone, glucose, temperature, and NaCl were identified as significant variables that affected lysozyme production, the formula was further optimized using a four factor central composite design (CCD) to understand their interaction and to determine their optimal levels. A quadratic model was developed and validated. Compared to the initial level (18.8 U/mL), the maximum lysozyme production (65.8 U/mL) observed was approximately increased by 3.5-fold under the optimized conditions. Cold-adapted lysozymes production was first optimized using statistical experimental methods. A 3.5-fold enhancement of microbial lysozyme was gained after optimization. Such an improved production will facilitate the application of microbial lysozyme. Thus, D. hansenii lysozyme may be a good and new resource for the industrial production of cold-adapted lysozymes. © 2012 Institute of Food Technologists®

Surface laser marking optimization using an experimental design approach

NASA Astrophysics Data System (ADS)

Brihmat-Hamadi, F.; Amara, E. H.; Lavisse, L.; Jouvard, J. M.; Cicala, E.; Kellou, H.

2017-04-01

Laser surface marking is performed on a titanium substrate using a pulsed frequency doubled Nd:YAG laser ( λ= 532 nm, τ pulse=5 ns) to process the substrate surface under normal atmospheric conditions. The aim of the work is to investigate, following experimental and statistical approaches, the correlation between the process parameters and the response variables (output), using a Design of Experiment method (DOE): Taguchi methodology and a response surface methodology (RSM). A design is first created using MINTAB program, and then the laser marking process is performed according to the planned design. The response variables; surface roughness and surface reflectance were measured for each sample, and incorporated into the design matrix. The results are then analyzed and the RSM model is developed and verified for predicting the process output for the given set of process parameters values. The analysis shows that the laser beam scanning speed is the most influential operating factor followed by the laser pumping intensity during marking, while the other factors show complex influences on the objective functions.

Design and experimental realization of an optimal scheme for teleportation of an n-qubit quantum state

NASA Astrophysics Data System (ADS)

Sisodia, Mitali; Shukla, Abhishek; Thapliyal, Kishore; Pathak, Anirban

2017-12-01

An explicit scheme (quantum circuit) is designed for the teleportation of an n-qubit quantum state. It is established that the proposed scheme requires an optimal amount of quantum resources, whereas larger amount of quantum resources have been used in a large number of recently reported teleportation schemes for the quantum states which can be viewed as special cases of the general n-qubit state considered here. A trade-off between our knowledge about the quantum state to be teleported and the amount of quantum resources required for the same is observed. A proof-of-principle experimental realization of the proposed scheme (for a 2-qubit state) is also performed using 5-qubit superconductivity-based IBM quantum computer. The experimental results show that the state has been teleported with high fidelity. Relevance of the proposed teleportation scheme has also been discussed in the context of controlled, bidirectional, and bidirectional controlled state teleportation.

Experimental design data for the biosynthesis of citric acid using Central Composite Design method.

PubMed

Kola, Anand Kishore; Mekala, Mallaiah; Goli, Venkat Reddy

2017-06-01

In the present investigation, we report that statistical design and optimization of significant variables for the microbial production of citric acid from sucrose in presence of filamentous fungi A. niger NCIM 705. Various combinations of experiments were designed with Central Composite Design (CCD) of Response Surface Methodology (RSM) for the production of citric acid as a function of six variables. The variables are; initial sucrose concentration, initial pH of medium, fermentation temperature, incubation time, stirrer rotational speed, and oxygen flow rate. From experimental data, a statistical model for this process has been developed. The optimum conditions reported in the present article are initial concentration of sucrose of 163.6 g/L, initial pH of medium 5.26, stirrer rotational speed of 247.78 rpm, incubation time of 8.18 days, fermentation temperature of 30.06 °C and flow rate of oxygen of 1.35 lpm. Under optimum conditions the predicted maximum citric acid is 86.42 g/L. The experimental validation carried out under the optimal values and reported citric acid to be 82.0 g/L. The model is able to represent the experimental data and the agreement between the model and experimental data is good.

Solar-Diesel Hybrid Power System Optimization and Experimental Validation

NASA Astrophysics Data System (ADS)

Jacobus, Headley Stewart

As of 2008 1.46 billion people, or 22 percent of the World's population, were without electricity. Many of these people live in remote areas where decentralized generation is the only method of electrification. Most mini-grids are powered by diesel generators, but new hybrid power systems are becoming a reliable method to incorporate renewable energy while also reducing total system cost. This thesis quantifies the measurable Operational Costs for an experimental hybrid power system in Sierra Leone. Two software programs, Hybrid2 and HOMER, are used during the system design and subsequent analysis. Experimental data from the installed system is used to validate the two programs and to quantify the savings created by each component within the hybrid system. This thesis bridges the gap between design optimization studies that frequently lack subsequent validation and experimental hybrid system performance studies.

A statistical approach to optimizing concrete mixture design.

PubMed

Ahmad, Shamsad; Alghamdi, Saeid A

2014-01-01

A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (3(3)). A total of 27 concrete mixtures with three replicates (81 specimens) were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48), cementitious materials content (350, 375, and 400 kg/m(3)), and fine/total aggregate ratio (0.35, 0.40, and 0.45). The experimental data were utilized to carry out analysis of variance (ANOVA) and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options.

A Statistical Approach to Optimizing Concrete Mixture Design

PubMed Central

Alghamdi, Saeid A.

2014-01-01

A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (33). A total of 27 concrete mixtures with three replicates (81 specimens) were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48), cementitious materials content (350, 375, and 400 kg/m3), and fine/total aggregate ratio (0.35, 0.40, and 0.45). The experimental data were utilized to carry out analysis of variance (ANOVA) and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options. PMID:24688405

Optimization of the capillary zone electrophoresis method for Huperzine A determination using experimental design and artificial neural networks.

PubMed

Hameda, A Ben; Elosta, S; Havel, J

2005-08-19

Huperzine A, natural product from Huperzia serrata, is quite an important compound used to treat the Alzheimer's disease as a food supplement and also proposed as a prospective and prophylactic antidote against organophosphate poisoning. In this work, simple and fast capillary electrophoresis (CE) procedure with UV detection (at 230 nm) for determination of Huperzine A was developed and optimized. Capillary electrophoresis determination of Huperzine A was optimized using a combination of the experimental design (ED) and the artificial neural networks (ANN). In the first stage of optimization, the experiments were done according to the appropriate ED. Data evaluated by ANN allowed finding the optimal values of several analytical parameters (peak area, peak height, and analysis time). Optimal conditions found were 50 mM acetate buffer, pH 4.6, separation voltage 10 kV, hydrodynamic injection time 10 s and temperature 25 degrees C. The developed method shows good repeatability as relative standard division (R.S.D. = 0.9%) and it has been applied for determination of Huperzine A in various pharmaceutical products and in biological liquids. The limit of detection (LOD) in aqueous media was 0.226 ng/ml and 0.233 ng/ml for determination in the serum.

"Real-time" disintegration analysis and D-optimal experimental design for the optimization of diclofenac sodium fast-dissolving films.

PubMed

El-Malah, Yasser; Nazzal, Sami

2013-01-01

The objective of this work was to study the dissolution and mechanical properties of fast-dissolving films prepared from a tertiary mixture of pullulan, polyvinylpyrrolidone and hypromellose. Disintegration studies were performed in real-time by probe spectroscopy to detect the onset of film disintegration. Tensile strength and elastic modulus of the films were measured by texture analysis. Disintegration time of the films ranged from 21 to 105 seconds whereas their mechanical properties ranged from approximately 2 to 49 MPa for tensile strength and 1 to 21 MPa% for young's modulus. After generating polynomial models correlating the variables using a D-Optimal mixture design, an optimal formulation with desired responses was proposed by the statistical package. For validation, a new film formulation loaded with diclofenac sodium based on the optimized composition was prepared and tested for dissolution and tensile strength. Dissolution of the optimized film was found to commence almost immediately with 50% of the drug released within one minute. Tensile strength and young's modulus of the film were 11.21 MPa and 6, 78 MPa%, respectively. Real-time spectroscopy in conjunction with statistical design were shown to be very efficient for the optimization and development of non-conventional intraoral delivery system such as fast dissolving films.

Optimality models in the age of experimental evolution and genomics.

PubMed

Bull, J J; Wang, I-N

2010-09-01

Optimality models have been used to predict evolution of many properties of organisms. They typically neglect genetic details, whether by necessity or design. This omission is a common source of criticism, and although this limitation of optimality is widely acknowledged, it has mostly been defended rather than evaluated for its impact. Experimental adaptation of model organisms provides a new arena for testing optimality models and for simultaneously integrating genetics. First, an experimental context with a well-researched organism allows dissection of the evolutionary process to identify causes of model failure--whether the model is wrong about genetics or selection. Second, optimality models provide a meaningful context for the process and mechanics of evolution, and thus may be used to elicit realistic genetic bases of adaptation--an especially useful augmentation to well-researched genetic systems. A few studies of microbes have begun to pioneer this new direction. Incompatibility between the assumed and actual genetics has been demonstrated to be the cause of model failure in some cases. More interestingly, evolution at the phenotypic level has sometimes matched prediction even though the adaptive mutations defy mechanisms established by decades of classic genetic studies. Integration of experimental evolutionary tests with genetics heralds a new wave for optimality models and their extensions that does not merely emphasize the forces driving evolution.

Optimization of photocatalytic degradation of methyl blue using silver ion doped titanium dioxide by combination of experimental design and response surface approach.

PubMed

Sahoo, C; Gupta, A K

2012-05-15

Photocatalytic degradation of methyl blue (MYB) was studied using Ag(+) doped TiO(2) under UV irradiation in a batch reactor. Catalytic dose, initial concentration of dye and pH of the reaction mixture were found to influence the degradation process most. The degradation was found to be effective in the range catalytic dose (0.5-1.5g/L), initial dye concentration (25-100ppm) and pH of reaction mixture (5-9). Using the three factors three levels Box-Behnken design of experiment technique 15 sets of experiments were designed considering the effective ranges of the influential parameters. The results of the experiments were fitted to two quadratic polynomial models developed using response surface methodology (RSM), representing functional relationship between the decolorization and mineralization of MYB and the experimental parameters. Design Expert software version 8.0.6.1 was used to optimize the effects of the experimental parameters on the responses. The optimum values of the parameters were dose of Ag(+) doped TiO(2) 0.99g/L, initial concentration of MYB 57.68ppm and pH of reaction mixture 7.76. Under the optimal condition the predicted decolorization and mineralization rate of MYB were 95.97% and 80.33%, respectively. Regression analysis with R(2) values >0.99 showed goodness of fit of the experimental results with predicted values. Copyright © 2012 Elsevier B.V. All rights reserved.

Development, optimization, and in vitro characterization of dasatinib-loaded PEG functionalized chitosan capped gold nanoparticles using Box-Behnken experimental design.

PubMed

Adena, Sandeep Kumar Reddy; Upadhyay, Mansi; Vardhan, Harsh; Mishra, Brahmeshwar

2018-03-01

The purpose of this research study was to develop, optimize, and characterize dasatinib loaded polyethylene glycol (PEG) stabilized chitosan capped gold nanoparticles (DSB-PEG-Ch-GNPs). Gold (III) chloride hydrate was reduced with chitosan and the resulting nanoparticles were coated with thiol-terminated PEG and loaded with dasatinib (DSB). Plackett-Burman design (PBD) followed by Box-Behnken experimental design (BBD) were employed to optimize the process parameters. Polynomial equations, contour, and 3D response surface plots were generated to relate the factors and responses. The optimized DSB-PEG-Ch-GNPs were characterized by FTIR, XRD, HR-SEM, EDX, TEM, SAED, AFM, DLS, and ZP. The results of the optimized DSB-PEG-Ch-GNPs showed particle size (PS) of 24.39 ± 1.82 nm, apparent drug content (ADC) of 72.06 ± 0.86%, and zeta potential (ZP) of -13.91 ± 1.21 mV. The responses observed and the predicted values of the optimized process were found to be close. The shape and surface morphology studies showed that the resulting DSB-PEG-Ch-GNPs were spherical and smooth. The stability and in vitro drug release studies confirmed that the optimized formulation was stable at different conditions of storage and exhibited a sustained drug release of the drug of up to 76% in 48 h and followed Korsmeyer-Peppas release kinetic model. A process for preparing gold nanoparticles using chitosan, anchoring PEG to the particle surface, and entrapping dasatinib in the chitosan-PEG surface corona was optimized.

Computational Optimization of a Natural Laminar Flow Experimental Wing Glove

NASA Technical Reports Server (NTRS)

Hartshom, Fletcher

2012-01-01

Computational optimization of a natural laminar flow experimental wing glove that is mounted on a business jet is presented and discussed. The process of designing a laminar flow wing glove starts with creating a two-dimensional optimized airfoil and then lofting it into a three-dimensional wing glove section. The airfoil design process does not consider the three dimensional flow effects such as cross flow due wing sweep as well as engine and body interference. Therefore, once an initial glove geometry is created from the airfoil, the three dimensional wing glove has to be optimized to ensure that the desired extent of laminar flow is maintained over the entire glove. TRANAIR, a non-linear full potential solver with a coupled boundary layer code was used as the main tool in the design and optimization process of the three-dimensional glove shape. The optimization process uses the Class-Shape-Transformation method to perturb the geometry with geometric constraints that allow for a 2-in clearance from the main wing. The three-dimensional glove shape was optimized with the objective of having a spanwise uniform pressure distribution that matches the optimized two-dimensional pressure distribution as closely as possible. Results show that with the appropriate inputs, the optimizer is able to match the two dimensional pressure distributions practically across the entire span of the wing glove. This allows for the experiment to have a much higher probability of having a large extent of natural laminar flow in flight.

Improving knowledge of garlic paste greening through the design of an experimental strategy.

PubMed

Aguilar, Miguel; Rincón, Francisco

2007-12-12

The furthering of scientific knowledge depends in part upon the reproducibility of experimental results. When experimental conditions are not set with sufficient precision, the resulting background noise often leads to poorly reproduced and even faulty experiments. An example of the catastrophic consequences of this background noise can be found in the design of strategies for the development of solutions aimed at preventing garlic paste greening, where reported results are contradictory. To avoid such consequences, this paper presents a two-step strategy based on the concept of experimental design. In the first step, the critical factors inherent to the problem are identified, using a 2(III)(7-4) Plackett-Burman experimental design, from a list of seven apparent critical factors (ACF); subsequently, the critical factors thus identified are considered as the factors to be optimized (FO), and optimization is performed using a Box and Wilson experimental design to identify the stationary point of the system. Optimal conditions for preventing garlic greening are examined after analysis of the complex process of green-pigment development, which involves both chemical and enzymatic reactions and is strongly influenced by pH, with an overall pH optimum of 4.5. The critical step in the greening process is the synthesis of thiosulfinates (allicin) from cysteine sulfoxides (alliin). Cysteine inhibits the greening process at this critical stage; no greening precursors are formed in the presence of around 1% cysteine. However, the optimal conditions for greening prevention are very sensitive both to the type of garlic and to manufacturing conditions. This suggests that optimal solutions for garlic greening prevention should be sought on a case-by-case basis, using the strategy presented here.

Optimization and characterization of liposome formulation by mixture design.

PubMed

Maherani, Behnoush; Arab-tehrany, Elmira; Kheirolomoom, Azadeh; Reshetov, Vadzim; Stebe, Marie José; Linder, Michel

2012-02-07

This study presents the application of the mixture design technique to develop an optimal liposome formulation by using the different lipids in type and percentage (DOPC, POPC and DPPC) in liposome composition. Ten lipid mixtures were generated by the simplex-centroid design technique and liposomes were prepared by the extrusion method. Liposomes were characterized with respect to size, phase transition temperature, ζ-potential, lamellarity, fluidity and efficiency in loading calcein. The results were then applied to estimate the coefficients of mixture design model and to find the optimal lipid composition with improved entrapment efficiency, size, transition temperature, fluidity and ζ-potential of liposomes. The response optimization of experiments was the liposome formulation with DOPC: 46%, POPC: 12% and DPPC: 42%. The optimal liposome formulation had an average diameter of 127.5 nm, a phase-transition temperature of 11.43 °C, a ζ-potential of -7.24 mV, fluidity (1/P)(TMA-DPH)((¬)) value of 2.87 and an encapsulation efficiency of 20.24%. The experimental results of characterization of optimal liposome formulation were in good agreement with those predicted by the mixture design technique.

Shape optimization techniques for musical instrument design

NASA Astrophysics Data System (ADS)

Henrique, Luis; Antunes, Jose; Carvalho, Joao S.

2002-11-01

The design of musical instruments is still mostly based on empirical knowledge and costly experimentation. One interesting improvement is the shape optimization of resonating components, given a number of constraints (allowed parameter ranges, shape smoothness, etc.), so that vibrations occur at specified modal frequencies. Each admissible geometrical configuration generates an error between computed eigenfrequencies and the target set. Typically, error surfaces present many local minima, corresponding to suboptimal designs. This difficulty can be overcome using global optimization techniques, such as simulated annealing. However these methods are greedy, concerning the number of function evaluations required. Thus, the computational effort can be unacceptable if complex problems, such as bell optimization, are tackled. Those issues are addressed in this paper, and a method for improving optimization procedures is proposed. Instead of using the local geometric parameters as searched variables, the system geometry is modeled in terms of truncated series of orthogonal space-funcitons, and optimization is performed on their amplitude coefficients. Fourier series and orthogonal polynomials are typical such functions. This technique reduces considerably the number of searched variables, and has a potential for significant computational savings in complex problems. It is illustrated by optimizing the shapes of both current and uncommon marimba bars.

Conceptual design optimization study

NASA Technical Reports Server (NTRS)

Hollowell, S. J.; Beeman, E. R., II; Hiyama, R. M.

1990-01-01

The feasibility of applying multilevel functional decomposition and optimization techniques to conceptual design of advanced fighter aircraft was investigated. Applying the functional decomposition techniques to the conceptual design phase appears to be feasible. The initial implementation of the modified design process will optimize wing design variables. A hybrid approach, combining functional decomposition techniques for generation of aerodynamic and mass properties linear sensitivity derivatives with existing techniques for sizing mission performance and optimization, is proposed.

Application of optimal design methodologies in clinical pharmacology experiments.

PubMed

Ogungbenro, Kayode; Dokoumetzidis, Aristides; Aarons, Leon

2009-01-01

Pharmacokinetics and pharmacodynamics data are often analysed by mixed-effects modelling techniques (also known as population analysis), which has become a standard tool in the pharmaceutical industries for drug development. The last 10 years has witnessed considerable interest in the application of experimental design theories to population pharmacokinetic and pharmacodynamic experiments. Design of population pharmacokinetic experiments involves selection and a careful balance of a number of design factors. Optimal design theory uses prior information about the model and parameter estimates to optimize a function of the Fisher information matrix to obtain the best combination of the design factors. This paper provides a review of the different approaches that have been described in the literature for optimal design of population pharmacokinetic and pharmacodynamic experiments. It describes options that are available and highlights some of the issues that could be of concern as regards practical application. It also discusses areas of application of optimal design theories in clinical pharmacology experiments. It is expected that as the awareness about the benefits of this approach increases, more people will embrace it and ultimately will lead to more efficient population pharmacokinetic and pharmacodynamic experiments and can also help to reduce both cost and time during drug development. Copyright (c) 2008 John Wiley & Sons, Ltd.

Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction.

PubMed

Šiljić Tomić, Aleksandra; Antanasijević, Davor; Ristić, Mirjana; Perić-Grujić, Aleksandra; Pocajt, Viktor

2018-04-01

This paper presents an application of experimental design for the optimization of artificial neural network (ANN) for the prediction of dissolved oxygen (DO) content in the Danube River. The aim of this research was to obtain a more reliable ANN model that uses fewer monitoring records, by simultaneous optimization of the following model parameters: number of monitoring sites, number of historical monitoring data (expressed in years), and number of input water quality parameters used. Box-Behnken three-factor at three levels experimental design was applied for simultaneous spatial, temporal, and input variables optimization of the ANN model. The prediction of DO was performed using a feed-forward back-propagation neural network (BPNN), while the selection of most important inputs was done off-model using multi-filter approach that combines a chi-square ranking in the first step with a correlation-based elimination in the second step. The contour plots of absolute and relative error response surfaces were utilized to determine the optimal values of design factors. From the contour plots, two BPNN models that cover entire Danube flow through Serbia are proposed: an upstream model (BPNN-UP) that covers 8 monitoring sites prior to Belgrade and uses 12 inputs measured in the 7-year period and a downstream model (BPNN-DOWN) which covers 9 monitoring sites and uses 11 input parameters measured in the 6-year period. The main difference between the two models is that BPNN-UP utilizes inputs such as BOD, P, and PO 4 3- , which is in accordance with the fact that this model covers northern part of Serbia (Vojvodina Autonomous Province) which is well-known for agricultural production and extensive use of fertilizers. Both models have shown very good agreement between measured and predicted DO (with R 2  ≥ 0.86) and demonstrated that they can effectively forecast DO content in the Danube River.

Optimal Design of Passive Flow Control for a Boundary-Layer-Ingesting Offset Inlet Using Design-of-Experiments

NASA Technical Reports Server (NTRS)

Allan, Brian G.; Owens, Lewis R.; Lin, John C.

2006-01-01

This research will investigate the use of Design-of-Experiments (DOE) in the development of an optimal passive flow control vane design for a boundary-layer-ingesting (BLI) offset inlet in transonic flow. This inlet flow control is designed to minimize the engine fan-face distortion levels and first five Fourier harmonic half amplitudes while maximizing the inlet pressure recovery. Numerical simulations of the BLI inlet are computed using the Reynolds-averaged Navier-Stokes (RANS) flow solver, OVERFLOW, developed at NASA. These simulations are used to generate the numerical experiments for the DOE response surface model. In this investigation, two DOE optimizations were performed using a D-Optimal Response Surface model. The first DOE optimization was performed using four design factors which were vane height and angles-of-attack for two groups of vanes. One group of vanes was placed at the bottom of the inlet and a second group symmetrically on the sides. The DOE design was performed for a BLI inlet with a free-stream Mach number of 0.85 and a Reynolds number of 2 million, based on the length of the fan-face diameter, matching an experimental wind tunnel BLI inlet test. The first DOE optimization required a fifth order model having 173 numerical simulation experiments and was able to reduce the DC60 baseline distortion from 64% down to 4.4%, while holding the pressure recovery constant. A second DOE optimization was performed holding the vanes heights at a constant value from the first DOE optimization with the two vane angles-of-attack as design factors. This DOE only required a second order model fit with 15 numerical simulation experiments and reduced DC60 to 3.5% with small decreases in the fourth and fifth harmonic amplitudes. The second optimal vane design was tested at the NASA Langley 0.3- Meter Transonic Cryogenic Tunnel in a BLI inlet experiment. The experimental results showed a 80% reduction of DPCP(sub avg), the circumferential distortion level at the

Optimal Design of Passive Flow Control for a Boundary-Layer-Ingesting Offset Inlet Using Design-of-Experiments

NASA Technical Reports Server (NTRS)

Allan, Brian G.; Owens, Lewis R., Jr.; Lin, John C.

2006-01-01

This research will investigate the use of Design-of-Experiments (DOE) in the development of an optimal passive flow control vane design for a boundary-layer-ingesting (BLI) offset inlet in transonic flow. This inlet flow control is designed to minimize the engine fan face distortion levels and first five Fourier harmonic half amplitudes while maximizing the inlet pressure recovery. Numerical simulations of the BLI inlet are computed using the Reynolds-averaged Navier-Stokes (RANS) flow solver, OVERFLOW, developed at NASA. These simulations are used to generate the numerical experiments for the DOE response surface model. In this investigation, two DOE optimizations were performed using a D-Optimal Response Surface model. The first DOE optimization was performed using four design factors which were vane height and angles-of-attack for two groups of vanes. One group of vanes was placed at the bottom of the inlet and a second group symmetrically on the sides. The DOE design was performed for a BLI inlet with a free-stream Mach number of 0.85 and a Reynolds number of 2 million, based on the length of the fan face diameter, matching an experimental wind tunnel BLI inlet test. The first DOE optimization required a fifth order model having 173 numerical simulation experiments and was able to reduce the DC60 baseline distortion from 64% down to 4.4%, while holding the pressure recovery constant. A second DOE optimization was performed holding the vanes heights at a constant value from the first DOE optimization with the two vane angles-of-attack as design factors. This DOE only required a second order model fit with 15 numerical simulation experiments and reduced DC60 to 3.5% with small decreases in the fourth and fifth harmonic amplitudes. The second optimal vane design was tested at the NASA Langley 0.3-Meter Transonic Cryogenic Tunnel in a BLI inlet experiment. The experimental results showed a 80% reduction of DPCPavg, the circumferential distortion level at the engine

Multi-objective experimental design for (13)C-based metabolic flux analysis.

PubMed

Bouvin, Jeroen; Cajot, Simon; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Van Impe, Jan; Geeraerd, Annemie; Bernaerts, Kristel

2015-10-01

(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi

Application of Box-Behnken experimental design to optimize the extraction of insecticidal Cry1Ac from soil.

PubMed

Li, Yan-Liang; Fang, Zhi-Xiang; You, Jing

2013-02-20

A validated method for analyzing Cry proteins is a premise to study the fate and ecological effects of contaminants associated with genetically engineered Bacillus thuringiensis crops. The current study has optimized the extraction method to analyze Cry1Ac protein in soil using a response surface methodology with a three-level-three-factor Box-Behnken experimental design (BBD). The optimum extraction conditions were at 21 °C and 630 rpm for 2 h. Regression analysis showed a good fit of the experimental data to the second-order polynomial model with a coefficient of determination of 0.96. The method was sensitive and precise with a method detection limit of 0.8 ng/g dry weight and relative standard deviations at 7.3%. Finally, the established method was applied for analyzing Cry1Ac protein residues in field-collected soil samples. Trace amounts of Cry1Ac protein were detected in the soils where transgenic crops have been planted for 8 and 12 years.

Optimal color design of psychological counseling room by design of experiments and response surface methodology.

PubMed

Liu, Wenjuan; Ji, Jianlin; Chen, Hua; Ye, Chenyu

2014-01-01

Color is one of the most powerful aspects of a psychological counseling environment. Little scientific research has been conducted on color design and much of the existing literature is based on observational studies. Using design of experiments and response surface methodology, this paper proposes an optimal color design approach for transforming patients' perception into color elements. Six indices, pleasant-unpleasant, interesting-uninteresting, exciting-boring, relaxing-distressing, safe-fearful, and active-inactive, were used to assess patients' impression. A total of 75 patients participated, including 42 for Experiment 1 and 33 for Experiment 2. 27 representative color samples were designed in Experiment 1, and the color sample (L = 75, a = 0, b = -60) was the most preferred one. In Experiment 2, this color sample was set as the 'central point', and three color attributes were optimized to maximize the patients' satisfaction. The experimental results show that the proposed method can get the optimal solution for color design of a counseling room.

Optimal Color Design of Psychological Counseling Room by Design of Experiments and Response Surface Methodology

PubMed Central

Chen, Hua; Ye, Chenyu

2014-01-01

Color is one of the most powerful aspects of a psychological counseling environment. Little scientific research has been conducted on color design and much of the existing literature is based on observational studies. Using design of experiments and response surface methodology, this paper proposes an optimal color design approach for transforming patients’ perception into color elements. Six indices, pleasant-unpleasant, interesting-uninteresting, exciting-boring, relaxing-distressing, safe-fearful, and active-inactive, were used to assess patients’ impression. A total of 75 patients participated, including 42 for Experiment 1 and 33 for Experiment 2. 27 representative color samples were designed in Experiment 1, and the color sample (L = 75, a = 0, b = -60) was the most preferred one. In Experiment 2, this color sample was set as the ‘central point’, and three color attributes were optimized to maximize the patients’ satisfaction. The experimental results show that the proposed method can get the optimal solution for color design of a counseling room. PMID:24594683

Principles of Experimental Design for Big Data Analysis.

PubMed

Drovandi, Christopher C; Holmes, Christopher; McGree, James M; Mengersen, Kerrie; Richardson, Sylvia; Ryan, Elizabeth G

2017-08-01

Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality. The purpose of this paper is to open a discourse on the potential for modern decision theoretic optimal experimental design methods, which by their very nature have traditionally been applied prospectively, to improve the analysis of Big Data through retrospective designed sampling in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has the potential for wide generality and advantageous inferential and computational properties. We highlight current hurdles and open research questions surrounding efficient computational optimisation in using retrospective designs, and in part this paper is a call to the optimisation and experimental design communities to work together in the field of Big Data analysis.

Principles of Experimental Design for Big Data Analysis

PubMed Central

Drovandi, Christopher C; Holmes, Christopher; McGree, James M; Mengersen, Kerrie; Richardson, Sylvia; Ryan, Elizabeth G

2016-01-01

Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality. The purpose of this paper is to open a discourse on the potential for modern decision theoretic optimal experimental design methods, which by their very nature have traditionally been applied prospectively, to improve the analysis of Big Data through retrospective designed sampling in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has the potential for wide generality and advantageous inferential and computational properties. We highlight current hurdles and open research questions surrounding efficient computational optimisation in using retrospective designs, and in part this paper is a call to the optimisation and experimental design communities to work together in the field of Big Data analysis. PMID:28883686

designGG: an R-package and web tool for the optimal design of genetical genomics experiments.

PubMed

Li, Yang; Swertz, Morris A; Vera, Gonzalo; Fu, Jingyuan; Breitling, Rainer; Jansen, Ritsert C

2009-06-18

High-dimensional biomolecular profiling of genetically different individuals in one or more environmental conditions is an increasingly popular strategy for exploring the functioning of complex biological systems. The optimal design of such genetical genomics experiments in a cost-efficient and effective way is not trivial. This paper presents designGG, an R package for designing optimal genetical genomics experiments. A web implementation for designGG is available at http://gbic.biol.rug.nl/designGG. All software, including source code and documentation, is freely available. DesignGG allows users to intelligently select and allocate individuals to experimental units and conditions such as drug treatment. The user can maximize the power and resolution of detecting genetic, environmental and interaction effects in a genome-wide or local mode by giving more weight to genome regions of special interest, such as previously detected phenotypic quantitative trait loci. This will help to achieve high power and more accurate estimates of the effects of interesting factors, and thus yield a more reliable biological interpretation of data. DesignGG is applicable to linkage analysis of experimental crosses, e.g. recombinant inbred lines, as well as to association analysis of natural populations.

Data-driven design optimization for composite material characterization

Treesearch

John G. Michopoulos; John C. Hermanson; Athanasios Iliopoulos; Samuel G. Lambrakos; Tomonari Furukawa

2011-06-01

The main goal of the present paper is to demonstrate the value of design optimization beyond its use for structural shape determination in the realm of the constitutive characterization of anisotropic material systems such as polymer matrix composites with or without damage. The approaches discussed are based on the availability of massive experimental data...

Structural Optimization in automotive design

NASA Technical Reports Server (NTRS)

Bennett, J. A.; Botkin, M. E.

1984-01-01

Although mathematical structural optimization has been an active research area for twenty years, there has been relatively little penetration into the design process. Experience indicates that often this is due to the traditional layout-analysis design process. In many cases, optimization efforts have been outgrowths of analysis groups which are themselves appendages to the traditional design process. As a result, optimization is often introduced into the design process too late to have a significant effect because many potential design variables have already been fixed. A series of examples are given to indicate how structural optimization has been effectively integrated into the design process.

Optimizing indomethacin-loaded chitosan nanoparticle size, encapsulation, and release using Box-Behnken experimental design.

PubMed

Abul Kalam, Mohd; Khan, Abdul Arif; Khan, Shahanavaj; Almalik, Abdulaziz; Alshamsan, Aws

2016-06-01

Indomethacin chitosan nanoparticles (NPs) were developed by ionotropic gelation and optimized by concentrations of chitosan and tripolyphosphate (TPP) and stirring time by 3-factor 3-level Box-Behnken experimental design. Optimal concentration of chitosan (A) and TPP (B) were found 0.6mg/mL and 0.4mg/mL with 120min stirring time (C), with applied constraints of minimizing particle size (R1) and maximizing encapsulation efficiency (R2) and drug release (R3). Based on obtained 3D response surface plots, factors A, B and C were found to give synergistic effect on R1, while factor A has a negative impact on R2 and R3. Interaction of AB was negative on R1 and R2 but positive on R3. The factor AC was having synergistic effect on R1 and on R3, while the same combination had a negative effect on R2. The interaction BC was positive on the all responses. NPs were found in the size range of 321-675nm with zeta potentials (+25 to +32mV) after 6 months storage. Encapsulation, drug release, and content were in the range of 56-79%, 48-73% and 98-99%, respectively. In vitro drug release data were fitted in different kinetic models and pattern of drug release followed Higuchi-matrix type. Copyright © 2016 Elsevier B.V. All rights reserved.

Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications.

PubMed

Bondi, Robert W; Igne, Benoît; Drennen, James K; Anderson, Carl A

2012-12-01

Near-infrared spectroscopy (NIRS) is a valuable tool in the pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms. The purpose of this work was to investigate the effect of experimental designs on prediction performance of quantitative models based on NIRS using a five-component formulation as a model system. The following experimental designs were evaluated: five-level, full factorial (5-L FF); three-level, full factorial (3-L FF); central composite; I-optimal; and D-optimal. The factors for all designs were acetaminophen content and the ratio of microcrystalline cellulose to lactose monohydrate. Other constituents included croscarmellose sodium and magnesium stearate (content remained constant). Partial least squares-based models were generated using data from individual experimental designs that related acetaminophen content to spectral data. The effect of each experimental design was evaluated by determining the statistical significance of the difference in bias and standard error of the prediction for that model's prediction performance. The calibration model derived from the I-optimal design had similar prediction performance as did the model derived from the 5-L FF design, despite containing 16 fewer design points. It also outperformed all other models estimated from designs with similar or fewer numbers of samples. This suggested that experimental-design selection for calibration-model development is critical, and optimum performance can be achieved with efficient experimental designs (i.e., optimal designs).

Application of an experimental design for the optimization and validation of a new HPLC method for the determination of vancomycin in an extemporaneous ophthalmic solution.

PubMed

Enrique, Montse; García-Montoya, Encarna; Miñarro, Montserrat; Orriols, Anna; Ticó, Joseph Ramon; Suñé-Negre, Joseph Maria; Pérez-Lozano, Pilar

2008-10-01

An experimental design has been used to develop and optimize a new high-performance liquid chromatographic (HPLC) method for the determination of Vancomycin in an extemporaneous ophthalmic solution. After the preliminary studies and literature review, the optimized method was carried out on a second generation of a C18 reverse-phase column (Luna 150 x 4.6 mm i.d., 5 microm particle size) and using methanol as organic phase, a less toxic solvent than acetonitrile, described in the extended literature. The experimental design consisted of a Placket-Burman design where six different variables were studied (flow rate, mL/min; temperature, degrees C; pH mobile phase; % buffer solution; wavelength; and injection volume) to obtain the best suitability parameters (Capacity factor-K', tailing factor, resolution, and theoretical plates). After the optimization of the chromatographic conditions and statistical treatment of the obtained results, the final method uses a mixture of a buffer solution of water-phosphoric acid (85%) (99.83:0.17, v/v) adjusted to pH 3.0 using triethylamine and mixed with methanol (87:13, v/v). The separation is achieved using a flow rate of 1.0 mL/min at 35 degrees C. The UV detector was operated at 280 nm. The validation study carried out, demonstrates the viability of the method, obtaining a good selectivity, linearity, precision, accuracy, and sensitivity.

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Man, Jun; Zhang, Jiangjiang; Li, Weixuan

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees ofmore » freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.« less

Aerodynamic design optimization using sensitivity analysis and computational fluid dynamics

NASA Technical Reports Server (NTRS)

Baysal, Oktay; Eleshaky, Mohamed E.

1991-01-01

A new and efficient method is presented for aerodynamic design optimization, which is based on a computational fluid dynamics (CFD)-sensitivity analysis algorithm. The method is applied to design a scramjet-afterbody configuration for an optimized axial thrust. The Euler equations are solved for the inviscid analysis of the flow, which in turn provides the objective function and the constraints. The CFD analysis is then coupled with the optimization procedure that uses a constrained minimization method. The sensitivity coefficients, i.e. gradients of the objective function and the constraints, needed for the optimization are obtained using a quasi-analytical method rather than the traditional brute force method of finite difference approximations. During the one-dimensional search of the optimization procedure, an approximate flow analysis (predicted flow) based on a first-order Taylor series expansion is used to reduce the computational cost. Finally, the sensitivity of the optimum objective function to various design parameters, which are kept constant during the optimization, is computed to predict new optimum solutions. The flow analysis of the demonstrative example are compared with the experimental data. It is shown that the method is more efficient than the traditional methods.

Experimental evaluation of HJB optimal controllers for the attitude dynamics of a multirotor aerial vehicle.

PubMed

Prado, Igor Afonso Acampora; Pereira, Mateus de Freitas Virgílio; de Castro, Davi Ferreira; Dos Santos, Davi Antônio; Balthazar, Jose Manoel

2018-06-01

The present paper is concerned with the design and experimental evaluation of optimal control laws for the nonlinear attitude dynamics of a multirotor aerial vehicle. Three design methods based on Hamilton-Jacobi-Bellman equation are taken into account. The first one is a linear control with guarantee of stability for nonlinear systems. The second and third are a nonlinear suboptimal control techniques. These techniques are based on an optimal control design approach that takes into account the nonlinearities present in the vehicle dynamics. The stability Proof of the closed-loop system is presented. The performance of the control system designed is evaluated via simulations and also via an experimental scheme using the Quanser 3-DOF Hover. The experiments show the effectiveness of the linear control method over the nonlinear strategy. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Development and experimental design of a novel controlled-release matrix tablet formulation for indapamide hemihydrate.

PubMed

Antovska, Packa; Ugarkovic, Sonja; Petruševski, Gjorgji; Stefanova, Bosilka; Manchevska, Blagica; Petkovska, Rumenka; Makreski, Petre

2017-11-01

Development, experimental design and in vitro in vivo correlation (IVIVC) of controlled-release matrix formulation. Development of novel oral controlled delivery system for indapamide hemihydrate, optimization of the formulation by experimental design and evaluation regarding IVIVC on a pilot scale batch as a confirmation of a well-established formulation. In vitro dissolution profiles of controlled-release tablets of indapamide hemihydrate from four different matrices had been evaluated in comparison to the originator's product Natrilix (Servier) as a direction for further development and optimization of a hydroxyethylcellulose-based matrix controlled-release formulation. A central composite factorial design had been applied for the optimization of a chosen controlled-release tablet formulation. The controlled-release tablets with appropriate physical and technological properties had been obtained with a matrix: binder concentration variations in the range: 20-40w/w% for the matrix and 1-3w/w% for the binder. The experimental design had defined the design space for the formulation and was prerequisite for extraction of a particular formulation that would be a subject for transfer on pilot scale and IVIV correlation. The release model of the optimized formulation has shown best fit to the zero order kinetics depicted with the Hixson-Crowell erosion-dependent mechanism of release. Level A correlation was obtained.

Experimental validation of an integrated controls-structures design methodology for a class of flexible space structures

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Gupta, Sandeep; Elliott, Kenny B.; Joshi, Suresh M.; Walz, Joseph E.

1994-01-01

This paper describes the first experimental validation of an optimization-based integrated controls-structures design methodology for a class of flexible space structures. The Controls-Structures-Interaction (CSI) Evolutionary Model, a laboratory test bed at Langley, is redesigned based on the integrated design methodology with two different dissipative control strategies. The redesigned structure is fabricated, assembled in the laboratory, and experimentally compared with the original test structure. Design guides are proposed and used in the integrated design process to ensure that the resulting structure can be fabricated. Experimental results indicate that the integrated design requires greater than 60 percent less average control power (by thruster actuators) than the conventional control-optimized design while maintaining the required line-of-sight performance, thereby confirming the analytical findings about the superiority of the integrated design methodology. Amenability of the integrated design structure to other control strategies is considered and evaluated analytically and experimentally. This work also demonstrates the capabilities of the Langley-developed design tool CSI DESIGN which provides a unified environment for structural and control design.

Structural Model Tuning Capability in an Object-Oriented Multidisciplinary Design, Analysis, and Optimization Tool

NASA Technical Reports Server (NTRS)

Lung, Shun-fat; Pak, Chan-gi

2008-01-01

Updating the finite element model using measured data is a challenging problem in the area of structural dynamics. The model updating process requires not only satisfactory correlations between analytical and experimental results, but also the retention of dynamic properties of structures. Accurate rigid body dynamics are important for flight control system design and aeroelastic trim analysis. Minimizing the difference between analytical and experimental results is a type of optimization problem. In this research, a multidisciplinary design, analysis, and optimization (MDAO) tool is introduced to optimize the objective function and constraints such that the mass properties, the natural frequencies, and the mode shapes are matched to the target data as well as the mass matrix being orthogonalized.

Structural Model Tuning Capability in an Object-Oriented Multidisciplinary Design, Analysis, and Optimization Tool

NASA Technical Reports Server (NTRS)

Lung, Shun-fat; Pak, Chan-gi

2008-01-01

Updating the finite element model using measured data is a challenging problem in the area of structural dynamics. The model updating process requires not only satisfactory correlations between analytical and experimental results, but also the retention of dynamic properties of structures. Accurate rigid body dynamics are important for flight control system design and aeroelastic trim analysis. Minimizing the difference between analytical and experimental results is a type of optimization problem. In this research, a multidisciplinary design, analysis, and optimization [MDAO] tool is introduced to optimize the objective function and constraints such that the mass properties, the natural frequencies, and the mode shapes are matched to the target data as well as the mass matrix being orthogonalized.

Reliability-based design optimization using a generalized subset simulation method and posterior approximation

NASA Astrophysics Data System (ADS)

Ma, Yuan-Zhuo; Li, Hong-Shuang; Yao, Wei-Xing

2018-05-01

The evaluation of the probabilistic constraints in reliability-based design optimization (RBDO) problems has always been significant and challenging work, which strongly affects the performance of RBDO methods. This article deals with RBDO problems using a recently developed generalized subset simulation (GSS) method and a posterior approximation approach. The posterior approximation approach is used to transform all the probabilistic constraints into ordinary constraints as in deterministic optimization. The assessment of multiple failure probabilities required by the posterior approximation approach is achieved by GSS in a single run at all supporting points, which are selected by a proper experimental design scheme combining Sobol' sequences and Bucher's design. Sequentially, the transformed deterministic design optimization problem can be solved by optimization algorithms, for example, the sequential quadratic programming method. Three optimization problems are used to demonstrate the efficiency and accuracy of the proposed method.

Applications of Chemiluminescence in the Teaching of Experimental Design

ERIC Educational Resources Information Center

Krawczyk, Tomasz; Slupska, Roksana; Baj, Stefan

2015-01-01

This work describes a single-session laboratory experiment devoted to teaching the principles of factorial experimental design. Students undertook the rational optimization of a luminol oxidation reaction, using a two-level experiment that aimed to create a long-lasting bright emission. During the session students used only simple glassware and…

Rationally reduced libraries for combinatorial pathway optimization minimizing experimental effort.

PubMed

Jeschek, Markus; Gerngross, Daniel; Panke, Sven

2016-03-31

Rational flux design in metabolic engineering approaches remains difficult since important pathway information is frequently not available. Therefore empirical methods are applied that randomly change absolute and relative pathway enzyme levels and subsequently screen for variants with improved performance. However, screening is often limited on the analytical side, generating a strong incentive to construct small but smart libraries. Here we introduce RedLibs (Reduced Libraries), an algorithm that allows for the rational design of smart combinatorial libraries for pathway optimization thereby minimizing the use of experimental resources. We demonstrate the utility of RedLibs for the design of ribosome-binding site libraries by in silico and in vivo screening with fluorescent proteins and perform a simple two-step optimization of the product selectivity in the branched multistep pathway for violacein biosynthesis, indicating a general applicability for the algorithm and the proposed heuristics. We expect that RedLibs will substantially simplify the refactoring of synthetic metabolic pathways.

Rotorcraft Optimization Tools: Incorporating Rotorcraft Design Codes into Multi-Disciplinary Design, Analysis, and Optimization

NASA Technical Reports Server (NTRS)

Meyn, Larry A.

2018-01-01

One of the goals of NASA's Revolutionary Vertical Lift Technology Project (RVLT) is to provide validated tools for multidisciplinary design, analysis and optimization (MDAO) of vertical lift vehicles. As part of this effort, the software package, RotorCraft Optimization Tools (RCOTOOLS), is being developed to facilitate incorporating key rotorcraft conceptual design codes into optimizations using the OpenMDAO multi-disciplinary optimization framework written in Python. RCOTOOLS, also written in Python, currently supports the incorporation of the NASA Design and Analysis of RotorCraft (NDARC) vehicle sizing tool and the Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics II (CAMRAD II) analysis tool into OpenMDAO-driven optimizations. Both of these tools use detailed, file-based inputs and outputs, so RCOTOOLS provides software wrappers to update input files with new design variable values, execute these codes and then extract specific response variable values from the file outputs. These wrappers are designed to be flexible and easy to use. RCOTOOLS also provides several utilities to aid in optimization model development, including Graphical User Interface (GUI) tools for browsing input and output files in order to identify text strings that are used to identify specific variables as optimization input and response variables. This paper provides an overview of RCOTOOLS and its use

Applied optimal shape design

NASA Astrophysics Data System (ADS)

Mohammadi, B.; Pironneau, O.

2002-12-01

This paper is a short survey of optimal shape design (OSD) for fluids. OSD is an interesting field both mathematically and for industrial applications. Existence, sensitivity, correct discretization are important theoretical issues. Practical implementation issues for airplane designs are critical too. The paper is also a summary of the material covered in our recent book, Applied Optimal Shape Design, Oxford University Press, 2001.

A FRAMEWORK TO DESIGN AND OPTIMIZE CHEMICAL FLOODING PROCESSES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mojdeh Delshad; Gary A. Pope; Kamy Sepehrnoori

2005-07-01

The goal of this proposed research is to provide an efficient and user friendly simulation framework for screening and optimizing chemical/microbial enhanced oil recovery processes. The framework will include (1) a user friendly interface to identify the variables that have the most impact on oil recovery using the concept of experimental design and response surface maps, (2) UTCHEM reservoir simulator to perform the numerical simulations, and (3) an economic model that automatically imports the simulation production data to evaluate the profitability of a particular design. Such a reservoir simulation framework is not currently available to the oil industry. The objectivesmore » of Task 1 are to develop three primary modules representing reservoir, chemical, and well data. The modules will be interfaced with an already available experimental design model. The objective of the Task 2 is to incorporate UTCHEM reservoir simulator and the modules with the strategic variables and developing the response surface maps to identify the significant variables from each module. The objective of the Task 3 is to develop the economic model designed specifically for the chemical processes targeted in this proposal and interface the economic model with UTCHEM production output. Task 4 is on the validation of the framework and performing simulations of oil reservoirs to screen, design and optimize the chemical processes.« less

A Framework to Design and Optimize Chemical Flooding Processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mojdeh Delshad; Gary A. Pope; Kamy Sepehrnoori

2006-08-31

The goal of this proposed research is to provide an efficient and user friendly simulation framework for screening and optimizing chemical/microbial enhanced oil recovery processes. The framework will include (1) a user friendly interface to identify the variables that have the most impact on oil recovery using the concept of experimental design and response surface maps, (2) UTCHEM reservoir simulator to perform the numerical simulations, and (3) an economic model that automatically imports the simulation production data to evaluate the profitability of a particular design. Such a reservoir simulation framework is not currently available to the oil industry. The objectivesmore » of Task 1 are to develop three primary modules representing reservoir, chemical, and well data. The modules will be interfaced with an already available experimental design model. The objective of the Task 2 is to incorporate UTCHEM reservoir simulator and the modules with the strategic variables and developing the response surface maps to identify the significant variables from each module. The objective of the Task 3 is to develop the economic model designed specifically for the chemical processes targeted in this proposal and interface the economic model with UTCHEM production output. Task 4 is on the validation of the framework and performing simulations of oil reservoirs to screen, design and optimize the chemical processes.« less

A FRAMEWORK TO DESIGN AND OPTIMIZE CHEMICAL FLOODING PROCESSES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mojdeh Delshad; Gary A. Pope; Kamy Sepehrnoori

2004-11-01

The goal of this proposed research is to provide an efficient and user friendly simulation framework for screening and optimizing chemical/microbial enhanced oil recovery processes. The framework will include (1) a user friendly interface to identify the variables that have the most impact on oil recovery using the concept of experimental design and response surface maps, (2) UTCHEM reservoir simulator to perform the numerical simulations, and (3) an economic model that automatically imports the simulation production data to evaluate the profitability of a particular design. Such a reservoir simulation framework is not currently available to the oil industry. The objectivesmore » of Task 1 are to develop three primary modules representing reservoir, chemical, and well data. The modules will be interfaced with an already available experimental design model. The objective of the Task 2 is to incorporate UTCHEM reservoir simulator and the modules with the strategic variables and developing the response surface maps to identify the significant variables from each module. The objective of the Task 3 is to develop the economic model designed specifically for the chemical processes targeted in this proposal and interface the economic model with UTCHEM production output. Task 4 is on the validation of the framework and performing simulations of oil reservoirs to screen, design and optimize the chemical processes.« less

Model Selection in Systems Biology Depends on Experimental Design

PubMed Central

Silk, Daniel; Kirk, Paul D. W.; Barnes, Chris P.; Toni, Tina; Stumpf, Michael P. H.

2014-01-01

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis. PMID:24922483

Model selection in systems biology depends on experimental design.

PubMed

Silk, Daniel; Kirk, Paul D W; Barnes, Chris P; Toni, Tina; Stumpf, Michael P H

2014-06-01

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.

Cr(VI) transport via a supported ionic liquid membrane containing CYPHOS IL101 as carrier: system analysis and optimization through experimental design strategies.

PubMed

Rodríguez de San Miguel, Eduardo; Vital, Xóchitl; de Gyves, Josefina

2014-05-30

Chromium(VI) transport through a supported liquid membrane (SLM) system containing the commercial ionic liquid CYPHOS IL101 as carrier was studied. A reducing stripping phase was used as a mean to increase recovery and to simultaneously transform Cr(VI) into a less toxic residue for disposal or reuse. General functions which describe the time-depending evolution of the metal fractions in the cell compartments were defined and used in data evaluation. An experimental design strategy, using factorial and central-composite design matrices, was applied to assess the influence of the extractant, NaOH and citrate concentrations in the different phases, while a desirability function scheme allowed the synchronized optimization of depletion and recovery of the analyte. The mechanism for chromium permeation was analyzed and discussed to contribute to the understanding of the transfer process. The influence of metal concentration was evaluated as well. The presence of different interfering ions (Ca(2+), Al(3+), NO3(-), SO4(2-), and Cl(-)) at several Cr(VI): interfering ion ratios was studied through the use of a Plackett and Burman experimental design matrix. Under optimized conditions 90% of recovery was obtained from a feed solution containing 7mgL(-1) of Cr(VI) in 0.01moldm(-3) HCl medium after 5h of pertraction. Copyright © 2014 Elsevier B.V. All rights reserved.

Microrandomized trials: An experimental design for developing just-in-time adaptive interventions.

PubMed

Klasnja, Predrag; Hekler, Eric B; Shiffman, Saul; Boruvka, Audrey; Almirall, Daniel; Tewari, Ambuj; Murphy, Susan A

2015-12-01

This article presents an experimental design, the microrandomized trial, developed to support optimization of just-in-time adaptive interventions (JITAIs). JITAIs are mHealth technologies that aim to deliver the right intervention components at the right times and locations to optimally support individuals' health behaviors. Microrandomized trials offer a way to optimize such interventions by enabling modeling of causal effects and time-varying effect moderation for individual intervention components within a JITAI. The article describes the microrandomized trial design, enumerates research questions that this experimental design can help answer, and provides an overview of the data analyses that can be used to assess the causal effects of studied intervention components and investigate time-varying moderation of those effects. Microrandomized trials enable causal modeling of proximal effects of the randomized intervention components and assessment of time-varying moderation of those effects. Microrandomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions' effects, enabling creation of more effective JITAIs. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Real-time PCR probe optimization using design of experiments approach.

PubMed

Wadle, S; Lehnert, M; Rubenwolf, S; Zengerle, R; von Stetten, F

2016-03-01

Primer and probe sequence designs are among the most critical input factors in real-time polymerase chain reaction (PCR) assay optimization. In this study, we present the use of statistical design of experiments (DOE) approach as a general guideline for probe optimization and more specifically focus on design optimization of label-free hydrolysis probes that are designated as mediator probes (MPs), which are used in reverse transcription MP PCR (RT-MP PCR). The effect of three input factors on assay performance was investigated: distance between primer and mediator probe cleavage site; dimer stability of MP and target sequence (influenza B virus); and dimer stability of the mediator and universal reporter (UR). The results indicated that the latter dimer stability had the greatest influence on assay performance, with RT-MP PCR efficiency increased by up to 10% with changes to this input factor. With an optimal design configuration, a detection limit of 3-14 target copies/10 μl reaction could be achieved. This improved detection limit was confirmed for another UR design and for a second target sequence, human metapneumovirus, with 7-11 copies/10 μl reaction detected in an optimum case. The DOE approach for improving oligonucleotide designs for real-time PCR not only produces excellent results but may also reduce the number of experiments that need to be performed, thus reducing costs and experimental times.

D-Optimal mixture experimental design for stealth biodegradable crosslinked docetaxel-loaded poly-ε-caprolactone nanoparticles manufactured by dispersion polymerization.

PubMed

Ogunwuyi, O; Adesina, S; Akala, E O

2015-03-01

We report here our efforts on the development of stealth biodegradable crosslinked poly-ε-caprolactone nanoparticles by free radical dispersion polymerization suitable for the delivery of bioactive agents. The uniqueness of the dispersion polymerization technique is that it is surfactant free, thereby obviating the problems known to be associated with the use of surfactants in the fabrication of nanoparticles for biomedical applications. Aided by a statistical software for experimental design and analysis, we used D-optimal mixture statistical experimental design to generate thirty batches of nanoparticles prepared by varying the proportion of the components (poly-ε-caprolactone macromonomer, crosslinker, initiators and stabilizer) in acetone/water system. Morphology of the nanoparticles was examined using scanning electron microscopy (SEM). Particle size and zeta potential were measured by dynamic light scattering (DLS). Scheffe polynomial models were generated to predict particle size (nm) and particle surface zeta potential (mV) as functions of the proportion of the components. Solutions were returned from simultaneous optimization of the response variables for component combinations to (a) minimize nanoparticle size (small nanoparticles are internalized into disease organs easily, avoid reticuloendothelial clearance and lung filtration) and (b) maximization of the negative zeta potential values, as it is known that, following injection into the blood stream, nanoparticles with a positive zeta potential pose a threat of causing transient embolism and rapid clearance compared to negatively charged particles. In vitro availability isotherms show that the nanoparticles sustained the release of docetaxel for 72 to 120 hours depending on the formulation. The data show that nanotechnology platforms for controlled delivery of bioactive agents can be developed based on the nanoparticles.

Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks.

PubMed

Flassig, R J; Sundmacher, K

2012-12-01

Biochemical reaction networks in the form of coupled ordinary differential equations (ODEs) provide a powerful modeling tool for understanding the dynamics of biochemical processes. During the early phase of modeling, scientists have to deal with a large pool of competing nonlinear models. At this point, discrimination experiments can be designed and conducted to obtain optimal data for selecting the most plausible model. Since biological ODE models have widely distributed parameters due to, e.g. biologic variability or experimental variations, model responses become distributed. Therefore, a robust optimal experimental design (OED) for model discrimination can be used to discriminate models based on their response probability distribution functions (PDFs). In this work, we present an optimal control-based methodology for designing optimal stimulus experiments aimed at robust model discrimination. For estimating the time-varying model response PDF, which results from the nonlinear propagation of the parameter PDF under the ODE dynamics, we suggest using the sigma-point approach. Using the model overlap (expected likelihood) as a robust discrimination criterion to measure dissimilarities between expected model response PDFs, we benchmark the proposed nonlinear design approach against linearization with respect to prediction accuracy and design quality for two nonlinear biological reaction networks. As shown, the sigma-point outperforms the linearization approach in the case of widely distributed parameter sets and/or existing multiple steady states. Since the sigma-point approach scales linearly with the number of model parameter, it can be applied to large systems for robust experimental planning. An implementation of the method in MATLAB/AMPL is available at http://www.uni-magdeburg.de/ivt/svt/person/rf/roed.html. flassig@mpi-magdeburg.mpg.de Supplementary data are are available at Bioinformatics online.

Taguchi method of experimental design in materials education

NASA Technical Reports Server (NTRS)

Weiser, Martin W.

1993-01-01

Some of the advantages and disadvantages of the Taguchi Method of experimental design as applied to Materials Science will be discussed. This is a fractional factorial method that employs the minimum number of experimental trials for the information obtained. The analysis is also very simple to use and teach, which is quite advantageous in the classroom. In addition, the Taguchi loss function can be easily incorporated to emphasize that improvements in reproducibility are often at least as important as optimization of the response. The disadvantages of the Taguchi Method include the fact that factor interactions are normally not accounted for, there are zero degrees of freedom if all of the possible factors are used, and randomization is normally not used to prevent environmental biasing. In spite of these disadvantages it is felt that the Taguchi Method is extremely useful for both teaching experimental design and as a research tool, as will be shown with a number of brief examples.

Fatigue design of a cellular phone folder using regression model-based multi-objective optimization

NASA Astrophysics Data System (ADS)

Kim, Young Gyun; Lee, Jongsoo

2016-08-01

In a folding cellular phone, the folding device is repeatedly opened and closed by the user, which eventually results in fatigue damage, particularly to the front of the folder. Hence, it is important to improve the safety and endurance of the folder while also reducing its weight. This article presents an optimal design for the folder front that maximizes its fatigue endurance while minimizing its thickness. Design data for analysis and optimization were obtained experimentally using a test jig. Multi-objective optimization was carried out using a nonlinear regression model. Three regression methods were employed: back-propagation neural networks, logistic regression and support vector machines. The AdaBoost ensemble technique was also used to improve the approximation. Two-objective Pareto-optimal solutions were identified using the non-dominated sorting genetic algorithm (NSGA-II). Finally, a numerically optimized solution was validated against experimental product data, in terms of both fatigue endurance and thickness index.

Optimization of the NIF ignition point design hohlraum

NASA Astrophysics Data System (ADS)

Callahan, D. A.; Hinkel, D. E.; Berger, R. L.; Divol, L.; Dixit, S. N.; Edwards, M. J.; Haan, S. W.; Jones, O. S.; Lindl, J. D.; Meezan, N. B.; Michel, P. A.; Pollaine, S. M.; Suter, L. J.; Town, R. P. J.; Bradley, P. A.

2008-05-01

In preparation for the start of NIF ignition experiments, we have designed a porfolio of targets that span the temperature range that is consistent with initial NIF operations: 300 eV, 285 eV, and 270 eV. Because these targets are quite complicated, we have developed a plan for choosing the optimum hohlraum for the first ignition attempt that is based on this portfolio of designs coupled with early NIF experiements using 96 beams. These early experiments will measure the laser plasma instabilities of the candidate designs and will demonstrate our ability to tune symmetry in these designs. These experimental results, coupled with the theory and simulations that went into the designs, will allow us to choose the optimal hohlraum for the first NIF ignition attempt.

Design Optimization Toolkit: Users' Manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aguilo Valentin, Miguel Alejandro

The Design Optimization Toolkit (DOTk) is a stand-alone C++ software package intended to solve complex design optimization problems. DOTk software package provides a range of solution methods that are suited for gradient/nongradient-based optimization, large scale constrained optimization, and topology optimization. DOTk was design to have a flexible user interface to allow easy access to DOTk solution methods from external engineering software packages. This inherent flexibility makes DOTk barely intrusive to other engineering software packages. As part of this inherent flexibility, DOTk software package provides an easy-to-use MATLAB interface that enables users to call DOTk solution methods directly from the MATLABmore » command window.« less

Design Optimization of Irregular Cellular Structure for Additive Manufacturing

NASA Astrophysics Data System (ADS)

Song, Guo-Hua; Jing, Shi-Kai; Zhao, Fang-Lei; Wang, Ye-Dong; Xing, Hao; Zhou, Jing-Tao

2017-09-01

Irregularcellular structurehas great potential to be considered in light-weight design field. However, the research on optimizing irregular cellular structures has not yet been reporteddue to the difficulties in their modeling technology. Based on the variable density topology optimization theory, an efficient method for optimizing the topology of irregular cellular structures fabricated through additive manufacturing processes is proposed. The proposed method utilizes tangent circles to automatically generate the main outline of irregular cellular structure. The topological layoutof each cellstructure is optimized using the relative density informationobtained from the proposed modified SIMP method. A mapping relationship between cell structure and relative densityelement is builtto determine the diameter of each cell structure. The results show that the irregular cellular structure can be optimized with the proposed method. The results of simulation and experimental test are similar for irregular cellular structure, which indicate that the maximum deformation value obtained using the modified Solid Isotropic Microstructures with Penalization (SIMP) approach is lower 5.4×10-5 mm than that using the SIMP approach under the same under the same external load. The proposed research provides the instruction to design the other irregular cellular structure.

Experimental design for estimating unknown groundwater pumping using genetic algorithm and reduced order model

NASA Astrophysics Data System (ADS)

Ushijima, Timothy T.; Yeh, William W.-G.

2013-10-01

An optimal experimental design algorithm is developed to select locations for a network of observation wells that provide maximum information about unknown groundwater pumping in a confined, anisotropic aquifer. The design uses a maximal information criterion that chooses, among competing designs, the design that maximizes the sum of squared sensitivities while conforming to specified design constraints. The formulated optimization problem is non-convex and contains integer variables necessitating a combinatorial search. Given a realistic large-scale model, the size of the combinatorial search required can make the problem difficult, if not impossible, to solve using traditional mathematical programming techniques. Genetic algorithms (GAs) can be used to perform the global search; however, because a GA requires a large number of calls to a groundwater model, the formulated optimization problem still may be infeasible to solve. As a result, proper orthogonal decomposition (POD) is applied to the groundwater model to reduce its dimensionality. Then, the information matrix in the full model space can be searched without solving the full model. Results from a small-scale test case show identical optimal solutions among the GA, integer programming, and exhaustive search methods. This demonstrates the GA's ability to determine the optimal solution. In addition, the results show that a GA with POD model reduction is several orders of magnitude faster in finding the optimal solution than a GA using the full model. The proposed experimental design algorithm is applied to a realistic, two-dimensional, large-scale groundwater problem. The GA converged to a solution for this large-scale problem.

Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial

PubMed Central

Ibrahim, Ahmed; Alfa, Attahiru

2017-01-01

This paper is intended to serve as an overview of, and mostly a tutorial to illustrate, the optimization techniques used in several different key design aspects that have been considered in the literature of wireless sensor networks (WSNs). It targets the researchers who are new to the mathematical optimization tool, and wish to apply it to WSN design problems. We hence divide the paper into two main parts. One part is dedicated to introduce optimization theory and an overview on some of its techniques that could be helpful in design problem in WSNs. In the second part, we present a number of design aspects that we came across in the WSN literature in which mathematical optimization methods have been used in the design. For each design aspect, a key paper is selected, and for each we explain the formulation techniques and the solution methods implemented. We also provide in-depth analyses and assessments of the problem formulations, the corresponding solution techniques and experimental procedures in some of these papers. The analyses and assessments, which are provided in the form of comments, are meant to reflect the points that we believe should be taken into account when using optimization as a tool for design purposes. PMID:28763039

Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial.

PubMed

Ibrahim, Ahmed; Alfa, Attahiru

2017-08-01

This paper is intended to serve as an overview of, and mostly a tutorial to illustrate, the optimization techniques used in several different key design aspects that have been considered in the literature of wireless sensor networks (WSNs). It targets the researchers who are new to the mathematical optimization tool, and wish to apply it to WSN design problems. We hence divide the paper into two main parts. One part is dedicated to introduce optimization theory and an overview on some of its techniques that could be helpful in design problem in WSNs. In the second part, we present a number of design aspects that we came across in the WSN literature in which mathematical optimization methods have been used in the design. For each design aspect, a key paper is selected, and for each we explain the formulation techniques and the solution methods implemented. We also provide in-depth analyses and assessments of the problem formulations, the corresponding solution techniques and experimental procedures in some of these papers. The analyses and assessments, which are provided in the form of comments, are meant to reflect the points that we believe should be taken into account when using optimization as a tool for design purposes.

A new efficient mixture screening design for optimization of media.

PubMed

Rispoli, Fred; Shah, Vishal

2009-01-01

Screening ingredients for the optimization of media is an important first step to reduce the many potential ingredients down to the vital few components. In this study, we propose a new method of screening for mixture experiments called the centroid screening design. Comparison of the proposed design with Plackett-Burman, fractional factorial, simplex lattice design, and modified mixture design shows that the centroid screening design is the most efficient of all the designs in terms of the small number of experimental runs needed and for detecting high-order interaction among ingredients. (c) 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009.

Optimal design of compact spur gear reductions

NASA Technical Reports Server (NTRS)

Savage, M.; Lattime, S. B.; Kimmel, J. A.; Coe, H. H.

1992-01-01

The optimal design of compact spur gear reductions includes the selection of bearing and shaft proportions in addition to gear mesh parameters. Designs for single mesh spur gear reductions are based on optimization of system life, system volume, and system weight including gears, support shafts, and the four bearings. The overall optimization allows component properties to interact, yielding the best composite design. A modified feasible directions search algorithm directs the optimization through a continuous design space. Interpolated polynomials expand the discrete bearing properties and proportions into continuous variables for optimization. After finding the continuous optimum, the designer can analyze near optimal designs for comparison and selection. Design examples show the influence of the bearings on the optimal configurations.

Ocean power technology design optimization

DOE PAGES

van Rij, Jennifer; Yu, Yi -Hsiang; Edwards, Kathleen; ...

2017-07-18

For this study, the National Renewable Energy Laboratory and Ocean Power Technologies (OPT) conducted a collaborative code validation and design optimization study for OPT's PowerBuoy wave energy converter (WEC). NREL utilized WEC-Sim, an open-source WEC simulator, to compare four design variations of OPT's PowerBuoy. As an input to the WEC-Sim models, viscous drag coefficients for the PowerBuoy floats were first evaluated using computational fluid dynamics. The resulting WEC-Sim PowerBuoy models were then validated with experimental power output and fatigue load data provided by OPT. The validated WEC-Sim models were then used to simulate the power performance and loads for operationalmore » conditions, extreme conditions, and directional waves, for each of the four PowerBuoy design variations, assuming the wave environment of Humboldt Bay, California. And finally, ratios of power-to-weight, power-to-fatigue-load, power-to-maximum-extreme-load, power-to-water-plane-area, and power-to-wetted-surface-area were used to make a final comparison of the potential PowerBuoy WEC designs. Lastly, the design comparison methodologies developed and presented in this study are applicable to other WEC devices and may be useful as a framework for future WEC design development projects.« less

Ocean power technology design optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

van Rij, Jennifer; Yu, Yi -Hsiang; Edwards, Kathleen

For this study, the National Renewable Energy Laboratory and Ocean Power Technologies (OPT) conducted a collaborative code validation and design optimization study for OPT's PowerBuoy wave energy converter (WEC). NREL utilized WEC-Sim, an open-source WEC simulator, to compare four design variations of OPT's PowerBuoy. As an input to the WEC-Sim models, viscous drag coefficients for the PowerBuoy floats were first evaluated using computational fluid dynamics. The resulting WEC-Sim PowerBuoy models were then validated with experimental power output and fatigue load data provided by OPT. The validated WEC-Sim models were then used to simulate the power performance and loads for operationalmore » conditions, extreme conditions, and directional waves, for each of the four PowerBuoy design variations, assuming the wave environment of Humboldt Bay, California. And finally, ratios of power-to-weight, power-to-fatigue-load, power-to-maximum-extreme-load, power-to-water-plane-area, and power-to-wetted-surface-area were used to make a final comparison of the potential PowerBuoy WEC designs. Lastly, the design comparison methodologies developed and presented in this study are applicable to other WEC devices and may be useful as a framework for future WEC design development projects.« less

Aerodynamic design using numerical optimization

NASA Technical Reports Server (NTRS)

Murman, E. M.; Chapman, G. T.

1983-01-01

The procedure of using numerical optimization methods coupled with computational fluid dynamic (CFD) codes for the development of an aerodynamic design is examined. Several approaches that replace wind tunnel tests, develop pressure distributions and derive designs, or fulfill preset design criteria are presented. The method of Aerodynamic Design by Numerical Optimization (ADNO) is described and illustrated with examples.

Optimization of the combined ultrasonic assisted/adsorption method for the removal of malachite green by gold nanoparticles loaded on activated carbon: Experimental design

NASA Astrophysics Data System (ADS)

Roosta, M.; Ghaedi, M.; Shokri, N.; Daneshfar, A.; Sahraei, R.; Asghari, A.

2014-01-01

The present study was aimed to experimental design optimization applied to removal of malachite green (MG) from aqueous solution by ultrasound-assisted removal onto the gold nanoparticles loaded on activated carbon (Au-NP-AC). This nanomaterial was characterized using different techniques such as FESEM, TEM, BET, and UV-vis measurements. The effects of variables such as pH, initial dye concentration, adsorbent dosage (g), temperature and sonication time on MG removal were studied using central composite design (CCD) and the optimum experimental conditions were found with desirability function (DF) combined response surface methodology (RSM). Fitting the experimental equilibrium data to various isotherm models such as Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show the suitability and applicability of the Langmuir model. Kinetic models such as pseudo -first order, pseudo-second order, Elovich and intraparticle diffusion models applicability was tested for experimental data and the second-order equation and intraparticle diffusion models control the kinetic of the adsorption process. The small amount of proposed adsorbent (0.015 g) is applicable for successful removal of MG (RE > 99%) in short time (4.4 min) with high adsorption capacity (140-172 mg g-1).

Optimization of the combined ultrasonic assisted/adsorption method for the removal of malachite green by gold nanoparticles loaded on activated carbon: experimental design.

PubMed

Roosta, M; Ghaedi, M; Shokri, N; Daneshfar, A; Sahraei, R; Asghari, A

2014-01-24

The present study was aimed to experimental design optimization applied to removal of malachite green (MG) from aqueous solution by ultrasound-assisted removal onto the gold nanoparticles loaded on activated carbon (Au-NP-AC). This nanomaterial was characterized using different techniques such as FESEM, TEM, BET, and UV-vis measurements. The effects of variables such as pH, initial dye concentration, adsorbent dosage (g), temperature and sonication time on MG removal were studied using central composite design (CCD) and the optimum experimental conditions were found with desirability function (DF) combined response surface methodology (RSM). Fitting the experimental equilibrium data to various isotherm models such as Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show the suitability and applicability of the Langmuir model. Kinetic models such as pseudo -first order, pseudo-second order, Elovich and intraparticle diffusion models applicability was tested for experimental data and the second-order equation and intraparticle diffusion models control the kinetic of the adsorption process. The small amount of proposed adsorbent (0.015 g) is applicable for successful removal of MG (RE>99%) in short time (4.4 min) with high adsorption capacity (140-172 mg g(-1)). Copyright © 2013. Published by Elsevier B.V.

Design optimization studies using COSMIC NASTRAN

NASA Technical Reports Server (NTRS)

Pitrof, Stephen M.; Bharatram, G.; Venkayya, Vipperla B.

1993-01-01

The purpose of this study is to create, test and document a procedure to integrate mathematical optimization algorithms with COSMIC NASTRAN. This procedure is very important to structural design engineers who wish to capitalize on optimization methods to ensure that their design is optimized for its intended application. The OPTNAST computer program was created to link NASTRAN and design optimization codes into one package. This implementation was tested using two truss structure models and optimizing their designs for minimum weight, subject to multiple loading conditions and displacement and stress constraints. However, the process is generalized so that an engineer could design other types of elements by adding to or modifying some parts of the code.

Design optimization of beta- and photovoltaic conversion devices

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wichner, R.; Blum, A.; Fischer-Colbrie, E.

1976-01-08

This report presents the theoretical and experimental results of an LLL Electronics Engineering research program aimed at optimizing the design and electronic-material parameters of beta- and photovoltaic p-n junction conversion devices. To meet this objective, a comprehensive computer code has been developed that can handle a broad range of practical conditions. The physical model upon which the code is based is described first. Then, an example is given of a set of optimization calculations along with the resulting optimized efficiencies for silicon (Si) and gallium-arsenide (GaAs) devices. The model we have developed, however, is not limited to these materials. Itmore » can handle any appropriate material--single or polycrystalline-- provided energy absorption and electron-transport data are available. To check code validity, the performance of experimental silicon p-n junction devices (produced in-house) were measured under various light intensities and spectra as well as under tritium beta irradiation. The results of these tests were then compared with predicted results based on the known or best estimated device parameters. The comparison showed very good agreement between the calculated and the measured results.« less

Design and optimization of an experimental bioregenerative life support system with higher plants and silkworms

NASA Astrophysics Data System (ADS)

Hu, Enzhu; Bartsev, Sergey I.; Zhao, Ming; Liu, Professor Hong

The conceptual scheme of an experimental bioregenerative life support system (BLSS) for planetary exploration was designed, which consisted of four elements - human metabolism, higher plants, silkworms and waste treatment. 15 kinds of higher plants, such as wheat, rice, soybean, lettuce, mulberry, et al., were selected as regenerative component of BLSS providing the crew with air, water, and vegetable food. Silkworms, which producing animal nutrition for crews, were fed by mulberry-leaves during the first three instars, and lettuce leaves last two instars. The inedible biomass of higher plants, human wastes and silkworm feces were composted into soil like substrate, which can be reused by higher plants cultivation. Salt, sugar and some household material such as soap, shampoo would be provided from outside. To support the steady state of BLSS the same amount and elementary composition of dehydrated wastes were removed periodically. The balance of matter flows between BLSS components was described by the system of algebraic equations. The mass flows between the components were optimized by EXCEL spreadsheets and using Solver. The numerical method used in this study was Newton's method.

Optimal Design of Multitype Groundwater Monitoring Networks Using Easily Accessible Tools.

PubMed

Wöhling, Thomas; Geiges, Andreas; Nowak, Wolfgang

2016-11-01

Monitoring networks are expensive to establish and to maintain. In this paper, we extend an existing data-worth estimation method from the suite of PEST utilities with a global optimization method for optimal sensor placement (called optimal design) in groundwater monitoring networks. Design optimization can include multiple simultaneous sensor locations and multiple sensor types. Both location and sensor type are treated simultaneously as decision variables. Our method combines linear uncertainty quantification and a modified genetic algorithm for discrete multilocation, multitype search. The efficiency of the global optimization is enhanced by an archive of past samples and parallel computing. We demonstrate our methodology for a groundwater monitoring network at the Steinlach experimental site, south-western Germany, which has been established to monitor river-groundwater exchange processes. The target of optimization is the best possible exploration for minimum variance in predicting the mean travel time of the hyporheic exchange. Our results demonstrate that the information gain of monitoring network designs can be explored efficiently and with easily accessible tools prior to taking new field measurements or installing additional measurement points. The proposed methods proved to be efficient and can be applied for model-based optimal design of any type of monitoring network in approximately linear systems. Our key contributions are (1) the use of easy-to-implement tools for an otherwise complex task and (2) yet to consider data-worth interdependencies in simultaneous optimization of multiple sensor locations and sensor types. © 2016, National Ground Water Association.

Pathway Design, Engineering, and Optimization.

PubMed

Garcia-Ruiz, Eva; HamediRad, Mohammad; Zhao, Huimin

The microbial metabolic versatility found in nature has inspired scientists to create microorganisms capable of producing value-added compounds. Many endeavors have been made to transfer and/or combine pathways, existing or even engineered enzymes with new function to tractable microorganisms to generate new metabolic routes for drug, biofuel, and specialty chemical production. However, the success of these pathways can be impeded by different complications from an inherent failure of the pathway to cell perturbations. Pursuing ways to overcome these shortcomings, a wide variety of strategies have been developed. This chapter will review the computational algorithms and experimental tools used to design efficient metabolic routes, and construct and optimize biochemical pathways to produce chemicals of high interest.

Multidisciplinary design optimization using genetic algorithms

NASA Technical Reports Server (NTRS)

Unal, Resit

1994-01-01

Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared

Flat-plate photovoltaic array design optimization

NASA Technical Reports Server (NTRS)

Ross, R. G., Jr.

1980-01-01

An analysis is presented which integrates the results of specific studies in the areas of photovoltaic structural design optimization, optimization of array series/parallel circuit design, thermal design optimization, and optimization of environmental protection features. The analysis is based on minimizing the total photovoltaic system life-cycle energy cost including repair and replacement of failed cells and modules. This approach is shown to be a useful technique for array optimization, particularly when time-dependent parameters such as array degradation and maintenance are involved.

Optimal experimental design for improving the estimation of growth parameters of Lactobacillus viridescens from data under non-isothermal conditions.

PubMed

Longhi, Daniel Angelo; Martins, Wiaslan Figueiredo; da Silva, Nathália Buss; Carciofi, Bruno Augusto Mattar; de Aragão, Gláucia Maria Falcão; Laurindo, João Borges

2017-01-02

In predictive microbiology, the model parameters have been estimated using the sequential two-step modeling (TSM) approach, in which primary models are fitted to the microbial growth data, and then secondary models are fitted to the primary model parameters to represent their dependence with the environmental variables (e.g., temperature). The Optimal Experimental Design (OED) approach allows reducing the experimental workload and costs, and the improvement of model identifiability because primary and secondary models are fitted simultaneously from non-isothermal data. Lactobacillus viridescens was selected to this study because it is a lactic acid bacterium of great interest to meat products preservation. The objectives of this study were to estimate the growth parameters of L. viridescens in culture medium from TSM and OED approaches and to evaluate both the number of experimental data and the time needed in each approach and the confidence intervals of the model parameters. Experimental data for estimating the model parameters with TSM approach were obtained at six temperatures (total experimental time of 3540h and 196 experimental data of microbial growth). Data for OED approach were obtained from four optimal non-isothermal profiles (total experimental time of 588h and 60 experimental data of microbial growth), two profiles with increasing temperatures (IT) and two with decreasing temperatures (DT). The Baranyi and Roberts primary model and the square root secondary model were used to describe the microbial growth, in which the parameters b and T min (±95% confidence interval) were estimated from the experimental data. The parameters obtained from TSM approach were b=0.0290 (±0.0020) [1/(h 0.5 °C)] and T min =-1.33 (±1.26) [°C], with R 2 =0.986 and RMSE=0.581, and the parameters obtained with the OED approach were b=0.0316 (±0.0013) [1/(h 0.5 °C)] and T min =-0.24 (±0.55) [°C], with R 2 =0.990 and RMSE=0.436. The parameters obtained from OED approach

Experimental validation of systematically designed acoustic hyperbolic meta material slab exhibiting negative refraction

NASA Astrophysics Data System (ADS)

Christiansen, Rasmus E.; Sigmund, Ole

2016-09-01

This Letter reports on the experimental validation of a two-dimensional acoustic hyperbolic metamaterial slab optimized to exhibit negative refractive behavior. The slab was designed using a topology optimization based systematic design method allowing for tailoring the refractive behavior. The experimental results confirm the predicted refractive capability as well as the predicted transmission at an interface. The study simultaneously provides an estimate of the attenuation inside the slab stemming from the boundary layer effects—insight which can be utilized in the further design of the metamaterial slabs. The capability of tailoring the refractive behavior opens possibilities for different applications. For instance, a slab exhibiting zero refraction across a wide angular range is capable of funneling acoustic energy through it, while a material exhibiting the negative refractive behavior across a wide angular range provides lensing and collimating capabilities.

Reliability based design optimization: Formulations and methodologies

NASA Astrophysics Data System (ADS)

Agarwal, Harish

Modern products ranging from simple components to complex systems should be designed to be optimal and reliable. The challenge of modern engineering is to ensure that manufacturing costs are reduced and design cycle times are minimized while achieving requirements for performance and reliability. If the market for the product is competitive, improved quality and reliability can generate very strong competitive advantages. Simulation based design plays an important role in designing almost any kind of automotive, aerospace, and consumer products under these competitive conditions. Single discipline simulations used for analysis are being coupled together to create complex coupled simulation tools. This investigation focuses on the development of efficient and robust methodologies for reliability based design optimization in a simulation based design environment. Original contributions of this research are the development of a novel efficient and robust unilevel methodology for reliability based design optimization, the development of an innovative decoupled reliability based design optimization methodology, the application of homotopy techniques in unilevel reliability based design optimization methodology, and the development of a new framework for reliability based design optimization under epistemic uncertainty. The unilevel methodology for reliability based design optimization is shown to be mathematically equivalent to the traditional nested formulation. Numerical test problems show that the unilevel methodology can reduce computational cost by at least 50% as compared to the nested approach. The decoupled reliability based design optimization methodology is an approximate technique to obtain consistent reliable designs at lesser computational expense. Test problems show that the methodology is computationally efficient compared to the nested approach. A framework for performing reliability based design optimization under epistemic uncertainty is also developed

Optimization of 3D Field Design

NASA Astrophysics Data System (ADS)

Logan, Nikolas; Zhu, Caoxiang

2017-10-01

Recent progress in 3D tokamak modeling is now leveraged to create a conceptual design of new external 3D field coils for the DIII-D tokamak. Using the IPEC dominant mode as a target spectrum, the Finding Optimized Coils Using Space-curves (FOCUS) code optimizes the currents and 3D geometry of multiple coils to maximize the total set's resonant coupling. The optimized coils are individually distorted in space, creating toroidal ``arrays'' containing a variety of shapes that often wrap around a significant poloidal extent of the machine. The generalized perturbed equilibrium code (GPEC) is used to determine optimally efficient spectra for driving total, core, and edge neoclassical toroidal viscosity (NTV) torque and these too provide targets for the optimization of 3D coil designs. These conceptual designs represent a fundamentally new approach to 3D coil design for tokamaks targeting desired plasma physics phenomena. Optimized coil sets based on plasma response theory will be relevant to designs for future reactors or on any active machine. External coils, in particular, must be optimized for reliable and efficient fusion reactor designs. Work supported by the US Department of Energy under DE-AC02-09CH11466.

Experimental design for evaluating WWTP data by linear mass balances.

PubMed

Le, Quan H; Verheijen, Peter J T; van Loosdrecht, Mark C M; Volcke, Eveline I P

2018-05-15

A stepwise experimental design procedure to obtain reliable data from wastewater treatment plants (WWTPs) was developed. The proposed procedure aims at determining sets of additional measurements (besides available ones) that guarantee the identifiability of key process variables, which means that their value can be calculated from other, measured variables, based on available constraints in the form of linear mass balances. Among all solutions, i.e. all possible sets of additional measurements allowing the identifiability of all key process variables, the optimal solutions were found taking into account two objectives, namely the accuracy of the identified key variables and the cost of additional measurements. The results of this multi-objective optimization problem were represented in a Pareto-optimal front. The presented procedure was applied to a full-scale WWTP. Detailed analysis of the relation between measurements allowed the determination of groups of overlapping mass balances. Adding measured variables could only serve in identifying key variables that appear in the same group of mass balances. Besides, the application of the experimental design procedure to these individual groups significantly reduced the computational effort in evaluating available measurements and planning additional monitoring campaigns. The proposed procedure is straightforward and can be applied to other WWTPs with or without prior data collection. Copyright © 2018 Elsevier Ltd. All rights reserved.

Experimental and Quasi-Experimental Design.

ERIC Educational Resources Information Center

Cottrell, Edward B.

With an emphasis on the problems of control of extraneous variables and threats to internal and external validity, the arrangement or design of experiments is discussed. The purpose of experimentation in an educational institution, and the principles governing true experimentation (randomization, replication, and control) are presented, as are…

Optimal design of a shear magnetorheological damper for turning vibration suppression

NASA Astrophysics Data System (ADS)

Zhou, Y.; Zhang, Y. L.

2013-09-01

The intelligent material, so-called magnetorheological (MR) fluid, is utilized to control turning vibration. According to the structure of a common lathe CA6140, a shear MR damper is conceived by designing its structure and magnetic circuit. The vibration suppression effect of the damper is proved with dynamic analysis and simulation. Further, the magnetic circuit of the damper is optimized with the ANSYS parametric design language (APDL). In the optimization course, the area of the magnetic circuit and the damping force are considered. After optimization, the damper’s structure and its efficiency of electrical energy consumption are improved. Additionally, a comparative study on damping forces acquired from the initial and optimal design is conducted. A prototype of the developed MR damper is fabricated and magnetic tests are performed to measure the magnetic flux intensities and the residual magnetism in four damping gaps. Then, the testing results are compared with the simulated results. Finally, the suppressing vibration experimental system is set up and cylindrical turning experiments are performed to investigate the working performance of the MR damper.

Optimal Designs for the Rasch Model

ERIC Educational Resources Information Center

Grasshoff, Ulrike; Holling, Heinz; Schwabe, Rainer

2012-01-01

In this paper, optimal designs will be derived for estimating the ability parameters of the Rasch model when difficulty parameters are known. It is well established that a design is locally D-optimal if the ability and difficulty coincide. But locally optimal designs require that the ability parameters to be estimated are known. To attenuate this…

Optimal Flow Control Design

NASA Technical Reports Server (NTRS)

Allan, Brian; Owens, Lewis

2010-01-01

In support of the Blended-Wing-Body aircraft concept, a new flow control hybrid vane/jet design has been developed for use in a boundary-layer-ingesting (BLI) offset inlet in transonic flows. This inlet flow control is designed to minimize the engine fan-face distortion levels and the first five Fourier harmonic half amplitudes while maximizing the inlet pressure recovery. This concept represents a potentially enabling technology for quieter and more environmentally friendly transport aircraft. An optimum vane design was found by minimizing the engine fan-face distortion, DC60, and the first five Fourier harmonic half amplitudes, while maximizing the total pressure recovery. The optimal vane design was then used in a BLI inlet wind tunnel experiment at NASA Langley's 0.3-meter transonic cryogenic tunnel. The experimental results demonstrated an 80-percent decrease in DPCPavg, the reduction in the circumferential distortion levels, at an inlet mass flow rate corresponding to the middle of the operational range at the cruise condition. Even though the vanes were designed at a single inlet mass flow rate, they performed very well over the entire inlet mass flow range tested in the wind tunnel experiment with the addition of a small amount of jet flow control. While the circumferential distortion was decreased, the radial distortion on the outer rings at the aerodynamic interface plane (AIP) increased. This was a result of the large boundary layer being distributed from the bottom of the AIP in the baseline case to the outer edges of the AIP when using the vortex generator (VG) vane flow control. Experimental results, as already mentioned, showed an 80-percent reduction of DPCPavg, the circumferential distortion level at the engine fan-face. The hybrid approach leverages strengths of vane and jet flow control devices, increasing inlet performance over a broader operational range with significant reduction in mass flow requirements. Minimal distortion level requirements

Habitat Design Optimization and Analysis

NASA Technical Reports Server (NTRS)

SanSoucie, Michael P.; Hull, Patrick V.; Tinker, Michael L.

2006-01-01

Long-duration surface missions to the Moon and Mars will require habitats for the astronauts. The materials chosen for the habitat walls play a direct role in the protection against the harsh environments found on the surface. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Advanced optimization techniques are necessary for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat design optimization tool utilizing genetic algorithms has been developed. Genetic algorithms use a "survival of the fittest" philosophy, where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multi-objective formulation of structural analysis, heat loss, radiation protection, and meteoroid protection. This paper presents the research and development of this tool.

Optimal design criteria - prediction vs. parameter estimation

NASA Astrophysics Data System (ADS)

Waldl, Helmut

2014-05-01

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

Comparison of Optimal Design Methods in Inverse Problems

PubMed Central

Banks, H. T.; Holm, Kathleen; Kappel, Franz

2011-01-01

Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher Information Matrix (FIM). A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criteria with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model [13], the standard harmonic oscillator model [13] and a popular glucose regulation model [16, 19, 29]. PMID:21857762

Experimental design

NASA Technical Reports Server (NTRS)

Lim, H. S.

1983-01-01

The design of long life, low weight nickel cadmium cells is studied. The status of a program to optimize nickel electrodes for the best performance is discussed. The pore size of the plaque, the mechanical strength and active material loading are considered in depth.

Optimization of permeability for quality improvement by using factorial design

NASA Astrophysics Data System (ADS)

Said, Rahaini Mohd; Miswan, Nor Hamizah; Juan, Ng Shu; Hussin, Nor Hafizah; Ahmad, Aminah; Kamal, Mohamad Ridzuan Mohamad

2017-05-01

Sand castings are used worldwide by the manufacturing process in Metal Casting Industry, whereby the green sand are the commonly used sand mould type in the industry of sand casting. The defects on the surface of casting product is one of the problems in the industry of sand casting. The problems that relates to the defect composition of green sand are such as blowholes, pinholes shrinkage and porosity. Our objective is to optimize the best composition of green sand in order to minimize the occurrence of defects. Sand specimen of difference parameters (Bentonite, Green Sand, Cold dust and water) were design and prepared to undergo permeability test. The 24 factorial design experiment with four factors at difference composition were runs, and the total of 16 runs experiment were conducted. The developed models based on the experimental design necessary models were obtained. The model with a high coefficient of determination (R2=0.9841) and model for predicted and actual fitted well with the experimental data. Using the Analysis of Design Expert software, we identified that bentonite and water are the main interaction effect in the experiments. The optimal settings for green sand composition are 100g silica sand, 21g bentonite, 6.5 g water and 6g coal dust. This composition gives an effect of permeability number 598.3GP.

Effects of experimental design on calibration curve precision in routine analysis

PubMed Central

Pimentel, Maria Fernanda; Neto, Benício de Barros; Saldanha, Teresa Cristina B.

1998-01-01

A computational program which compares the effciencies of different experimental designs with those of maximum precision (D-optimized designs) is described. The program produces confidence interval plots for a calibration curve and provides information about the number of standard solutions, concentration levels and suitable concentration ranges to achieve an optimum calibration. Some examples of the application of this novel computational program are given, using both simulated and real data. PMID:18924816

Modified optimal control pilot model for computer-aided design and analysis

NASA Technical Reports Server (NTRS)

Davidson, John B.; Schmidt, David K.

1992-01-01

This paper presents the theoretical development of a modified optimal control pilot model based upon the optimal control model (OCM) of the human operator developed by Kleinman, Baron, and Levison. This model is input compatible with the OCM and retains other key aspects of the OCM, such as a linear quadratic solution for the pilot gains with inclusion of control rate in the cost function, a Kalman estimator, and the ability to account for attention allocation and perception threshold effects. An algorithm designed for each implementation in current dynamic systems analysis and design software is presented. Example results based upon the analysis of a tracking task using three basic dynamic systems are compared with measured results and with similar analyses performed with the OCM and two previously proposed simplified optimal pilot models. The pilot frequency responses and error statistics obtained with this modified optimal control model are shown to compare more favorably to the measured experimental results than the other previously proposed simplified models evaluated.

Experimental design and optimization of leaching process for recovery of valuable chemical elements (U, La, V, Mo, Yb and Th) from low-grade uranium ore.

PubMed

Zakrzewska-Koltuniewicz, Grażyna; Herdzik-Koniecko, Irena; Cojocaru, Corneliu; Chajduk, Ewelina

2014-06-30

The paper deals with experimental design and optimization of leaching process of uranium and associated metals from low-grade, Polish ores. The chemical elements of interest for extraction from the ore were U, La, V, Mo, Yb and Th. Sulphuric acid has been used as leaching reagent. Based on the design of experiments the second-order regression models have been constructed to approximate the leaching efficiency of elements. The graphical illustrations using 3-D surface plots have been employed in order to identify the main, quadratic and interaction effects of the factors. The multi-objective optimization method based on desirability approach has been applied in this study. The optimum condition have been determined as P=5 bar, T=120 °C and t=90 min. Under these optimal conditions, the overall extraction performance is 81.43% (for U), 64.24% (for La), 98.38% (for V), 43.69% (for Yb) and 76.89% (for Mo) and 97.00% (for Th). Copyright © 2014 Elsevier B.V. All rights reserved.

Entropy-Based Search Algorithm for Experimental Design

NASA Astrophysics Data System (ADS)

Malakar, N. K.; Knuth, K. H.

2011-03-01

The scientific method relies on the iterated processes of inference and inquiry. The inference phase consists of selecting the most probable models based on the available data; whereas the inquiry phase consists of using what is known about the models to select the most relevant experiment. Optimizing inquiry involves searching the parameterized space of experiments to select the experiment that promises, on average, to be maximally informative. In the case where it is important to learn about each of the model parameters, the relevance of an experiment is quantified by Shannon entropy of the distribution of experimental outcomes predicted by a probable set of models. If the set of potential experiments is described by many parameters, we must search this high-dimensional entropy space. Brute force search methods will be slow and computationally expensive. We present an entropy-based search algorithm, called nested entropy sampling, to select the most informative experiment for efficient experimental design. This algorithm is inspired by Skilling's nested sampling algorithm used in inference and borrows the concept of a rising threshold while a set of experiment samples are maintained. We demonstrate that this algorithm not only selects highly relevant experiments, but also is more efficient than brute force search. Such entropic search techniques promise to greatly benefit autonomous experimental design.

Optimal design of a piezoelectric transducer for exciting guided wave ultrasound in rails

NASA Astrophysics Data System (ADS)

Ramatlo, Dineo A.; Wilke, Daniel N.; Loveday, Philip W.

2017-02-01

An existing Ultrasonic Broken Rail Detection System installed in South Africa on a heavy duty railway line is currently being upgraded to include defect detection and location. To accomplish this, an ultrasonic piezoelectric transducer to strongly excite a guided wave mode with energy concentrated in the web (web mode) of a rail is required. A previous study demonstrated that the recently developed SAFE-3D (Semi-Analytical Finite Element - 3 Dimensional) method can effectively predict the guided waves excited by a resonant piezoelectric transducer. In this study, the SAFE-3D model is used in the design optimization of a rail web transducer. A bound-constrained optimization problem was formulated to maximize the energy transmitted by the transducer in the web mode when driven by a pre-defined excitation signal. Dimensions of the transducer components were selected as the three design variables. A Latin hypercube sampled design of experiments that required a total of 500 SAFE-3D analyses in the design space was employed in a response surface-based optimization approach. The Nelder-Mead optimization algorithm was then used to find an optimal transducer design on the constructed response surface. The radial basis function response surface was first verified by comparing a number of predicted responses against the computed SAFE-3D responses. The performance of the optimal transducer predicted by the optimization algorithm on the response surface was also verified to be sufficiently accurate using SAFE-3D. The computational advantages of SAFE-3D in optimal transducer design are noteworthy as more than 500 analyses were performed. The optimal design was then manufactured and experimental measurements were used to validate the predicted performance. The adopted design method has demonstrated the capability to automate the design of transducers for a particular rail cross-section and frequency range.

Optimal Learning for Efficient Experimentation in Nanotechnology and Biochemistry

DTIC Science & Technology

2015-12-22

AFRL-AFOSR-VA-TR-2016-0018 Optimal Learning for Efficient Experimentation in Nanotechnology , Biochemistry Warren Powell TRUSTEES OF PRINCETON...3. DATES COVERED (From - To) 01-07-2012 to 30-09-2015 4. TITLE AND SUBTITLE Optimal Learning for Efficient Experimentation in Nanotechnology and...in Nanotechnology and Biochemistry Principal Investigators: Warren B. Powell Princeton University Department of Operations Research and

Micro-Randomized Trials: An Experimental Design for Developing Just-in-Time Adaptive Interventions

PubMed Central

Klasnja, Predrag; Hekler, Eric B.; Shiffman, Saul; Boruvka, Audrey; Almirall, Daniel; Tewari, Ambuj; Murphy, Susan A.

2015-01-01

Objective This paper presents an experimental design, the micro-randomized trial, developed to support optimization of just-in-time adaptive interventions (JITAIs). JITAIs are mHealth technologies that aim to deliver the right intervention components at the right times and locations to optimally support individuals’ health behaviors. Micro-randomized trials offer a way to optimize such interventions by enabling modeling of causal effects and time-varying effect moderation for individual intervention components within a JITAI. Methods The paper describes the micro-randomized trial design, enumerates research questions that this experimental design can help answer, and provides an overview of the data analyses that can be used to assess the causal effects of studied intervention components and investigate time-varying moderation of those effects. Results Micro-randomized trials enable causal modeling of proximal effects of the randomized intervention components and assessment of time-varying moderation of those effects. Conclusions Micro-randomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions’ effects, enabling creation of more effective JITAIs. PMID:26651463

A framework for accelerated phototrophic bioprocess development: integration of parallelized microscale cultivation, laboratory automation and Kriging-assisted experimental design.

PubMed

Morschett, Holger; Freier, Lars; Rohde, Jannis; Wiechert, Wolfgang; von Lieres, Eric; Oldiges, Marco

2017-01-01

Even though microalgae-derived biodiesel has regained interest within the last decade, industrial production is still challenging for economic reasons. Besides reactor design, as well as value chain and strain engineering, laborious and slow early-stage parameter optimization represents a major drawback. The present study introduces a framework for the accelerated development of phototrophic bioprocesses. A state-of-the-art micro-photobioreactor supported by a liquid-handling robot for automated medium preparation and product quantification was used. To take full advantage of the technology's experimental capacity, Kriging-assisted experimental design was integrated to enable highly efficient execution of screening applications. The resulting platform was used for medium optimization of a lipid production process using Chlorella vulgaris toward maximum volumetric productivity. Within only four experimental rounds, lipid production was increased approximately threefold to 212 ± 11 mg L -1  d -1 . Besides nitrogen availability as a key parameter, magnesium, calcium and various trace elements were shown to be of crucial importance. Here, synergistic multi-parameter interactions as revealed by the experimental design introduced significant further optimization potential. The integration of parallelized microscale cultivation, laboratory automation and Kriging-assisted experimental design proved to be a fruitful tool for the accelerated development of phototrophic bioprocesses. By means of the proposed technology, the targeted optimization task was conducted in a very timely and material-efficient manner.

Wrinkle-free design of thin membrane structures using stress-based topology optimization

NASA Astrophysics Data System (ADS)

Luo, Yangjun; Xing, Jian; Niu, Yanzhuang; Li, Ming; Kang, Zhan

2017-05-01

Thin membrane structures would experience wrinkling due to local buckling deformation when compressive stresses are induced in some regions. Using the stress criterion for membranes in wrinkled and taut states, this paper proposed a new stress-based topology optimization methodology to seek the optimal wrinkle-free design of macro-scale thin membrane structures under stretching. Based on the continuum model and linearly elastic assumption in the taut state, the optimization problem is defined as to maximize the structural stiffness under membrane area and principal stress constraints. In order to make the problem computationally tractable, the stress constraints are reformulated into equivalent ones and relaxed by a cosine-type relaxation scheme. The reformulated optimization problem is solved by a standard gradient-based algorithm with the adjoint-variable sensitivity analysis. Several examples with post-bulking simulations and experimental tests are given to demonstrate the effectiveness of the proposed optimization model for eliminating stress-related wrinkles in the novel design of thin membrane structures.

Heat Sink Design and Optimization

DTIC Science & Technology

2015-12-01

HEAT SINK DESIGN AND OPTIMIZATION I...REPORT DATE (DD-MM-YYYY) December 2015 2. REPORT TYPE Final 3. DATES COVERED (From – To) 4. TITLE AND SUBTITLE HEAT SINK DESIGN AND OPTIMIZATION...distribution is unlimited. 13. SUPPLEMENTARY NOTES 14. ABSTRACT Heat sinks are devices that are used to enhance heat dissipation

Formulation, optimization and characterization of cationic polymeric nanoparticles of mast cell stabilizing agent using the Box-Behnken experimental design.

PubMed

Gajra, Balaram; Patel, Ravi R; Dalwadi, Chintan

2016-01-01

The present research work was intended to develop and optimize sustained release of biodegradable chitosan nanoparticles (CSNPs) as delivery vehicle for sodium cromoglicate (SCG) using the circumscribed Box-Behnken experimental design (BBD) and evaluate its potential for oral permeability enhancement. The 3-factor, 3-level BBD was employed to investigate the combined influence of formulation variables on particle size and entrapment efficiency (%EE) of SCG-CSNPs prepared by ionic gelation method. The generated polynomial equation was validated and desirability function was utilized for optimization. Optimized SCG-CSNPs were evaluated for physicochemical, morphological, in-vitro characterizations and permeability enhancement potential by ex-vivo and uptake study using CLSM. SCG-CSNPs exhibited particle size of 200.4 ± 4.06 nm and %EE of 62.68 ± 2.4% with unimodal size distribution having cationic, spherical, smooth surface. Physicochemical and in-vitro characterization revealed existence of SCG in amorphous form inside CSNPs without interaction and showed sustained release profile. Ex-vivo and uptake study showed the permeability enhancement potential of CSNPs. The developed SCG-CSNPs can be considered as promising delivery strategy with respect to improved permeability and sustained drug release, proving importance of CSNPs as potential oral delivery system for treatment of allergic rhinitis. Hence, further studies should be performed for establishing the pharmacokinetic potential of the CSNPs.

Design and optimization of membrane-type acoustic metamaterials

NASA Astrophysics Data System (ADS)

Blevins, Matthew Grant

One of the most common problems in noise control is the attenuation of low frequency noise. Typical solutions require barriers with high density and/or thickness. Membrane-type acoustic metamaterials are a novel type of engineered material capable of high low-frequency transmission loss despite their small thickness and light weight. These materials are ideally suited to applications with strict size and weight limitations such as aircraft, automobiles, and buildings. The transmission loss profile can be manipulated by changing the micro-level substructure, stacking multiple unit cells, or by creating multi-celled arrays. To date, analysis has focused primarily on experimental studies in plane-wave tubes and numerical modeling using finite element methods. These methods are inefficient when used for applications that require iterative changes to the structure of the material. To facilitate design and optimization of membrane-type acoustic metamaterials, computationally efficient dynamic models based on the impedance-mobility approach are proposed. Models of a single unit cell in a waveguide and in a baffle, a double layer of unit cells in a waveguide, and an array of unit cells in a baffle are studied. The accuracy of the models and the validity of assumptions used are verified using a finite element method. The remarkable computational efficiency of the impedance-mobility models compared to finite element methods enables implementation in design tools based on a graphical user interface and in optimization schemes. Genetic algorithms are used to optimize the unit cell design for a variety of noise reduction goals, including maximizing transmission loss for broadband, narrow-band, and tonal noise sources. The tools for design and optimization created in this work will enable rapid implementation of membrane-type acoustic metamaterials to solve real-world noise control problems.

Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain

NASA Astrophysics Data System (ADS)

Beck, Joakim; Dia, Ben Mansour; Espath, Luis F. R.; Long, Quan; Tempone, Raúl

2018-06-01

In calculating expected information gain in optimal Bayesian experimental design, the computation of the inner loop in the classical double-loop Monte Carlo requires a large number of samples and suffers from underflow if the number of samples is small. These drawbacks can be avoided by using an importance sampling approach. We present a computationally efficient method for optimal Bayesian experimental design that introduces importance sampling based on the Laplace method to the inner loop. We derive the optimal values for the method parameters in which the average computational cost is minimized according to the desired error tolerance. We use three numerical examples to demonstrate the computational efficiency of our method compared with the classical double-loop Monte Carlo, and a more recent single-loop Monte Carlo method that uses the Laplace method as an approximation of the return value of the inner loop. The first example is a scalar problem that is linear in the uncertain parameter. The second example is a nonlinear scalar problem. The third example deals with the optimal sensor placement for an electrical impedance tomography experiment to recover the fiber orientation in laminate composites.

Optimization of the fabrication of novel stealth PLA-based nanoparticles by dispersion polymerization using D-optimal mixture design

PubMed Central

Adesina, Simeon K.; Wight, Scott A.; Akala, Emmanuel O.

2015-01-01

Purpose Nanoparticle size is important in drug delivery. Clearance of nanoparticles by cells of the reticuloendothelial system has been reported to increase with increase in particle size. Further, nanoparticles should be small enough to avoid lung or spleen filtering effects. Endocytosis and accumulation in tumor tissue by the enhanced permeability and retention effect are also processes that are influenced by particle size. We present the results of studies designed to optimize crosslinked biodegradable stealth polymeric nanoparticles fabricated by dispersion polymerization. Methods Nanoparticles were fabricated using different amounts of macromonomer, initiators, crosslinking agent and stabilizer in a dioxane/DMSO/water solvent system. Confirmation of nanoparticle formation was by scanning electron microscopy (SEM). Particle size was measured by dynamic light scattering (DLS). D-optimal mixture statistical experimental design was used for the experimental runs, followed by model generation (Scheffe polynomial) and optimization with the aid of a computer software. Model verification was done by comparing particle size data of some suggested solutions to the predicted particle sizes. Results and Conclusion Data showed that average particle sizes follow the same trend as predicted by the model. Negative terms in the model corresponding to the crosslinking agent and stabilizer indicate the important factors for minimizing particle size. PMID:24059281

Optimization of the fabrication of novel stealth PLA-based nanoparticles by dispersion polymerization using D-optimal mixture design.

PubMed

Adesina, Simeon K; Wight, Scott A; Akala, Emmanuel O

2014-11-01

Nanoparticle size is important in drug delivery. Clearance of nanoparticles by cells of the reticuloendothelial system has been reported to increase with increase in particle size. Further, nanoparticles should be small enough to avoid lung or spleen filtering effects. Endocytosis and accumulation in tumor tissue by the enhanced permeability and retention effect are also processes that are influenced by particle size. We present the results of studies designed to optimize cross-linked biodegradable stealth polymeric nanoparticles fabricated by dispersion polymerization. Nanoparticles were fabricated using different amounts of macromonomer, initiators, crosslinking agent and stabilizer in a dioxane/DMSO/water solvent system. Confirmation of nanoparticle formation was by scanning electron microscopy (SEM). Particle size was measured by dynamic light scattering (DLS). D-optimal mixture statistical experimental design was used for the experimental runs, followed by model generation (Scheffe polynomial) and optimization with the aid of a computer software. Model verification was done by comparing particle size data of some suggested solutions to the predicted particle sizes. Data showed that average particle sizes follow the same trend as predicted by the model. Negative terms in the model corresponding to the cross-linking agent and stabilizer indicate the important factors for minimizing particle size.

Improving spacecraft design using a multidisciplinary design optimization methodology

NASA Astrophysics Data System (ADS)

Mosher, Todd Jon

2000-10-01

Spacecraft design has gone from maximizing performance under technology constraints to minimizing cost under performance constraints. This is characteristic of the "faster, better, cheaper" movement that has emerged within NASA. Currently spacecraft are "optimized" manually through a tool-assisted evaluation of a limited set of design alternatives. With this approach there is no guarantee that a systems-level focus will be taken and "feasibility" rather than "optimality" is commonly all that is achieved. To improve spacecraft design in the "faster, better, cheaper" era, a new approach using multidisciplinary design optimization (MDO) is proposed. Using MDO methods brings structure to conceptual spacecraft design by casting a spacecraft design problem into an optimization framework. Then, through the construction of a model that captures design and cost, this approach facilitates a quicker and more straightforward option synthesis. The final step is to automatically search the design space. As computer processor speed continues to increase, enumeration of all combinations, while not elegant, is one method that is straightforward to perform. As an alternative to enumeration, genetic algorithms are used and find solutions by reviewing fewer possible solutions with some limitations. Both methods increase the likelihood of finding an optimal design, or at least the most promising area of the design space. This spacecraft design methodology using MDO is demonstrated on three examples. A retrospective test for validation is performed using the Near Earth Asteroid Rendezvous (NEAR) spacecraft design. For the second example, the premise that aerobraking was needed to minimize mission cost and was mission enabling for the Mars Global Surveyor (MGS) mission is challenged. While one might expect no feasible design space for an MGS without aerobraking mission, a counterintuitive result is discovered. Several design options that don't use aerobraking are feasible and cost

Computational design optimization for microfluidic magnetophoresis

PubMed Central

Plouffe, Brian D.; Lewis, Laura H.; Murthy, Shashi K.

2011-01-01

Current macro- and microfluidic approaches for the isolation of mammalian cells are limited in both efficiency and purity. In order to design a robust platform for the enumeration of a target cell population, high collection efficiencies are required. Additionally, the ability to isolate pure populations with minimal biological perturbation and efficient off-chip recovery will enable subcellular analyses of these cells for applications in personalized medicine. Here, a rational design approach for a simple and efficient device that isolates target cell populations via magnetic tagging is presented. In this work, two magnetophoretic microfluidic device designs are described, with optimized dimensions and operating conditions determined from a force balance equation that considers two dominant and opposing driving forces exerted on a magnetic-particle-tagged cell, namely, magnetic and viscous drag. Quantitative design criteria for an electromagnetic field displacement-based approach are presented, wherein target cells labeled with commercial magnetic microparticles flowing in a central sample stream are shifted laterally into a collection stream. Furthermore, the final device design is constrained to fit on standard rectangular glass coverslip (60 (L)×24 (W)×0.15 (H) mm3) to accommodate small sample volume and point-of-care design considerations. The anticipated performance of the device is examined via a parametric analysis of several key variables within the model. It is observed that minimal currents (<500 mA) are required to generate magnetic fields sufficient to separate cells from the sample streams flowing at rate as high as 7 ml∕h, comparable to the performance of current state-of-the-art magnet-activated cell sorting systems currently used in clinical settings. Experimental validation of the presented model illustrates that a device designed according to the derived rational optimization can effectively isolate (∼100%) a magnetic-particle-tagged cell

Post-Optimality Analysis In Aerospace Vehicle Design

NASA Technical Reports Server (NTRS)

Braun, Robert D.; Kroo, Ilan M.; Gage, Peter J.

1993-01-01

This analysis pertains to the applicability of optimal sensitivity information to aerospace vehicle design. An optimal sensitivity (or post-optimality) analysis refers to computations performed once the initial optimization problem is solved. These computations may be used to characterize the design space about the present solution and infer changes in this solution as a result of constraint or parameter variations, without reoptimizing the entire system. The present analysis demonstrates that post-optimality information generated through first-order computations can be used to accurately predict the effect of constraint and parameter perturbations on the optimal solution. This assessment is based on the solution of an aircraft design problem in which the post-optimality estimates are shown to be within a few percent of the true solution over the practical range of constraint and parameter variations. Through solution of a reusable, single-stage-to-orbit, launch vehicle design problem, this optimal sensitivity information is also shown to improve the efficiency of the design process, For a hierarchically decomposed problem, this computational efficiency is realized by estimating the main-problem objective gradient through optimal sep&ivity calculations, By reducing the need for finite differentiation of a re-optimized subproblem, a significant decrease in the number of objective function evaluations required to reach the optimal solution is obtained.

Integrated multidisciplinary design optimization of rotorcraft

NASA Technical Reports Server (NTRS)

Adelman, Howard M.; Mantay, Wayne R.

1989-01-01

The NASA/Army research plan for developing the logic elements for helicopter rotor design optimization by integrating appropriate disciplines and accounting for important interactions among the disciplines is discussed. The paper describes the optimization formulation in terms of the objective function, design variables, and constraints. The analysis aspects are discussed, and an initial effort at defining the interdisciplinary coupling is summarized. Results are presented on the achievements made in the rotor aerodynamic performance optimization for minimum hover horsepower, rotor dynamic optimization for vibration reduction, rotor structural optimization for minimum weight, and integrated aerodynamic load/dynamics optimization for minimum vibration and weight.

Teaching experimental design.

PubMed

Fry, Derek J

2014-01-01

Awareness of poor design and published concerns over study quality stimulated the development of courses on experimental design intended to improve matters. This article describes some of the thinking behind these courses and how the topics can be presented in a variety of formats. The premises are that education in experimental design should be undertaken with an awareness of educational principles, of how adults learn, and of the particular topics in the subject that need emphasis. For those using laboratory animals, it should include ethical considerations, particularly severity issues, and accommodate learners not confident with mathematics. Basic principles, explanation of fully randomized, randomized block, and factorial designs, and discussion of how to size an experiment form the minimum set of topics. A problem-solving approach can help develop the skills of deciding what are correct experimental units and suitable controls in different experimental scenarios, identifying when an experiment has not been properly randomized or blinded, and selecting the most efficient design for particular experimental situations. Content, pace, and presentation should suit the audience and time available, and variety both within a presentation and in ways of interacting with those being taught is likely to be effective. Details are given of a three-day course based on these ideas, which has been rated informative, educational, and enjoyable, and can form a postgraduate module. It has oral presentations reinforced by group exercises and discussions based on realistic problems, and computer exercises which include some analysis. Other case studies consider a half-day format and a module for animal technicians. © The Author 2014. Published by Oxford University Press on behalf of the Institute for Laboratory Animal Research. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Graphical Models for Quasi-Experimental Designs

ERIC Educational Resources Information Center

Kim, Yongnam; Steiner, Peter M.; Hall, Courtney E.; Su, Dan

2016-01-01

Experimental and quasi-experimental designs play a central role in estimating cause-effect relationships in education, psychology, and many other fields of the social and behavioral sciences. This paper presents and discusses the causal graphs of experimental and quasi-experimental designs. For quasi-experimental designs the authors demonstrate…

Optimization of ultrasound-assisted dispersive solid-phase microextraction based on nanoparticles followed by spectrophotometry for the simultaneous determination of dyes using experimental design.

PubMed

Asfaram, Arash; Ghaedi, Mehrorang; Goudarzi, Alireza

2016-09-01

A simple, low cost and ultrasensitive method for the simultaneous preconcentration and determination of trace amount of auramine-O and malachite green in aqueous media following accumulation on novel and lower toxicity nanomaterials by ultrasound-assisted dispersive solid phase micro-extraction (UA-DSPME) procedure combined with spectrophotometric has been described. The Mn doped ZnS nanoparticles loaded on activated carbon were characterized by Field emission scanning electron microscopy (FE-SEM), particle size distribution, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR) analyses and subsequently were used as green and efficient material for dyes accumulation. Contribution of experimental variables such as ultrasonic time, ultrasonic temperature, adsorbent mass, vortex time, ionic strength, pH and elution volume were optimized through experimental design, and while the preconcentrated analytes were efficiently eluted by acetone. Preliminary Plackett-Burman design was applied for selection of most significant factors and giving useful information about their main and interaction part of significant variables like ultrasonic time, adsorbent mass, elution volume and pH were obtained by central composite design combined with response surface analysis and optimum experimental conditions was set at pH of 8.0, 1.2mg of adsorbent, 150μL eluent and 3.7min sonication. Under optimized conditions, the average recoveries (five replicates) for two dyes (spiked at 500.0ngmL(-1)) changes in the range of 92.80-97.70% with acceptable RSD% less than 4.0% over a linear range of 3.0-5000.0ngmL(-1) for the AO and MG in water samples with regression coefficients (R(2)) of 0.9975 and 0.9977, respectively. Acceptable limits of detection of 0.91 and 0.61ngmL(-1) for AO and MG, respectively and high accuracy and repeatability are unique advantages of present method to improve the figures of merit for their accurate determination at trace level in complicated

Optimization methods applied to hybrid vehicle design

NASA Technical Reports Server (NTRS)

Donoghue, J. F.; Burghart, J. H.

1983-01-01

The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.

Characterization and Optimization Design of the Polymer-Based Capacitive Micro-Arrayed Ultrasonic Transducer

NASA Astrophysics Data System (ADS)

Chiou, De-Yi; Chen, Mu-Yueh; Chang, Ming-Wei; Deng, Hsu-Cheng

2007-11-01

This study constructs an electromechanical finite element model of the polymer-based capacitive micro-arrayed ultrasonic transducer (P-CMUT). The electrostatic-structural coupled-field simulations are performed to investigate the operational characteristics, such as collapse voltage and resonant frequency. The numerical results are found to be in good agreement with experimental observations. The study of influence of each defined parameter on the collapse voltage and resonant frequency are also presented. To solve some conflict problems in diversely physical fields, an integrated design method is developed to optimize the geometric parameters of the P-CMUT. The optimization search routine conducted using the genetic algorithm (GA) is connected with the commercial FEM software ANSYS to obtain the best design variable using multi-objective functions. The results show that the optimal parameter values satisfy the conflicting objectives, namely to minimize the collapse voltage while simultaneously maintaining a customized frequency. Overall, the present result indicates that the combined FEM/GA optimization scheme provides an efficient and versatile approach of optimization design of the P-CMUT.

Optimal pupil design for confocal microscopy

NASA Astrophysics Data System (ADS)

Patel, Yogesh G.; Rajadhyaksha, Milind; DiMarzio, Charles A.

2010-02-01

Confocal reflectance microscopy may enable screening and diagnosis of skin cancers noninvasively and in real-time, as an adjunct to biopsy and pathology. Current instruments are large, complex, and expensive. A simpler, confocal line-scanning microscope may accelerate the translation of confocal microscopy in clinical and surgical dermatology. A confocal reflectance microscope may use a beamsplitter, transmitting and detecting through the pupil, or a divided pupil, or theta configuration, with half used for transmission and half for detection. The divided pupil may offer better sectioning and contrast. We present a Fourier optics model and compare the on-axis irradiance of a confocal point-scanning microscope in both pupil configurations, optimizing the profile of a Gaussian beam in a circular or semicircular aperture. We repeat both calculations with a cylindrical lens which focuses the source to a line. The variable parameter is the fillfactor, h, the ratio of the 1/e2 diameter of the Gaussian beam to the diameter of the full aperture. The optimal values of h, for point scanning are 0.90 (full) and 0.66 for the half-aperture. For line-scanning, the fill-factors are 1.02 (full) and 0.52 (half). Additional parameters to consider are the optimal location of the point-source beam in the divided-pupil configuration, the optimal line width for the line-source, and the width of the aperture in the divided-pupil configuration. Additional figures of merit are field-of-view and sectioning. Use of optimal designs is critical in comparing the experimental performance of the different configurations.

Optimized structural designs for stretchable silicon integrated circuits.

PubMed

Kim, Dae-Hyeong; Liu, Zhuangjian; Kim, Yun-Soung; Wu, Jian; Song, Jizhou; Kim, Hoon-Sik; Huang, Yonggang; Hwang, Keh-Chih; Zhang, Yongwei; Rogers, John A

2009-12-01

Materials and design strategies for stretchable silicon integrated circuits that use non-coplanar mesh layouts and elastomeric substrates are presented. Detailed experimental and theoretical studies reveal many of the key underlying aspects of these systems. The results shpw, as an example, optimized mechanics and materials for circuits that exhibit maximum principal strains less than 0.2% even for applied strains of up to approximately 90%. Simple circuits, including complementary metal-oxide-semiconductor inverters and n-type metal-oxide-semiconductor differential amplifiers, validate these designs. The results suggest practical routes to high-performance electronics with linear elastic responses to large strain deformations, suitable for diverse applications that are not readily addressed with conventional wafer-based technologies.

Design optimization of space structures

NASA Technical Reports Server (NTRS)

Felippa, Carlos

1991-01-01

The topology-shape-size optimization of space structures is investigated through Kikuchi's homogenization method. The method starts from a 'design domain block,' which is a region of space into which the structure is to materialize. This domain is initially filled with a finite element mesh, typically regular. Force and displacement boundary conditions corresponding to applied loads and supports are applied at specific points in the domain. An optimal structure is to be 'carved out' of the design under two conditions: (1) a cost function is to be minimized, and (2) equality or inequality constraints are to be satisfied. The 'carving' process is accomplished by letting microstructure holes develop and grow in elements during the optimization process. These holes have a rectangular shape in two dimensions and a cubical shape in three dimensions, and may also rotate with respect to the reference axes. The properties of the perforated element are obtained through an homogenization procedure. Once a hole reaches the volume of the element, that element effectively disappears. The project has two phases. In the first phase the method was implemented as the combination of two computer programs: a finite element module, and an optimization driver. In the second part, focus is on the application of this technique to planetary structures. The finite element part of the method was programmed for the two-dimensional case using four-node quadrilateral elements to cover the design domain. An element homogenization technique different from that of Kikuchi and coworkers was implemented. The optimization driver is based on an augmented Lagrangian optimizer, with the volume constraint treated as a Courant penalty function. The optimizer has to be especially tuned to this type of optimization because the number of design variables can reach into the thousands. The driver is presently under development.

Bioresorbable scaffolds for bone tissue engineering: optimal design, fabrication, mechanical testing and scale-size effects analysis.

PubMed

Coelho, Pedro G; Hollister, Scott J; Flanagan, Colleen L; Fernandes, Paulo R

2015-03-01

Bone scaffolds for tissue regeneration require an optimal trade-off between biological and mechanical criteria. Optimal designs may be obtained using topology optimization (homogenization approach) and prototypes produced using additive manufacturing techniques. However, the process from design to manufacture remains a research challenge and will be a requirement of FDA design controls to engineering scaffolds. This work investigates how the design to manufacture chain affects the reproducibility of complex optimized design characteristics in the manufactured product. The design and prototypes are analyzed taking into account the computational assumptions and the final mechanical properties determined through mechanical tests. The scaffold is an assembly of unit-cells, and thus scale size effects on the mechanical response considering finite periodicity are investigated and compared with the predictions from the homogenization method which assumes in the limit infinitely repeated unit cells. Results show that a limited number of unit-cells (3-5 repeated on a side) introduce some scale-effects but the discrepancies are below 10%. Higher discrepancies are found when comparing the experimental data to numerical simulations due to differences between the manufactured and designed scaffold feature shapes and sizes as well as micro-porosities introduced by the manufacturing process. However good regression correlations (R(2) > 0.85) were found between numerical and experimental values, with slopes close to 1 for 2 out of 3 designs. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

The Experimental Design Assistant.

PubMed

Percie du Sert, Nathalie; Bamsey, Ian; Bate, Simon T; Berdoy, Manuel; Clark, Robin A; Cuthill, Innes; Fry, Derek; Karp, Natasha A; Macleod, Malcolm; Moon, Lawrence; Stanford, S Clare; Lings, Brian

2017-09-01

Addressing the common problems that researchers encounter when designing and analysing animal experiments will improve the reliability of in vivo research. In this article, the Experimental Design Assistant (EDA) is introduced. The EDA is a web-based tool that guides the in vivo researcher through the experimental design and analysis process, providing automated feedback on the proposed design and generating a graphical summary that aids communication with colleagues, funders, regulatory authorities, and the wider scientific community. It will have an important role in addressing causes of irreproducibility.

Design optimization for active twist rotor blades

NASA Astrophysics Data System (ADS)

Mok, Ji Won

This dissertation introduces the process of optimizing active twist rotor blades in the presence of embedded anisotropic piezo-composite actuators. Optimum design of active twist blades is a complex task, since it involves a rich design space with tightly coupled design variables. The study presents the development of an optimization framework for active helicopter rotor blade cross-sectional design. This optimization framework allows for exploring a rich and highly nonlinear design space in order to optimize the active twist rotor blades. Different analytical components are combined in the framework: cross-sectional analysis (UM/VABS), an automated mesh generator, a beam solver (DYMORE), a three-dimensional local strain recovery module, and a gradient based optimizer within MATLAB. Through the mathematical optimization problem, the static twist actuation performance of a blade is maximized while satisfying a series of blade constraints. These constraints are associated with locations of the center of gravity and elastic axis, blade mass per unit span, fundamental rotating blade frequencies, and the blade strength based on local three-dimensional strain fields under worst loading conditions. Through pre-processing, limitations of the proposed process have been studied. When limitations were detected, resolution strategies were proposed. These include mesh overlapping, element distortion, trailing edge tab modeling, electrode modeling and foam implementation of the mesh generator, and the initial point sensibility of the current optimization scheme. Examples demonstrate the effectiveness of this process. Optimization studies were performed on the NASA/Army/MIT ATR blade case. Even though that design was built and shown significant impact in vibration reduction, the proposed optimization process showed that the design could be improved significantly. The second example, based on a model scale of the AH-64D Apache blade, emphasized the capability of this framework to

Numerical and experimental analysis of a ducted propeller designed by a fully automated optimization process under open water condition

NASA Astrophysics Data System (ADS)

Yu, Long; Druckenbrod, Markus; Greve, Martin; Wang, Ke-qi; Abdel-Maksoud, Moustafa

2015-10-01

A fully automated optimization process is provided for the design of ducted propellers under open water conditions, including 3D geometry modeling, meshing, optimization algorithm and CFD analysis techniques. The developed process allows the direct integration of a RANSE solver in the design stage. A practical ducted propeller design case study is carried out for validation. Numerical simulations and open water tests are fulfilled and proved that the optimum ducted propeller improves hydrodynamic performance as predicted.

Designing electronic properties of two-dimensional crystals through optimization of deformations

NASA Astrophysics Data System (ADS)

Jones, Gareth W.; Pereira, Vitor M.

2014-09-01

One of the enticing features common to most of the two-dimensional (2D) electronic systems that, in the wake of (and in parallel with) graphene, are currently at the forefront of materials science research is the ability to easily introduce a combination of planar deformations and bending in the system. Since the electronic properties are ultimately determined by the details of atomic orbital overlap, such mechanical manipulations translate into modified (or, at least, perturbed) electronic properties. Here, we present a general-purpose optimization framework for tailoring physical properties of 2D electronic systems by manipulating the state of local strain, allowing a one-step route from their design to experimental implementation. A definite example, chosen for its relevance in light of current experiments in graphene nanostructures, is the optimization of the experimental parameters that generate a prescribed spatial profile of pseudomagnetic fields (PMFs) in graphene. But the method is general enough to accommodate a multitude of possible experimental parameters and conditions whereby deformations can be imparted to the graphene lattice, and complies, by design, with graphene's elastic equilibrium and elastic compatibility constraints. As a result, it efficiently answers the inverse problem of determining the optimal values of a set of external or control parameters (such as substrate topography, sample shape, load distribution, etc) that result in a graphene deformation whose associated PMF profile best matches a prescribed target. The ability to address this inverse problem in an expedited way is one key step for practical implementations of the concept of 2D systems with electronic properties strain-engineered to order. The general-purpose nature of this calculation strategy means that it can be easily applied to the optimization of other relevant physical quantities which directly depend on the local strain field, not just in graphene but in other 2D

D-OPTIMAL EXPERIMENTAL DESIGNS TO TEST FOR DEPARTURE FROM ADDITIVITY IN A FIXED-RATIO MIXTURE RAY.

EPA Science Inventory

Humans are exposed to mixtures of environmental compounds. A regulatory assumption is that the mixtures of chemicals act in an additive manner. However, this assumption requires experimental validation. Traditional experimental designs (full factorial) require a large number of e...

Globally optimal trial design for local decision making.

PubMed

Eckermann, Simon; Willan, Andrew R

2009-02-01

Value of information methods allows decision makers to identify efficient trial design following a principle of maximizing the expected value to decision makers of information from potential trial designs relative to their expected cost. However, in health technology assessment (HTA) the restrictive assumption has been made that, prospectively, there is only expected value of sample information from research commissioned within jurisdiction. This paper extends the framework for optimal trial design and decision making within jurisdiction to allow for optimal trial design across jurisdictions. This is illustrated in identifying an optimal trial design for decision making across the US, the UK and Australia for early versus late external cephalic version for pregnant women presenting in the breech position. The expected net gain from locally optimal trial designs of US$0.72M is shown to increase to US$1.14M with a globally optimal trial design. In general, the proposed method of globally optimal trial design improves on optimal trial design within jurisdictions by: (i) reflecting the global value of non-rival information; (ii) allowing optimal allocation of trial sample across jurisdictions; (iii) avoiding market failure associated with free-rider effects, sub-optimal spreading of fixed costs and heterogeneity of trial information with multiple trials. Copyright (c) 2008 John Wiley & Sons, Ltd.

The Experimental Design Assistant

PubMed Central

Bamsey, Ian; Bate, Simon T.; Berdoy, Manuel; Clark, Robin A.; Cuthill, Innes; Fry, Derek; Karp, Natasha A.; Macleod, Malcolm; Moon, Lawrence; Stanford, S. Clare; Lings, Brian

2017-01-01

Addressing the common problems that researchers encounter when designing and analysing animal experiments will improve the reliability of in vivo research. In this article, the Experimental Design Assistant (EDA) is introduced. The EDA is a web-based tool that guides the in vivo researcher through the experimental design and analysis process, providing automated feedback on the proposed design and generating a graphical summary that aids communication with colleagues, funders, regulatory authorities, and the wider scientific community. It will have an important role in addressing causes of irreproducibility. PMID:28957312

Optimization of sample preparation variables for wedelolactone from Eclipta alba using Box-Behnken experimental design followed by HPLC identification.

PubMed

Patil, A A; Sachin, B S; Shinde, D B; Wakte, P S

2013-07-01

Coumestan wedelolactone is an important phytocomponent from Eclipta alba (L.) Hassk. It possesses diverse pharmacological activities, which have prompted the development of various extraction techniques and strategies for its better utilization. The aim of the present study is to develop and optimize supercritical carbon dioxide assisted sample preparation and HPLC identification of wedelolactone from E. alba (L.) Hassk. The response surface methodology was employed to study the optimization of sample preparation using supercritical carbon dioxide for wedelolactone from E. alba (L.) Hassk. The optimized sample preparation involves the investigation of quantitative effects of sample preparation parameters viz. operating pressure, temperature, modifier concentration and time on yield of wedelolactone using Box-Behnken design. The wedelolactone content was determined using validated HPLC methodology. The experimental data were fitted to second-order polynomial equation using multiple regression analysis and analyzed using the appropriate statistical method. By solving the regression equation and analyzing 3D plots, the optimum extraction conditions were found to be: extraction pressure, 25 MPa; temperature, 56 °C; modifier concentration, 9.44% and extraction time, 60 min. Optimum extraction conditions demonstrated wedelolactone yield of 15.37 ± 0.63 mg/100 g E. alba (L.) Hassk, which was in good agreement with the predicted values. Temperature and modifier concentration showed significant effect on the wedelolactone yield. The supercritical carbon dioxide extraction showed higher selectivity than the conventional Soxhlet assisted extraction method. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Rigorous ILT optimization for advanced patterning and design-process co-optimization

NASA Astrophysics Data System (ADS)

Selinidis, Kosta; Kuechler, Bernd; Cai, Howard; Braam, Kyle; Hoppe, Wolfgang; Domnenko, Vitaly; Poonawala, Amyn; Xiao, Guangming

2018-03-01

Despite the large difficulties involved in extending 193i multiple patterning and the slow ramp of EUV lithography to full manufacturing readiness, the pace of development for new technology node variations has been accelerating. Multiple new variations of new and existing technology nodes have been introduced for a range of device applications; each variation with at least a few new process integration methods, layout constructs and/or design rules. This had led to a strong increase in the demand for predictive technology tools which can be used to quickly guide important patterning and design co-optimization decisions. In this paper, we introduce a novel hybrid predictive patterning method combining two patterning technologies which have each individually been widely used for process tuning, mask correction and process-design cooptimization. These technologies are rigorous lithography simulation and inverse lithography technology (ILT). Rigorous lithography simulation has been extensively used for process development/tuning, lithography tool user setup, photoresist hot-spot detection, photoresist-etch interaction analysis, lithography-TCAD interactions/sensitivities, source optimization and basic lithography design rule exploration. ILT has been extensively used in a range of lithographic areas including logic hot-spot fixing, memory layout correction, dense memory cell optimization, assist feature (AF) optimization, source optimization, complex patterning design rules and design-technology co-optimization (DTCO). The combined optimization capability of these two technologies will therefore have a wide range of useful applications. We investigate the benefits of the new functionality for a few of these advanced applications including correction for photoresist top loss and resist scumming hotspots.

Experimental design for the optimization of the derivatization reaction in determining chlorophenols and chloroanisoles by headspace-solid-phase microextraction-gas chromatography/mass spectrometry.

PubMed

Morales, Rocío; Sarabia, Luis A; Sánchez, M Sagrario; Ortiz, M Cruz

2013-06-28

The paper shows some tools (its interpretation and usefulness) to optimize a derivatization reaction and to more easily interpret and visualize the effect that some experimental factors exert on several analytical responses of interest when these responses are in conflict. The entire proposed procedure has been applied in the optimization of equilibrium/extraction temperature and extraction time in the acetylation reaction of 2,4,6-trichlorophenol; 2,3,4,6-tetrachlorophenol, pentachlorophenol and 2,4,6-tribromophenol as internal standard (IS) in presence of 2,4,6-trichloroanisole, 2,3,5,6-tetrachloroanisole, pentachloroanisole and 2,4,6-trichloroanisole-d5 as IS. The procedure relies on the second order advantage of PARAFAC (parallel factor analysis) that allows the unequivocal identification and quantification, mandatory according international regulations (in this paper the EU document SANCO/12495/2011), of the acetyl-chlorophenols and chloroanisoles that are determined by means of a HS-SPME-GC/MS automated device. The joint use of a PARAFAC decomposition and a Doehlert design provides the data to fit a response surface for each analyte. With the fitted surfaces, the overall desirability function and the Pareto-optimal front are used to describe the relation between the conditions of the derivatization reaction and the quantity extracted of each analyte. The visualization by using a parallel coordinates plot allows a deeper knowledge about the problem at hand as well as the wise selection of the conditions of the experimental factors for achieving specific goals about the responses. In the optimal experimental conditions (45°C and 25min) the determination by means of an automated HS-SPME-GC/MS system is carried out. By using the regression line fitted between calculated and true concentrations, it has been checked that the procedure has neither proportional nor constant bias. The decision limits, CCa, for probability a of false positive set to 0.05, vary between

Design Oriented Structural Modeling for Airplane Conceptual Design Optimization

NASA Technical Reports Server (NTRS)

Livne, Eli

1999-01-01

The main goal for research conducted with the support of this grant was to develop design oriented structural optimization methods for the conceptual design of airplanes. Traditionally in conceptual design airframe weight is estimated based on statistical equations developed over years of fitting airplane weight data in data bases of similar existing air- planes. Utilization of such regression equations for the design of new airplanes can be justified only if the new air-planes use structural technology similar to the technology on the airplanes in those weight data bases. If any new structural technology is to be pursued or any new unconventional configurations designed the statistical weight equations cannot be used. In such cases any structural weight estimation must be based on rigorous "physics based" structural analysis and optimization of the airframes under consideration. Work under this grant progressed to explore airframe design-oriented structural optimization techniques along two lines of research: methods based on "fast" design oriented finite element technology and methods based on equivalent plate / equivalent shell models of airframes, in which the vehicle is modelled as an assembly of plate and shell components, each simulating a lifting surface or nacelle / fuselage pieces. Since response to changes in geometry are essential in conceptual design of airplanes, as well as the capability to optimize the shape itself, research supported by this grant sought to develop efficient techniques for parametrization of airplane shape and sensitivity analysis with respect to shape design variables. Towards the end of the grant period a prototype automated structural analysis code designed to work with the NASA Aircraft Synthesis conceptual design code ACS= was delivered to NASA Ames.

Optimization of fruit punch using mixture design.

PubMed

Kumar, S Bharath; Ravi, R; Saraswathi, G

2010-01-01

A highly acceptable dehydrated fruit punch was developed with selected fruits, namely lemon, orange, and mango, using a mixture design and optimization technique. The fruit juices were freeze dried, powdered, and used in the reconstitution studies. Fruit punches were prepared according to the experimental design combinations (total 10) based on a mixture design and then subjected to sensory evaluation for acceptability. Response surfaces of sensory attributes were also generated as a function of fruit juices. Analysis of data revealed that the fruit punch prepared using 66% of mango, 33% of orange, and 1% of lemon had highly desirable sensory scores for color (6.00), body (5.92), sweetness (5.68), and pleasantness (5.94). The aroma pattern of individual as well as combinations of fruit juices were also analyzed by electronic nose. The electronic nose could discriminate the aroma patterns of individual as well as fruit juice combinations by mixture design. The results provide information on the sensory quality of best fruit punch formulations liked by the consumer panel based on lemon, orange, and mango.

Multidisciplinary Optimization Methods for Aircraft Preliminary Design

NASA Technical Reports Server (NTRS)

Kroo, Ilan; Altus, Steve; Braun, Robert; Gage, Peter; Sobieski, Ian

1994-01-01

This paper describes a research program aimed at improved methods for multidisciplinary design and optimization of large-scale aeronautical systems. The research involves new approaches to system decomposition, interdisciplinary communication, and methods of exploiting coarse-grained parallelism for analysis and optimization. A new architecture, that involves a tight coupling between optimization and analysis, is intended to improve efficiency while simplifying the structure of multidisciplinary, computation-intensive design problems involving many analysis disciplines and perhaps hundreds of design variables. Work in two areas is described here: system decomposition using compatibility constraints to simplify the analysis structure and take advantage of coarse-grained parallelism; and collaborative optimization, a decomposition of the optimization process to permit parallel design and to simplify interdisciplinary communication requirements.

Integrated topology and shape optimization in structural design

NASA Technical Reports Server (NTRS)

Bremicker, M.; Chirehdast, M.; Kikuchi, N.; Papalambros, P. Y.

1990-01-01

Structural optimization procedures usually start from a given design topology and vary its proportions or boundary shapes to achieve optimality under various constraints. Two different categories of structural optimization are distinguished in the literature, namely sizing and shape optimization. A major restriction in both cases is that the design topology is considered fixed and given. Questions concerning the general layout of a design (such as whether a truss or a solid structure should be used) as well as more detailed topology features (e.g., the number and connectivities of bars in a truss or the number of holes in a solid) have to be resolved by design experience before formulating the structural optimization model. Design quality of an optimized structure still depends strongly on engineering intuition. This article presents a novel approach for initiating formal structural optimization at an earlier stage, where the design topology is rigorously generated in addition to selecting shape and size dimensions. A three-phase design process is discussed: an optimal initial topology is created by a homogenization method as a gray level image, which is then transformed to a realizable design using computer vision techniques; this design is then parameterized and treated in detail by sizing and shape optimization. A fully automated process is described for trusses. Optimization of two dimensional solid structures is also discussed. Several application-oriented examples illustrate the usefulness of the proposed methodology.

Computer-aided design and experimental investigation of a hydrodynamic device: the microwire electrode

PubMed

Fulian; Gooch; Fisher; Stevens; Compton

2000-08-01

The development and application of a new electrochemical device using a computer-aided design strategy is reported. This novel design is based on the flow of electrolyte solution past a microwire electrode situated centrally within a large duct. In the design stage, finite element simulations were employed to evaluate feasible working geometries and mass transport rates. The computer-optimized designs were then exploited to construct experimental devices. Steady-state voltammetric measurements were performed for a reversible one-electron-transfer reaction to establish the experimental relationship between electrolysis current and solution velocity. The experimental results are compared to those predicted numerically, and good agreement is found. The numerical studies are also used to establish an empirical relationship between the mass transport limited current and the volume flow rate, providing a simple and quantitative alternative for workers who would prefer to exploit this device without the need to develop the numerical aspects.

Review of design optimization methods for turbomachinery aerodynamics

NASA Astrophysics Data System (ADS)

Li, Zhihui; Zheng, Xinqian

2017-08-01

In today's competitive environment, new turbomachinery designs need to be not only more efficient, quieter, and ;greener; but also need to be developed at on much shorter time scales and at lower costs. A number of advanced optimization strategies have been developed to achieve these requirements. This paper reviews recent progress in turbomachinery design optimization to solve real-world aerodynamic problems, especially for compressors and turbines. This review covers the following topics that are important for optimizing turbomachinery designs. (1) optimization methods, (2) stochastic optimization combined with blade parameterization methods and the design of experiment methods, (3) gradient-based optimization methods for compressors and turbines and (4) data mining techniques for Pareto Fronts. We also present our own insights regarding the current research trends and the future optimization of turbomachinery designs.

Design, Modeling and Performance Optimization of a Novel Rotary Piezoelectric Motor

NASA Technical Reports Server (NTRS)

Duong, Khanh A.; Garcia, Ephrahim

1997-01-01

This work has demonstrated a proof of concept for a torsional inchworm type motor. The prototype motor has shown that piezoelectric stack actuators can be used for rotary inchworm motor. The discrete linear motion of piezoelectric stacks can be converted into rotary stepping motion. The stacks with its high force and displacement output are suitable actuators for use in piezoelectric motor. The designed motor is capable of delivering high torque and speed. Critical issues involving the design and operation of piezoelectric motors were studied. The tolerance between the contact shoes and the rotor has proved to be very critical to the performance of the motor. Based on the prototype motor, a waveform optimization scheme was proposed and implemented to improve the performance of the motor. The motor was successfully modeled in MATLAB. The model closely represents the behavior of the prototype motor. Using the motor model, the input waveforms were successfully optimized to improve the performance of the motor in term of speed, torque, power and precision. These optimized waveforms drastically improve the speed of the motor at different frequencies and loading conditions experimentally. The optimized waveforms also increase the level of precision of the motor. The use of the optimized waveform is a break-away from the traditional use of sinusoidal and square waves as the driving signals. This waveform optimization scheme can be applied to any inchworm motors to improve their performance. The prototype motor in this dissertation as a proof of concept was designed to be robust and large. Future motor can be designed much smaller and more efficient with lessons learned from the prototype motor.

Optimization of headspace solid-phase microextraction by means of an experimental design for the determination of methyl tert.-butyl ether in water by gas chromatography-flame ionization detection.

PubMed

Dron, Julien; Garcia, Rosa; Millán, Esmeralda

2002-07-19

A procedure for determination of methyl tert.-butyl ether (MTBE) in water by headspace solid-phase microextraction (HS-SPME) has been developed. The analysis was carried out by gas chromatography with flame ionization detection. The extraction procedure, using a 65-microm poly(dimethylsiloxane)-divinylbenzene SPME fiber, was optimized following experimental design. A fractional factorial design for screening and a central composite design for optimizing the significant variables were applied. Extraction temperature and sodium chloride concentration were significant variables, and 20 degrees C and 300 g/l were, respectively chosen for the best extraction response. With these conditions, an extraction time of 5 min was sufficient to extract MTBE. The calibration linear range for MTBE was 5-500 microg/l and the detection limit 0.45 microg/l. The relative standard deviation, for seven replicates of 250 microg/l MTBE in water, was 6.3%.

Ultrafiltration membrane reactors for enzymatic resolution of amino acids: design model and optimization.

PubMed

Bódalo, A; Gómez, J L.; Gómez, E; Bastida, J; Máximo, M F.; Montiel, M C.

2001-03-08

In this paper the possibility of continuous resolution of DL-phenylalanine, catalyzed by L-aminoacylase in a ultrafiltration membrane reactor (UFMR) is presented. A simple design model, based on previous kinetic studies, has been demonstrated to be capable of describing the behavior of the experimental system. The model has been used to determine the optimal experimental conditions to carry out the asymmetrical hydrolysis of N-acetyl-DL-phenylalanine.

Optimal control design of turbo spin‐echo sequences with applications to parallel‐transmit systems

PubMed Central

Hoogduin, Hans; Hajnal, Joseph V.; van den Berg, Cornelis A. T.; Luijten, Peter R.; Malik, Shaihan J.

2016-01-01

Purpose The design of turbo spin‐echo sequences is modeled as a dynamic optimization problem which includes the case of inhomogeneous transmit radiofrequency fields. This problem is efficiently solved by optimal control techniques making it possible to design patient‐specific sequences online. Theory and Methods The extended phase graph formalism is employed to model the signal evolution. The design problem is cast as an optimal control problem and an efficient numerical procedure for its solution is given. The numerical and experimental tests address standard multiecho sequences and pTx configurations. Results Standard, analytically derived flip angle trains are recovered by the numerical optimal control approach. New sequences are designed where constraints on radiofrequency total and peak power are included. In the case of parallel transmit application, the method is able to calculate the optimal echo train for two‐dimensional and three‐dimensional turbo spin echo sequences in the order of 10 s with a single central processing unit (CPU) implementation. The image contrast is maintained through the whole field of view despite inhomogeneities of the radiofrequency fields. Conclusion The optimal control design sheds new light on the sequence design process and makes it possible to design sequences in an online, patient‐specific fashion. Magn Reson Med 77:361–373, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine PMID:26800383

D-OPTIMAL EXPERIMENTAL DESIGNS TO TEST FOR DEPARTURE FROM ADDITIVITY IN A FIXED-RATIO MIXTURE RAY.

EPA Science Inventory

Traditional factorial designs for evaluating interactions among chemicals in a mixture are prohibitive when the number of chemicals is large. However, recent advances in statistically-based experimental design have made it easier to evaluate interactions involving many chemicals...

A Matrix-Free Algorithm for Multidisciplinary Design Optimization

NASA Astrophysics Data System (ADS)

Lambe, Andrew Borean

Multidisciplinary design optimization (MDO) is an approach to engineering design that exploits the coupling between components or knowledge disciplines in a complex system to improve the final product. In aircraft design, MDO methods can be used to simultaneously design the outer shape of the aircraft and the internal structure, taking into account the complex interaction between the aerodynamic forces and the structural flexibility. Efficient strategies are needed to solve such design optimization problems and guarantee convergence to an optimal design. This work begins with a comprehensive review of MDO problem formulations and solution algorithms. First, a fundamental MDO problem formulation is defined from which other formulations may be obtained through simple transformations. Using these fundamental problem formulations, decomposition methods from the literature are reviewed and classified. All MDO methods are presented in a unified mathematical notation to facilitate greater understanding. In addition, a novel set of diagrams, called extended design structure matrices, are used to simultaneously visualize both data communication and process flow between the many software components of each method. For aerostructural design optimization, modern decomposition-based MDO methods cannot efficiently handle the tight coupling between the aerodynamic and structural states. This fact motivates the exploration of methods that can reduce the computational cost. A particular structure in the direct and adjoint methods for gradient computation motivates the idea of a matrix-free optimization method. A simple matrix-free optimizer is developed based on the augmented Lagrangian algorithm. This new matrix-free optimizer is tested on two structural optimization problems and one aerostructural optimization problem. The results indicate that the matrix-free optimizer is able to efficiently solve structural and multidisciplinary design problems with thousands of variables and

A Matrix-Free Algorithm for Multidisciplinary Design Optimization

NASA Astrophysics Data System (ADS)

Lambe, Andrew Borean

Multidisciplinary design optimization (MDO) is an approach to engineering design that exploits the coupling between components or knowledge disciplines in a complex system to improve the final product. In aircraft design, MDO methods can be used to simultaneously design the outer shape of the aircraft and the internal structure, taking into account the complex interaction between the aerodynamic forces and the structural flexibility. Efficient strategies are needed to solve such design optimization problems and guarantee convergence to an optimal design. This work begins with a comprehensive review of MDO problem formulations and solution algorithms. First, a fundamental MDO problem formulation is defined from which other formulations may be obtained through simple transformations. Using these fundamental problem formulations, decomposition methods from the literature are reviewed and classified. All MDO methods are presented in a unified mathematical notation to facilitate greater understanding. In addition, a novel set of diagrams, called extended design structure matrices, are used to simultaneously visualize both data communication and process flow between the many software components of each method. For aerostructural design optimization, modern decomposition-based MDO methods cannot efficiently handle the tight coupling between the aerodynamic and structural states. This fact motivates the exploration of methods that can reduce the computational cost. A particular structure in the direct and adjoint methods for gradient computation. motivates the idea of a matrix-free optimization method. A simple matrix-free optimizer is developed based on the augmented Lagrangian algorithm. This new matrix-free optimizer is tested on two structural optimization problems and one aerostructural optimization problem. The results indicate that the matrix-free optimizer is able to efficiently solve structural and multidisciplinary design problems with thousands of variables and

Application of Taguchi L32 orthogonal array design to optimize copper biosorption by using Spaghnum moss.

PubMed

Ozdemir, Utkan; Ozbay, Bilge; Ozbay, Ismail; Veli, Sevil

2014-09-01

In this work, Taguchi L32 experimental design was applied to optimize biosorption of Cu(2+) ions by an easily available biosorbent, Spaghnum moss. With this aim, batch biosorption tests were performed to achieve targeted experimental design with five factors (concentration, pH, biosorbent dosage, temperature and agitation time) at two different levels. Optimal experimental conditions were determined by calculated signal-to-noise ratios. "Higher is better" approach was followed to calculate signal-to-noise ratios as it was aimed to obtain high metal removal efficiencies. The impact ratios of factors were determined by the model. Within the study, Cu(2+) biosorption efficiencies were also predicted by using Taguchi method. Results of the model showed that experimental and predicted values were close to each other demonstrating the success of Taguchi approach. Furthermore, thermodynamic, isotherm and kinetic studies were performed to explain the biosorption mechanism. Calculated thermodynamic parameters were in good accordance with the results of Taguchi model. Copyright © 2014 Elsevier Inc. All rights reserved.

Quasi-Experimental Designs for Causal Inference

ERIC Educational Resources Information Center

Kim, Yongnam; Steiner, Peter

2016-01-01

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Optimal Pulse Configuration Design for Heart Stimulation. A Theoretical, Numerical and Experimental Study.

NASA Astrophysics Data System (ADS)

Hardy, Neil; Dvir, Hila; Fenton, Flavio

Existing pacemakers consider the rectangular pulse to be the optimal form of stimulation current. However, other waveforms for the use of pacemakers could save energy while still stimulating the heart. We aim to find the optimal waveform for pacemaker use, and to offer a theoretical explanation for its advantage. Since the pacemaker battery is a charge source, here we probe the stimulation current waveforms with respect to the total charge delivery. In this talk we present theoretical analysis and numerical simulations of myocyte ion-channel currents acting as an additional source of charge that adds to the external stimulating charge for stimulation purposes. Therefore, we find that as the action potential emerges, the external stimulating current can be reduced accordingly exponentially. We then performed experimental studies in rabbit and cat hearts and showed that indeed exponential truncated pulses with less total charge can still induce activation in the heart. From the experiments, we present curves showing the savings in charge as a function of exponential waveform and we calculated that the longevity of the pacemaker battery would be ten times higher for the exponential current compared to the rectangular waveforms. Thanks to Petit Undergraduate Research Scholars Program and NSF# 1413037.

[Optimization of vacuum belt drying process of Gardeniae Fructus in Reduning injection by Box-Behnken design-response surface methodology].

PubMed

Huang, Dao-sheng; Shi, Wei; Han, Lei; Sun, Ke; Chen, Guang-bo; Wu Jian-xiong; Xu, Gui-hong; Bi, Yu-an; Wang, Zhen-zhong; Xiao, Wei

2015-06-01

To optimize the belt drying process conditions optimization of Gardeniae Fructus extract from Reduning injection by Box-Behnken design-response surface methodology, on the basis of single factor experiment, a three-factor and three-level Box-Behnken experimental design was employed to optimize the drying technology of Gardeniae Fructus extract from Reduning injection. With drying temperature, drying time, feeding speed as independent variables and the content of geniposide as dependent variable, the experimental data were fitted to a second order polynomial equation, establishing the mathematical relationship between the content of geniposide and respective variables. With the experimental data analyzed by Design-Expert 8. 0. 6, the optimal drying parameter was as follows: the drying temperature was 98.5 degrees C , the drying time was 89 min, the feeding speed was 99.8 r x min(-1). Three verification experiments were taked under this technology and the measured average content of geniposide was 564. 108 mg x g(-1), which was close to the model prediction: 563. 307 mg x g(-1). According to the verification test, the Gardeniae Fructus belt drying process is steady and feasible. So single factor experiments combined with response surface method (RSM) could be used to optimize the drying technology of Reduning injection Gardenia extract.

Optimization of phase feeding of starter, grower, and finisher diets for male broilers by mixture experimental design: forty-eight-day production period.

PubMed

Roush, W B; Boykin, D; Branton, S L

2004-08-01

A mixture experiment, a variant of response surface methodology, was designed to determine the proportion of time to feed broiler starter (23% protein), grower (20% protein), and finisher (18% protein) diets to optimize production and processing variables based on a total production time of 48 d. Mixture designs are useful for proportion problems where the components of the experiment (i.e., length of time the diets were fed) add up to a unity (48 d). The experiment was conducted with day-old male Ross x Ross broiler chicks. The birds were placed 50 birds per pen in each of 60 pens. The experimental design was a 10-point augmented simplex-centroid (ASC) design with 6 replicates of each point. Each design point represented the portion(s) of the 48 d that each of the diets was fed. Formulation of the diets was based on NRC standards. At 49 d, each pen of birds was evaluated for production data including BW, feed conversion, and cost of feed consumed. Then, 6 birds were randomly selected from each pen for processing data. Processing variables included live weight, hot carcass weight, dressing percentage, fat pad percentage, and breast yield (pectoralis major and pectoralis minor weights). Production and processing data were fit to simplex regression models. Model terms determined not to be significant (P > 0.05) were removed. The models were found to be statistically adequate for analysis of the response surfaces. A compromise solution was calculated based on optimal constraints designated for the production and processing data. The results indicated that broilers fed a starter and finisher diet for 30 and 18 d, respectively, would meet the production and processing constraints. Trace plots showed that the production and processing variables were not very sensitive to the grower diet.

Complex optimization for big computational and experimental neutron datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bao, Feng; Oak Ridge National Lab.; Archibald, Richard

Here, we present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. Finally, we use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, andmore » refine first principles calculations to better describe the experimental data.« less

Complex optimization for big computational and experimental neutron datasets

DOE PAGES

Bao, Feng; Oak Ridge National Lab.; Archibald, Richard; ...

2016-11-07

Here, we present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. Finally, we use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, andmore » refine first principles calculations to better describe the experimental data.« less

Angular rate optimal design for the rotary strapdown inertial navigation system.

PubMed

Yu, Fei; Sun, Qian

2014-04-22

Due to the characteristics of high precision for a long duration, the rotary strapdown inertial navigation system (RSINS) has been widely used in submarines and surface ships. Nowadays, the core technology, the rotating scheme, has been studied by numerous researchers. It is well known that as one of the key technologies, the rotating angular rate seriously influences the effectiveness of the error modulating. In order to design the optimal rotating angular rate of the RSINS, the relationship between the rotating angular rate and the velocity error of the RSINS was analyzed in detail based on the Laplace transform and the inverse Laplace transform in this paper. The analysis results showed that the velocity error of the RSINS depends on not only the sensor error, but also the rotating angular rate. In order to minimize the velocity error, the rotating angular rate of the RSINS should match the sensor error. One optimal design method for the rotating rate of the RSINS was also proposed in this paper. Simulation and experimental results verified the validity and superiority of this optimal design method for the rotating rate of the RSINS.

Angular Rate Optimal Design for the Rotary Strapdown Inertial Navigation System

PubMed Central

Yu, Fei; Sun, Qian

2014-01-01

Due to the characteristics of high precision for a long duration, the rotary strapdown inertial navigation system (RSINS) has been widely used in submarines and surface ships. Nowadays, the core technology, the rotating scheme, has been studied by numerous researchers. It is well known that as one of the key technologies, the rotating angular rate seriously influences the effectiveness of the error modulating. In order to design the optimal rotating angular rate of the RSINS, the relationship between the rotating angular rate and the velocity error of the RSINS was analyzed in detail based on the Laplace transform and the inverse Laplace transform in this paper. The analysis results showed that the velocity error of the RSINS depends on not only the sensor error, but also the rotating angular rate. In order to minimize the velocity error, the rotating angular rate of the RSINS should match the sensor error. One optimal design method for the rotating rate of the RSINS was also proposed in this paper. Simulation and experimental results verified the validity and superiority of this optimal design method for the rotating rate of the RSINS. PMID:24759115

Optimal design of geodesically stiffened composite cylindrical shells

NASA Technical Reports Server (NTRS)

Gendron, G.; Guerdal, Z.

1992-01-01

An optimization system based on the finite element code Computations Structural Mechanics (CSM) Testbed and the optimization program, Automated Design Synthesis (ADS), is described. The optimization system can be used to obtain minimum-weight designs of composite stiffened structures. Ply thickness, ply orientations, and stiffener heights can be used as design variables. Buckling, displacement, and material failure constraints can be imposed on the design. The system is used to conduct a design study of geodesically stiffened shells. For comparison purposes, optimal designs of unstiffened shells and shells stiffened by rings and stingers are also obtained. Trends in the design of geodesically stiffened shells are identified. An approach to include local stress concentrations during the design optimization process is then presented. The method is based on a global/local analysis technique. It employs spline interpolation functions to determine displacements and rotations from a global model which are used as 'boundary conditions' for the local model. The organization of the strategy in the context of an optimization process is described. The method is validated with an example.

Optimal flexible sample size design with robust power.

PubMed

Zhang, Lanju; Cui, Lu; Yang, Bo

2016-08-30

It is well recognized that sample size determination is challenging because of the uncertainty on the treatment effect size. Several remedies are available in the literature. Group sequential designs start with a sample size based on a conservative (smaller) effect size and allow early stop at interim looks. Sample size re-estimation designs start with a sample size based on an optimistic (larger) effect size and allow sample size increase if the observed effect size is smaller than planned. Different opinions favoring one type over the other exist. We propose an optimal approach using an appropriate optimality criterion to select the best design among all the candidate designs. Our results show that (1) for the same type of designs, for example, group sequential designs, there is room for significant improvement through our optimization approach; (2) optimal promising zone designs appear to have no advantages over optimal group sequential designs; and (3) optimal designs with sample size re-estimation deliver the best adaptive performance. We conclude that to deal with the challenge of sample size determination due to effect size uncertainty, an optimal approach can help to select the best design that provides most robust power across the effect size range of interest. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Design optimization of an ironless inductive position sensor for the LHC collimators

NASA Astrophysics Data System (ADS)

Danisi, A.; Masi, A.; Losito, R.; Perriard, Y.

2013-09-01

The Ironless Inductive Position Sensor (I2PS) is an air-cored displacement sensor which has been conceived to be totally immune to external DC/slowly-varying magnetic fields. It can thus be used as a valid alternative to Linear Variable Differential Transformers (LVDTs), which can show a position error in magnetic environments. In addition, since it retains the excellent properties of LVDTs, the I2PS can be used in harsh environments, such as nuclear plants, plasma control and particle accelerators. This paper focuses on the design optimization of the sensor, considering the CERN LHC Collimators as application. In particular, the optimization comes after a complete review of the electromagnetic and thermal modeling of the sensor, as well as the proper choice of the reading technique. The design optimization stage is firmly based on these preliminary steps. Therefore, the paper summarises the sensor's complete development, from its modeling to its actual implementation. A set of experimental measurements demonstrates the sensor's performances to be those expected in the design phase.

Polymeric behavior evaluation of PVP K30-poloxamer binary carrier for solid dispersed nisoldipine by experimental design.

PubMed

Kyaw Oo, May; Mandal, Uttam K; Chatterjee, Bappaditya

2017-02-01

High melting point polymeric carrier without plasticizer is unacceptable for solid dispersion (SD) by melting method. Combined polymer-plasticizer carrier significantly affects drug solubility and tableting property of SD. To evaluate and optimize the combined effect of a binary carrier consisting PVP K30 and poloxamer 188, on nisoldipine solubility and tensile strength of amorphous SD compact (SD compact ) by experimental design. SD of nisoldpine (SD nisol ) was prepared by melt mixing with different PVP K30 and poloxamer amount. A 3 2 factorial design was employed using nisoldipine solubility and tensile strength of SD compact as response variables. Statistical optimization by design expert software, and SD nisol characterization using ATR FTIR, DSC and microscopy were done. PVP K30:poloxamer, at a ratio of 3.73:6.63, was selected as the optimized combination of binary polymeric carrier resulting nisoldipine solubility of 115 μg/mL and tensile strength of 1.19 N/m 2 . PVP K30 had significant positive effect on both responses. Increase in poloxamer concentration after a certain level decreased nisoldipine solubility and tensile strength of SD compact . An optimized PVP K30-poloxamer binary composition for SD carrier was developed. Tensile strength of SD compact can be considered as a response for experimental design to optimize SD.

Implications of optimization cost for balancing exploration and exploitation in global search and for experimental optimization

NASA Astrophysics Data System (ADS)

Chaudhuri, Anirban

Global optimization based on expensive and time consuming simulations or experiments usually cannot be carried out to convergence, but must be stopped because of time constraints, or because the cost of the additional function evaluations exceeds the benefits of improving the objective(s). This dissertation sets to explore the implications of such budget and time constraints on the balance between exploration and exploitation and the decision of when to stop. Three different aspects are considered in terms of their effects on the balance between exploration and exploitation: 1) history of optimization, 2) fixed evaluation budget, and 3) cost as a part of objective function. To this end, this research develops modifications to the surrogate-based optimization technique, Efficient Global Optimization algorithm, that controls better the balance between exploration and exploitation, and stopping criteria facilitated by these modifications. Then the focus shifts to examining experimental optimization, which shares the issues of cost and time constraints. Through a study on optimization of thrust and power for a small flapping wing for micro air vehicles, important differences and similarities between experimental and simulation-based optimization are identified. The most important difference is that reduction of noise in experiments becomes a major time and cost issue, and a second difference is that parallelism as a way to cut cost is more challenging. The experimental optimization reveals the tendency of the surrogate to display optimistic bias near the surrogate optimum, and this tendency is then verified to also occur in simulation based optimization.

Teaching-learning-based Optimization Algorithm for Parameter Identification in the Design of IIR Filters

NASA Astrophysics Data System (ADS)

Singh, R.; Verma, H. K.

2013-12-01

This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

Optimization Under Uncertainty for Electronics Cooling Design

NASA Astrophysics Data System (ADS)

Bodla, Karthik K.; Murthy, Jayathi Y.; Garimella, Suresh V.

Optimization under uncertainty is a powerful methodology used in design and optimization to produce robust, reliable designs. Such an optimization methodology, employed when the input quantities of interest are uncertain, produces output uncertainties, helping the designer choose input parameters that would result in satisfactory thermal solutions. Apart from providing basic statistical information such as mean and standard deviation in the output quantities, auxiliary data from an uncertainty based optimization, such as local and global sensitivities, help the designer decide the input parameter(s) to which the output quantity of interest is most sensitive. This helps the design of experiments based on the most sensitive input parameter(s). A further crucial output of such a methodology is the solution to the inverse problem - finding the allowable uncertainty range in the input parameter(s), given an acceptable uncertainty range in the output quantity of interest...

Acoustic design by topology optimization

NASA Astrophysics Data System (ADS)

Dühring, Maria B.; Jensen, Jakob S.; Sigmund, Ole

2008-11-01

To bring down noise levels in human surroundings is an important issue and a method to reduce noise by means of topology optimization is presented here. The acoustic field is modeled by Helmholtz equation and the topology optimization method is based on continuous material interpolation functions in the density and bulk modulus. The objective function is the squared sound pressure amplitude. First, room acoustic problems are considered and it is shown that the sound level can be reduced in a certain part of the room by an optimized distribution of reflecting material in a design domain along the ceiling or by distribution of absorbing and reflecting material along the walls. We obtain well defined optimized designs for a single frequency or a frequency interval for both 2D and 3D problems when considering low frequencies. Second, it is shown that the method can be applied to design outdoor sound barriers in order to reduce the sound level in the shadow zone behind the barrier. A reduction of up to 10 dB for a single barrier and almost 30 dB when using two barriers are achieved compared to utilizing conventional sound barriers.

Development and optimization of a self-microemulsifying drug delivery system for atorvastatin calcium by using D-optimal mixture design.

PubMed

Yeom, Dong Woo; Song, Ye Seul; Kim, Sung Rae; Lee, Sang Gon; Kang, Min Hyung; Lee, Sangkil; Choi, Young Wook

2015-01-01

In this study, we developed and optimized a self-microemulsifying drug delivery system (SMEDDS) formulation for improving the dissolution and oral absorption of atorvastatin calcium (ATV), a poorly water-soluble drug. Solubility and emulsification tests were performed to select a suitable combination of oil, surfactant, and cosurfactant. A D-optimal mixture design was used to optimize the concentration of components used in the SMEDDS formulation for achieving excellent physicochemical characteristics, such as small droplet size and high dissolution. The optimized ATV-loaded SMEDDS formulation containing 7.16% Capmul MCM (oil), 48.25% Tween 20 (surfactant), and 44.59% Tetraglycol (cosurfactant) significantly enhanced the dissolution rate of ATV in different types of medium, including simulated intestinal fluid, simulated gastric fluid, and distilled water, compared with ATV suspension. Good agreement was observed between predicted and experimental values for mean droplet size and percentage of the drug released in 15 minutes. Further, pharmacokinetic studies in rats showed that the optimized SMEDDS formulation considerably enhanced the oral absorption of ATV, with 3.4-fold and 4.3-fold increases in the area under the concentration-time curve and time taken to reach peak plasma concentration, respectively, when compared with the ATV suspension. Thus, we successfully developed an optimized ATV-loaded SMEDDS formulation by using the D-optimal mixture design, that could potentially be used for improving the oral absorption of poorly water-soluble drugs.

Optimization design of multiphase pump impeller based on combined genetic algorithm and boundary vortex flux diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Jin-ya; Cai, Shu-jie; Li, Yong-jiang; Li, Yong-jiang; Zhang, Yong-xue

2017-12-01

A novel optimization design method for the multiphase pump impeller is proposed through combining the quasi-3D hydraulic design (Q3DHD), the boundary vortex flux (BVF) diagnosis, and the genetic algorithm (GA). The BVF diagnosis based on the Q3DHD is used to evaluate the objection function. Numerical simulations and hydraulic performance tests are carried out to compare the impeller designed only by the Q3DHD method and that optimized by the presented method. The comparisons of both the flow fields simulated under the same condition show that (1) the pressure distribution in the optimized impeller is more reasonable and the gas-liquid separation is more efficiently inhibited, (2) the scales of the gas pocket and the vortex decrease remarkably for the optimized impeller, (3) the unevenness of the BVF distributions near the shroud of the original impeller is effectively eliminated in the optimized impeller. The experimental results show that the differential pressure and the maximum efficiency of the optimized impeller are increased by 4% and 2.5%, respectively. Overall, the study indicates that the optimization design method proposed in this paper is feasible.

Integrating uniform design and response surface methodology to optimize thiacloprid suspension

PubMed Central

Li, Bei-xing; Wang, Wei-chang; Zhang, Xian-peng; Zhang, Da-xia; Mu, Wei; Liu, Feng

2017-01-01

A model 25% suspension concentrate (SC) of thiacloprid was adopted to evaluate an integrative approach of uniform design and response surface methodology. Tersperse2700, PE1601, xanthan gum and veegum were the four experimental factors, and the aqueous separation ratio and viscosity were the two dependent variables. Linear and quadratic polynomial models of stepwise regression and partial least squares were adopted to test the fit of the experimental data. Verification tests revealed satisfactory agreement between the experimental and predicted data. The measured values for the aqueous separation ratio and viscosity were 3.45% and 278.8 mPa·s, respectively, and the relative errors of the predicted values were 9.57% and 2.65%, respectively (prepared under the proposed conditions). Comprehensive benefits could also be obtained by appropriately adjusting the amount of certain adjuvants based on practical requirements. Integrating uniform design and response surface methodology is an effective strategy for optimizing SC formulas. PMID:28383036

Program Aids Analysis And Optimization Of Design

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Lamarsh, William J., II

1994-01-01

NETS/ PROSSS (NETS Coupled With Programming System for Structural Synthesis) computer program developed to provide system for combining NETS (MSC-21588), neural-network application program and CONMIN (Constrained Function Minimization, ARC-10836), optimization program. Enables user to reach nearly optimal design. Design then used as starting point in normal optimization process, possibly enabling user to converge to optimal solution in significantly fewer iterations. NEWT/PROSSS written in C language and FORTRAN 77.

Design approaches to experimental mediation☆

PubMed Central

Pirlott, Angela G.; MacKinnon, David P.

2016-01-01

Identifying causal mechanisms has become a cornerstone of experimental social psychology, and editors in top social psychology journals champion the use of mediation methods, particularly innovative ones when possible (e.g. Halberstadt, 2010, Smith, 2012). Commonly, studies in experimental social psychology randomly assign participants to levels of the independent variable and measure the mediating and dependent variables, and the mediator is assumed to causally affect the dependent variable. However, participants are not randomly assigned to levels of the mediating variable(s), i.e., the relationship between the mediating and dependent variables is correlational. Although researchers likely know that correlational studies pose a risk of confounding, this problem seems forgotten when thinking about experimental designs randomly assigning participants to levels of the independent variable and measuring the mediator (i.e., “measurement-of-mediation” designs). Experimentally manipulating the mediator provides an approach to solving these problems, yet these methods contain their own set of challenges (e.g., Bullock, Green, & Ha, 2010). We describe types of experimental manipulations targeting the mediator (manipulations demonstrating a causal effect of the mediator on the dependent variable and manipulations targeting the strength of the causal effect of the mediator) and types of experimental designs (double randomization, concurrent double randomization, and parallel), provide published examples of the designs, and discuss the strengths and challenges of each design. Therefore, the goals of this paper include providing a practical guide to manipulation-of-mediator designs in light of their challenges and encouraging researchers to use more rigorous approaches to mediation because manipulation-of-mediator designs strengthen the ability to infer causality of the mediating variable on the dependent variable. PMID:27570259

Design approaches to experimental mediation.

PubMed

Pirlott, Angela G; MacKinnon, David P

2016-09-01

Identifying causal mechanisms has become a cornerstone of experimental social psychology, and editors in top social psychology journals champion the use of mediation methods, particularly innovative ones when possible (e.g. Halberstadt, 2010, Smith, 2012). Commonly, studies in experimental social psychology randomly assign participants to levels of the independent variable and measure the mediating and dependent variables, and the mediator is assumed to causally affect the dependent variable. However, participants are not randomly assigned to levels of the mediating variable(s), i.e., the relationship between the mediating and dependent variables is correlational. Although researchers likely know that correlational studies pose a risk of confounding, this problem seems forgotten when thinking about experimental designs randomly assigning participants to levels of the independent variable and measuring the mediator (i.e., "measurement-of-mediation" designs). Experimentally manipulating the mediator provides an approach to solving these problems, yet these methods contain their own set of challenges (e.g., Bullock, Green, & Ha, 2010). We describe types of experimental manipulations targeting the mediator (manipulations demonstrating a causal effect of the mediator on the dependent variable and manipulations targeting the strength of the causal effect of the mediator) and types of experimental designs (double randomization, concurrent double randomization, and parallel), provide published examples of the designs, and discuss the strengths and challenges of each design. Therefore, the goals of this paper include providing a practical guide to manipulation-of-mediator designs in light of their challenges and encouraging researchers to use more rigorous approaches to mediation because manipulation-of-mediator designs strengthen the ability to infer causality of the mediating variable on the dependent variable.

Experimental analysis of the performance of optimized fin structures in a latent heat energy storage test rig

NASA Astrophysics Data System (ADS)

Johnson, Maike; Hübner, Stefan; Reichmann, Carsten; Schönberger, Manfred; Fiß, Michael

2017-06-01

Energy storage systems are a key technology for developing a more sustainable energy supply system and lowering overall CO2 emissions. Among the variety of storage technologies, high temperature phase change material (PCM) storage is a promising option with a wide range of applications. PCM storages using an extended finned tube storage concept have been designed and techno-economically optimized for solar thermal power plant operations. These finned tube components were experimentally tested in order to validate the optimized design and simulation models used. Analysis of the charging and discharging characteristics of the storage at the pilot scale gives insight into the heat distribution both axially as well as radially in the storage material, thereby allowing for a realistic validation of the design. The design was optimized for discharging of the storage, as this is the more critical operation mode in power plant applications. The data show good agreement between the model and the experiments for discharging.

Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems

PubMed Central

Junker, Astrid; Muraya, Moses M.; Weigelt-Fischer, Kathleen; Arana-Ceballos, Fernando; Klukas, Christian; Melchinger, Albrecht E.; Meyer, Rhonda C.; Riewe, David; Altmann, Thomas

2015-01-01

Detailed and standardized protocols for plant cultivation in environmentally controlled conditions are an essential prerequisite to conduct reproducible experiments with precisely defined treatments. Setting up appropriate and well defined experimental procedures is thus crucial for the generation of solid evidence and indispensable for successful plant research. Non-invasive and high throughput (HT) phenotyping technologies offer the opportunity to monitor and quantify performance dynamics of several hundreds of plants at a time. Compared to small scale plant cultivations, HT systems have much higher demands, from a conceptual and a logistic point of view, on experimental design, as well as the actual plant cultivation conditions, and the image analysis and statistical methods for data evaluation. Furthermore, cultivation conditions need to be designed that elicit plant performance characteristics corresponding to those under natural conditions. This manuscript describes critical steps in the optimization of procedures for HT plant phenotyping systems. Starting with the model plant Arabidopsis, HT-compatible methods were tested, and optimized with regard to growth substrate, soil coverage, watering regime, experimental design (considering environmental inhomogeneities) in automated plant cultivation and imaging systems. As revealed by metabolite profiling, plant movement did not affect the plants' physiological status. Based on these results, procedures for maize HT cultivation and monitoring were established. Variation of maize vegetative growth in the HT phenotyping system did match well with that observed in the field. The presented results outline important issues to be considered in the design of HT phenotyping experiments for model and crop plants. It thereby provides guidelines for the setup of HT experimental procedures, which are required for the generation of reliable and reproducible data of phenotypic variation for a broad range of applications. PMID

Optimal designs based on the maximum quasi-likelihood estimator

PubMed Central

Shen, Gang; Hyun, Seung Won; Wong, Weng Kee

2016-01-01

We use optimal design theory and construct locally optimal designs based on the maximum quasi-likelihood estimator (MqLE), which is derived under less stringent conditions than those required for the MLE method. We show that the proposed locally optimal designs are asymptotically as efficient as those based on the MLE when the error distribution is from an exponential family, and they perform just as well or better than optimal designs based on any other asymptotically linear unbiased estimators such as the least square estimator (LSE). In addition, we show current algorithms for finding optimal designs can be directly used to find optimal designs based on the MqLE. As an illustrative application, we construct a variety of locally optimal designs based on the MqLE for the 4-parameter logistic (4PL) model and study their robustness properties to misspecifications in the model using asymptotic relative efficiency. The results suggest that optimal designs based on the MqLE can be easily generated and they are quite robust to mis-specification in the probability distribution of the responses. PMID:28163359

A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models

PubMed Central

Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung

2015-01-01

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237

Optimization of digital designs

NASA Technical Reports Server (NTRS)

Miles, Lowell H. (Inventor); Whitaker, Sterling R. (Inventor)

2009-01-01

An application specific integrated circuit is optimized by translating a first representation of its digital design to a second representation. The second representation includes multiple syntactic expressions that admit a representation of a higher-order function of base Boolean values. The syntactic expressions are manipulated to form a third representation of the digital design.

Optimization applications in aircraft engine design and test

NASA Technical Reports Server (NTRS)

Pratt, T. K.

1984-01-01

Starting with the NASA-sponsored STAEBL program, optimization methods based primarily upon the versatile program COPES/CONMIN were introduced over the past few years to a broad spectrum of engineering problems in structural optimization, engine design, engine test, and more recently, manufacturing processes. By automating design and testing processes, many repetitive and costly trade-off studies have been replaced by optimization procedures. Rather than taking engineers and designers out of the loop, optimization has, in fact, put them more in control by providing sophisticated search techniques. The ultimate decision whether to accept or reject an optimal feasible design still rests with the analyst. Feedback obtained from this decision process has been invaluable since it can be incorporated into the optimization procedure to make it more intelligent. On several occasions, optimization procedures have produced novel designs, such as the nonsymmetric placement of rotor case stiffener rings, not anticipated by engineering designers. In another case, a particularly difficult resonance contraint could not be satisfied using hand iterations for a compressor blade, when the STAEBL program was applied to the problem, a feasible solution was obtained in just two iterations.

Sparse and optimal acquisition design for diffusion MRI and beyond

PubMed Central

Koay, Cheng Guan; Özarslan, Evren; Johnson, Kevin M.; Meyerand, M. Elizabeth

2012-01-01

strategy was found to be effective in finding the optimum configuration. It was found that the square design is the most robust (i.e., with stable condition numbers and A-optimal measures under varying experimental conditions) among many other possible designs of the same sample size. Under the same performance evaluation, the square design was found to be more robust than the widely used sampling schemes similar to that of 3D radial MRI and of diffusion spectrum imaging (DSI). Conclusions: A novel optimality criterion for sparse multiple-shell acquisition and quasimultiple-shell designs in diffusion MRI and an effective search strategy for finding the best configuration have been developed. The results are very promising, interesting, and practical for diffusion MRI acquisitions. PMID:22559620

Multiobjective optimization techniques for structural design

NASA Technical Reports Server (NTRS)

Rao, S. S.

1984-01-01

The multiobjective programming techniques are important in the design of complex structural systems whose quality depends generally on a number of different and often conflicting objective functions which cannot be combined into a single design objective. The applicability of multiobjective optimization techniques is studied with reference to simple design problems. Specifically, the parameter optimization of a cantilever beam with a tip mass and a three-degree-of-freedom vabration isolation system and the trajectory optimization of a cantilever beam are considered. The solutions of these multicriteria design problems are attempted by using global criterion, utility function, game theory, goal programming, goal attainment, bounded objective function, and lexicographic methods. It has been observed that the game theory approach required the maximum computational effort, but it yielded better optimum solutions with proper balance of the various objective functions in all the cases.

On the Constitutive Response Characterization for Composite Materials Via Data-Driven Design Optimization

Treesearch

John G. Michopoulos; John G. Hermanson; Athanasios lliopoulos; Samuel Lambrakos; Tomonari Furukawa

2011-01-01

In the present paper we focus on demonstrating the use of design optimization for the constitutive characterization of anisotropic material systems such as polymer matrix composites, with or without damage. All approaches are based on the availability of experimental data originating from mechatronic material testing systems that can expose specimens to...

Telemanipulator design and optimization software

NASA Astrophysics Data System (ADS)

Cote, Jean; Pelletier, Michel

1995-12-01

For many years, industrial robots have been used to execute specific repetitive tasks. In those cases, the optimal configuration and location of the manipulator only has to be found once. The optimal configuration or position where often found empirically according to the tasks to be performed. In telemanipulation, the nature of the tasks to be executed is much wider and can be very demanding in terms of dexterity and workspace. The position/orientation of the robot's base could be required to move during the execution of a task. At present, the choice of the initial position of the teleoperator is usually found empirically which can be sufficient in the case of an easy or repetitive task. In the converse situation, the amount of time wasted to move the teleoperator support platform has to be taken into account during the execution of the task. Automatic optimization of the position/orientation of the platform or a better designed robot configuration could minimize these movements and save time. This paper will present two algorithms. The first algorithm is used to optimize the position and orientation of a given manipulator (or manipulators) with respect to the environment on which a task has to be executed. The second algorithm is used to optimize the position or the kinematic configuration of a robot. For this purpose, the tasks to be executed are digitized using a position/orientation measurement system and a compact representation based on special octrees. Given a digitized task, the optimal position or Denavit-Hartenberg configuration of the manipulator can be obtained numerically. Constraints on the robot design can also be taken into account. A graphical interface has been designed to facilitate the use of the two optimization algorithms.

Experimental validation of a new heterogeneous mechanical test design

NASA Astrophysics Data System (ADS)

Aquino, J.; Campos, A. Andrade; Souto, N.; Thuillier, S.

2018-05-01

Standard material parameters identification strategies generally use an extensive number of classical tests for collecting the required experimental data. However, a great effort has been made recently by the scientific and industrial communities to support this experimental database on heterogeneous tests. These tests can provide richer information on the material behavior allowing the identification of a more complete set of material parameters. This is a result of the recent development of full-field measurements techniques, like digital image correlation (DIC), that can capture the heterogeneous deformation fields on the specimen surface during the test. Recently, new specimen geometries were designed to enhance the richness of the strain field and capture supplementary strain states. The butterfly specimen is an example of these new geometries, designed through a numerical optimization procedure where an indicator capable of evaluating the heterogeneity and the richness of strain information. However, no experimental validation was yet performed. The aim of this work is to experimentally validate the heterogeneous butterfly mechanical test in the parameter identification framework. For this aim, DIC technique and a Finite Element Model Up-date inverse strategy are used together for the parameter identification of a DC04 steel, as well as the calculation of the indicator. The experimental tests are carried out in a universal testing machine with the ARAMIS measuring system to provide the strain states on the specimen surface. The identification strategy is accomplished with the data obtained from the experimental tests and the results are compared to a reference numerical solution.

Design, analysis, optimization and control of rotor tip flows

NASA Astrophysics Data System (ADS)

Maesschalck, Cis Guy M. De

Developments in turbomachinery focus on efficiency and reliability enhancements, while reducing the production costs. In spite of the many noteworthy experimental and numerical investigations over the past decades, the turbine tip design presents numerous challenges to the engine manufacturers, and remains the primary factor defining the machine durability for the periodic removal of the turbine components during overhaul. Due to the hot gases coming from the upstream combustion chamber, the turbine blades are subjected to temperatures far above the metal creep temperature, combined with severe thermal stresses induced within the blade material. Inadequate designs cause early tip burnouts leading to considerable performance degradations, or even a catastrophic turbine failure. Moreover, the leakage spillage, nowadays often exceeding the transonic regime, generates large aerodynamic penalties which are responsible for about one third of the turbine losses. In this view, the current doctoral research exploits the potential through the modification and optimization of the blade tip shape as a means to control the tip leakage flow aerodynamics and manage the heat load distribution over the blade profile to improve the turbine efficiency and durability. Three main design strategies for unshrouded turbine blade tips were analyzed and optimized: tight running clearances, blade tip contouring and the use of complex squealer-like geometries. The altered overtip flow physics and heat transfer characteristics were simulated for tight gap sizes as low as 0.5% down to 0.1% of the blade height, occurring during engine transients and soon to be expected due to recent developments in active clearance control strategies. The potential of fully 3D contoured blade top surfaces, allowing to adapt the profile locally to the changing flow conditions throughout the camberline, is quantified. First adopting a quasi-3D approach and subsequently using a full 3D optimization. For the

True and Quasi-Experimental Designs. ERIC/AE Digest.

ERIC Educational Resources Information Center

Gribbons, Barry; Herman, Joan

Among the different types of experimental design are two general categories: true experimental designs and quasi- experimental designs. True experimental designs include more than one purposively created group, common measured outcomes, and random assignment. Quasi-experimental designs are commonly used when random assignment is not practical or…

MEMS resonant load cells for micro-mechanical test frames: feasibility study and optimal design

NASA Astrophysics Data System (ADS)

Torrents, A.; Azgin, K.; Godfrey, S. W.; Topalli, E. S.; Akin, T.; Valdevit, L.

2010-12-01

This paper presents the design, optimization and manufacturing of a novel micro-fabricated load cell based on a double-ended tuning fork. The device geometry and operating voltages are optimized for maximum force resolution and range, subject to a number of manufacturing and electromechanical constraints. All optimizations are enabled by analytical modeling (verified by selected finite elements analyses) coupled with an efficient C++ code based on the particle swarm optimization algorithm. This assessment indicates that force resolutions of ~0.5-10 nN are feasible in vacuum (~1-50 mTorr), with force ranges as large as 1 N. Importantly, the optimal design for vacuum operation is independent of the desired range, ensuring versatility. Experimental verifications on a sub-optimal device fabricated using silicon-on-glass technology demonstrate a resolution of ~23 nN at a vacuum level of ~50 mTorr. The device demonstrated in this article will be integrated in a hybrid micro-mechanical test frame for unprecedented combinations of force resolution and range, displacement resolution and range, optical (or SEM) access to the sample, versatility and cost.

Marine Steam Condenser Design Optimization.

DTIC Science & Technology

1983-12-01

to make design decisions to obtain a feasible design. CONNIN, as do most optimizers, requires complete control in determining all iterative design...neutralize all the places where such design decisions are made. By removing the ability for CONDIP to make any design decisions it became totally passive...dependent on CONNIN for design decisions , does not have that capability. Pemeabering that CONHIN requires a complete once-through analysis in order to

Efficient Optimization of Stimuli for Model-Based Design of Experiments to Resolve Dynamical Uncertainty.

PubMed

Mdluli, Thembi; Buzzard, Gregery T; Rundell, Ann E

2015-09-01

This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm's scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements.

Efficient Optimization of Stimuli for Model-Based Design of Experiments to Resolve Dynamical Uncertainty

PubMed Central

Mdluli, Thembi; Buzzard, Gregery T.; Rundell, Ann E.

2015-01-01

This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm’s scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements. PMID:26379275

Turbomachinery Airfoil Design Optimization Using Differential Evolution

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)

2002-01-01

An aerodynamic design optimization procedure that is based on a evolutionary algorithm known at Differential Evolution is described. Differential Evolution is a simple, fast, and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems, including highly nonlinear systems with discontinuities and multiple local optima. The method is combined with a Navier-Stokes solver that evaluates the various intermediate designs and provides inputs to the optimization procedure. An efficient constraint handling mechanism is also incorporated. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated. Substantial reductions in the overall computing time requirements are achieved by using the algorithm in conjunction with neural networks.

Optimal design of a thermally stable composite optical bench

NASA Technical Reports Server (NTRS)

Gray, C. E., Jr.

1985-01-01

The Lidar Atmospheric Sensing Experiment will be performed aboard an ER-2 aircraft; the lidar system used will be mounted on a lightweight, thermally stable graphite/epoxy optical bench whose design is presently subjected to analytical study and experimental validation. Attention is given to analytical methods for the selection of such expected laminate properties as the thermal expansion coefficient, the apparent in-plane moduli, and ultimate strength. For a symmetric laminate in which one of the lamina angles remains variable, an optimal lamina angle is selected to produce a design laminate with a near-zero coefficient of thermal expansion. Finite elements are used to model the structural concept of the design, with a view to the optical bench's thermal structural response as well as the determination of the degree of success in meeting the experiment's alignment tolerances.

Design Optimization of Composite Structures under Uncertainty

NASA Technical Reports Server (NTRS)

Haftka, Raphael T.

2003-01-01

Design optimization under uncertainty is computationally expensive and is also challenging in terms of alternative formulation. The work under the grant focused on developing methods for design against uncertainty that are applicable to composite structural design with emphasis on response surface techniques. Applications included design of stiffened composite plates for improved damage tolerance, the use of response surfaces for fitting weights obtained by structural optimization, and simultaneous design of structure and inspection periods for fail-safe structures.

Box-Behnken statistical design to optimize thermal performance of energy storage systems

NASA Astrophysics Data System (ADS)

Jalalian, Iman Joz; Mohammadiun, Mohammad; Moqadam, Hamid Hashemi; Mohammadiun, Hamid

2018-05-01

Latent heat thermal storage (LHTS) is a technology that can help to reduce energy consumption for cooling applications, where the cold is stored in phase change materials (PCMs). In the present study a comprehensive theoretical and experimental investigation is performed on a LHTES system containing RT25 as phase change material (PCM). Process optimization of the experimental conditions (inlet air temperature and velocity and number of slabs) was carried out by means of Box-Behnken design (BBD) of Response surface methodology (RSM). Two parameters (cooling time and COP value) were chosen to be the responses. Both of the responses were significantly influenced by combined effect of inlet air temperature with velocity and number of slabs. Simultaneous optimization was performed on the basis of the desirability function to determine the optimal conditions for the cooling time and COP value. Maximum cooling time (186 min) and COP value (6.04) were found at optimum process conditions i.e. inlet temperature of (32.5), air velocity of (1.98) and slab number of (7).

Simulation-Driven Design Approach for Design and Optimization of Blankholder

NASA Astrophysics Data System (ADS)

Sravan, Tatipala; Suddapalli, Nikshep R.; Johan, Pilthammar; Mats, Sigvant; Christian, Johansson

2017-09-01

Reliable design of stamping dies is desired for efficient and safe production. The design of stamping dies are today mostly based on casting feasibility, although it can also be based on criteria for fatigue, stiffness, safety, economy. Current work presents an approach that is built on Simulation Driven Design, enabling Design Optimization to address this issue. A structural finite element model of a stamping die, used to produce doors for Volvo V70/S80 car models, is studied. This die had developed cracks during its usage. To understand the behaviour of stress distribution in the stamping die, structural analysis of the die is conducted and critical regions with high stresses are identified. The results from structural FE-models are compared with analytical calculations pertaining to fatigue properties of the material. To arrive at an optimum design with increased stiffness and lifetime, topology and free-shape optimization are performed. In the optimization routine, identified critical regions of the die are set as design variables. Other optimization variables are set to maintain manufacturability of the resultant stamping die. Thereafter a CAD model is built based on geometrical results from topology and free-shape optimizations. Then the CAD model is subjected to structural analysis to visualize the new stress distribution. This process is iterated until a satisfactory result is obtained. The final results show reduction in stress levels by 70% with a more homogeneous distribution. Even though mass of the die is increased by 17 %, overall, a stiffer die with better lifetime is obtained. Finally, by reflecting on the entire process, a coordinated approach to handle such situations efficiently is presented.

Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

NASA Astrophysics Data System (ADS)

Devra, Amit; Prabhu, Prithviraj; Singh, Harpreet; Arvind; Dorai, Kavita

2018-03-01

We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the "Luck-Choose" mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.

Solid oxide fuel cell simulation and design optimization with numerical adjoint techniques

NASA Astrophysics Data System (ADS)

Elliott, Louie C.

This dissertation reports on the application of numerical optimization techniques as applied to fuel cell simulation and design. Due to the "multi-physics" inherent in a fuel cell, which results in a highly coupled and non-linear behavior, an experimental program to analyze and improve the performance of fuel cells is extremely difficult. This program applies new optimization techniques with computational methods from the field of aerospace engineering to the fuel cell design problem. After an overview of fuel cell history, importance, and classification, a mathematical model of solid oxide fuel cells (SOFC) is presented. The governing equations are discretized and solved with computational fluid dynamics (CFD) techniques including unstructured meshes, non-linear solution methods, numerical derivatives with complex variables, and sensitivity analysis with adjoint methods. Following the validation of the fuel cell model in 2-D and 3-D, the results of the sensitivity analysis are presented. The sensitivity derivative for a cost function with respect to a design variable is found with three increasingly sophisticated techniques: finite difference, direct differentiation, and adjoint. A design cycle is performed using a simple optimization method to improve the value of the implemented cost function. The results from this program could improve fuel cell performance and lessen the world's dependence on fossil fuels.

Design optimization of axial flow hydraulic turbine runner: Part II - multi-objective constrained optimization method

NASA Astrophysics Data System (ADS)

Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji

2002-06-01

This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright

Automatic Optimization of Wayfinding Design.

PubMed

Huang, Haikun; Lin, Ni-Ching; Barrett, Lorenzo; Springer, Darian; Wang, Hsueh-Cheng; Pomplun, Marc; Yu, Lap-Fai

2017-10-10

Wayfinding signs play an important role in guiding users to navigate in a virtual environment and in helping pedestrians to find their ways in a real-world architectural site. Conventionally, the wayfinding design of a virtual environment is created manually, so as the wayfinding design of a real-world architectural site. The many possible navigation scenarios, and the interplay between signs and human navigation, can make the manual design process overwhelming and non-trivial. As a result, creating a wayfinding design for a typical layout can take months to several years. In this paper, we introduce the Way to Go! approach for automatically generating a wayfinding design for a given layout. The designer simply has to specify some navigation scenarios; our approach will automatically generate an optimized wayfinding design with signs properly placed considering human agents' visibility and possibility of making navigation mistakes. We demonstrate the effectiveness of our approach in generating wayfinding designs for different layouts. We evaluate our results by comparing different wayfinding designs and show that our optimized designs can guide pedestrians to their destinations effectively. Our approach can also help the designer visualize the accessibility of a destination from different locations, and correct any "blind zone" with additional signs.

A design optimization process for Space Station Freedom

NASA Technical Reports Server (NTRS)

Chamberlain, Robert G.; Fox, George; Duquette, William H.

1990-01-01

The Space Station Freedom Program is used to develop and implement a process for design optimization. Because the relative worth of arbitrary design concepts cannot be assessed directly, comparisons must be based on designs that provide the same performance from the point of view of station users; such designs can be compared in terms of life cycle cost. Since the technology required to produce a space station is widely dispersed, a decentralized optimization process is essential. A formulation of the optimization process is provided and the mathematical models designed to facilitate its implementation are described.

Electrochemical production and use of free chlorine for pollutant removal: an experimental design approach.

PubMed

Antonelli, Raissa; de Araújo, Karla Santos; Pires, Ricardo Francisco; Fornazari, Ana Luiza de Toledo; Granato, Ana Claudia; Malpass, Geoffroy Roger Pointer

2017-10-28

The present paper presents the study of (1) the optimization of electrochemical-free chlorine production using an experimental design approach, and (2) the application of the optimum conditions obtained for the application in photo-assisted electrochemical degradation of simulated textile effluent. In the experimental design the influence of inter-electrode gap, pH, NaCl concentration and current was considered. It was observed that the four variables studied are significant for the process, with NaCl concentration and current being the most significant variables for free chlorine production. The maximum free chlorine production was obtained at a current of 2.33 A and NaCl concentrations in 0.96 mol dm -3 . The application of the optimized conditions with simultaneous UV irradiation resulted in up to 83.1% Total Organic Carbon removal and 100% of colour removal over 180 min of electrolysis. The results indicate that a systematic (statistical) approach to the electrochemical treatment of pollutants can save time and reagents.

Development of a fast, lean and agile direct pelletization process using experimental design techniques.

PubMed

Politis, Stavros N; Rekkas, Dimitrios M

2017-04-01

A novel hot melt direct pelletization method was developed, characterized and optimized, using statistical thinking and experimental design tools. Mixtures of carnauba wax (CW) and HPMC K100M were spheronized using melted gelucire 50-13 as a binding material (BM). Experimentation was performed sequentially; a fractional factorial design was set up initially to screen the factors affecting the process, namely spray rate, quantity of BM, rotor speed, type of rotor disk, lubricant-glidant presence, additional spheronization time, powder feeding rate and quantity. From the eight factors assessed, three were further studied during process optimization (spray rate, quantity of BM and powder feeding rate), at different ratios of the solid mixture of CW and HPMC K100M. The study demonstrated that the novel hot melt process is fast, efficient, reproducible and predictable. Therefore, it can be adopted in a lean and agile manufacturing setting for the production of flexible pellet dosage forms with various release rates easily customized between immediate and modified delivery.

Experimental validation of 3D printed material behaviors and their influence on the structural topology design

NASA Astrophysics Data System (ADS)

Yang, Kai Ke; Zhu, Ji Hong; Wang, Chuang; Jia, Dong Sheng; Song, Long Long; Zhang, Wei Hong

2018-05-01

The purpose of this paper is to investigate the structures achieved by topology optimization and their fabrications by 3D printing considering the particular features of material microstructures and macro mechanical performances. Combining Digital Image Correlation and Optical Microscope, this paper experimentally explored the anisotropies of stiffness and strength existing in the 3D printed polymer material using Stereolithography (SLA) and titanium material using Selective Laser Melting (SLM). The standard specimens and typical structures obtained by topology optimization were fabricated along different building directions. On the one hand, the experimental results of these SLA produced structures showed stable properties and obviously anisotropic rules in stiffness, ultimate strengths and places of fractures. Further structural designs were performed using topology optimization when the particular mechanical behaviors of SLA printed materials were considered, which resulted in better structural performances compared to the optimized designs using `ideal' isotropic material model. On the other hand, this paper tested the mechanical behaviors of SLM printed multiscale lattice structures which were fabricated using the same metal powder and the same machine. The structural stiffness values are generally similar while the strength behaviors show a difference, which are mainly due to the irregular surface quality of the tiny structural branches of the lattice. The above evidences clearly show that the consideration of the particular behaviors of 3D printed materials is therefore indispensable for structural design and optimization in order to improve the structural performance and strengthen their practical significance.

Experimental validation of 3D printed material behaviors and their influence on the structural topology design

NASA Astrophysics Data System (ADS)

Yang, Kai Ke; Zhu, Ji Hong; Wang, Chuang; Jia, Dong Sheng; Song, Long Long; Zhang, Wei Hong

2018-02-01

The purpose of this paper is to investigate the structures achieved by topology optimization and their fabrications by 3D printing considering the particular features of material microstructures and macro mechanical performances. Combining Digital Image Correlation and Optical Microscope, this paper experimentally explored the anisotropies of stiffness and strength existing in the 3D printed polymer material using Stereolithography (SLA) and titanium material using Selective Laser Melting (SLM). The standard specimens and typical structures obtained by topology optimization were fabricated along different building directions. On the one hand, the experimental results of these SLA produced structures showed stable properties and obviously anisotropic rules in stiffness, ultimate strengths and places of fractures. Further structural designs were performed using topology optimization when the particular mechanical behaviors of SLA printed materials were considered, which resulted in better structural performances compared to the optimized designs using `ideal' isotropic material model. On the other hand, this paper tested the mechanical behaviors of SLM printed multiscale lattice structures which were fabricated using the same metal powder and the same machine. The structural stiffness values are generally similar while the strength behaviors show a difference, which are mainly due to the irregular surface quality of the tiny structural branches of the lattice. The above evidences clearly show that the consideration of the particular behaviors of 3D printed materials is therefore indispensable for structural design and optimization in order to improve the structural performance and strengthen their practical significance.

A robust optimization methodology for preliminary aircraft design

NASA Astrophysics Data System (ADS)

Prigent, S.; Maréchal, P.; Rondepierre, A.; Druot, T.; Belleville, M.

2016-05-01

This article focuses on a robust optimization of an aircraft preliminary design under operational constraints. According to engineers' know-how, the aircraft preliminary design problem can be modelled as an uncertain optimization problem whose objective (the cost or the fuel consumption) is almost affine, and whose constraints are convex. It is shown that this uncertain optimization problem can be approximated in a conservative manner by an uncertain linear optimization program, which enables the use of the techniques of robust linear programming of Ben-Tal, El Ghaoui, and Nemirovski [Robust Optimization, Princeton University Press, 2009]. This methodology is then applied to two real cases of aircraft design and numerical results are presented.

Weight optimal design of lateral wing upper covers made of composite materials

NASA Astrophysics Data System (ADS)

Barkanov, Evgeny; Eglītis, Edgars; Almeida, Filipe; Bowering, Mark C.; Watson, Glenn

2016-09-01

The present investigation is devoted to the development of a new optimal design of lateral wing upper covers made of advanced composite materials, with special emphasis on closer conformity of the developed finite element analysis and operational requirements for aircraft wing panels. In the first stage, 24 weight optimization problems based on linear buckling analysis were solved for the laminated composite panels with three types of stiffener, two stiffener pitches and four load levels, taking into account manufacturing, reparability and damage tolerance requirements. In the second stage, a composite panel with the best weight/design performance from the previous study was verified by nonlinear buckling analysis and optimization to investigate the effect of shear and fuel pressure on the performance of stiffened panels, and their behaviour under skin post-buckling. Three rib-bay laminated composite panels with T-, I- and HAT-stiffeners were modelled with ANSYS, NASTRAN and ABAQUS finite element codes to study their buckling behaviour as a function of skin and stiffener lay-ups, stiffener height, stiffener top and root width. Owing to the large dimension of numerical problems to be solved, an optimization methodology was developed employing the method of experimental design and response surface technique. Optimal results obtained in terms of cross-sectional areas were verified successfully using ANSYS and ABAQUS shared-node models and a NASTRAN rigid-linked model, and were used later to estimate the weight of the Advanced Low Cost Aircraft Structures (ALCAS) lateral wing upper cover.

Design principles and operating principles: the yin and yang of optimal functioning.

PubMed

Voit, Eberhard O

2003-03-01

Metabolic engineering has as a goal the improvement of yield of desired products from microorganisms and cell lines. This goal has traditionally been approached with experimental biotechnological methods, but it is becoming increasingly popular to precede the experimental phase by a mathematical modeling step that allows objective pre-screening of possible improvement strategies. The models are either linear and represent the stoichiometry and flux distribution in pathways or they are non-linear and account for the full kinetic behavior of the pathway, which is often significantly effected by regulatory signals. Linear flux analysis is simpler and requires less input information than a full kinetic analysis, and the question arises whether the consideration of non-linearities is really necessary for devising optimal strategies for yield improvements. The article analyzes this question with a generic, representative pathway. It shows that flux split ratios, which are the key criterion for linear flux analysis, are essentially sufficient for unregulated, but not for regulated branch points. The interrelationships between regulatory design on one hand and optimal patterns of operation on the other suggest the investigation of operating principles that complement design principles, like a user's manual complements the hardwiring of electronic equipment.

Quality by design: optimization of a freeze-drying cycle via design space in case of heterogeneous drying behavior and influence of the freezing protocol.

PubMed

Pisano, Roberto; Fissore, Davide; Barresi, Antonello A; Brayard, Philippe; Chouvenc, Pierre; Woinet, Bertrand

2013-02-01

This paper shows how to optimize the primary drying phase, for both product quality and drying time, of a parenteral formulation via design space. A non-steady state model, parameterized with experimentally determined heat and mass transfer coefficients, is used to define the design space when the heat transfer coefficient varies with the position of the vial in the array. The calculations recognize both equipment and product constraints, and also take into account model parameter uncertainty. Examples are given of cycles designed for the same formulation, but varying the freezing conditions and the freeze-dryer scale. These are then compared in terms of drying time. Furthermore, the impact of inter-vial variability on design space, and therefore on the optimized cycle, is addressed. With this regard, a simplified method is presented for the cycle design, which reduces the experimental effort required for the system qualification. The use of mathematical modeling is demonstrated to be very effective not only for cycle development, but also for solving problem of process transfer. This study showed that inter-vial variability remains significant when vials are loaded on plastic trays, and how inter-vial variability can be taken into account during process design.

The effects of experimental pain and induced optimism on working memory task performance.

PubMed

Boselie, Jantine J L M; Vancleef, Linda M G; Peters, Madelon L

2016-07-01

Pain can interrupt and deteriorate executive task performance. We have previously shown that experimentally induced optimism can diminish the deteriorating effect of cold pressor pain on a subsequent working memory task (i.e., operation span task). In two successive experiments we sought further evidence for the protective role of optimism on pain-induced working memory impairments. We used another working memory task (i.e., 2-back task) that was performed either after or during pain induction. Study 1 employed a 2 (optimism vs. no-optimism)×2 (pain vs. no-pain)×2 (pre-score vs. post-score) mixed factorial design. In half of the participants optimism was induced by the Best Possible Self (BPS) manipulation, which required them to write and visualize about a life in the future where everything turned out for the best. In the control condition, participants wrote and visualized a typical day in their life (TD). Next, participants completed either the cold pressor task (CPT) or a warm water control task (WWCT). Before (baseline) and after the CPT or WWCT participants working memory performance was measured with the 2-back task. The 2-back task measures the ability to monitor and update working memory representation by asking participants to indicate whether the current stimulus corresponds to the stimulus that was presented 2 stimuli ago. Study 2 had a 2 (optimism vs. no-optimism)×2 (pain vs. no-pain) mixed factorial design. After receiving the BPS or control manipulation, participants completed the 2-back task twice: once with painful heat stimulation, and once without any stimulation (counter-balanced order). Continuous heat stimulation was used with temperatures oscillating around 1°C above and 1°C below the individual pain threshold. In study 1, the results did not show an effect of cold pressor pain on subsequent 2-back task performance. Results of study 2 indicated that heat pain impaired concurrent 2-back task performance. However, no evidence was found

The Experimental Design Ability Test (EDAT)

ERIC Educational Resources Information Center

Sirum, Karen; Humburg, Jennifer

2011-01-01

Higher education goals include helping students develop evidence based reasoning skills; therefore, scientific thinking skills such as those required to understand the design of a basic experiment are important. The Experimental Design Ability Test (EDAT) measures students' understanding of the criteria for good experimental design through their…

Quasi experimental designs in pharmacist intervention research.

PubMed

Krass, Ines

2016-06-01

Background In the field of pharmacist intervention research it is often difficult to conform to the rigorous requirements of the "true experimental" models, especially the requirement of randomization. When randomization is not feasible, a practice based researcher can choose from a range of "quasi-experimental designs" i.e., non-randomised and at time non controlled. Objective The aim of this article was to provide an overview of quasi-experimental designs, discuss their strengths and weaknesses and to investigate their application in pharmacist intervention research over the previous decade. Results In the literature quasi experimental studies may be classified into five broad categories: quasi-experimental design without control groups; quasi-experimental design that use control groups with no pre-test; quasi-experimental design that use control groups and pre-tests; interrupted time series and stepped wedge designs. Quasi-experimental study design has consistently featured in the evolution of pharmacist intervention research. The most commonly applied of all quasi experimental designs in the practice based research literature are the one group pre-post-test design and the non-equivalent control group design i.e., (untreated control group with dependent pre-tests and post-tests) and have been used to test the impact of pharmacist interventions in general medications management as well as in specific disease states. Conclusion Quasi experimental studies have a role to play as proof of concept, in the pilot phases of interventions when testing different intervention components, especially in complex interventions. They serve to develop an understanding of possible intervention effects: while in isolation they yield weak evidence of clinical efficacy, taken collectively, they help build a body of evidence in support of the value of pharmacist interventions across different practice settings and countries. However, when a traditional RCT is not feasible for

Spin bearing retainer design optimization

NASA Technical Reports Server (NTRS)

Boesiger, Edward A.; Warner, Mark H.

1991-01-01

The dynamics behavior of spin bearings for momentum wheels (control-moment gyroscope, reaction wheel assembly) is critical to satellite stability and life. Repeated bearing retainer instabilities hasten lubricant deterioration and can lead to premature bearing failure and/or unacceptable vibration. These instabilities are typically distinguished by increases in torque, temperature, audible noise, and vibration induced by increases into the bearing cartridge. Ball retainer design can be optimized to minimize these occurrences. A retainer was designed using a previously successful smaller retainer as an example. Analytical methods were then employed to predict its behavior and optimize its configuration.

MDTri: robust and efficient global mixed integer search of spaces of multiple ternary alloys: A DIRECT-inspired optimization algorithm for experimentally accessible computational material design

DOE PAGES

Graf, Peter A.; Billups, Stephen

2017-07-24

Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less

MDTri: robust and efficient global mixed integer search of spaces of multiple ternary alloys: A DIRECT-inspired optimization algorithm for experimentally accessible computational material design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Graf, Peter A.; Billups, Stephen

Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less

Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature.

PubMed

Van Derlinden, E; Bernaerts, K; Van Impe, J F

2010-05-21

Optimal experiment design for parameter estimation (OED/PE) has become a popular tool for efficient and accurate estimation of kinetic model parameters. When the kinetic model under study encloses multiple parameters, different optimization strategies can be constructed. The most straightforward approach is to estimate all parameters simultaneously from one optimal experiment (single OED/PE strategy). However, due to the complexity of the optimization problem or the stringent limitations on the system's dynamics, the experimental information can be limited and parameter estimation convergence problems can arise. As an alternative, we propose to reduce the optimization problem to a series of two-parameter estimation problems, i.e., an optimal experiment is designed for a combination of two parameters while presuming the other parameters known. Two different approaches can be followed: (i) all two-parameter optimal experiments are designed based on identical initial parameter estimates and parameters are estimated simultaneously from all resulting experimental data (global OED/PE strategy), and (ii) optimal experiments are calculated and implemented sequentially whereby the parameter values are updated intermediately (sequential OED/PE strategy). This work exploits OED/PE for the identification of the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993). This kinetic model describes the effect of temperature on the microbial growth rate and encloses four parameters. The three OED/PE strategies are considered and the impact of the OED/PE design strategy on the accuracy of the CTMI parameter estimation is evaluated. Based on a simulation study, it is observed that the parameter values derived from the sequential approach deviate more from the true parameters than the single and global strategy estimates. The single and global OED/PE strategies are further compared based on experimental data obtained from design implementation in a bioreactor

An equivalent method for optimization of particle tuned mass damper based on experimental parametric study

NASA Astrophysics Data System (ADS)

Lu, Zheng; Chen, Xiaoyi; Zhou, Ying

2018-04-01

A particle tuned mass damper (PTMD) is a creative combination of a widely used tuned mass damper (TMD) and an efficient particle damper (PD) in the vibration control area. The performance of a one-storey steel frame attached with a PTMD is investigated through free vibration and shaking table tests. The influence of some key parameters (filling ratio of particles, auxiliary mass ratio, and particle density) on the vibration control effects is investigated, and it is shown that the attenuation level significantly depends on the filling ratio of particles. According to the experimental parametric study, some guidelines for optimization of the PTMD that mainly consider the filling ratio are proposed. Furthermore, an approximate analytical solution based on the concept of an equivalent single-particle damper is proposed, and it shows satisfied agreement between the simulation and experimental results. This simplified method is then used for the preliminary optimal design of a PTMD system, and a case study of a PTMD system attached to a five-storey steel structure following this optimization process is presented.

Application of optimal control theory to the design of broadband excitation pulses for high-resolution NMR.

PubMed

Skinner, Thomas E; Reiss, Timo O; Luy, Burkhard; Khaneja, Navin; Glaser, Steffen J

2003-07-01

Optimal control theory is considered as a methodology for pulse sequence design in NMR. It provides the flexibility for systematically imposing desirable constraints on spin system evolution and therefore has a wealth of applications. We have chosen an elementary example to illustrate the capabilities of the optimal control formalism: broadband, constant phase excitation which tolerates miscalibration of RF power and variations in RF homogeneity relevant for standard high-resolution probes. The chosen design criteria were transformation of I(z)-->I(x) over resonance offsets of +/- 20 kHz and RF variability of +/-5%, with a pulse length of 2 ms. Simulations of the resulting pulse transform I(z)-->0.995I(x) over the target ranges in resonance offset and RF variability. Acceptably uniform excitation is obtained over a much larger range of RF variability (approximately 45%) than the strict design limits. The pulse performs well in simulations that include homonuclear and heteronuclear J-couplings. Experimental spectra obtained from 100% 13C-labeled lysine show only minimal coupling effects, in excellent agreement with the simulations. By increasing pulse power and reducing pulse length, we demonstrate experimental excitation of 1H over +/-32 kHz, with phase variations in the spectra <8 degrees and peak amplitudes >93% of maximum. Further improvements in broadband excitation by optimized pulses (BEBOP) may be possible by applying more sophisticated implementations of the optimal control formalism.

Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems

DOE PAGES

Rangarajan, Srinivas; Maravelias, Christos T.; Mavrikakis, Manos

2017-11-09

Here, we present a general optimization-based framework for (i) ab initio and experimental data driven mechanistic modeling and (ii) optimal catalyst design of heterogeneous catalytic systems. Both cases are formulated as a nonlinear optimization problem that is subject to a mean-field microkinetic model and thermodynamic consistency requirements as constraints, for which we seek sparse solutions through a ridge (L 2 regularization) penalty. The solution procedure involves an iterative sequence of forward simulation of the differential algebraic equations pertaining to the microkinetic model using a numerical tool capable of handling stiff systems, sensitivity calculations using linear algebra, and gradient-based nonlinear optimization.more » A multistart approach is used to explore the solution space, and a hierarchical clustering procedure is implemented for statistically classifying potentially competing solutions. An example of methanol synthesis through hydrogenation of CO and CO 2 on a Cu-based catalyst is used to illustrate the framework. The framework is fast, is robust, and can be used to comprehensively explore the model solution and design space of any heterogeneous catalytic system.« less

Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rangarajan, Srinivas; Maravelias, Christos T.; Mavrikakis, Manos

Here, we present a general optimization-based framework for (i) ab initio and experimental data driven mechanistic modeling and (ii) optimal catalyst design of heterogeneous catalytic systems. Both cases are formulated as a nonlinear optimization problem that is subject to a mean-field microkinetic model and thermodynamic consistency requirements as constraints, for which we seek sparse solutions through a ridge (L 2 regularization) penalty. The solution procedure involves an iterative sequence of forward simulation of the differential algebraic equations pertaining to the microkinetic model using a numerical tool capable of handling stiff systems, sensitivity calculations using linear algebra, and gradient-based nonlinear optimization.more » A multistart approach is used to explore the solution space, and a hierarchical clustering procedure is implemented for statistically classifying potentially competing solutions. An example of methanol synthesis through hydrogenation of CO and CO 2 on a Cu-based catalyst is used to illustrate the framework. The framework is fast, is robust, and can be used to comprehensively explore the model solution and design space of any heterogeneous catalytic system.« less

A sequential linear optimization approach for controller design

NASA Technical Reports Server (NTRS)

Horta, L. G.; Juang, J.-N.; Junkins, J. L.

1985-01-01

A linear optimization approach with a simple real arithmetic algorithm is presented for reliable controller design and vibration suppression of flexible structures. Using first order sensitivity of the system eigenvalues with respect to the design parameters in conjunction with a continuation procedure, the method converts a nonlinear optimization problem into a maximization problem with linear inequality constraints. The method of linear programming is then applied to solve the converted linear optimization problem. The general efficiency of the linear programming approach allows the method to handle structural optimization problems with a large number of inequality constraints on the design vector. The method is demonstrated using a truss beam finite element model for the optimal sizing and placement of active/passive-structural members for damping augmentation. Results using both the sequential linear optimization approach and nonlinear optimization are presented and compared. The insensitivity to initial conditions of the linear optimization approach is also demonstrated.

Monitoring and optimizing the co-composting of dewatered sludge: a mixture experimental design approach.

PubMed

Komilis, Dimitrios; Evangelou, Alexandros; Voudrias, Evangelos

2011-09-01

The management of dewatered wastewater sludge is a major issue worldwide. Sludge disposal to landfills is not sustainable and thus alternative treatment techniques are being sought. The objective of this work was to determine optimal mixing ratios of dewatered sludge with other organic amendments in order to maximize the degradability of the mixtures during composting. This objective was achieved using mixture experimental design principles. An additional objective was to study the impact of the initial C/N ratio and moisture contents on the co-composting process of dewatered sludge. The composting process was monitored through measurements of O(2) uptake rates, CO(2) evolution, temperature profile and solids reduction. Eight (8) runs were performed in 100 L insulated air-tight bioreactors under a dynamic air flow regime. The initial mixtures were prepared using dewatered wastewater sludge, mixed paper wastes, food wastes, tree branches and sawdust at various initial C/N ratios and moisture contents. According to empirical modeling, mixtures of sludge and food waste mixtures at 1:1 ratio (ww, wet weight) maximize degradability. Structural amendments should be maintained below 30% to reach thermophilic temperatures. The initial C/N ratio and initial moisture content of the mixture were not found to influence the decomposition process. The bio C/bio N ratio started from around 10, for all runs, decreased during the middle of the process and increased to up to 20 at the end of the process. The solid carbon reduction of the mixtures without the branches ranged from 28% to 62%, whilst solid N reductions ranged from 30% to 63%. Respiratory quotients had a decreasing trend throughout the composting process. Copyright © 2011 Elsevier Ltd. All rights reserved.

Aerodynamic design and optimization in one shot

NASA Technical Reports Server (NTRS)

Ta'asan, Shlomo; Kuruvila, G.; Salas, M. D.

1992-01-01

This paper describes an efficient numerical approach for the design and optimization of aerodynamic bodies. As in classical optimal control methods, the present approach introduces a cost function and a costate variable (Lagrange multiplier) in order to achieve a minimum. High efficiency is achieved by using a multigrid technique to solve for all the unknowns simultaneously, but restricting work on a design variable only to grids on which their changes produce nonsmooth perturbations. Thus, the effort required to evaluate design variables that have nonlocal effects on the solution is confined to the coarse grids. However, if a variable has a nonsmooth local effect on the solution in some neighborhood, it is relaxed in that neighborhood on finer grids. The cost of solving the optimal control problem is shown to be approximately two to three times the cost of the equivalent analysis problem. Examples are presented to illustrate the application of the method to aerodynamic design and constraint optimization.

Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality.

PubMed

Otero-Muras, Irene; Banga, Julio R

2017-07-21

In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.

Experimental design based response surface methodology optimization of ultrasonic assisted adsorption of safaranin O by tin sulfide nanoparticle loaded on activated carbon.

PubMed

Roosta, M; Ghaedi, M; Daneshfar, A; Sahraei, R

2014-03-25

In this research, the adsorption rate of safranine O (SO) onto tin sulfide nanoparticle loaded on activated carbon (SnS-NPAC) was accelerated by the ultrasound. SnS-NP-AC was characterized by different techniques such as SEM, XRD and UV-Vis measurements. The present results confirm that the ultrasound assisted adsorption method has remarkable ability to improve the adsorption efficiency. The influence of parameters such as the sonication time, adsorbent dosage, pH and initial SO concentration was examined and evaluated by central composite design (CCD) combined with response surface methodology (RSM) and desirability function (DF). Conducting adsorption experiments at optimal conditions set as 4 min of sonication time, 0.024 g of adsorbent, pH 7 and 18 mg L(-1) SO make admit to achieve high removal percentage (98%) and high adsorption capacity (50.25 mg g(-)(1)). A good agreement between experimental and predicted data in this study was observed. The experimental equilibrium data fitting to Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show that the Langmuir model is a good and suitable model for evaluation and the actual behavior of adsorption. Kinetic evaluation of experimental data showed that the adsorption processes followed well pseudo-second-order and intraparticle diffusion models. Copyright © 2013. Published by Elsevier B.V.

Experimental design based response surface methodology optimization of ultrasonic assisted adsorption of safaranin O by tin sulfide nanoparticle loaded on activated carbon

NASA Astrophysics Data System (ADS)

Roosta, M.; Ghaedi, M.; Daneshfar, A.; Sahraei, R.

2014-03-01

In this research, the adsorption rate of safranine O (SO) onto tin sulfide nanoparticle loaded on activated carbon (SnS-NPAC) was accelerated by the ultrasound. SnS-NP-AC was characterized by different techniques such as SEM, XRD and UV-Vis measurements. The present results confirm that the ultrasound assisted adsorption method has remarkable ability to improve the adsorption efficiency. The influence of parameters such as the sonication time, adsorbent dosage, pH and initial SO concentration was examined and evaluated by central composite design (CCD) combined with response surface methodology (RSM) and desirability function (DF). Conducting adsorption experiments at optimal conditions set as 4 min of sonication time, 0.024 g of adsorbent, pH 7 and 18 mg L-1 SO make admit to achieve high removal percentage (98%) and high adsorption capacity (50.25 mg g-1). A good agreement between experimental and predicted data in this study was observed. The experimental equilibrium data fitting to Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show that the Langmuir model is a good and suitable model for evaluation and the actual behavior of adsorption. Kinetic evaluation of experimental data showed that the adsorption processes followed well pseudo-second-order and intraparticle diffusion models.

Experimental optimization of directed field ionization

NASA Astrophysics Data System (ADS)

Liu, Zhimin Cheryl; Gregoric, Vincent C.; Carroll, Thomas J.; Noel, Michael W.

2017-04-01

The state distribution of an ensemble of Rydberg atoms is commonly measured using selective field ionization. The resulting time resolved ionization signal from a single energy eigenstate tends to spread out due to the multiple avoided Stark level crossings atoms must traverse on the way to ionization. The shape of the ionization signal can be modified by adding a perturbation field to the main field ramp. Here, we present experimental results of the manipulation of the ionization signal using a genetic algorithm. We address how both the genetic algorithm and the experimental parameters were adjusted to achieve an optimized result. This work was supported by the National Science Foundation under Grants No. 1607335 and No. 1607377.

Multidisciplinary design optimization - An emerging new engineering discipline

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1993-01-01

A definition of the multidisciplinary design optimization (MDO) is introduced, and functionality and relationship of the MDO conceptual components are examined. The latter include design-oriented analysis, approximation concepts, mathematical system modeling, design space search, an optimization procedure, and a humane interface.

Evolutionary optimization methods for accelerator design

NASA Astrophysics Data System (ADS)

Poklonskiy, Alexey A.

Many problems from the fields of accelerator physics and beam theory can be formulated as optimization problems and, as such, solved using optimization methods. Despite growing efficiency of the optimization methods, the adoption of modern optimization techniques in these fields is rather limited. Evolutionary Algorithms (EAs) form a relatively new and actively developed optimization methods family. They possess many attractive features such as: ease of the implementation, modest requirements on the objective function, a good tolerance to noise, robustness, and the ability to perform a global search efficiently. In this work we study the application of EAs to problems from accelerator physics and beam theory. We review the most commonly used methods of unconstrained optimization and describe the GATool, evolutionary algorithm and the software package, used in this work, in detail. Then we use a set of test problems to assess its performance in terms of computational resources, quality of the obtained result, and the tradeoff between them. We justify the choice of GATool as a heuristic method to generate cutoff values for the COSY-GO rigorous global optimization package for the COSY Infinity scientific computing package. We design the model of their mutual interaction and demonstrate that the quality of the result obtained by GATool increases as the information about the search domain is refined, which supports the usefulness of this model. We Giscuss GATool's performance on the problems suffering from static and dynamic noise and study useful strategies of GATool parameter tuning for these and other difficult problems. We review the challenges of constrained optimization with EAs and methods commonly used to overcome them. We describe REPA, a new constrained optimization method based on repairing, in exquisite detail, including the properties of its two repairing techniques: REFIND and REPROPT. We assess REPROPT's performance on the standard constrained

Multidisciplinary design optimization using multiobjective formulation techniques

NASA Technical Reports Server (NTRS)

Chattopadhyay, Aditi; Pagaldipti, Narayanan S.

1995-01-01

This report addresses the development of a multidisciplinary optimization procedure using an efficient semi-analytical sensitivity analysis technique and multilevel decomposition for the design of aerospace vehicles. A semi-analytical sensitivity analysis procedure is developed for calculating computational grid sensitivities and aerodynamic design sensitivities. Accuracy and efficiency of the sensitivity analysis procedure is established through comparison of the results with those obtained using a finite difference technique. The developed sensitivity analysis technique are then used within a multidisciplinary optimization procedure for designing aerospace vehicles. The optimization problem, with the integration of aerodynamics and structures, is decomposed into two levels. Optimization is performed for improved aerodynamic performance at the first level and improved structural performance at the second level. Aerodynamic analysis is performed by solving the three-dimensional parabolized Navier Stokes equations. A nonlinear programming technique and an approximate analysis procedure are used for optimization. The proceduredeveloped is applied to design the wing of a high speed aircraft. Results obtained show significant improvements in the aircraft aerodynamic and structural performance when compared to a reference or baseline configuration. The use of the semi-analytical sensitivity technique provides significant computational savings.

The role of the optimization process in illumination design

NASA Astrophysics Data System (ADS)

Gauvin, Michael A.; Jacobsen, David; Byrne, David J.

2015-07-01

This paper examines the role of the optimization process in illumination design. We will discuss why the starting point of the optimization process is crucial to a better design and why it is also important that the user understands the basic design problem and implements the correct merit function. Both a brute force method and the Downhill Simplex method will be used to demonstrate optimization methods with focus on using interactive design tools to create better starting points to streamline the optimization process.

Stimulated Brillouin scattering materials, experimental design and applications: A review

NASA Astrophysics Data System (ADS)

Bai, Zhenxu; Yuan, Hang; Liu, Zhaohong; Xu, Pengbai; Gao, Qilin; Williams, Robert J.; Kitzler, Ondrej; Mildren, Richard P.; Wang, Yulei; Lu, Zhiwei

2018-01-01

Stimulated Brillouin scattering (SBS), as one type of third-order nonlinear optics effect, is extensively exploited and rapidly developed in the field of lasers and optoelectronics. A large number of theoretical and experimental studies on SBS have been carried out in the past decades. Especially, the exploration of new SBS materials and new types of SBS modulation methods have been engaged simultaneously, as the properties of different materials have great influence on the SBS performance such as generation threshold, Brillouin amplification efficiency, frequency shift, breakdown threshold, etc. This article provides a comprehensive review of the characteristics of different types of SBS materials, SBS applications, experimental design methods, as well as the parameter optimization method, which is expected to provide reference and guidance to SBS related experiments.

A Library of Optimization Algorithms for Organizational Design

DTIC Science & Technology

2005-01-01

N00014-98-1-0465 and #N00014-00-1-0101 A Library of Optimization Algorithms for Organizational Design Georgiy M. Levchuk Yuri N. Levchuk Jie Luo...E-mail: Krishna@engr.uconn.edu Abstract This paper presents a library of algorithms to solve a broad range of optimization problems arising in the...normative design of organizations to execute a specific mission. The use of specific optimization algorithms for different phases of the design process

Progress in multidisciplinary design optimization at NASA Langley

NASA Technical Reports Server (NTRS)

Padula, Sharon L.

1993-01-01

Multidisciplinary Design Optimization refers to some combination of disciplinary analyses, sensitivity analysis, and optimization techniques used to design complex engineering systems. The ultimate objective of this research at NASA Langley Research Center is to help the US industry reduce the costs associated with development, manufacturing, and maintenance of aerospace vehicles while improving system performance. This report reviews progress towards this objective and highlights topics for future research. Aerospace design problems selected from the author's research illustrate strengths and weaknesses in existing multidisciplinary optimization techniques. The techniques discussed include multiobjective optimization, global sensitivity equations and sequential linear programming.

On the proper study design applicable to experimental balneology.

PubMed

Varga, Csaba

2016-08-01

The simple message of this paper is that it is the high time to reevaluate the strategies and optimize the efforts for investigation of thermal (spa) waters. Several articles trying to clear mode of action of medicinal waters have been published up to now. Almost all studies apply the unproven hypothesis, namely the inorganic ingredients are in close connection with healing effects of bathing. Change of paradigm would be highly necessary in this field taking into consideration the presence of several biologically active organic substances in these waters. A successful design for experimental mechanistic studies is approved.

Optimal designs of staggered dean vortex micromixers.

PubMed

Chen, Jyh Jian; Chen, Chun Huei; Shie, Shian Ruei

2011-01-01

A novel parallel laminar micromixer with a two-dimensional staggered Dean Vortex micromixer is optimized and fabricated in our study. Dean vortices induced by centrifugal forces in curved rectangular channels cause fluids to produce secondary flows. The split-and-recombination (SAR) structures of the flow channels and the impinging effects result in the reduction of the diffusion distance of two fluids. Three different designs of a curved channel micromixer are introduced to evaluate the mixing performance of the designed micromixer. Mixing performances are demonstrated by means of a pH indicator using an optical microscope and fluorescent particles via a confocal microscope at different flow rates corresponding to Reynolds numbers (Re) ranging from 0.5 to 50. The comparison between the experimental data and numerical results shows a very reasonable agreement. At a Re of 50, the mixing length at the sixth segment, corresponding to the downstream distance of 21.0 mm, can be achieved in a distance 4 times shorter than when the Re equals 1. An optimization of this micromixer is performed with two geometric parameters. These are the angle between the lines from the center to two intersections of two consecutive curved channels, θ, and the angle between two lines of the centers of three consecutive curved channels, ϕ. It can be found that the maximal mixing index is related to the maximal value of the sum of θ and ϕ, which is equal to 139.82°.

Optimal Designs of Staggered Dean Vortex Micromixers

PubMed Central

Chen, Jyh Jian; Chen, Chun Huei; Shie, Shian Ruei

2011-01-01

A novel parallel laminar micromixer with a two-dimensional staggered Dean Vortex micromixer is optimized and fabricated in our study. Dean vortices induced by centrifugal forces in curved rectangular channels cause fluids to produce secondary flows. The split-and-recombination (SAR) structures of the flow channels and the impinging effects result in the reduction of the diffusion distance of two fluids. Three different designs of a curved channel micromixer are introduced to evaluate the mixing performance of the designed micromixer. Mixing performances are demonstrated by means of a pH indicator using an optical microscope and fluorescent particles via a confocal microscope at different flow rates corresponding to Reynolds numbers (Re) ranging from 0.5 to 50. The comparison between the experimental data and numerical results shows a very reasonable agreement. At a Re of 50, the mixing length at the sixth segment, corresponding to the downstream distance of 21.0 mm, can be achieved in a distance 4 times shorter than when the Re equals 1. An optimization of this micromixer is performed with two geometric parameters. These are the angle between the lines from the center to two intersections of two consecutive curved channels, θ, and the angle between two lines of the centers of three consecutive curved channels, ϕ. It can be found that the maximal mixing index is related to the maximal value of the sum of θ and ϕ, which is equal to 139.82°. PMID:21747691

Comparison of Traditional Design Nonlinear Programming Optimization and Stochastic Methods for Structural Design

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Pai, Shantaram S.; Coroneos, Rula M.

2010-01-01

Structural design generated by traditional method, optimization method and the stochastic design concept are compared. In the traditional method, the constraints are manipulated to obtain the design and weight is back calculated. In design optimization, the weight of a structure becomes the merit function with constraints imposed on failure modes and an optimization algorithm is used to generate the solution. Stochastic design concept accounts for uncertainties in loads, material properties, and other parameters and solution is obtained by solving a design optimization problem for a specified reliability. Acceptable solutions were produced by all the three methods. The variation in the weight calculated by the methods was modest. Some variation was noticed in designs calculated by the methods. The variation may be attributed to structural indeterminacy. It is prudent to develop design by all three methods prior to its fabrication. The traditional design method can be improved when the simplified sensitivities of the behavior constraint is used. Such sensitivity can reduce design calculations and may have a potential to unify the traditional and optimization methods. Weight versus reliabilitytraced out an inverted-S-shaped graph. The center of the graph corresponded to mean valued design. A heavy design with weight approaching infinity could be produced for a near-zero rate of failure. Weight can be reduced to a small value for a most failure-prone design. Probabilistic modeling of load and material properties remained a challenge.

A case study on topology optimized design for additive manufacturing

NASA Astrophysics Data System (ADS)

Gebisa, A. W.; Lemu, H. G.

2017-12-01

Topology optimization is an optimization method that employs mathematical tools to optimize material distribution in a part to be designed. Earlier developments of topology optimization considered conventional manufacturing techniques that have limitations in producing complex geometries. This has hindered the topology optimization efforts not to fully be realized. With the emergence of additive manufacturing (AM) technologies, the technology that builds a part layer upon a layer directly from three dimensional (3D) model data of the part, however, producing complex shape geometry is no longer an issue. Realization of topology optimization through AM provides full design freedom for the design engineers. The article focuses on topologically optimized design approach for additive manufacturing with a case study on lightweight design of jet engine bracket. The study result shows that topology optimization is a powerful design technique to reduce the weight of a product while maintaining the design requirements if additive manufacturing is considered.