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  • richardmitnick 1:46 pm on January 22, 2020 Permalink | Reply
    Tags: "Beyond the tunnel", (LES)-large-eddy simulation, ANL MIRA supercomputer, , , , How turbulence affects aircraft during flight, , Stanford-led team turns to Argonne’s Mira to fine-tune a computational route around aircraft wind-tunnel testing.,   

    From ASCR Discovery: “Beyond the tunnel” 

    From ASCR Discovery

    Stanford-led team turns to Argonne’s Mira to fine-tune a computational route around aircraft wind-tunnel testing.

    MIRA IBM Blue Gene Q supercomputer at the Argonne Leadership Computing Facility

    The white lines represent simulated air-flow on a wing surface, including an eddy (the circular pattern at the tip). Engineers can use supercomputing, in particular large-eddy simulation (LES), to study how turbulence affects flight. LES techniques applied to commercial aircraft promise a cost-effective alternative to wind-tunnel testing. Image courtesy of Stanford University and Cascade Technologies.

    For aircraft designers, modeling and wind-tunnel testing one iteration after another consumes time and may inadequately recreate the conditions planes encounter during free flight – especially take-off and landing. “Prototyping that aircraft every time you change something in the design would be infeasible and expensive,” says Parviz Moin, a Stanford University professor of mechanical engineering.

    Over the past five years, researchers have explored the use of high-fidelity numerical simulations to predict unsteady airflow and forces such as lift and drag on commercial aircraft, particularly under challenging operating conditions such as takeoff and landing.

    Moin has led much of this research as founding director of the Center for Turbulence Research at Stanford. With help from the Department of Energy’s Innovative and Novel Computational Impact on Theory and Experiment (INCITE) grants, he and colleagues at Stanford and nearby Cascade Technologies in Palo Alto, California, have used supercomputing to see whether large-eddy simulation (LES) of commercial aircraft is both cost effective and sufficiently accurate to help designers. They’ve used 240 million core-hours on Mira, the Blue Gene/Q at the Argonne Leadership Computing Facility, a DOE user facility at Argonne National Laboratories, to conduct these simulations. The early results are “very encouraging,” Moin says.

    Specifically, Moin and colleagues – including INCITE co-investigators George Park of the University of Pennsylvania and Cascade Technologies’ Sanjeeb Bose, a DOE Computational Science Graduate Fellowship (DOE CSGF) alumnus – study how turbulence affects aircraft during flight. Flow of air about a plane in flight is always turbulent, wherein patches of swirling fluid – eddies – move seemingly at random.

    Because it happens on multiple scales, engineers and physicists find aircraft turbulence difficult to describe mathematically. The Navier-Stokes equations are known to govern all flows of engineering interest, Moin explains, including those involving gases and liquids and flows inside or outside a given object. Eddies can be several meters long in the atmosphere but only microns big in the aircraft surface’s vicinity. This means computationally solving the Navier-Stokes equations to describe all the fluid-motion scales would be prohibitively expensive and computationally taxing. For years, engineers have used Reynolds-averaged Navier-Stokes (RANS) equations to predict averaged quantities of engineering interest such as lift and drag forces. RANS equations, however, contain certain modeling assumptions that are not based on first principles, which can result in inaccurate predictions of complex flows.

    LES, on the other hand, offers a compromise, Moin says, between capturing the spectrum of eddy motions or ignoring them all. With LES, researchers can compute the effect of energy-containing eddies on an aircraft while modeling small-scale motions. Although LES predictions are more accurate than RANS approaches, the computational cost of LES has so far been a barrier to widespread use. But supercomputers and recent algorithm advances have rendered LES computations and specialized variations of them more feasible recently, Moin says.

    Eddies get smaller and smaller as they approach a wall – or a surface like an aircraft wing – and capturing these movements has historically been computationally challenging. To avoid these issues, Moin and his colleagues instead model the small-scale near-wall turbulence using a technique they call wall-modeled LES. In wall-modeled LES, the near-wall-eddy effect on the large-scale motions away from the wall are accounted for by a simpler system model.

    Moin and his colleagues have used two commercial aircraft models to validate their large-eddy simulation results: NASA’s Common Research Model and the Japan Aerospace Exploration Agency’s (JAXA’s) Standard Model. They’ve studied each at about a dozen operating conditions to see how the simulations agreed with physical measurements in wind tunnels.

    These early results show that the large-eddy simulations are capable of predicting quantities of engineering interest resulting from turbulent flow around an aircraft. This proof of concept, Moin says, is the first step. “We can compute these flows without tuning model parameters and predict experimental results. Once we have confidence as we compute many cases, then we can start looking into manipulating the flow using passive or active flow-control strategies.” The speed and accuracy of the computations, Moin notes, have been surprising. Researchers commonly thought the calculations could not have been realized until 2030, he says.

    Ultimately, these simulations will help engineers to make protrusions or other modifications of airplane wing surfaces to increase lift during take-off conditions or to design more efficient engines.

    Moin is eager to see more engineers use large eddy simulations and supercomputing to study the effect of turbulence on commercial aircraft and other applications.

    “The future of aviation is bright and needs more development,” he says. “I think with time – and hopefully it won’t take too long – aerospace engineers will start to see the advantage of these high-fidelity computations in engineering analysis and design.”

    See the full article here.


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    ASCRDiscovery is a publication of The U.S. Department of Energy

  • richardmitnick 3:39 pm on September 25, 2018 Permalink | Reply
    Tags: , ANL MIRA supercomputer, , Argonne's Theta supercomputer, , , , , , , ,   

    From Argonne National Laboratory ALCF: “Argonne team brings leadership computing to CERN’s Large Hadron Collider” 

    Argonne Lab
    News from Argonne National Laboratory

    From Argonne National Laboratory ALCF

    ANL ALCF Cetus IBM supercomputer

    ANL ALCF Theta Cray supercomputer

    ANL ALCF Cray Aurora supercomputer

    ANL ALCF MIRA IBM Blue Gene Q supercomputer at the Argonne Leadership Computing Facility

    September 25, 2018
    Madeleine O’Keefe

    CERN’s Large Hadron Collider (LHC), the world’s largest particle accelerator, expects to produce around 50 petabytes of data this year. This is equivalent to nearly 15 million high-definition movies—an amount so enormous that analyzing it all poses a serious challenge to researchers.


    CERN map

    CERN LHC Tunnel

    CERN LHC particles

    A team of collaborators from the U.S. Department of Energy’s (DOE) Argonne National Laboratory is working to address this issue with computing resources at the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science User Facility. Since 2015, this team has worked with the ALCF on multiple projects to explore ways supercomputers can help meet the growing needs of the LHC’s ATLAS experiment.

    The efforts are especially important given what is coming up for the accelerator. In 2026, the LHC will undergo an ambitious upgrade to become the High-Luminosity LHC (HL-LHC). The aim of this upgrade is to increase the LHC’s luminosity—the number of events detected per second—by a factor of 10. “This means that the HL-LHC will be producing about 20 times more data per year than what ATLAS will have on disk at the end of 2018,” says Taylor Childers, a member of the ATLAS collaboration and computer scientist at the ALCF who is leading the effort at the facility. “CERN’s computing resources are not going to grow by that factor.”

    Luckily for CERN, the ALCF already operates some of the world’s most powerful supercomputers for science, and the facility is in the midst of planning for an upgrade of its own. In 2021, Aurora—the ALCF’s next-generation system, and the first exascale machine in the country—is scheduled to come online.

    It will provide the ATLAS experiment with an unprecedented resource for analyzing the data coming out of the LHC—and soon, the HL-LHC.

    CERN/ATLAS detector

    Why ALCF?

    CERN may be best known for smashing particles, which physicists do to study the fundamental laws of nature and gather clues about how the particles interact. This involves a lot of computationally intense calculations that benefit from the use of the DOE’s powerful computing systems.

    The ATLAS detector is an 82-foot-tall, 144-foot-long cylinder with magnets, detectors, and other instruments layered around the central beampipe like an enormous 7,000-ton Swiss roll. When protons collide in the detector, they send a spray of subatomic particles flying in all directions, and this particle debris generates signals in the detector’s instruments. Scientists can use these signals to discover important information about the collision and the particles that caused it in a computational process called reconstruction. Childers compares this process to arriving at the scene of a car crash that has nearly completely obliterated the vehicles and trying to figure out the makes and models of the cars and how fast they were going. Reconstruction is also performed on simulated data in the ATLAS analysis framework, called Athena.

    An ATLAS physics analysis consists of three steps. First, in event generation, researchers use the physics that they know to model the kinds of particle collisions that take place in the LHC. In the next step, simulation, they generate the subsequent measurements the ATLAS detector would make. Finally, reconstruction algorithms are run on both simulated and real data, the output of which can be compared to see differences between theoretical prediction and measurement.

    “If we understand what’s going on, we should be able to simulate events that look very much like the real ones,” says Tom LeCompte, a physicist in Argonne’s High Energy Physics division and former physics coordinator for ATLAS.

    “And if we see the data deviate from what we know, then we know we’re either wrong, we have a bug, or we’ve found new physics,” says Childers.

    Some of these simulations, however, are too complicated for the Worldwide LHC Computing Grid, which LHC scientists have used to handle data processing and analysis since 2002.

    MonALISA LHC Computing GridMap http:// monalisa.caltech.edu/ml/_client.beta

    The Grid is an international distributed computing infrastructure that links 170 computing centers across 42 countries, allowing data to be accessed and analyzed in near real-time by an international community of more than 10,000 physicists working on various LHC experiments.

    The Grid has served the LHC well so far, but as demand for new science increases, so does the required computing power.

    That’s where the ALCF comes in.

    In 2011, when LeCompte returned to Argonne after serving as ATLAS physics coordinator, he started looking for the next big problem he could help solve. “Our computing needs were growing faster than it looked like we would be able to fulfill them, and we were beginning to notice that there were problems we were trying to solve with existing computing that just weren’t able to be solved,” he says. “It wasn’t just an issue of having enough computing; it was an issue of having enough computing in the same place. And that’s where the ALCF really shines.”

    LeCompte worked with Childers and ALCF computer scientist Tom Uram to use Mira, the ALCF’s 10-petaflops IBM Blue Gene/Q supercomputer, to carry out calculations to improve the performance of the ATLAS software.

    MIRA IBM Blue Gene Q supercomputer at the Argonne Leadership Computing Facility

    Together they scaled Alpgen, a Monte Carlo-based event generator, to run efficiently on Mira, enabling the generation of millions of particle collision events in parallel. “From start to finish, we ended up processing events more than 20 times as fast, and used all of Mira’s 49,152 processors to run the largest-ever event generation job,” reports Uram.

    But they weren’t going to stop there. Simulation, which takes up around five times more Grid computing than event generation, was the next challenge to tackle.
    Moving forward with Theta

    In 2017, Childers and his colleagues were awarded a two-year allocation from the ALCF Data Science Program (ADSP), a pioneering initiative designed to explore and improve computational and data science methods that will help researchers gain insights into very large datasets produced by experimental, simulation, or observational methods. The goal is to deploy Athena on Theta, the ALCF’s 11.69-petaflops Intel-Cray supercomputer, and develop an end-to-end workflow to couple all the steps together to improve upon the current execution model for ATLAS jobs which involves a many­step workflow executed on the Grid.

    ANL ALCF Theta Cray XC40 supercomputer

    “Each of those steps—event generation, simulation, and reconstruction—has input data and output data, so if you do them in three different locations on the Grid, you have to move the data with it,” explains Childers. “Ideally, you do all three steps back-to-back on the same machine, which reduces the amount of time you have to spend moving data around.”

    Enabling portions of this workload on Theta promises to expedite the production of simulation results, discovery, and publications, as well as increase the collaboration’s data analysis reach, thus moving scientists closer to new particle physics.

    One challenge the group has encountered so far is that, unlike other computers on the Grid, Theta cannot reach out to the job server at CERN to receive computing tasks. To solve this, the ATLAS software team developed Harvester, a Python edge service that can retrieve jobs from the server and submit them to Theta. In addition, Childers developed Yoda, an MPI-enabled wrapper that launches these jobs on each compute node.

    Harvester and Yoda are now being integrated into the ATLAS production system. The team has just started testing this new workflow on Theta, where it has already simulated over 12 million collision events. Simulation is the only step that is “production-ready,” meaning it can accept jobs from the CERN job server.

    The team also has a running end-to-end workflow—which includes event generation and reconstruction—for ALCF resources. For now, the local ATLAS group is using it to run simulations investigating if machine learning techniques can be used to improve the way they identify particles in the detector. If it works, machine learning could provide a more efficient, less resource-intensive method for handling this vital part of the LHC scientific process.

    “Our traditional methods have taken years to develop and have been highly optimized for ATLAS, so it will be hard to compete with them,” says Childers. “But as new tools and technologies continue to emerge, it’s important that we explore novel approaches to see if they can help us advance science.”
    Upgrade computing, upgrade science

    As CERN’s quest for new science gets more and more intense, as it will with the HL-LHC upgrade in 2026, the computational requirements to handle the influx of data become more and more demanding.

    “With the scientific questions that we have right now, you need that much more data,” says LeCompte. “Take the Higgs boson, for example. To really understand its properties and whether it’s the only one of its kind out there takes not just a little bit more data but takes a lot more data.”

    This makes the ALCF’s resources—especially its next-generation exascale system, Aurora—more important than ever for advancing science.

    Depiction of ANL ALCF Cray Shasta Aurora exascale supercomputer

    Aurora, scheduled to come online in 2021, will be capable of one billion billion calculations per second—that’s 100 times more computing power than Mira. It is just starting to be integrated into the ATLAS efforts through a new project selected for the Aurora Early Science Program (ESP) led by Jimmy Proudfoot, an Argonne Distinguished Fellow in the High Energy Physics division. Proudfoot says that the effective utilization of Aurora will be key to ensuring that ATLAS continues delivering discoveries on a reasonable timescale. Since increasing compute resources increases the analyses that are able to be done, systems like Aurora may even enable new analyses not yet envisioned.

    The ESP project, which builds on the progress made by Childers and his team, has three components that will help prepare Aurora for effective use in the search for new physics: enable ATLAS workflows for efficient end-to-end production on Aurora, optimize ATLAS software for parallel environments, and update algorithms for exascale machines.

    “The algorithms apply complex statistical techniques which are increasingly CPU-intensive and which become more tractable—and perhaps only possible—with the computing resources provided by exascale machines,” explains Proudfoot.

    In the years leading up to Aurora’s run, Proudfoot and his team, which includes collaborators from the ALCF and Lawrence Berkeley National Laboratory, aim to develop the workflow to run event generation, simulation, and reconstruction. Once Aurora becomes available in 2021, the group will bring their end-to-end workflow online.

    The stated goals of the ATLAS experiment—from searching for new particles to studying the Higgs boson—only scratch the surface of what this collaboration can do. Along the way to groundbreaking science advancements, the collaboration has developed technology for use in fields beyond particle physics, like medical imaging and clinical anesthesia.

    These contributions and the LHC’s quickly growing needs reinforce the importance of the work that LeCompte, Childers, Proudfoot, and their colleagues are doing with ALCF computing resources.

    “I believe DOE’s leadership computing facilities are going to play a major role in the processing and simulation of the future rounds of data that will come from the ATLAS experiment,” says LeCompte.

    This research is supported by the DOE Office of Science. ALCF computing time and resources were allocated through the ASCR Leadership Computing Challenge, the ALCF Data Science Program, and the Early Science Program for Aurora.

    See the full article here .


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    Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science. For more visit http://www.anl.gov.

    About ALCF

    The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing facilities in partnership with the computational science community.

    We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and expertise.

    ALCF projects cover many scientific disciplines, ranging from chemistry and biology to physics and materials science. Examples include modeling and simulation efforts to:

    Discover new materials for batteries
    Predict the impacts of global climate change
    Unravel the origins of the universe
    Develop renewable energy technologies

    Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

    Argonne Lab Campus

  • richardmitnick 2:02 pm on February 9, 2018 Permalink | Reply
    Tags: ANL MIRA supercomputer, , Astrophysicists settle century-old cosmic debate on magnetism of planets and stars, , , , OMEGA Laser Facility in Rochester N.Y,   

    From University of Chicago: “Astrophysicists settle century-old cosmic debate on magnetism of planets and stars” 

    U Chicago bloc

    University of Chicago

    February 9, 2018
    Rob Mitchum

    Laser experiments verify ‘turbulent dynamo’ theory of how cosmic magnetic fields are created

    Three-dimensional FLASH simulation of the experimental platform, performed on the Mira supercomputer. Shown are renderings of the simulated magnetic fields before the flows collide. Courtesy of the Flash Center for Computational Science.

    The universe is highly magnetic, with everything from stars to planets to galaxies producing their own magnetic fields. Astrophysicists have long puzzled over these surprisingly strong and long-lived fields, with theories and simulations seeking a mechanism that explains their generation.

    Using one of the world’s most powerful laser facilities, a team led by University of Chicago scientists experimentally confirmed one of the most popular theories for cosmic magnetic field generation: the turbulent dynamo. By creating a hot turbulent plasma the size of a penny, which lasts a few billionths of a second, the researchers recorded how the turbulent motions can amplify a weak magnetic field to the strengths of those observed in our sun, distant stars and galaxies.

    The paper, published this week in Nature Communications, is the first laboratory demonstration of a theory explaining the magnetic field of numerous cosmic bodies, which has been debated by physicists for nearly a century. Using the FLASH physics simulation code, developed by the Flash Center for Computational Science at UChicago, the researchers designed an experiment conducted at the OMEGA Laser Facility in Rochester, N.Y. to recreate turbulent dynamo conditions.

    U Rochester Omega Laser

    Confirming decades of numerical simulations, the experiment revealed that turbulent plasma could dramatically boost a weak magnetic field up to the magnitude observed by astronomers in stars and galaxies.

    “We now know for sure that turbulent dynamo exists, and that it’s one of the mechanisms that can actually explain magnetization of the universe,” said Petros Tzeferacos, research assistant professor of astronomy and astrophysics at the University of Chicago and associate director of the Flash Center. “This is something that we hoped we knew, but now we do.”

    A mechanical dynamo produces an electric current by rotating coils through a magnetic field. In astrophysics, dynamo theory proposes the reverse: the motion of electrically-conducting fluid creates and maintains a magnetic field. In the early 20th century, physicist Joseph Larmor proposed that such a mechanism could explain the magnetism of the Earth and sun, inspiring decades of scientific debate and inquiry.

    While numerical simulations demonstrated that turbulent plasma can generate magnetic fields at the scale of those observed in stars, planets and galaxies, creating a turbulent dynamo in the laboratory was far more difficult. Confirming the theory requires producing plasma at an extremely high temperature and volatility to produce the sufficient turbulence to fold, stretch and amplify the magnetic field.

    To design an experiment that creates those conditions, Tzeferacos and colleagues at UChicago and the University of Oxford ran hundreds of two- and three-dimensional simulations with FLASH on the Mira supercomputer at Argonne National Laboratory.

    MIRA IBM Blue Gene Q supercomputer at the Argonne Leadership Computing Facility

    The final setup involved blasting two penny-sized pieces of foil with powerful lasers, propelling two jets of plasma through grids and into collision with each other, creating turbulent fluid motion.


    “People have dreamed of doing this experiment with lasers for a long time, but it really took the ingenuity of this team to make this happen,” said Donald Lamb, the Robert A. Millikan Distinguished Service Professor Emeritus in Astronomy and Astrophysics and director of the Flash Center. “This is a huge breakthrough.”

    The team also used FLASH simulations to develop two independent methods for measuring the magnetic field produced by the plasma: proton radiography, the subject of a recent paper [AIP]from the FLASH group, and polarized light, based on how astronomers measure the magnetic fields of distant objects. Both measurements tracked the growth in mere nanoseconds of the magnetic field from its weak initial state to over 100 kiloGauss—stronger than a high-resolution MRI scanner and a million times stronger than the magnetic field of the Earth.

    “This work opens up the opportunity to experimentally verify ideas and concepts about the origin of magnetic fields in the universe that have been proposed and studied theoretically over the better part of a century,” said Fausto Cattaneo, professor of astronomy and astrophysics at the University of Chicago and a co-author of the paper.

    Now that a turbulent dynamo can be created in a laboratory, scientists can explore deeper questions about its function: How quickly does the magnetic field increase in strength? How strong can the field get? How does the magnetic field alter the turbulence that amplified it?

    “It’s one thing to have well-developed theories, but it’s another thing to really demonstrate it in a controlled laboratory setting where you can make all these kinds of measurements about what’s going on,” Lamb said. “Now that we can do it, we can poke it and probe it.”

    In addition to Tzeferacos and Lamb, UChicago co-authors on the paper include Carlo Graziani and Gianluca Gregori, who is also professor of physics at the University of Oxford. The research was funded by the European Research Council and the U.S. Department of Energy.

    See the full article here .

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