Showing papers by "Baskar Ganapathysubramanian published in 2013"

Engineering fluid flow using sequenced microstructures

Abstract: Controlling the shape of fluid streams is important across scales: from industrial processing to control of biomolecular interactions. Previous approaches to control fluid streams have focused mainly on creating chaotic flows to enhance mixing. Here we develop an approach to apply order using sequences of fluid transformations rather than enhancing chaos. We investigate the inertial flow deformations around a library of single cylindrical pillars within a microfluidic channel and assemble these net fluid transformations to engineer fluid streams. As these transformations provide a deterministic mapping of fluid elements from upstream to downstream of a pillar, we can sequentially arrange pillars to apply the associated nested maps and, therefore, create complex fluid structures without additional numerical simulation. To show the range of capabilities, we present sequences that sculpt the cross-sectional shape of a stream into complex geometries, move and split a fluid stream, perform solution exchange and achieve particle separation. A general strategy to engineer fluid streams into a broad class of defined configurations in which the complexity of the nonlinear equations of fluid motion are abstracted from the user is a first step to programming streams of any desired shape, which would be useful for biological, chemical and materials automation.

Quantifying organic solar cell morphology: a computational study of three-dimensional maps

Abstract: Establishing how fabrication conditions quantitatively affect the morphology of organic blends opens the possibility of rationally designing higher efficiency materials; yet such a relationship remains elusive. One of the major challenges stems from incomplete three-dimensional representations of morphology, which is due to the difficulties of performing accurate morphological measurements. Recently, three-dimensional measurements of mixed organic layers using electron tomography with high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) provided maps of morphology with high resolution and detail. Using a simple, yet powerful, computational tool kit, these complex 3D datasets are converted into a set of physically meaningful morphology descriptors. These descriptors provide means for converting these large, complicated datasets (∼5 × 107 voxels) into simple, descriptive parameters, enabling a quantitative comparison among morphologies fabricated under different conditions. A set of P3HT:endohedral fullerene bulk-heterojunctions, fabricated under conditions specifically chosen to yield a wide range of morphologies, are examined. The effects of processing conditions and electrode presence on interfacial area, domain size distribution, connectivity, and tortuosity of charge transport paths are herein determined directly from real-space data for the first time. Through this characterization, quantitative insights into the role of processing in morphology are provided, as well as a more complete picture of the consequences of a three-phase morphology. The analysis demonstrates a methodology which can enable a deeper understanding into morphology control.

Exploring the Use of Elastic Resource Federations for Enabling Large-Scale Scientific Workflows

Abstract: An important class of scientific and engineering workflows, e.g. those used for uncertainty quantification, design optimization and parametric studies, naturally map onto the Many-Task Computing (MTC) paradigm. However, what distinguishes these workloads is a unique combination of dynamically changing resource requirements and very large computational and throughput demands. Such workflows can benefit from an elastic execution infrastructure that is based on the dynamic federation of resources. The overarching goal of this paper is to explore the nature of such an elastic, dynamically federated platform, and to experimentally demonstrate that it can effectively support the targeted class of scientific and engineering workflows. As a driving application for our study we use the problem of constructing a phase diagram in microfluidics, which is representative for a broader class of parameter space interrogation techniques. To satisfy its computational demands of 2.5 million corehours within reasonable time limits, we construct a dynamic federation of ten HPC resources from six different computing centers. This experiment delivers the most comprehensive data on fluid flow in a microchannel with an obstacle. Moreover, it offers important insights that enable us to identify key requirements and architectural components that a platform based on federated resources must provide in order to efficiently handle considered scientific MTC workloads.

Data Dimensionality Reduction in Materials Science

Abstract: Materials science research has witnessed an increasing use of data-mining techniques in establishing structure–process–property relationships. Significant advances in high-throughput experiments and computational capability have resulted in the generation of huge amounts of data. Various statistical methods are currently employed to reduce the noise, redundancy, and dimensionality of the data to make analysis more tractable. Popular methods for reduction (such as principal component analysis) assume a linear relationship between the input and output variables. Recent developments in nonlinear reduction (neural networks, self-organizing maps), though successful, have computational issues associated with convergence and scalability. This chapter reviews various spectral-based techniques that efficiently unravel linear and nonlinear structures in the data, which can subsequently be used to tractably investigate structure–property–process relationships. We compare and contrast the advantages and disadvantages of these techniques and discuss the mathematical and algorithmic underpinning of these methods. In addition, we describe techniques (based on graph-theoretic analysis) to estimate the optimal dimensionality of the low-dimensional parametric representation. We show how these techniques can be packaged into a modular, computationally scalable software framework with a graphical user interface – Scalable Extensible Toolkit for Dimensionality Reduction (SETDiR). This interface helps to separate out the mathematics and computational aspects from the material science applications, thus significantly enhancing utility to the materials science community. The applicability of the framework in constructing reduced order models of complicated materials data sets is illustrated.

Engineering fluid flow using sequenced

Abstract: Controlling the shape of fluid streams is important across scales: from industrial processing to control of biomolecular interactions. Previous approaches to control fluid streams have focused mainly on creating chaotic flows to enhance mixing. Here we develop an approach to apply order using sequences of fluid transformations rather than enhancing chaos. We investigate the inertial flow deformations around a library of single cylindrical pillars within a microfluidic channel and assemble these net fluid transformations to engineer fluid streams. As these transformations provide a deterministic mapping of fluid elements from upstream to downstream of a pillar, we can sequentially arrange pillars to apply the associated nested maps and, therefore, create complex fluid structures without additional numerical simulation. To show the range of capabilities, we present sequences that sculpt the cross-sectional shape of a stream into complex geometries, move and split a fluid stream, perform solution exchange and achieve particle separation. A general strategy to engineer fluid streams into a broad class of defined configurations in which the complexity of the nonlinear equations of fluid motion are abstracted from the user is a first step to programming streams of any desired shape, which would be useful for biological, chemical and materials automation.

Application of Computational Homology and Graph-Theoretic Approaches for Quantitative Chemical Imaging in Atom Probe Tomography

Abstract: Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.