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Bodony D.J.,University of Illinois at Urbana - Champaign | Zagaris G.,Computational Science and Engineering
Journal of Sound and Vibration | Year: 2011

The simulation of sound generating flows in complex geometries requires accurate numerical methods that are non-dissipative and stable, and well-posed boundary conditions. A structured mesh approach is often desired for a higher-order discretization that better uses the provided grids, but at the expense of complex geometry capabilities relative to techniques for unstructured grids. One solution is to use an overset mesh-based discretization where locally structured meshes are globally assembled in an unstructured manner. This article discusses recent advancements in overset methods, also called Chimera methods, concerning boundary conditions, parallel methods for overset grid management, and stable and accurate interpolation between the grids. Several examples are given, some of which include moving grids. © 2011 Elsevier Ltd.


News Article | October 28, 2016
Site: phys.org

The bear population of each area is too small to prevent inbreeding and loss of genetic diversity in this endangered species. Inbreeding can pass on too many deleterious recessive traits, and lack of genetic diversity makes a population vulnerable to catastrophic events such as the introduction of a disease. Local land trusts have set out to help by acquiring land to create corridors through which animals might gradually migrate between the two preserves. At the same time, they have been designing corridors for also endangered wolverines and lynx. Now, researchers at Cornell, Georgia Tech and the U.S. Forest Service have found that when a corridor includes areas that are hospitable to two species, the cost is far less than it would be to create separate corridors for each one. This also means more animals can be helped within the same limited budget. "This work opens up new directions in terms of understanding tradeoffs for different species," said Carla Gomes, professor of computing and information science and director of the Cornell Institute for Computational Sustainability. "The land trusts have very limited resources. Now that we can get synthesis rather than just optimize for one species, it's economically more efficient." Gomes and colleagues began by creating optimization programs to design corridors just for grizzly bears, later applying the same techniques for other animals. Now they have updated their systems to work with multiple species, most recently trying it out with bears and wolverines. Given the suitability of every available parcel of land, along with the purchase price, a computer can evaluate, in a "smart way," many possible combinations of connected parcels to find a route that best satisfies all the animals' needs at the lowest total cost. Gomes is co-author of a paper, "Trade-offs and efficiencies in optimal budget-constrained multispecies corridor networks." published in the Sept. 27 online edition of the journal Conservation Biology. Cornell-related co-authors include Bistra Dilkina Ph.D. '12, now assistant professor at the School of Computational Science and Engineering at Georgia Tech, and Richard Bernstein, a staff programmer in the Institute for Computational Sustainability. Also participating are authors at the College of Forestry at Oregon State University. "We were very fortunate to come across Carla and Bistra." said Michael Schwartz, director of the National Genomics center for Wildlife and Fish Conservation, an agency of the U.S. Forest Service, who is also a co-author of the paper. "I've talked to many land trusts and they are very enthusiastic to use this. This puts it in an economic framework that people can use in Western Montana or upstate New York." Schwartz and colleagues in the Forest Service oversee 210 million acres of western land. They have collected genetic samples from more than 200 wolverines in the wild. "What got me interested is that I've seen our data being used to justify purchases," he said. In the computer analysis, each parcel of land is assigned a "resistance score" that represents the difficulty a particular animal might have dwelling in that environment – and conversely, how much it might be attracted there. Bears, for example, like canopied forest. Wolverines prefer land that has snow on the ground into early spring. Both of them want to avoid predators, and none want to run into humans. The latter takes care of itself in the optimization process, Gomes notes, because land near towns is more expensive. The computer's job is to evaluate many possible combinations until it finds a group of connected parcels that offer the lowest total of resistance scores and purchase prices. The computer scientist's job is to design an algorithm that will accomplish this in a reasonable amount of processing time – which gets harder as the number of species whose scores must be evaluated increases. In tryouts, the researchers found there are many "tradeoffs," like accepting parcels that have higher resistance scores but are less expensive, or deciding to weight one species over another for ecological reasons or just to satisfy human preferences. (Bears seem to be more charismatic.) The problem is common in computer science, Gomes said, using, for example, the same algorithms that might be used to route data packets around the Internet. The research paper describes new methods to deal with optimization problems that might otherwise require too much computer time to solve. One answer, Gomes explained, is for the computer to take a "reasoned approach," skipping combinations that start off looking worse than what was already available. Sustainability work often pays back with new insights for common problems in computer science, Gomes pointed out.


News Article | February 28, 2017
Site: www.cemag.us

It’s not enough to design new drugs. For drugs to be effective, they have to be delivered safely and intact to affected areas of the body. And drug delivery, much like drug design, is an immensely complex task. Cutting-edge research and development like that conducted at the U.S. Department of Energy’s Oak Ridge National Laboratory can help solve some of the challenges associated with drug delivery. In fact, ORNL researchers and collaborators at Wayne State University recently used a unique combination of experimentation and simulation to shed light on the design principles for improved delivery of RNA drugs, which are promising candidates in the treatment of a number of medical conditions including cancers and genetic disorders. Specifically, the research team discovered that the motions of a tRNA (or transfer RNA) model system can be enhanced when coupled with nanodiamonds, or diamond nanoparticles approximately 5 to 10 nanometers in size. Nanodiamonds are good delivery candidates due to their spherical shape, biocompatibility and low toxicity. And because their surfaces can be easily tailored to facilitate the attachment of various medicinal molecules, nanodiamonds have tremendous potential for the delivery of a vast range of therapies. The discovery involved ORNL’s Spallation Neutron Source, which provides the most intense pulsed neutron beams in the world for scientific research and industrial development, and ORNL’s Titan supercomputer, the nation’s most powerful for open science -- a one-two punch for illuminating the physical properties of potential drugs that inform new design principles for safer, improved delivery platforms. By comparing the SNS neutron scattering data with the data from the team’s molecular dynamics simulations on Titan, the researchers have confirmed that nanodiamonds enhance the dynamics of tRNA when in the presence of water. This cross-disciplinary research was profiled in Journal of Physical Chemistry B. The project began when ORNL’s P. Ganesh and Xiang-Qiang Chu of Wayne State University wondered how the water-phobic surfaces of nanoparticles alter the dynamics of biomolecules coated with water, and if it might be something that they could eventually control. They then formed a team including Gurpreet Dhindsa, Hugh O’Neill, Debsindhu Bhowmik, and Eugene Mamontov of ORNL and Liang Hong of Shanghai Jiao Tong University in China to observe the motions of hydrogen atoms from the model system, tRNA, in water using SNS’s BASIS neutron backscattering spectrometer, SNS beam line 2. Hydration is essential for biomolecules to function, and neutrons are excellent at distinguishing between the motions of hydration water molecules and the biomolecule they are surrounding. Therefore, by measuring the atoms’ neutron scattering signals, the team was able to discern the movement of tRNA in water, providing valuable insight into how the large molecule relaxes in different environmental conditions. After comparing the results of the individual atoms, it was clear that the nanodiamonds were having a profound effect on their companion RNA molecules. The results were somewhat baffling because similar experiments had demonstrated that companion solid materials (such as nanodiamonds) tended to dampen biomolecule dynamics. Surprisingly however, nanodiamonds did the opposite for tRNA. “Scientists are always interested in the bio-nano interactions,” says Chu. “While the interfacial layer of the bio-nano systems has very distinctive properties, it is very hard to study this mysterious zone without neutron scattering, which only sees hydrogen.” To realize the potential of nanodiamonds in the delivery of biomolecules using tRNA as a model, the team turned to Titan to shed a much-needed light on the underlying physics. “Molecular dynamics simulation can really tell those stories that current experimental advancement might not be able to,” says Bhowmik of ORNL’s Computational Science and Engineering Division, who set up and conducted the simulations alongside Monojoy Goswami of the laboratory’s Computer Science and Mathematics Division and Hong of Shanghai Jiao Tong University. “By combining these two techniques, you can enter a whole new world.” These simulations revealed that the “weak dynamic heterogeneity” of RNA molecules in the presence of nanodiamonds was responsible for the enhanced effect. In other words, the reactions among the nanodiamonds, water and the RNA molecule forms a water layer on the nanodiamond surface, which then blocks it and prevents strong RNA contact to the nanodiamond. Since RNA is hydrophilic, or “likes water,” the molecules on the nanodiamond surface swell with excess hydration and weaken the heterogeneous dynamics of the molecules. “You can fine-tune these dynamics with chemical functionalization on the nanodiamond surface, further enhancing its effectiveness,” says Goswami. The findings will likely guide future studies not only on the potential of nanodiamonds in drug delivery but also on fighting bacteria and treating viral diseases. Using simulation to confirm and gain insight into experiments is nothing new. But mimicking large-scale systems precisely is often a challenge, and the lack of quantitative consistency between the two disciplines makes data comparison difficult and answers more elusive to researchers. This lack of precision, and by extension lack of consistency, is largely driven by the uncertainty surrounding force-field parameters or the interaction criteria between different particles. The exact parameters are scarce for many macromolecules, often forcing researchers to use parameters that closely, but not exactly, match the experiment. Miscalculating the precision of these parameters can have major consequences for the interpretation of the experimental results. To ensure the calculations were correct, Goswami worked with Jose Borreguero and Vickie Lynch, both of ORNL’s Neutron Data Analysis and Visualization Division and Center for Accelerated Materials Modeling, to develop a workflow optimization technique known as Pegasus. This method compares molecular dynamics simulations with neutron scattering data and refines the simulation parameters to validate the results with the proper experimental precision. “Using the Pegasus workflow to run simulations sampling, the force-field parameter space saved time and eliminated input errors,” says Lynch. These parameters also helped researchers better characterize the nanodiamond-water interactions and tRNA dynamics in the presence of nanodiamonds. The researchers then developed an automated system capable of optimizing parameters across a wide spectrum of simulation systems and neutron experiments, an effort that will be of great worth to similar experiments going forward. This new workflow is also compatible with the laboratory’s Compute and Data Environment for Science (CADES), which assists experimentalists with the analysis of vast quantities of data. “Users of the CADES infrastructure can carry the optimization of the simulations within the Bellerophon Environment for the Analysis of Materials, in active development at ORNL,” says Borreguero. The Bellerophon Environment for the Analysis of Materials (BEAM) is an end-to-end workflow software system, developed at ORNL, enabling user-friendly, remote access to robust data storage and compute capabilities offered at CADES and the Oak Ridge Leadership Computing Facility, home of Titan, for scalable data analysis and modeling. It’s these in-house resources that make ORNL a world leader in experimentation, modeling, and the nexus in between and that make discoveries like this possible.


In fact, ORNL researchers and collaborators at Wayne State University recently used a unique combination of experimentation and simulation to shed light on the design principles for improved delivery of RNA drugs, which are promising candidates in the treatment of a number of medical conditions including cancers and genetic disorders. Specifically, the research team discovered that the motions of a tRNA (or transfer RNA) model system can be enhanced when coupled with nanodiamonds, or diamond nanoparticles approximately 5 to 10 nanometers in size. Nanodiamonds are good delivery candidates due to their spherical shape, biocompatibility and low toxicity. And because their surfaces can be easily tailored to facilitate the attachment of various medicinal molecules, nanodiamonds have tremendous potential for the delivery of a vast range of therapies. The discovery involved ORNL's Spallation Neutron Source, which provides the most intense pulsed neutron beams in the world for scientific research and industrial development, and ORNL's Titan supercomputer, the nation's most powerful for open science—a one-two punch for illuminating the physical properties of potential drugs that inform new design principles for safer, improved delivery platforms. By comparing the SNS neutron scattering data with the data from the team's molecular dynamics simulations on Titan, the researchers have confirmed that nanodiamonds enhance the dynamics of tRNA when in the presence of water. This cross-disciplinary research was profiled in Journal of Physical Chemistry B. The best of both worlds The project began when ORNL's P. Ganesh and Xiang-Qiang Chu of Wayne State University wondered how the water-phobic surfaces of nanoparticles alter the dynamics of biomolecules coated with water, and if it might be something that they could eventually control. They then formed a team including Gurpreet Dhindsa, Hugh O'Neill, Debsindhu Bhowmik and Eugene Mamontov of ORNL and Liang Hong of Shanghai Jiao Tong University in China to observe the motions of hydrogen atoms from the model system, tRNA, in water using SNS's BASIS neutron backscattering spectrometer, SNS beam line 2. Hydration is essential for biomolecules to function, and neutrons are excellent at distinguishing between the motions of hydration water molecules and the biomolecule they are surrounding. Therefore, by measuring the atoms' neutron scattering signals, the team was able to discern the movement of tRNA in water, providing valuable insight into how the large molecule relaxes in different environmental conditions. After comparing the results of the individual atoms, it was clear that the nanodiamonds were having a profound effect on their companion RNA molecules. The results were somewhat baffling because similar experiments had demonstrated that companion solid materials (such as nanodiamonds) tended to dampen biomolecule dynamics. Surprisingly however, nanodiamonds did the opposite for tRNA. "Scientists are always interested in the bio-nano interactions," said Chu. "While the interfacial layer of the bio-nano systems has very distinctive properties, it is very hard to study this mysterious zone without neutron scattering, which only sees hydrogen." To realize the potential of nanodiamonds in the delivery of biomolecules using tRNA as a model, the team turned to Titan to shed a much-needed light on the underlying physics. "Molecular dynamics simulation can really tell those stories that current experimental advancement might not be able to," said Bhowmik of ORNL's Computational Science and Engineering Division, who set up and conducted the simulations alongside Monojoy Goswami of the laboratory's Computer Science and Mathematics Division and Hong of Shanghai Jiao Tong University. "By combining these two techniques, you can enter a whole new world." These simulations revealed that the "weak dynamic heterogeneity" of RNA molecules in the presence of nanodiamonds was responsible for the enhanced effect. In other words, the reactions among the nanodiamonds, water and the RNA molecule forms a water layer on the nanodiamond surface, which then blocks it and prevents strong RNA contact to the nanodiamond. Since RNA is hydrophilic, or "likes water," the molecules on the nanodiamond surface swell with excess hydration and weaken the heterogeneous dynamics of the molecules. "You can fine-tune these dynamics with chemical functionalization on the nanodiamond surface, further enhancing its effectiveness," said Goswami. The findings will likely guide future studies not only on the potential of nanodiamonds in drug delivery but also on fighting bacteria and treating viral diseases. Using simulation to confirm and gain insight into experiments is nothing new. But mimicking large-scale systems precisely is often a challenge, and the lack of quantitative consistency between the two disciplines makes data comparison difficult and answers more elusive to researchers. This lack of precision, and by extension lack of consistency, is largely driven by the uncertainty surrounding force-field parameters or the interaction criteria between different particles. The exact parameters are scarce for many macromolecules, often forcing researchers to use parameters that closely, but not exactly, match the experiment. Miscalculating the precision of these parameters can have major consequences for the interpretation of the experimental results. To ensure the calculations were correct, Goswami worked with Jose Borreguero and Vickie Lynch, both of ORNL's Neutron Data Analysis and Visualization Division and Center for Accelerated Materials Modeling, to develop a workflow optimization technique known as Pegasus. This method compares molecular dynamics simulations with neutron scattering data and refines the simulation parameters to validate the results with the proper experimental precision. "Using the Pegasus workflow to run simulations sampling, the force-field parameter space saved time and eliminated input errors," said Lynch. These parameters also helped researchers better characterize the nanodiamond-water interactions and tRNA dynamics in the presence of nanodiamonds. The researchers then developed an automated system capable of optimizing parameters across a wide spectrum of simulation systems and neutron experiments, an effort that will be of great worth to similar experiments going forward. This new workflow is also compatible with the laboratory's Compute and Data Environment for Science (CADES), which assists experimentalists with the analysis of vast quantities of data. "Users of the CADES infrastructure can carry the optimization of the simulations within the Bellerophon Environment for the Analysis of Materials, in active development at ORNL," said Borreguero. The Bellerophon Environment for the Analysis of Materials (BEAM) is an end-to-end workflow software system, developed at ORNL, enabling user-friendly, remote access to robust data storage and compute capabilities offered at CADES and the Oak Ridge Leadership Computing Facility, home of Titan, for scalable data analysis and modeling. It's these in-house resources that make ORNL a world leader in experimentation, modeling and the nexus in between and that make discoveries like this possible. Explore further: High-performance simulation, neutrons uncover three classes of protein motion More information: Gurpreet K. Dhindsa et al. Enhanced Dynamics of Hydrated tRNA on Nanodiamond Surfaces: A Combined Neutron Scattering and MD Simulation Study, The Journal of Physical Chemistry B (2016). DOI: 10.1021/acs.jpcb.6b07511


News Article | March 30, 2016
Site: www.scientificcomputing.com

Today’s installment is the third in a series covering how researchers from national laboratories and scientific research centers are updating popular molecular dynamics, quantum chemistry and quantum materials code to take advantage of hardware advances, such as the next-generation Intel Xeon Phi processors. Georgia Institute of Technology, known as Georgia Tech, is an Intel Parallel Computing Center (Intel PCC) that focuses on modernizing the performance and functionality of software on advanced HPC systems used in scientific discovery. Georgia Tech developed a new HPC software package, called GTFock, and the SIMINT library to make quantum chemistry and materials simulations run faster on servers and supercomputers using Intel Xeon processors and Intel Xeon Phi coprocessors. These tools, which continue to be improved, provide an increase in processing speed over the best state-of-the-art quantum chemistry codes in existence. “GTFock and SIMINT allow us to perform quantum chemistry simulations faster and with less expense, which can help in solving large-scale problems from fundamental chemistry and biochemistry to pharmaceutical and materials design,” states Edmond Chow, Associate Professor of Computational Science and Engineering and Director of the Georgia Institute of Technology Intel PCC. The Intel PCC at Georgia Tech has been simulating the binding of the drug Indinavir with human immunodeficiency virus (HIV) II protease. Indinavir is a protease inhibitor that competitively binds to the active site of HIV II protease to disrupt normal function as part of HIV treatment therapy. Such systems are too large to study quantum mechanically, so only a part of the protease closest to the drug is typically simulated. The aim of the work at Georgia Tech is to quantify the discrepancy in the binding energy when such truncated models of the protease are used. To do this, simulations with increasing larger portions of the protease are performed. These are enabled by the GTFock code, developed at the Georgia Tech Intel PCC in collaboration with Intel, which has been designed to scale efficiently on large cluster computers, including Intel Many Integrated Core (MIC) architecture clusters. Calculations were performed at the Hartree-Fock level of theory. The largest simulations included residues of the protease more than 18 Angstroms away from the drug molecule. These simulations involved almost 3000 atoms and were performed on more than 1.6 million compute cores of the Tianhe-2 supercomputer (an Intel Xeon processor and Intel Xeon Phi processor-based system that is currently number one on the TOP500 list). The results of this work so far show variations in binding energy that persist throughout the range up to 18 Angstroms. This suggests that at even relatively large cutoff distances, leading to very large model complexes (much larger than are typically possible with conventional codes and computing resources), the binding energy is not converged to within chemical accuracy. Further work is planned to validate these results as well as to study additional protein-ligand systems. New quantum chemistry code: GTFock The GTFock code was developed by the Georgia Tech Intel PCC in conjunction with the Intel Parallel Computing Lab. GTFock addresses one of the main challenges of quantum chemistry, which is the ability to run more accurate simulations and simulations of larger molecules through exploiting distributed memory processing. GTFock was designed as a new toolkit with optimized and scalable code for Hartree-Fock self-consistent field iterations and the distributed computation of the Fock matrix in quantum chemistry. The Hartree-Fock (HF) method is the one of most fundamental methods in quantum chemistry for approximately solving the electronic Schrödinger equation. The solution of the equation, called the wavefunction, can be used to determine properties of the molecule. Georgia Tech’s goals in the code design of GTFock include scalability to large numbers of nodes and the capability to simultaneously use CPUs and Intel Xeon Phi coprocessors. GTFock also includes infrastructure for performing self-consistent field (SCF) iterations to solve for the Hartree-Fock approximation and uses a new distributed algorithm for load balancing and reducing communication. GTFock code can be integrated into existing quantum chemistry packages and can be used for experimentation as a benchmark for high-performance computing. The code is capable of separately computing the Coulomb and exchange matrices and, thus, can be used as a core routine in many quantum chemistry methods. As part of IPCC collaborations, Georgia Tech graduate student Xing Liu and Intel researcher Sanchit Misra spent a month in China optimizing and running GTFock on Tianhe-2. During testing, the team encountered scalability problems when scaling up the code to 8100 nodes on Tianhe-2. They resolved these issues by using a better static partitioning and a better work stealing algorithm than used in previous work. They utilized the Intel Xeon Phi coprocessors on Tianhe-2 by using a dedicated thread on each node to manage offload to coprocessors and to use work stealing to dynamically balance the work between CPUs and coprocessors. The electron repulsion integral (ERI) calculations were also optimized for modern processors including the Intel Xeon Phi coprocessor. The partitioning framework used in GTFock is useful for comparing existing and future partitioning techniques. The best partitioning scheme may depend on the size of the problem, the computing system used and the parallelism available. In Fock matrix construction, each thread sums to its own copy of Fock submatrices in order to avoid contention for a single copy of the Fock matrix on a node. However, accelerators including Intel Xeon Phi coprocessors have limited memory per core, making this strategy impossible for reduction across many threads. Thus, novel solutions had to be designed. Figure 2 shows speed up results from running the GTFock code. A deficiency in quantum chemistry codes that Georgia Tech saw had to be addressed is the bottleneck of computing quantities called electron repulsion integrals. This calculation is a very computationally intensive step: there are many of these integrals to calculate and these calculations do not run efficiently on modern processors, including the Intel Xeon processor. One of the reasons is that the existing codes do not take advantage of single instruction, multiple data (SIMD) processing that is available on these processors. It is difficult for algorithms to exploit SIMD operations because of the structure of the algorithms. The existing algorithms that are used are recursive in multiple dimensions and require substantial amounts of intermediate data. In general, it is difficult to vectorize these calculations. Many attempts in the past involved taking existing libraries and rearranging code elements to try to optimize and speed up the calculations. The Georgia Tech team felt it was necessary to create a new library for electron integral calculations from scratch. The library they created is called SIMINT, which means Single Instruction Multiple Integral (named by SIMINT library developer Ben Pritchard). This library applies SIMD instructions to compute multiple integrals at the same time, which is the efficient mode of operation of Intel Xeon processors as well as the Intel Xeon Phi microarchitecture (MIC), which has wide SIMD units. SIMINT is a library for calculating electron repulsion integrals. The Georgia Tech PCC team designed it to use the SIMD features of Intel Xeon processors — it is highly efficient and faster than other state-of-the-art ERI codes. The approach is to use horizontal vectorization; thus, you must compute batches of integrals of the same type together. The Georgia Tech team has posted information so that users can take a look. The team uses Intel VTune amplifier extensively in optimizing SIMINT, because it helps tune the vectorization and cache performance. Developers know how fast the processor can go and the speed limits of the calculation because of the instructions they need to perform. Intel VTune amplifier provides a variety of statistics at a line of code level that help determine why they may not be reaching the expected performance. Figure 3 shows an approximate 2x speedup over libint with a test case that has many worst-case configurations. Figure 4 shows a 3x speedup for another basis set without worst-case configurations. “SIMINT has been designed specifically to efficiently use SIMD features of Intel processors and co-processors. As a result, we’re already seeing speedups of 2x to 3x over the best existing codes.” Edmond Chow, Associate Professor of Computational Science and Engineering and Director of the Georgia Institute of Technology Intel PCC. “GTFock has attracted the attention of other developers of quantum chemistry packages. We have already integrated GTFock into PSI4 to provide distributed memory parallel capabilities to that package. In addition, we have exchanged visits with the developers of the NWChem package to initiate integration of GTFock into NWChem (joint work with Edo Apra and Karol Kowalski, PNNL). Along with SIMINT, we hope to help quantum chemists get their simulations — and their science — done faster,” states Chow. Other articles in this series covering the modernization of popular chemistry codes include: Linda Barney is the founder and owner of Barney and Associates, a technical/marketing writing, training and web design firm in Beaverton, OR. R&D 100 AWARD ENTRIES NOW OPEN: Establish your company as a technology leader! For more than 50 years, the R&D 100 Awards have showcased new products of technological significance. You can join this exclusive community! .


Stone C.,Computational Science and Engineering | Lynch C.E.,Georgia Institute of Technology | Smith M.J.,Georgia Institute of Technology
48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition | Year: 2010

The NASA/NREL Phase-VI Unsteady Aerodynamic Experiment horizontal axis wind turbine has been computationally modeled using both structured and unstructured overset, dynamic grid algorithms. The influence of geometry fidelity and turbulence modeling on the prediction capability of state-of-the-art Computational Fluid Dynamics (CFD) methods is discussed. Hybrid RANS/LES turbulence methods are shown to improve the ability of CFD to capture separated flows compared with 2-equation RANS turbulence models. The addition of the tower and nacelle configuration did not improve the time-averaged rotor loads predictions for the upwind case, although they are required to determine the unsteady load interactions of the blades and tower. © 2010 by Christopher Stone, Eric Lynch and Marilyn Smith.


Annafi T.A.,Computational Science and Engineering | Gyeabour A.A.I.,Computational Science and Engineering | Akaho E.H.K.,Computational Science and Engineering | Annor-Nyarko M.,Computational Science and Engineering | Quaye C.R.,Computational Science and Engineering
Annals of Nuclear Energy | Year: 2014

Mathematical model of the transient heat distribution within Ghana Research Reactor-1 (GHARR-1) fuel element and related shutdown heat generation rates have been developed. The shutdown heats considered were residual fission and fission product decay heat. A finite difference scheme for the discretization by implicit method was used. Solution algorithms were developed and MATLAB program implemented to determine the temperature distributions within the fuel element after shutdown due to reactivity insertion accident. The simulations showed a steady state temperature of about 341.3 K which deviated from that reported in the GHARR-1 safety analysis report by 2% error margin. The average temperature obtained under transient condition was found to be approximately 444 K which was lower than the melting point of 913 K for the aluminium cladding. Thus, the GHARR-1 fuel element was stable and there would be no release of radioactivity in the coolant during accident conditions.© 2014 Elsevier Ltd. All rights reserved.


News Article | November 18, 2015
Site: www.rdmag.com

Using large-scale computer modeling, researchers have shown the effects of confinement on macromolecules inside cells—and taken the first steps toward simulating a living cell, a capability that could allow them to ask “what-if” questions impossible to ask in real organisms. The work could help scientists better understand signaling between cells, and provide insights for designing new classes of therapeutics. For instance, the simulations showed that particles within the crowded cells tend to linger near cell walls, while confinement in the viscous liquid inside cells causes particles to move about more slowly than they would in unconfined spaces. The research is believed to be the first to consider the effects of confinement on intracellular macromolecular dynamics. Supported by the National Science Foundation, the results are reported in the Proceedings of the National Academy of Sciences. The study is an interdisciplinary collaboration between Edmond Chow, an associate professor in the Georgia Tech School of Computational Science and Engineering, and Jeffrey Skolnick, a professor in the Georgia Tech School of Biology. Their goal is to develop and study models for simulating the motions of molecules inside a cell, and also to develop advanced algorithms and computational techniques for performing large-scale simulations. “We are setting the stage for what we need to do to simulate a real cell,” said Skolnick. “We would like to put enough of a real cell together to be able to understand all of the cellular biochemical principles of life. That would allow us to ask questions that we can’t ask now.” Earlier simulations, which produced much less fidelity, had assumed that movement within a cell was the same as movement in an unconfined space. Skolnick compared the interior of a living cell to a large New Year’s Eve party, perhaps even in Times Square. “It’s kind of like a crowded party that has big people and little people, snakes—DNA strands—running around, some really large molecules and some very small molecules,” he said. “It’s a very heterogeneous and dense environment with as much as 40% of the volume occupied.” The simulations showed that molecules near the cell walls tend to remain there for extended periods of time, just as a newcomer might be pushed toward the walls of the New Year’s Eve party. Motions of nearby particles also tended to be correlated, and those correlations appeared linked to hydrodynamic forces. “The lifetimes of these interactions get enhanced, and that is what’s needed there for biological interactions to occur within the cell,” said Skolnick. “This lingering near the wall could be important for understanding other interactions because if there are signaling proteins arriving from other cells, they would associate with those particles first. This could have important consequences for how signals are transduced.” For particles in the middle of the cell, however, things are different. These molecules interact primarily with nearby molecules, but they still feel the effects of the cell wall, even if it is relatively far away. “Things move more slowly in the middle of the cell than they would if the cell were infinitely big,” Skolnick said. “This may increase the likelihood of having metabolic fluxes because you have to bring molecules around partners. If they are moving slowly, they have more time to react because intimate interactions by accident are unavoidable.” While the rate of activity slows quantitatively, qualitatively it is the same kind of motion. “Slowed motion is a double-edged sword,” Skolnick explained. “If you happen to be nearby, it is likely that you are going to have interactions if you are slower. But if you are not nearby, being slower makes it difficult to be nearby, affecting potential interactions.” The researchers also compared the activities of systems of particles with different sizes, finding that having particles of different sizes didn’t make an appreciable difference in the overall behavior of the molecules. While the simulations didn’t include the DNA strands or metabolite particles also found in cells, they did include up to a half-million objects. Using Brownian and Stokesian physics principles, Skolnick and Chow considered what the particles would do within the confined spherical cell a few microns in diameter. “From the results of the computer simulations, we can measure things that we think might be interesting, such as the diffusion rates near the walls and away from the walls,” said Chow. “We often don’t know what we are looking for until we find something that forces us to ask more questions and analyze more data.” Such simulations take a lot of computational time, so the algorithms used must be efficient enough to be completed in a reasonable time. The “art” of the algorithms is trading off fidelity with processing time. Even though the simulations were very large, they managed to study the actions of the confined particles for no more than milliseconds. “Part of the art of this is guessing what will be a reasonable approximation that will mimic the system, but not be so simple to be trivial or too complicated that you can’t take more than a few steps of the simulation,” Chow explained. Scientists, of course, can study real cells. But the simulation offers something the real thing can’t do: The ability to turn certain forces on or off to isolate the effects of other processes. For instance, in the simulated cell Skolnick and Chow hope to build, they’ll be able to turn on and off the hydrodynamic forces, allowing them to study the importance of these forces to the functioning of real cells. Results from the simulation can suggest hypotheses to be confirmed or rejected by experiment, which can then lead to further questions and simulations. “This becomes a tool you can use to understand real cells,” said Chow. “It’s a virtual system, and you can play all the games you want with it.”


News Article | November 16, 2015
Site: phys.org

The work could help scientists better understand signaling between cells, and provide insights for designing new classes of therapeutics. For instance, the simulations showed that particles within the crowded cells tend to linger near cell walls, while confinement in the viscous liquid inside cells causes particles to move about more slowly than they would in unconfined spaces. The research is believed to be the first to consider the effects of confinement on intracellular macromolecular dynamics. Supported by the National Science Foundation, the results are reported Nov. 16 in the journal Proceedings of the National Academy of Sciences. The research is an interdisciplinary collaboration between Edmond Chow, an associate professor in the Georgia Tech School of Computational Science and Engineering, and Jeffrey Skolnick, a professor in the Georgia Tech School of Biology. Their goal is to develop and study models for simulating the motions of molecules inside a cell, and also to develop advanced algorithms and computational techniques for performing large-scale simulations. "We are setting the stage for what we need to do to simulate a real cell," said Skolnick. "We would like to put enough of a real cell together to be able to understand all of the cellular biochemical principles of life. That would allow us to ask questions that we can't ask now." Earlier simulations, which produced much less fidelity, had assumed that movement within a cell was the same as movement in an unconfined space. Skolnick compared the interior of a living cell to a large New Year's Eve party, perhaps even in Times Square. "It's kind of like a crowded party that has big people and little people, snakes—DNA strands—running around, some really large molecules and some very small molecules," he said. "It's a very heterogeneous and dense environment with as much as 40 percent of the volume occupied." The simulations showed that molecules near the cell walls tend to remain there for extended periods of time, just as a newcomer might be pushed toward the walls of the New Year's Eve party. Motions of nearby particles also tended to be correlated, and those correlations appeared linked to hydrodynamic forces. "The lifetimes of these interactions get enhanced, and that is what's needed there for biological interactions to occur within the cell," said Skolnick. "This lingering near the wall could be important for understanding other interactions because if there are signaling proteins arriving from other cells, they would associate with those particles first. This could have important consequences for how signals are transduced." For particles in the middle of the cell, however, things are different. These molecules interact primarily with nearby molecules, but they still feel the effects of the cell wall, even if it is relatively far away. "Things move more slowly in the middle of the cell than they would if the cell were infinitely big," Skolnick said. "This may increase the likelihood of having metabolic fluxes because you have to bring molecules around partners. If they are moving slowly, they have more time to react because intimate interactions by accident are unavoidable." While the rate of activity slows quantitatively, qualitatively it is the same kind of motion. "Slowed motion is a double-edged sword," Skolnick explained. "If you happen to be nearby, it is likely that you are going to have interactions if you are slower. But if you are not nearby, being slower makes it difficult to be nearby, affecting potential interactions." The researchers also compared the activities of systems of particles with different sizes, finding that having particles of different sizes didn't make an appreciable difference in the overall behavior of the molecules. While the simulations didn't include the DNA strands or metabolite particles also found in cells, they did include up to a half-million objects. Using Brownian and Stokesian physics principles, Skolnick and Chow considered what the particles would do within the confined spherical cell a few microns in diameter. "From the results of the computer simulations, we can measure things that we think might be interesting, such as the diffusion rates near the walls and away from the walls," said Chow. "We often don't know what we are looking for until we find something that forces us to ask more questions and analyze more data." Such simulations take a lot of computational time, so the algorithms used must be efficient enough to be completed in a reasonable time. The 'art' of the algorithms is trading off fidelity with processing time. Even though the simulations were very large, they managed to study the actions of the confined particles for no more than milliseconds. "Part of the art of this is guessing what will be a reasonable approximation that will mimic the system, but not be so simple to be trivial or too complicated that you can't take more than a few steps of the simulation," Chow explained. Scientists, of course, can study real cells. But the simulation offers something the real thing can't do: The ability to turn certain forces on or off to isolate the effects of other processes. For instance, in the simulated cell Skolnick and Chow hope to build, they'll be able to turn on and off the hydrodynamic forces, allowing them to study the importance of these forces to the functioning of real cells. Results from the simulation can suggest hypotheses to be confirmed or rejected by experiment, which can then lead to further questions and simulations. "This becomes a tool you can use to understand real cells," said Chow. "It's a virtual system, and you can play all the games you want with it." More information: Edmond Chow and Jeffrey Skolnick, "Effects of confinement on models of intracellular macromolecular dynamics," Proceedings of the National Academy of Sciences, 2015. www.pnas.org/cgi/doi/10.1073/pnas.1514757112


News Article | November 18, 2015
Site: www.scientificcomputing.com

Using large-scale computer modeling, researchers have shown the effects of confinement on macromolecules inside cells — and taken the first steps toward simulating a living cell, a capability that could allow them to ask “what-if” questions impossible to ask in real organisms. The work could help scientists better understand signaling between cells, and provide insights for designing new classes of therapeutics. For instance, the simulations showed that particles within the crowded cells tend to linger near cell walls, while confinement in the viscous liquid inside cells causes particles to move about more slowly than they would in unconfined spaces. The research is believed to be the first to consider the effects of confinement on intracellular macromolecular dynamics. Supported by the National Science Foundation, the results are reported November 16, 2015, in the journal Proceedings of the National Academy of Sciences. The study is an interdisciplinary collaboration between Edmond Chow, an associate professor in the Georgia Tech School of Computational Science and Engineering, and Jeffrey Skolnick, a professor in the Georgia Tech School of Biology. Their goal is to develop and study models for simulating the motions of molecules inside a cell, and also to develop advanced algorithms and computational techniques for performing large-scale simulations. “We are setting the stage for what we need to do to simulate a real cell,” said Skolnick. “We would like to put enough of a real cell together to be able to understand all of the cellular biochemical principles of life. That would allow us to ask questions that we can’t ask now.” Earlier simulations, which produced much less fidelity, had assumed that movement within a cell was the same as movement in an unconfined space. Skolnick compared the interior of a living cell to a large New Year’s Eve party, perhaps even in Times Square. “It’s kind of like a crowded party that has big people and little people, snakes — DNA strands — running around, some really large molecules and some very small molecules,” he said. “It’s a very heterogeneous and dense environment with as much as 40 percent of the volume occupied.” The simulations showed that molecules near the cell walls tend to remain there for extended periods of time, just as a newcomer might be pushed toward the walls of the New Year’s Eve party. Motions of nearby particles also tended to be correlated, and those correlations appeared linked to hydrodynamic forces. “The lifetimes of these interactions get enhanced, and that is what’s needed there for biological interactions to occur within the cell,” said Skolnick. “This lingering near the wall could be important for understanding other interactions, because if there are signaling proteins arriving from other cells, they would associate with those particles first. This could have important consequences for how signals are transduced.” For particles in the middle of the cell, however, things are different. These molecules interact primarily with nearby molecules, but they still feel the effects of the cell wall, even if it is relatively far away. “Things move more slowly in the middle of the cell than they would if the cell were infinitely big,” Skolnick said. “This may increase the likelihood of having metabolic fluxes, because you have to bring molecules around partners. If they are moving slowly, they have more time to react, because intimate interactions by accident are unavoidable.” While the rate of activity slows quantitatively, qualitatively it is the same kind of motion. “Slowed motion is a double-edged sword,” Skolnick explained. “If you happen to be nearby, it is likely that you are going to have interactions if you are slower. But if you are not nearby, being slower makes it difficult to be nearby, affecting potential interactions.” The researchers also compared the activities of systems of particles with different sizes, finding that having particles of different sizes didn’t make an appreciable difference in the overall behavior of the molecules. While the simulations didn’t include the DNA strands or metabolite particles also found in cells, they did include up to a half-million objects. Using Brownian and Stokesian physics principles, Skolnick and Chow considered what the particles would do within the confined spherical cell a few microns in diameter. “From the results of the computer simulations, we can measure things that we think might be interesting, such as the diffusion rates near the walls and away from the walls,” said Chow. “We often don’t know what we are looking for until we find something that forces us to ask more questions and analyze more data.” Such simulations take a lot of computational time, so the algorithms used must be efficient enough to be completed in a reasonable time. The “art” of the algorithms is trading off fidelity with processing time. Even though the simulations were very large, they managed to study the actions of the confined particles for no more than milliseconds. “Part of the art of this is guessing what will be a reasonable approximation that will mimic the system, but not be so simple to be trivial or too complicated that you can’t take more than a few steps of the simulation,” Chow explained. Scientists, of course, can study real cells. But the simulation offers something the real thing can’t do: The ability to turn certain forces on or off to isolate the effects of other processes. For instance, in the simulated cell Skolnick and Chow hope to build, they’ll be able to turn on and off the hydrodynamic forces, allowing them to study the importance of these forces to the functioning of real cells. Results from the simulation can suggest hypotheses to be confirmed or rejected by experiment, which can then lead to further questions and simulations. “This becomes a tool you can use to understand real cells,” said Chow. “It’s a virtual system, and you can play all the games you want with it.” Citation: Edmond Chow and Jeffrey Skolnick, “Effects of confinement on models of intracellular macromolecular dynamics,” (Proceedings of the National Academy of Sciences, 2015). www.pnas.org/cgi/doi/10.1073/pnas.1514757112

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