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Results for “fractional factorial”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Using Bayesian variable selection to analyze regular resolution IV two-level fractional factorial designs

Regular two-level fractional factorial designs have complete aliasing in which the associated columns of multiple effects are identical. Here, we show how Bayesian variable selection can be used to analyze experiments that use such designs. In addition to sparsity and hierarchy, Bayesian variable selection naturally incorporates heredity . This prior information is used to identify the most likely combinations of active terms. We also demonstrate the method on simulated and real experiments.

42 ENGINEERING↗
NASA NTRS1973

An algorithm for generating all possible 2(p-q) fractional factorial designs and its use in scientific experimentation

An algorithm and computer program are presented for generating all the distinct 2(p-q) fractional factorial designs. Some applications of this algorithm to the construction of tables of designs and of designs for nonstandard situations and its use in Bayesian design are discussed. An appendix includes a discussion of an actual experiment whose design was facilitated by the algorithm.

Sidik, S. M.↗
DOE OSTI2022

Optimizing the Fabrication of Electrospun Nanofibrous Membrane Using Fractional Factorial Design

Electrospinning, a simple and easily tunable technique that utilizes the force balance between the electrostatic force and the solution surface tension to fabricate membranes out of 1D nanostructured fibers, has garnered much interest especially from the nanotechnology community. Depending on the morphology of the fibers, electrospun membrane has found its way in a wide range of applications, such as oil-water separation, desalination, tissue engineering, and batteries. During an electrospinning process, many factors can affect the morphology of the products, such as viscosity of the solution, voltage supply, discharge distance, feed rate of the solution, and electrospinning time. In this work, we investigated the impacts of the most important parameters on the morphology of electrospun fibrous membrane based on the fractional factorial design. We evaluated the electrospun fibrous membrane morphology using scanning electron microscopy (SEM) and capillary flow porometry. Poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) was chosen for the current study due to its interesting and versatile functionalities including superior hydrophobicity, high free volume, and piezoelectricity. This work provides valuable insight for the fabrication of electrospun membranes, especially for tailoring membrane properties in accordance with any targeted applications.

Zhao, Yajing↗
NASA NTRS2002

Fractional Factorial Experiment Designs to Minimize Configuration Changes in Wind Tunnel Testing

This paper serves as a tutorial to introduce the wind tunnel research community to configuration experiment designs that can satisfy resource constraints in a configuration study involving several variables, without arbitrarily eliminating any of them from the experiment initially. The special case of a configuration study featuring variables at two levels is examined in detail. This is the type of study in which each configuration variable has two natural states - 'on or off', 'deployed or not deployed', 'low or high', and so forth. The basic principles are illustrated by results obtained in configuration studies conducted in the Langley National Transonic Facility and in the ViGYAN Low Speed Tunnel in Hampton, Virginia. The crucial role of interactions among configuration variables is highlighted with an illustration of difficulties that can be encountered when they are not properly taken into account.

DeLoach, Richard↗
DOE OSTI2016

Introduction to Statistically Designed Experiments

Statistically designed experiments can save researchers time and money by reducing the number of necessary experimental trials, while resulting in more conclusive experimental results. Surprisingly, many researchers are still not aware of this efficient and effective experimental methodology. As reported in a 2013 article from Chemical & Engineering News, there has been a resurgence of this methodology in recent years (http://cen.acs.org/articles/91/i13/Design-Experiments-Makes-Comeback.html?h=2027056365). This presentation will provide a brief introduction to statistically designed experiments. The main advantages will be reviewed along with the some basic concepts such as factorial and fractional factorial designs. The recommended sequential approach to experiments will be introduced and finally a case study will be presented to demonstrate this methodology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Examining robustness of model selection with half-normal and LASSO plots for unreplicated factorial designs

When there are constraints on resources, an unreplicated factorial or fractional factorial design can allow efficient exploration of numerous factor and interaction effects. A half-normal plot is a common graphical tool used to compare the relative magnitude of effects and to identify important effects from these experiments when no estimate of error from the experiment is available. An alternative is to use a least absolute shrinkage and selection operation plot to examine the pattern of model selection terms from an experiment. We examine how both the half-normal and least absolute shrinkage and selection operation plots are impacted by the absence of individual observations or an outlier, and the robustness of conclusions obtained from these 2 techniques for identifying important effects from factorial experiments. As a result, the methods are illustrated with 2 examples from the literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Characterization of a Solid Oxide Fuel Cell Gas Turbine Hybrid System Based on a Factorial Design of Experiments Using Hardware Simulation

A full factorial experimental design and a replicated fractional factorial design were carried out using the Hybrid Performance (HyPer) project facility installed at the National Energy Technology Laboratory (NETL), U.S. Department of Energy to simulate gasifer/fuel cell/turbine hybrid power systems. The HyPer facility uses hardware in the loop (HIL) technology that couples a modified recuperated gas turbine cycle with hardware driven by a solid oxide fuel cell model. A 34 full factorial design (FFD) was selected to study the effects of four factors: cold-air, hot-air, bleed-air bypass valves, and the electric load on different parameters such as cathode and turbine inlet temperatures, pressure and mass flow. The results obtained, compared with former results where the experiments were made using one-factor-at-a-time (OFAT), show that no strong interactions between the factors are present in the different parameters of the system. This work also presents a fractional factorial design (ffd) 34-2 in order to analyze replication of the experiments. In addition, a new envelope is described based on the results of the design of experiments (DoE), compared with OFAT experiments, and analyzed in an off-design integrated fuel cell/gas turbine framework. This paper describes the methodology, strategy, and results of these experiments that bring new knowledge concerning the operating state space for this kind of power generation system.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1988

Microcomputer versus mainframe simulations: A case study

The research was conducted to two parts. Part one consisted of a study of the feasibility of running the Space Transportation Model simulation on an office IBM-AT. The second part was to design simulation runs so as to study the effects of certain performance factors on the execution of the simulation model. The results of this research are given in the two reports which follow: Microcomputer vs. Mainframe Simulation: A Case Study and Fractional Factorial Designs of Simulation Runs for the Space Transportation System Operations Model. In the first part, a DOS batch job was written in order to simplify the execution of the simulation model on an office microcomputer. A comparison study was then performed of running the model on NASA-Langley's mainframe computer vs. running on the IBM-AT microcomputer. This was done in order to find the advantages and disadvantages of running the model on each machine with the objective of determining if running of the office PC was practical. The study concluded that it was. The large number of performance parameters in the Space Transportation model precluded running a full factorial design needed to determine the most significant design factors. The second report gives several suggested fractional factorial designs which require far fewer simulation runs in order to determine which factors have significant influence on results.

Bengtson, Neal M.↗
NASA NTRS1972

NAMER: A FORTRAN 4 program for use in optimizing designs of two-level factorial experiments given partial prior information

Under certain specified conditions, the Bayes procedure for designing two-level fractional factorial experiments is that which maximizes the expected utility over all possible choices of parameter-estimator matchings, physical-design variable matchings, defining parameter groups, and sequences of telescoping groups. NAMER computes the utility of all possible matchings of physical variables to design variables and parameters to estimators for a specified choice of defining parameter group or groups. The matching yielding the maximum expected utility is indicated, and detailed information is provided about the optimal matchings and utilities. Complete documentation is given; and an example illustrates input, output, and usage.

Sidik, S. M.↗
NASA NTRS1986

Subset Regression

Backward elimination procedure minimizes prediction errors of equations fitted to factorial experiments. Problem of minimizing prediction error solved for two-level full- or fractional-factorial experiment with 16 factorial points and 0 to 6 center points. Such experiment provides 16 orthogonal coefficient estimates for empirical polynomial model. Each coefficient estimated with low variance error estimated from 16 observations.

Holms, A. G.↗
DOE OSTI2014

Evaluating sub-national building-energy efficiency policy options under uncertainty: Efficient sensitivity testing of alternative climate, technolgical, and socioeconomic futures in a regional intergrated-assessment model.

Improving the energy efficiency of the building stock, commercial equipment and household appliances can have a major impact on energy use, carbon emissions, and building services. Subnational regions such as U.S. states wish to increase their energy efficiency, reduce carbon emissions or adapt to climate change. Evaluating subnational policies to reduce energy use and emissions is difficult because of the uncertainties in socioeconomic factors, technology performance and cost, and energy and climate policies. Climate change may undercut such policies. Assessing these uncertainties can be a significant modeling and computation burden. As part of this uncertainty assessment, this paper demonstrates how a decision-focused sensitivity analysis strategy using fractional factorial methods can be applied to reveal the important drivers for detailed uncertainty analysis.

climate change↗
DOE OSTI2021

Statistical Multiobjective Optimization of Thiospinel CoNi 2 S 4 Nanocrystal Synthesis via Design of Experiments

Thiospinels, such as CoNi 2 S 4 , are showing promise for numerous applications, including as catalysts for the hydrogen evolution reaction, hydrodesulfurization, and oxygen evolution and reduction reactions; however, CoNi 2 S 4 has not been synthesized as small, colloidal nanocrystals with high surface-area-to-volume ratios. Traditional optimization methods to control nanocrystal attributes such as size typically rely upon one variable at a time (OVAT) methods that are not only time and labor intensive but also lack the ability to identify higher-order interactions between experimental variables that affect target outcomes. Herein, we demonstrate that a statistical design of experiments (DoE) approach can optimize the synthesis of CoNi 2 S 4 nanocrystals, allowing for control over the responses of nanocrystal size, size distribution, and isolated yield. After implementing a 2 5–2 fractional factorial design, the statistical screening of five different experimental variables identified temperature, Co:Ni precursor ratio, Co:thiol ratio, and their higher-order interactions as the most critical factors in influencing the aforementioned responses. Second-order design with a Doehlert matrix yielded polynomial functions used to predict the reaction parameters needed to individually optimize all three responses. A multiobjective optimization, allowing for the simultaneous optimization of size, size distribution, and isolated yield, predicted the synthetic conditions needed to achieve a minimum nanocrystal size of 6.1 nm, a minimum polydispersity (σ/$\bar{d}$) of 10%, and a maximum isolated yield of 99%, with a desirability of 96%. The resulting model was experimentally verified by performing reactions under the specified conditions. Furthermore, our work illustrates the advantage of multivariate experimental design as a powerful tool for accelerating control and optimization in nanocrystal syntheses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

A minimum information standard for reproducing bench-scale bacterial cell growth and productivity

Reproducing, exchanging, comparing, and building on each other’s work is foundational to technology advances. Advancing biotechnology calls for reliable reuse of engineered organisms. Reliable reuse of engineered organisms requires reproducible growth and productivity. Here, we identify the experimental factors that have the greatest effect on the growth and productivity of our engineered organisms in order to demonstrate reproducibility for biotechnology. Here, we present a draft of a Minimum Information Standard for Engineered Organism Experiments based on this method. We evaluate the effect of 22 factors on Escherichia coli engineered to produce the small molecule lycopene, and 18 factors on E. coli engineered to produce red fluorescent protein. Container geometry and shaking have the greatest effect on product titer and yield. We reproduce our results under two different conditions of reproducibility: conditions of use (different fractional factorial experiments), and time (48 biological replicates performed on 12 different days over four months).

59 BASIC BIOLOGICAL SCIENCES↗