Showing papers by "Baskar Ganapathysubramanian published in 2014"
TL;DR: A new software framework to capture various traits from a single image of seedling roots based on the mathematical notion of converting images of roots into an equivalent graph is developed, which allows automated querying of multiple traits simply as graph operations.
Abstract: The maize root system is crucial for plant establishment as well as water and nutrient uptake. There is substantial genetic and phenotypic variation for root architecture, which gives opportunity for selection. Root traits, however, have not been used as selection criterion mainly due to the difficulty in measuring them, as well as their quantitative mode of inheritance. Seedling root traits offer an opportunity to study multiple individuals and to enable repeated measurements per year as compared to adult root phenotyping. We developed a new software framework to capture various traits from a single image of seedling roots. This framework is based on the mathematical notion of converting images of roots into an equivalent graph. This allows automated querying of multiple traits simply as graph operations. This framework is furthermore extendable to 3D tomography image data. In order to evaluate this tool, a subset of the 384 inbred lines from the Ames panel, for which extensive genotype by sequencing data are available, was investigated. A genome wide association study was applied to this panel for two traits, Total Root Length and Total Surface Area, captured from seedling root images from WinRhizo Pro 9.0 and the current framework (called ARIA) for comparison using 135,311 single nucleotide polymorphism markers. The trait Total Root Length was found to have significant SNPs in similar regions of the genome when analyzed by both programs. This high-throughput trait capture software system allows for large phenotyping experiments and can help to establish relationships between developmental stages between seedling and adult traits in the future.
89 citations
TL;DR: Eight transformations that could serve as a partial basis for more complex transformations that include making a fluid stream concave and convex, tilting, stretching, splitting, adding a vertex, shifting, and encapsulating another flow stream are shown.
Abstract: The ability to control the shape of a flow in a passive microfluidic device enables potential applications in chemical reaction control, particle separation, and complex material fabrication. Recent work has demonstrated the concept of sculpting fluid streams in a microchannel using a set of pillars or other structures that individually deform a flow in a predictable pre-computed manner. These individual pillars are then placed in a defined sequence within the channel to yield the composition of the individual flow deformations – and ultimately complex user-defined flow shapes. In this way, an elegant mathematical operation can yield the final flow shape for a sequence without an experiment or additional numerical simulation. Although these approaches allow for programming complex flow shapes without understanding the detailed fluid mechanics, the design of an arbitrary flow shape of interest remains difficult, requiring significant design iteration. The development of intuitive basic operations (i.e. higher-level functions that consist of combinations of obstacles) that act on the flow field to create a basis for more complex transformations would be useful in systematically achieving a desired flow shape. Here, we show eight transformations that could serve as a partial basis for more complex transformations. We initially used in-house, freely available custom software (uFlow), which allowed us to arrive at these transformations that include making a fluid stream concave and convex, tilting, stretching, splitting, adding a vertex, shifting, and encapsulating another flow stream. The pillar sequences corresponding to these transformations were subsequently fabricated and optically analyzed using confocal imaging – yielding close agreement with uFlow-predicted shapes. We performed topological analysis on each transformation, characterizing potential sequences leading to these outputs and trends associated with changing diameter and placement of the pillars. We classify operations into four sets of sequence-building concatenations: stacking, recursion, mirroring, and shaping. The developed basis should help in the design of microfluidic systems that have a phenomenal variety of applications, such as optofluidic lensing, enhanced heat transfer, or new polymer fiber design.
43 citations
TL;DR: In this article, the authors identify four modes of phase formation and subsequent propagation within the thinning film during solvent-based fabrication and construct a mode diagram that maps processing conditions with individual modes.
Abstract: Solvent-based fabrication is a flexible and affordable approach to manufacture polymer thin films. The properties of products made from such films can be tailored by the internal organization (morphology) of the films. However, a precise knowledge of morphology evolution leading to the final film structure remains elusive, thus limiting morphology control to a trial and error approach. In particular, understanding when and where phases are formed, and how they evolve would provide rational guidelines for more rigorous control. Here, we identify four modes of phase formation and subsequent propagation within the thinning film during solvent-based fabrication. We unravel the origin and propagation characteristics of each of these modes. Finally, we construct a mode diagram that maps processing conditions with individual modes. The idea introduced here enables choosing processing conditions to tailor film morphology characteristics and paves the ground for a deeper understanding of morphology control with the ultimate goal of precise, yet affordable, morphology manipulation for a large spectrum of applications.
40 citations
TL;DR: It's argued that the fusion of federated computing and real-life engineering problems can be brought to the average user if relevant middleware is provided and it's possible to build a computational federation that's easy for end users to implement, and is elastic, resilient, and scalable.
Abstract: The complexity of many problems in science and engineering requires computational capacity exceeding what the average user can expect from a single computational center. While many of these problems can be viewed as a set of independent tasks, their collective complexity easily requires millions of core-hours on any high-power computing (HPC) resource, and throughput that can't be sustained by a single, multiuser queuing system. An exploration of the use of aggregated HPC resources to solve large-scale engineering problems shows that it's possible to build a computational federation that's easy for end users to implement, and is elastic, resilient, and scalable. Here, the authors argue that the fusion of federated computing and real-life engineering problems can be brought to the average user if relevant middleware is provided. They report on the use of federation of 10 distributed heterogeneous HPC resources to perform a large-scale interrogation of the parameter space in the microscale fluid flow problem.
33 citations
TL;DR: Analysis in terms of fraction of intra- and interchain charge transports revealed that processing schedule affects the average vertical orientation of polymer chains, which may be crucial for enhanced charge transport, nongeminate recombination, and charge collection.
Abstract: The nanomorphologies of the bulk heterojunction (BHJ) layer of polymer solar cells are extremely sensitive to the electrode materials and thermal annealing conditions. In this work, the correlations of electrode materials, thermal annealing sequences, and resultant BHJ nanomorphological details of P3HT:PCBM BHJ polymer solar cell are studied by a series of large-scale, coarse-grained (CG) molecular simulations of system comprised of PEDOT:PSS/P3HT:PCBM/Al layers. Simulations are performed for various configurations of electrode materials as well as processing temperature. The complex CG molecular data are characterized using a novel extension of our graph-based framework to quantify morphology and establish a link between morphology and processing conditions. Our analysis indicates that vertical phase segregation of P3HT:PCBM blend strongly depends on the electrode material and thermal annealing schedule. A thin P3HT-rich film is formed on the top, regardless of bottom electrode material, when the BHJ lay...
25 citations
29 Jun 2014
TL;DR: This paper reviews various spectral‐based techniques that efficiently unravel linear and non‐linear structures in the data which can be used to tractably investigate process‐structure‐property relationships and shows 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).
Abstract: Materials science research has witnessed an increasing use of data mining techniques in establishing process‐structure‐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 the dimensionality of the data to make analysis more tractable. Popular methods for reduction (like principal component analysis) assume a linear relationship between the input and output variables. Recent developments in non‐linear reduction (neural networks, self‐organizing maps), though successful, have computational issues associated with convergence and scalability. Another significant barrier to use dimensionality reduction techniques in materials science is the lack of ease of use owing to their complex mathematical formulations. This paper reviews various spectral‐based techniques that efficiently unravel linear and non‐linear structures in the data which can subsequently be used to tractably investigate process‐structure‐property relationships. 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 materials science applications, thus significantly enhancing utility to the materials science community. The applicability of this framework in constructing reduced order models of complicated materials dataset is illustrated with an example dataset of apatites described in structural descriptor space. Cluster analysis of the low‐dimensional plots yielded interesting insights into the correlation between several structural descriptors like ionic radius and covalence with characteristic properties like apatite stability. This information is crucial as it can promote the use of apatite materials as a potential host system for immobilizing toxic elements.
2 citations
01 Jan 2014
TL;DR: Fontanini et al. as mentioned in this paper investigated the long-term thermal behavior of a 10,000 ft2 building that utilizes a linear air dispersion system by utilizing three dimensional Computational Fluid Dynamic (CFD) simulations.
Abstract: Buildings consume 40% of the U.S. annual energy usage. The energy usage of building can drastically be reduced by proper management of the thermal load in large interior spaces. A well designed space should include systems that balance both occupant comfort and efficiency. Recently linear dispersion ductwork systems have been shown to be a promising solution for creating both comfortable and efficient spaces. We investigate long-term thermal behavior of a 10,000 ft2 building that utilizes a linear air dispersion system. We optimize the operating point of the HVAC system by utilizing three dimensional Computational Fluid Dynamic (CFD) simulations. The long-term air flow, comfort, and energy metrics of the building are analyzed for a number of different flow rates. The steady state simulations consist of 86 million degrees of freedom and resolve physics for length scales that span three orders of magnitude. The simulations utilize High Performance Computing (HPC) and use hundreds of processors to perform the simulations. Finally, challenges associated with the analysis and visualization of the extremely large data sets is discussed. Disciplines Acoustics, Dynamics, and Controls | Computer-Aided Engineering and Design | Mechanical Engineering Comments This proceeding is published as Fontanini, Anthony. "High-Resolution Performance Analysis of a Large Building with Linear Dispersion Ductwork System." ASHRAE Transactions 120 (2014): S1. Posted with permission. This conference proceeding is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/me_conf/186 A. Fontanini is PhD student in the Department of Mechanical Engineering, Iowa State University, Ames, IA. A. Passalacqua is a professor in the Department of Mechanical Engineering, Iowa State University, Ames, IA. U. Vaidya is a professor in the Department of Electrical and Computer Engineering, Iowa State University, Ames, IA. M. Olsen is a professor in the Department of Mechanical Engineering, Iowa State University, Ames, IA. B. Ganapathysubramanian is a professor in the Department of Mechanical Engineering, Iowa State University, Ames, IA. High-Resolution Performance Analysis of a Large Building with Linear Dispersion Ductwork System Anthony Fontanini Alberto Passalacqua, PhD Umesh Vaidya, PhD Michael Olsen, PhD Baskar Ganapathysubramanian, PhD
1 citations