The Experts below are selected from a list of 7599 Experts worldwide ranked by ideXlab platform
D. R. Shires - One of the best experts on this subject based on the ideXlab platform.
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Computational Environment for the Multiscale, Multi-Physics Resin Transfer Molding Process
2008Co-Authors: B. J. Henz, D. R. ShiresAbstract:Summary The capability to predict the residual stresses induced during the manufacturing process in composite components is necessary for the timely fielding of new combat systems. At the U.S. Army Research Laboratory we have developed a Computational Environment to model the resin flow, heat transfer, curing, and residual stresses in composite components manufactured with the resin transfer molding (RTM) process. This Computational Environment uses object-oriented programming methods to provide model coupling capabilities and access to high performance computing assets. In this paper we will provide details of the physical models, software, and the validation/verification procedure used to develop this RTM simulation package.
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A Parallel Computational Environment for Modeling the Resin Transfer Molding Process
AIP Conference Proceedings, 2004Co-Authors: B. J. Henz, D. R. Shires, Ram MohanAbstract:A parallel Computational Environment for modeling the resin transfer molding manufacturing process has been developed at the U.S. Army Research Laboratory. This Environment utilizes an implicit numerical method for modeling resin flow, a thermal model for analyzing convection and conduction, and a resin kinetics model to compute the heat generated and degree of resin cure during the curing process. The computing Environment also includes a multiscale thermal residual stress model for computing the distortions and residual stresses in the composite component caused by the manufacturing process. All of these models have been tied together within a parallel object‐oriented programming framework. This paper will discuss the computing Environment in detail and its utility to real world applications.
Jonathan Miodownik - One of the best experts on this subject based on the ideXlab platform.
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Research Report: Analysis of Software for Restricted Computational Environment Applicability
2016 IEEE Security and Privacy Workshops (SPW), 2016Co-Authors: Jacob I. Torrey, Jonathan MiodownikAbstract:Preliminary experiment design and research goals are presented to measure the applicability of restricted Computational complexity Environments in general purpose development efforts. The Linux kernel is examined through the lens of LangSec in order to gain insight into the make-up of the kernel code vis-à-vis the complexity class of recognizer for input to each component on the Chomsky Hierarchy. Manual analysis is assisted with LLVM Passes and comparison with the real-time Linux fork. This paper describes an on-going effort with the goals of justifying further research in the field of restricted Computational Environments.
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IEEE Symposium on Security and Privacy Workshops - Research Report: Analysis of Software for Restricted Computational Environment Applicability
2016 IEEE Security and Privacy Workshops (SPW), 2016Co-Authors: Jacob I. Torrey, Jonathan MiodownikAbstract:Preliminary experiment design and research goals are presented to measure the applicability of restricted Computational complexity Environments in general purpose development efforts. The Linux kernel is examined through the lens of LangSec in order to gain insight into the make-up of the kernel code vis-a-vis the complexity class of recognizer for input to each component on the Chomsky Hierarchy. Manual analysis is assisted with LLVM Passes and comparison with the real-time Linux fork. This paper describes an on-going effort with the goals of justifying further research in the field of restricted Computational Environments.
B. J. Henz - One of the best experts on this subject based on the ideXlab platform.
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Computational Environment for the Multiscale, Multi-Physics Resin Transfer Molding Process
2008Co-Authors: B. J. Henz, D. R. ShiresAbstract:Summary The capability to predict the residual stresses induced during the manufacturing process in composite components is necessary for the timely fielding of new combat systems. At the U.S. Army Research Laboratory we have developed a Computational Environment to model the resin flow, heat transfer, curing, and residual stresses in composite components manufactured with the resin transfer molding (RTM) process. This Computational Environment uses object-oriented programming methods to provide model coupling capabilities and access to high performance computing assets. In this paper we will provide details of the physical models, software, and the validation/verification procedure used to develop this RTM simulation package.
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A Parallel Computational Environment for Modeling the Resin Transfer Molding Process
AIP Conference Proceedings, 2004Co-Authors: B. J. Henz, D. R. Shires, Ram MohanAbstract:A parallel Computational Environment for modeling the resin transfer molding manufacturing process has been developed at the U.S. Army Research Laboratory. This Environment utilizes an implicit numerical method for modeling resin flow, a thermal model for analyzing convection and conduction, and a resin kinetics model to compute the heat generated and degree of resin cure during the curing process. The computing Environment also includes a multiscale thermal residual stress model for computing the distortions and residual stresses in the composite component caused by the manufacturing process. All of these models have been tied together within a parallel object‐oriented programming framework. This paper will discuss the computing Environment in detail and its utility to real world applications.
Thomas Taylor - One of the best experts on this subject based on the ideXlab platform.
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Stanford University Unstructured (SU2): An open-source integrated Computational Environment for multi-physics simulation and design
51st AIAA Aerospace Sciences Meeting, 2013Co-Authors: Juan Alonso, Michael Colonno, Jason Hicken, Aniket Aranake, Alejandro Campos, Sean Copeland, Thomas Economon, Amrita Lonkar, Trent Lukaczyk, Karthikeyan Duraisamy, Thomas TaylorAbstract:This paper describes the history, objectives, structure, and current capabilities of the Stanford University Unstructured (SU2) tool suite. This Computational analysis and design software collection is being developed to solve complex, multi-physics analysis and optimization tasks using arbitrary unstructured meshes, and it has been designed so that it is easily extensible for the solution of Partial Differential Equation-based (PDE) problems not directly envisioned by the authors. At its core, SU2 is an open-source collection of C++ software tools to discretize and solve problems described by PDEs and is able to solve PDE-constrained optimization problems, including optimal shape design. Although the toolset has been designed with Computational Fluid Dynamics (CFD) and aerodynamic shape optimization in mind, it has also been extended to treat other sets of governing equations including potential flow, electrodynamics, chemically reacting flows, and several others. In our experience, capabilities for Computational analysis and optimization have improved considerably over the past two decades. However, the ability to integrate the resulting software packages into coupled multi-physics analysis and design optimization solvers has remained a challenge: the variety of approaches chosen for the independent components of the overall problem (flow solvers, adjoint solvers, optimizers, shape parameterization, shape deformation, mesh adaption, mesh deformation, etc) make it difficult to (a) expand the range of applicability to situations not originally envisioned, and (b) to reduce the overall burden of creating integrated applications. By leveraging well-established object-oriented software architectures (using C++) and by enabling a common interface for all the necessary components, SU2 is able to remove these barriers for both the beginner and the seasoned analyst. In this paper we attempt to describe our efforts to develop SU2 as an integrated platform. In some senses, the paper can also be used as a software reference manual for those who might be interested in modifying it to suit their own needs. We carefully describe the C++ framework and object hierarchy, the sets of equations that can be currently modeled by SU2, the available choices for numerical discretization, and conclude with a set of relevant validation and verification test cases that are included with the SU2 distribution. We intend for SU2 to remain open source and to serve as a starting point for new capabilities not included in SU2 today, that will hopefully be contributed by users in both academic and industrial Environments. © 2013 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
C C Hilgetag - One of the best experts on this subject based on the ideXlab platform.
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The portable UNIX programming system (PUPS) and CANTOR: a Computational Environment for dynamical representation and analysis of complex neurobiological data
Philosophical Transactions of the Royal Society of London Series B-Biological Sciences, 2001Co-Authors: M. A. O'neill, C C HilgetagAbstract:Many problems in analytical biology, such as the classification of organisms, the modelling of macromolecules, or the structural analysis of metabolic or neural networks, involve complex relational data. Here, we describe a software Environment, the portable UNIX programming system (PUPS), which has been developed to allow efficient Computational representation and analysis of such data. The system can also be used as a general development tool for database and classification applications. As the complexity of analytical biology problems may lead to computation times of several days or weeks even on powerful computer hardware, the PUPS Environment gives support for persistent computations by providing mechanisms for dynamic interaction and homeostatic protection of processes. Biological objects and their interrelations are also represented in a homeostatic way in PUPS. Object relationships are maintained and updated by the objects themselves, thus providing a flexible, scalable and current data representation. Based on the PUPS Environment, we have developed an optimization package, CANTOR, which can be applied to a wide range of relational data and which has been employed in different analyses of neuroanatomical connectivity. The CANTOR package makes use of the PUPS system features by modifying candidate arrangements of objects within the system's database. This restructuring is carried out via optimization algorithms that are based on user-defined cost functions, thus providing flexible and powerful tools for the structural analysis of the database content. The use of stochastic optimization also enables the CANTOR system to deal effectively with incomplete and inconsistent data. Prototypical forms of PUPS and CANTOR have been coded and used successfully in the analysis of anatomical and functional mammalian brain connectivity, involving complex and inconsistent experimental data. In addition, PUPS has been used for solving multivariate engineering optimization problems and to implement the digital identification system (DAISY), a system for the automated classification of biological objects. PUPS is implemented in ANSI-C under the POSIX.1 standard and is to a great extent architecture- and operating-system independent. The software is supported by systems libraries that allow multi-threading (the concurrent processing of several database operations), as well as the distribution of the dynamic data objects and library operations over clusters of computers. These attributes make the system easily scalable, and in principle allow the representation and analysis of arbitrarily large sets of relational data. PUPS and CANTOR are freely distributed (http://www.pups.org.uk) as open-source software under the GNU license agreement.