126 research outputs found
Bayesian estimation of Pseudomonas aeruginosa viscoelastic properties based on creep responses of wild type, rugose, and mucoid variant biofilms
No full textPseudomonas aeruginosa biofilms are relevant for a variety of disease settings, including pulmonary infections in people with cystic fibrosis. Biofilms are initiated by individual bacteria that undergo a phenotypic switch and produce an extracellular polymeric slime (EPS). However, the viscoelastic characteristics of biofilms at different stages of formation and the contributions of different EPS constituents have not been fully explored. For this purpose, we develop and parameterize a mathematical model to study the rheological behavior of three biofilms — P. aeruginosa wild type PAO1, isogenic rugose small colony variant (RSCV), and mucoid variant biofilms against a range of experimental data. Using Bayesian inference to estimate these viscoelastic properties, we quantify the rheological characteristics of the biofilm EPS. We employ a Monte Carlo Markov Chain algorithm to estimate these properties of P. aeruginosa variant biofilms in comparison to those of wild type. This information helps us understand the rheological behavior of biofilms at different stages of their development. The mechanical properties of wild type biofilms change significantly over time and are more sensitive to small changes in their composition than the other two mutants
Die stress and internal friction during quasi-static and dynamic powder compaction
No full textThree commercial iron powders have been studied in terms of their particle morphology and frictional characteristics. The frictional characteristics were measured in shear cell experiments at normal applied loads of up to 4.7 MPa and deduced from measurements of axial stress and radial stress in an instrumented die used in quasi-static and dynamic compaction experiments at axial stresses of up to 300 MPa. Particle shape factor and specific surface area did not greatly affect the coefficient of internal friction. For the range of powder parameters considered, it was most affected by particle size with the smaller particle samples having the higher friction. There was good qualitative agreement between the coefficient of internal friction deduced from shear cell and die pressing experiments. Despite evidence of the development of cohesion in the powders in the die pressing experiments, the linearity of the axial stress-radial stress relations suggested that the powder response could be characterised in terms of a constant value of the coefficient of internal friction. In dynamic compaction, appreciably lower radial stresses are developed. This is consistent with a higher value of internal friction under these loading conditions
Fast orthogonal derivatives on the star
No full textAbstractIn many numerical problems there is the need for obtaining derivatives in the X and Y directions of m variables at each point on an n×n plane. We consider the case where these derivatives are obtained using spectral methods (i.e. n fast Fourier transforms of length n are taken for each component, multiplied by the wave numbers and reverse transformed).On the CDC STAR-100 all data points corresponding to a plane must be stored in contiguous locations if advantage is to be taken of the powerful pipeline hardware of the machine. This means that derivatives in one direction are obtained very efficiently while derivatives in the orthogonal direction require either the substantial overhead of transposition or the use of scalar operations with no benefits of pipelining.An algorithm is described that overcomes this problem by taking derivatives of all components simultaneously. This is made possible by perfect shuffling of data to effect a pseudo-transposition that permits the FFT routine to take transforms of all m components on a plane at one time. Practical experience with this algorithm for m=5 and n=32 shows a 10% speedup for X-derivatives and a 32% speedup for Y-derivatives over the conventional algorithms (in which X and Y derivatives are taken one component at a time and Y derivatives require transposition of data).A theoretical analysis based on available STAR-100 vector instruction timing data predicts that this algorithm is superior to the conventional algorithm for M ≥ 2, n ≤ 128 (problem sizes of practical interest). We show how further improvement in running time may be obtained if derivatives of several components on more than one plane are required.This analysis is applicable to the new generation of STAR computers (the CDC Cyber 203s) since vector instruction timings are essentially unchanged in the new machines
Global Sensitivity Analysis for the Rothermel Model Based on High Dimensional Model Representation
No full textRothermel’s wildland surface fire spread model is widely used in North America. The model outputs depend on a number of input parameters, which can be broadly categorized as fuel model, fuel moisture, terrain and wind parameters. Due to the inevitable presence of uncertainty in the input parameters, the sensitivity of the model output to a given input parameter can be very useful for understanding and controlling the sources of parametric uncertainty. Instead of obtaining the local sensitivity indices, we perform a global sensitivity analysis that considers the synchronous changes of parameters in their respective ranges. The global sensitivity indices corresponding to different parameter groups are computed by constructing the truncated ANOVA-high dimensional model representation for the model outputs with a polynomial expansion approach. We apply global sensitivity analysis to six standard fuel models, namely, short grass, tall grass, chaparral, hardwood litter, timber and light logging slash. Our sensitivity results show similarities as well as differences between fuel models. For example, the sensitivities of the input parameters fuel depth, low heat content, and wind, are large in all fuel models, and as high as 85% of the total model variance in the fuel model light logging slash. On the other hand, the fuel depth explains around 40% of the total variance in the fuel model light logging slash, but only 12% for the fuel model short grass. The quantification of the importance of parameters across fuel models helps identify the parameters for which additional resources should be used to lower their uncertainty, leading to effective fire management.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
PREDICTION OF NOISE GENERATED BY A ROUND NOZZLE JET FLOW USING COMPUTATIONAL AEROACOUSTICS
No full text This paper demonstrates an application of computational aeroacoustics to the prediction of noise generated by a round nozzle jet flow. In this study, the nozzle internal flow and the free jet flow outside are computed simultaneously by a high-order accurate, multi-block, large-eddy simulation (LES) code with overset grid capability. To simulate the jet flow field and its radiated noise, we solve the governing equations on approximately 370 million grid points using high-fidelity numerical schemes developed for computational aeroacoustics. Projection of the near-field noise to the far-field is accomplished by coupling the LES data with the Ffowcs Williams–Hawkings method. The main emphasis of these simulations is to compute the jet flow in sufficient detail to accurately capture the physical processes that lead to noise generation. Two separate simulations are performed using turbulent and laminar inflow conditions at the jet nozzle inlet. Simulation results are compared with the corresponding experimental measurements. Results show that nozzle inflow conditions have an influence on the jet flow field and far-field noise. </jats:p
A Stochastic Collocation Algorithm for Uncertainty Analysis
No full textThis report describes a stochastic collocation method to adequately handle a physically intrinsic uncertainty in the variables of a numerical simulation. For instance, while the standard Galerkin approach to Polynomial Chaos requires multi-dimensional summations over the stochastic basis functions, the stochastic collocation method enables to collapse those summations to a one-dimensional summation only. This report furnishes the essential algorithmic details of the new stochastic collocation method and provides as a numerical example the solution of the Riemann problem with the stochastic collocation method used for the discretization of the stochastic parameters
- …
