The Process of Discovery: NCSA Science Highlights-1991 3 Director's statement 4 Developing the environment 7 Researching today's challenges 8 Beyond the big bang: Stars and stellar evolution W. David Arnett and Bruce A. Fryxell 10 At the forefront: Toward the operational prediction of thunderstorms Kelvin K. Droegemeier 12 Where there's smoke: Multipronged approach to fire modeling Kwang-tzu Yang 14 Banding together: Coupling in high Tc superconductors Ronald E. Cohen 16 Going with the flow: Vortex simulation of combustion dynamics Ahmed F. Ghoniem 18 Quantum leaps: Electronic properties of clusters and solids Marvin L. Cohen and Steven G. Louie 20 Cracking the protein folding code: Protein tertiary structure recognition Peter G. Wolynes 22 Adrift in an electric sea: Linear and circular polymer gel electrophoresis Monica Olvera de la Cruz 24 The cortical connection: Simulation of complex neural tissues Klaus J. Schulten 26 Turning up the heat: Development of phased arrays for hyperthermia Emad S. Ebbini 28 Educating tomorrow's scientists Director's statement Predicting storms, sequencing human chromosomes, discovering new materials-- these and the other projects described in this report represent only a small fraction of the exciting work being carried out by users of the National Center for Supercomputing Applications. These are examples of emerging grand challenges-- fundamental problems in science or engineering, with potentially broad economic, social, or scientific impact--that can be advanced by applying high-performance computing resources. These problems, in their full complexity, require interdisciplinary team efforts on a national scale, where collaborators are tied together by the National Research and Education Network. In order to answer these challenges, NCSA is transforming its distributed heterogeneous computing environment into a single integrated metacomputer. Our application teams are working with local and remote users to drive or probe the technological implications of the metacomputer. This new infrastructure is being furthered by the High Performance Computing and Communications Initiative--an unprecedented commitment from the federal government to accelerate the utilization of new architectures. NCSA is committed to providing its diverse constituencies with production and experi- mental high-performance computing and communications resources. Talented, dedicated staff support these communities by helping them make the most effective use of current and emerging technologies. U.S. industrial researchers benefit from access to powerful new applications designed to take full advantage of the metacomputer--accelerating the movement in American industry from using high-performance computing and communications for analysis to using it for the total design process. The nation's future scientists are also active at the center. As an example, teams from four high schools were selected through a national competition to come to NCSA for SuperQuest '91, a three-week intensive high-performance computing institute. Each student submitted an individual proposal describing a research problem requiring a supercomputer. After returning to their high schools, these students continue their work remotely via the Internet using workstations donated to the schools. Other students also benefit because the workstations are used in local programs. NCSA is confident that these efforts of the entire national computational community will transform our world by the start of the twenty-first century. Larry Smarr, Director Developing the environment The computational environment of the 1980s was characterized by a small set of loosely coupled computers: one for processing, one for storage, and one for the user interface. The need for better performance, greater speed, and more data storage, combined with ease of use, has led to a new level of computing--the metacomputer. Larry Smarr, director of NCSA, describes the metacomputer as a gigabit-per-second network of heterogeneous, interactive, computational resources linked by software in such a way that it can be used almost as easily as a personal computer. The processors of a metacomputer encompass a range of architectures: massively parallel machines, vector multiprocessors, and superscalar systems. A worldwide infrastructure of interconnected computer networks will allow a researcher to reach out across national and international networks and obtain whatever computational resources are appropriate for the research needs of the moment. The development of a national scale metacom-puter, with its attendant national file system, is a new mission of the four NSF supercomputing centers. Combining intellectual and computational resources into a national metacenter will expedite R&D ventures in the areas of managing large data-sets, enhancing and expanding the National Research and Education Network (NREN), and developing software programs that allow communication between all the computing components. Improving experimental data analysis with the metacomputer Driving the technological development is a series of metacomputer probe projects. These projects are chosen to span the wide range of applications used by our national community of users and to address both experimental and computational approaches to science. Two metacomputer probe projects--as dissimilar as mapping the skies and analyzing heart motion--are using innovative metacomputer technology to manipulate observational and instrumental data. Typically, radio astronomers record their raw observational data on tape and then process it weeks or months later to obtain images. This time delay makes it difficult to study time-variable phenomena or to follow up on unexpected phenomena using radio telescopes. Richard Crutcher (of NCSA and University of Illinois at Urbana-Champaign professor of astronomy) would like to examine data from his telescope while his observational experiment is underway. This could be brought about by connecting the telescope system directly to the NCSA metacom-puter via a long-range, high-speed network. The telescope system is BIMA (Berkeley-Illinois-Maryland Array); the network is BLANCA (a gigabit network testbed funded by NSF, the Defense Advanced Research Projects Agency, and AT&T through the Corporation for National Research Initiatives). Eric Hoffman is chief of the cardiothoracic imaging research section and associate professor of radiologic science and physiology at the University of Pennsylvania School of Medicine. While at the Mayo Clinic, he acquired volumes of computerized tomography (CT) data from an instrument called the Dynamic Spatial Reconstructor (DSR). Unlike other scanners, which reconstruct still 3D images, the DSR collects 3D images consisting of 2 million volume elements at up to 60 images per second. Hoffman has been able to visualize interactively his dataset describing a dog's beating heart using the CRAY Y-MP and an SGI workstation. "At the moment, it's very time-consuming to build 3D images of these beating hearts," says Hoffman. "We can do them now only on rare, selected cases. If it became easier, we'd probably do it routinely, which would be a big plus for cardiac analysis." NCSA's Biomedical Imaging Group led by Clint Potter, NCSA research programmer, is developing a testbed for a distributed biomedical imaging laboratory (DBIL) that would allow biomedical instruments producing datasets like Hoffman's to be networked transparently to the metacomputer. Simulating reality on the metacomputer Numerical experiments study the behavior of complex systems under controlled conditions and ask the question "What happens?"--not just "What is the answer?" Therein lies the need for the metacom-puter-- no single computer provides the complete computational environment for computing, storing, visualizing, and analyzing the data produced by very large simulations, as in the three examples that follow. Princeton University astrophysicists are collaborating with scientists at NCSA to develop computer models and visualizations of galaxy cluster formation in the early universe. The team, led by Princeton Department of Astrophysical Sciences chair Jeremiah Ostriker and NCSA research scientist in astronomy and astrophysics Michael Norman, has developed the most comprehensive model to date. The ultimate aim is to create a "numerical laboratory" for physical cosmology simulations that can address a variety of topics, including large-scale structure, galaxy formation, and cluster evolution. Using this model, Norman and Ostriker used the NCSA CONVEX vector multiprocessor to compile a simulation consisting of 100 files of data, each a snapshot in time, containing as many as 170,000 galaxy positions in three dimensions. In order to navigate this 4D data, custom software was developed for the Silicon Graphics 4D/360 VGX high-performance workstation at NCSA. By mounting the CONVEX file systems on the Network File System, these data were read into the SGI over the network, providing a seamless connection between simulation and visualization. "Using this system, we can interactively explore the cluster formation process in space and time," says Norman. "This is one aspect of the metacomputer as applied to this project. The entire project, from initial simulation to video post-production," says Norman, "was accomplished without file transfers--an important aspect of metacomputing." Jack Burns, professor of astronomy at New Mexico State University, and Philip Hardee, professor of astronomy at the University of Alabama, are investigating various aspects of radio galaxies--and in particular, radio jets--using the ZEUS family of codes (developed by NCSA scientists David Clarke, Jim Stone, and Norman). These codes have forged many collaborations between NCSA and various institutions across the country. The ZEUS codes are designed to solve the equations of gas dynamics--including the effects of gravity, magnetism, and radiation--and have applications in virtually all branches of astrophysics. These include stellar winds, supernovae, galactic structure, and even cosmology. NCSA's integrated environment allows ZEUS simulations to be performed on the Cray systems, CONVEX, or Connection Machine, depending on the requirements of the particular problem and the researcher's approach. Bob Wilhelmson, atmospheric research scientist at NCSA, and his group build a thunderstorm model from a set of equations that describe the dynamics of atmospheric variables: temperature, pressure, wind speed and direction, moisture, water, and ice content. Wilhelmson says, "Gigabytes of model data must be saved, analyzed, and displayed during and after a model simulation." "The concept of using two or more computers to work on a problem has been around a long time," says Wilhelmson. "Since the early eighties we've used the Cray to run model simulations. When it was finished and the data were stored in files, we'd transfer the files to a high-powered workstation to do the visualization. Now with current technology, it's possible to consider connecting those two computers [SGI 4D/360 VGX and CRAY-2] and to have them working simultaneously on their specific tasks. What we'd like to create is a tightly coupled system of model simulation/data generation, analysis, and visualization, using multiple machines to improve throughput and exercise different computer capabilities." Metacomputing and grand challenge teams Projects such as those described above are prototyping the computational environment that will support the effort of grand challenge teams in the coming decade. Similar experiments are underway at each of the NSF supercomputer centers, as well as at other federal- and state- supported institutions--creating a single national information fabric. As grand challenge teams develop from the kinds of research described in this report, team members will become both users and co-architects of the emerging national metacomputer. Additional information about the NCSA metacomputing environment is available in the NCSA magazine, access, beginning with the September-December 1991 issue. Researching today's challenges "The process of scientific discovery is, in effect, a continued flight from wonder." -- Albert Einstein Beyond the big bang Stars and stellar evolution W. David Arnett and Bruce A. Fryxell Department of Physics University of Arizona Supernova explosions occur when a massive star exhausts its nuclear fuel and collapses under its own weight. They are among the most violent events in the universe and are extremely rare. On February 23, 1987, a supernova exploded in the Large Magellanic Cloud (a satellite galaxy of the Milky Way), affording scientists a once-in-a-lifetime opportunity to study the brightest supernova event since the invention of the telescope. Because of the relative closeness of the star and the high intrinsic luminosity of supernovae, it was possible for astronomers to obtain an unprecedented amount of data--not only from ground-based telescopes, but also from detectors on satellites, balloons, and rockets. A burst of neutrinos emitted during the collapse of the star's dense core just before the explosion was also detected. This data has produced many surprises, some of which can best be explained by nonlinear fluid instabilities which create strong mixing in the ejecta. To study these processes requires numerical hydrodynamical simulations in two and three dimensions--challenging the capabilities of even the largest high-performance computers. For the most part, these observations confirmed the existing theories of how a massive star dies. Although the exact explosion mechanism is still uncertain, the neutrino detections verified that the process is initiated by the collapse of the stellar core, forming a neutron star. Subsequently, the remainder of the star is ejected, constituting the visual display seen on earth. Dave Arnett and Bruce Fryxell, professors of physics at the University of Arizona, have been calculating the nonspherical motions, fluid instabilities, and mixing that occurred during the supernova explosion by performing two-dimensional hydrodynamic simulations on the CRAY- 2 and CRAY Y-MP systems at NCSA. In order to resolve the extremely intricate structures that develop in the flow, very large computational grids are required, making the use of a supercomputer essential. As a result of the vast amount of data collected during the explosion, it is clear that there are serious deficiencies in current models. In particular, there are many features that are impossible to explain if the explosion is spherically symmetric. For example, spherical models predict that the hydrogen in the outer envelope should be ejected at the greatest velocity, while the heavy iron-group elements--formed near the core during the explosion-- should be moving much more slowly. However, observations of various spectral lines indicate that some of the iron- group elements are moving at velocities in excess of 3,000 kilometers per second (km/s), while there is a significant amount of hydrogen at velocities less than 1,000 km/s. This situation can occur only if nonspherical motions take place during the explosion, causing the original layered composition distribution to become mixed. Arnett and Fryxell's calculations indicate that the amount of mixing obtained is not sufficient to explain all of the observations. In particular, it appears difficult--if not impossible--to accelerate the iron-group elements to the observed velocities in this way. This result points back to the uncertainties in the explosion mechanism. It now appears that the only way to accelerate the heavy elements to sufficient velocities is for substantial mixing to occur during the very early stages of the collapse or explosion. By investigating the multidimensional hydrodynamics of these early stages, Arnett and Fryxell hope to place constraints on the actual explosion mechanism for massive stars. (left) Composition structure four hours after the explosion. The light blue region represents the low-density hydrogen envelope. The yellow and orange region is the remnant of the helium shell which started as a thin spherical shell. The dark area in the center is composed of the heavier elements. (above) Density structures four hours after the explosion. The flow shows the low-density bubbles separated by dense fingers topped by "mushroom caps" which are characteristic of the Rayleigh-Taylor instability. The images were created on a Silicon Graphics 240 workstation using software written at the University of Arizona. W. David Arnett (left) Bruce Fryxell "With the CAPS model in place, the nature of numerical weather prediction and forecasting will be forever changed." At the forefront Toward the operational prediction of thunderstorms Kelvin K. Droegemeier Center for Analysis and Prediction of Storms University of Oklahoma at Norman Weather prediction has been identified as one of the principal components of the Grand Challenge Program, and for good reason. Historically, numerical weather prediction has been a driving force behind the advent of digital computing. John von Neumann, generally regarded as the father of computing, realized the potential of computers in weather forecasting and, in the late 1940s, created the now historical first hemispheric forecast using the ENIAC computer in collaboration with meteorologists Jule Charney and Ragnar Fjrtft. The Center for Analysis and Prediction of Storms (CAPS)--one of the first eleven Science and Technology Centers created in 1988 by the National Science Foundation--continues this tradition of innovation. CAPS' mission is to develop techniques for the practical prediction of weather phenomena on scales ranging from a few kilometers and tens of minutes (individual thunderstorms) to hundreds of kilo-meters and several hours (storm complexes and mesoscale systems). Two major developments during the past few years provided the impetus for proposing the creation of CAPS and for moving from a mode of storm simulation to one of prediction. The first is a national multiagency effort known as NEXRAD (NEXt generation RADar), which will result in the placement of some 175 scanning Doppler radars across the continental U.S. by the late 1990s, providing nearly continuous single-Doppler coverage of the scales relevant to storm prediction. The second development, stimulated by the first and now the key element for making storm- scale prediction a reality, concerns techniques developed at the University of Oklahoma for retrieving unobserved quantities from single-Doppler data. Generally referred to as Single Doppler Velocity Retrieval (SDVR), this class of methods allows researchers to recover, using a time series of a single observed wind component, the other two wind components and the mass field to yield a complete and dynamically consistent set of observations with which a storm- scale prediction model can be initialized. Kelvin Droegemeier, deputy director of CAPS, and his team are capitalizing on these developments. The CAPS research effort is divided into four major thrusts: prediction model development, data assimilation techniques, small-scale atmospheric predictability, and implementation. Prediction model development. After an extensive study of existing storm-scale simulation codes, CAPS chose to develop an entirely new sequence of models known as the Advanced Regional Prediction System or ARPS. ARPS is designed using new discrete operator algorithms that greatly simplify the code structure and enhance flexibility. It is completely portable among a variety of computers, including those of the massively parallel class. Droegemeier and his team have been evaluating the ARPS model for NCSA's CRAY-2, Connection Machine Model 2, and IBM RS/6000 systems. Data assimilation techniques. The accuracy of a numerical forecast is highly dependent upon the accuracy of the model's initial conditions. The time honored computer adage "garbage in, garbage out" is certainly true in meteorology! One method, called the adjoint method, involves adjusting the model's initial condition until the most accurate initial state is reached. Originally thousands of complex iterations were required. Recent work has shown that perhaps as few as 50 or 100 iterations might suffice, though experiments involving more complete model physics and real data are needed before the technique can be viewed as successful. Small-scale atmospheric predictability. CAPS is attempting to understand which types of thunderstorms tend to be the most predictable, and the degree to which storm evolution and type depend upon the model's initial state. Implementation. The many facets of the research and development program will be joined for a preliminary evaluation in 1994. Results from this operational test will serve as a starting point for the future of regional storm-scale weather prediction. Droegemeier envisions CAPS research as culminating in a prototype for self-contained regional numerical prediction centers (e.g., one per state or every two states), each of which would ingest its own data from a few nearby NEXRAD systems, perform all computing locally, and disseminate its information to a regional constituency. It is estimated that commercial airlines alone could, with accurate regional forecasts, save millions of dollars each year in revenues presently lost to unanticipated weather delays and traffic rerouting. Accurate forecasts of electrical storms would aid rerouting and switching by power and communication utilities. Numerous benefits concerning logistical planning would be available to the defense and space flight communities. Droegemeier is confident that, with the CAPS model in place, the nature of numerical weather prediction and forecasting will be forever changed. (left) Different timesteps of a strongly rotating thunderstorm. The simulation was run on the CRAY-2, and the images were created using the Plot-3D package on a Silicon Graphics IRIS 4D workstation. (above) Temperature field of a turbulent thermal rising in a 2D numerical model using the Piecewise Parabolic Method. The image was made on a Gould IP8500 system with software written at the Uni-versity of Oklahoma. Where there's smoke Multipronged approach to fire modeling Kwang-tzu Yang Department of Aerospace and Mechanical Engineering University of Notre Dame For as long as man has known about the wonders of fire, he has also recognized its ability to destroy. By learning how fire and smoke spread in confined and ventilated spaces we hope to eventually develop ways to reduce fire hazards and losses in both human lives and properties. It is now generally recognized that fire hazards in rooms, passageways, and other confined spaces can only be reduced by a multipronged approach. One such strategy is fire modeling, which develops mathematical models that describe the physical and chemical processes of how fire spreads as a function of the ignition source, space geometry, and material content. Once validated by experiments in small-scale laboratory tests or full-scale fire tests, these mathematical models become computer-based simulation models to determine the effects of significant parameters of fire-spread phenomena. The simulation results can then be used to develop fire reduction measures and to provide a rational basis for post-fire investigations. Fire modeling significantly reduces the need for full-scale fire tests, which are extremely expensive and time consuming. Fire models can be categorized as either zone models or field models. Zone models divide the fire-affected environment or space into distinct zones that can be analyzed separately-- either empirically or theoretically--in terms of input and output information based on mass and energy balances. While zone models are generally computationally efficient, they have shortcomings: models for some zones are not adequately known and quantified, and the validity of zone models is not certain. Field models, on the other hand, are inherently more rational and capable of revealing important large- and small-scale phenomena in a fire-spread problem. The primary shortcoming of field models is that they are computationally intensive, requiring supercomputers to execute numerical solutions. The research effort at the University of Notre Dame, led by Dr. K. T. Yang, the Viola D. Hank Professor of Engineering in the Department of Aerospace and Mechanical Engineering, concentrates on developing field models based on numerical solutions to the governing differential field equations of the conservation of momentum, mass, energy, and spaces. The field models are based on three-dimensional, finite- difference, primitive-variable, and microcontrol volume time-dependent formulations. They use a high- order differencing scheme for advection terms in the governing equations to minimize numerical diffusion errors. The models now include the physical effects of large buoyancy, turbulence, surface and gaseous radiation, wall losses, forced ventilation, partitions, and pressurization. A combustion model based on laminar flamelets is being incorporated into the basic algorithm. Another significant feature of the current models is that different complex geometries of the fire-affected space can be accommodated without disturbing the basic algorithm. The Notre Dame field models have successfully simulated full-scale fire tests in rooms, aircraft cabins, and ventilated and unventilated underwater vehicles--all carried out in a decommissioned German nuclear-reactor containment building. NCSA's CRAY Y-MP supercomputer has been used in this study to generate numerical results that are compared with results of the full-scale fire test. Comparisons have been made with the detailed transient temperature field inside the burn room. A supercomputing environment is required because the computations deal with the incredibly complex numerical solutions to the unsteady compressible Navier-Stokes equations, the continuity equations, and the full energy equation (an integral-differential equation that incorporates thermal radiation effects of a participating medium). The future direction of this effort lies in incorporating a realistic combustion model compatible with other physical effects that are already in the current models; incorporating a more rational turbulence model (this effort is now well under way at Notre Dame); further validating field models with results from full-scale fire testing; and recasting the algorithm for parallel processing (the current code is fully vectorized). (top left) Contours of velocity along roll cell close to the end wall. (bottom left) Contours of velocity along roll cell showing central symmetry. (above) Velocity vectors of rolls and contours of velocity along roll cell. The images were created using Wavefront Technologies' DataVisualizer on a Silicon Graphics 4D/25G workstation. (Courtesy Mike Krogh.) Banding together Coupling in high Tc superconductors Ronald E. Cohen Geophysical Laboratory Carnegie Institution of Washington Are high-temperature superconductors conventional metals, or are they exotic, "new forms" of matter? Ronald Cohen is investigating this question with colleagues Warren E. Pickett and David Singh at the Naval Research Laboratory, and Henry Krakauer at the College of William and Mary. Using the CRAY-2 system at NCSA and the IBM 3090 at the Cornell Theory Center, they are performing large scale electronic structure calculations on the oxide superconductors YBa2Cu3O7-D, La2-x(Ba,Sr)xCuO4, and (Ba,K)BiO3 using conventional band theory within the local density approximation (LDA). These are first-principles calculations in the sense that no experimental data are used. The only inputs are the nuclear charges and positions in the crystal. LDA is known to work well for most conventional metals, semiconductors, and insulators. However, if the high Tc superconductors are exotic materials to which band theory does not apply, LDA predictions would be expected to disagree with experiment. In fact, there is a major discrepancy--LDA predicts pure La2CuO4 and YBa2Cu3O6 to be metallic, whereas they are actually insulators. This result is one of the main reasons many researchers have assumed that the high- temperature superconductors could not be treated by conventional techniques. When doped, the superconductors exhibit metallic conductivity in the normal state. Cohen has found that LDA correctly predicts properties of the doped, metallic superconductors. Extensions to LDA appear necessary to treat the insulating parent compounds of the high Tc superconductors, but the accuracy of LDA is comparable for superconductors as for other conventional materials. Cohen made special efforts to highly converge his computations to confirm that agreement or disagreement with experimental data is not due to numerical approximations in the calculations. The complexity of the high Tc superconductors, coupled with the requirements for high accuracy, makes the computations very difficult and time- consuming. Three types of properties are being investigated for high Tc superconductors: electronic properties such as the Fermi surface that characterizes the quasiparticle states available for scattering in a conventional metal; vibrational properties such as phonon frequencies, eigenvectors, and anharmonicity; and electron-phonon coupling, which leads to superconductivity in conventional superconductors. The Fermi surface separates occupied from unoccupied electronic states in the ground state of a metal, with all of the quasiparticle scattering occurring at the Fermi surface. The shape of the Fermi surface governs many properties of metals. For example, in a magnetic field the quasiparticles follow orbits in real space that follow the contours of the Fermi surface. The Fermi surface for YBa2Cu3O7 was calculated shortly after its discovery and more recently Cohen's group has performed more detailed and highly converged calculations that emphasize the 3D electronic structure. Until lately it was not clear that the superconductors even have Fermi surfaces. The presence of a Fermi surface and the excellent agreement between band theory and experiment strongly suggest that the high Tc superconductors are indeed conventional metals that can be described with the well-developed apparatus known collectively as Fermi liquid theory. While investigating phonons in YBa2Cu3O7 and La2CuO4, Cohen generally found good agreement between calculated and observed vibrational frequencies. This indicates that band theory gives the correct charge density and static density response for the high Tc superconductors--further evidence that they are conventional materials. Highly anhar-monic modes were also studied; these are related to phase transitions and anomalous dynamical properties, and also influence superconductivity. The goal of studying high Tc superconductors is twofold: to determine whether these are electron-phonon superconductors similar to conventional low Tc superconductors and to understand why Tc is so high. Cohen and his team have found indications that the electron- phonon interaction is indeed very strong in the high Tc superconductors. The main difference between conventional and high Tc superconductors is that in conventional superconductors the interactions between atomic motions and the electrons are local. In high Tc superconductors, moving an atom affects the electronic interaction on other atoms due to the low density of states and ionicity in the oxide superconductors. This effect greatly increases the electron-phonon coupling strength. The calculations indicate that three things are needed to achieve high Tc superconductivity: a low density of states at the Fermi level, significant ionicity, and low-mass atoms, such as oxygen. This grand challenge research in molecular and crystalline structure and in improving and understanding the nature of materials could eventually lead to using liquid nitrogen rather than liquid helium in semiconductor design--a substantial monetary savings. In turn, this could produce more powerful, smaller motors, and smaller, faster computers. (left) Calculated Fermi surface of YBa2Cu3O7. The green and red surfaces are for the Cu-O planes and have been found by photoelectron spectroscopy (Arko et al., 1989); the blue and pink surfaces are chain related and have been observed by de Haas van Alphen (Fowler et al., 1991) and positron annihilation (Haghighi et al., 1991), respectively. The image was created using SunVision on a Sun SPARCstation 2. (above) Change in charge density in La2CuO4 with Oz displacement. Displacing the oxygen changes the charge density and potential in the Cu-O plane, which greatly increases the electron-phonon interaction for this mode. Data processed with DISSPLA. "Armed with supercomputing power, it has been possible to make some serious attempts to solve the equations governing turbulent combustion and to use these solutions to probe into the details of this complex phenomenon." Going with the flow Vortex simulation of combustion dynamics Ahmed F. Ghoniem Department of Mechanical Engineering Massachusetts Institute of Technology From the flints and dry kindling used a million years ago to the pressurized combustion chambers and ultralow emission fuels of today, sweeping changes have occurred in the technology of combustion. Improved combustion has economic and environmental--and thus, political--implications. Judicious use of fossil fuels is one issue. Safety and health considerations associated with the burning of those fuels, local and global environmental impacts of the combustion process, and propulsion systems for the 21st century broaden the picture. To understand the dynamics of combustion, researchers study the physics of turbulent reacting flows--the tumbling eddies of gaseous oxidizers and fuels in which, when the proper molecular mix has occurred, chemical energy is converted into heat and mechanical energy. These studies have been conducted using experimental methods, analysis, and more recently, computational modeling. The first approach is expensive and is limited by what can be accurately measured in the hostile environment of intense flames. The second approach is encumbered by the complexity of the mathematical equations used to model turbulent combustion. Computational fluid dynamics, known as CFD, enables scientists to study combustion and other aspects of fluid flow via supercomputing tools in a theoretical construct. In this framework, rapidly evolving interacting processes--modeled by a large set of analytically unsolvable equations--can be studied without the need to invoke simplifying assumptions. Ahmed Ghoniem, CFD expert and professor of mechanical engineering at MIT, has found a broad range of current engineering applications for his combustion research. These include propulsion of hypersonic planes; improved design of utility and domestic burners; safe and efficient disposal of toxic wastes in incinerators; cleaner, more efficient automotive engines; reduced noise and air pollution; and fire control and suppression. With this diversified applications base, Ghoniem's work is supported by many different governmental agencies, the automotive industry, the aerospace sector, and the utility industry. Ghoniem's approach has been to model turbulent flows in the absence of combustion, then to successively introduce the additional physics that modify a turbulent flow when combustion occurs--variable temperature, density and pressure effects, and other changes due to energy transfer. It has been recognized that fast and efficient burning can be achieved if turbulence is properly employed to promote mixing among the reacting species without causing the disintegration of the reaction zone or the generation of instabilities. With the plans to develop propulsion systems for supersonic aircraft in which the rates of energy conversion are much higher than in traditional subsonic propulsion, reducing toxic emissions and noise, and understanding the role of turbulence in combustion have become even more urgent. Armed with supercomputing power, it has been possible to make serious attempts to solve the equations governing turbulent combustion and to use these solutions to probe into the physical details and practical implications of this complex phenomenon. By refining their numerical models, Ghoniem and his colleagues hope to develop better engineering tools for optimizing the design of combustion devices. Having access to these powerful tools will benefit both science and industry. Researchers can investigate new concepts, test the validity of new ideas, and demonstrate the applicability of new inventions. Industry hopes to reduce the time and cost of designing new products--a process that currently relies heavily on traditional methods. This concerted effort should improve national productivity and competitiveness. The leap toward modeling such complex phenomena as those encountered in turbulent combustion was made possible through the availability of supercomputers. The vast memory and enormous speed of these machines are indispensable in carrying out computations that represent rapidly evolving, spatially tangled physical and chemical processes such as combustion in automobile engines. Numerical methods that take full advantage of the computer architecture, while maintaining accuracy and stability, continue to improve the interface between physical reality and the computer. Finally, by using visualization hardware and software, scientists and engineers are able to interpret computer output in familiar terms. (left and above) Turbulent mixing. Stills from a simulation show development of a complex, 3D structure in a turbulent mixing layer into a chemical reaction between two streams of fuel and oxidizer. Two cross sections of concentration are shown. Red/yellow indicates high concentration; blue/green low concentration. The images were processed from CRAY Y-MP data using NCAR software on a MicroVAX II computer. Quantum leaps Electronic properties of clusters and solids Marvin L. Cohen and Steven G. Louie Department of Physics University of California, Berkeley One of the goals of condensed matter physics since the late 1920s has been to explain and predict the properties of solids using only quantum theory and input information about the constituent atoms. Marvin Cohen and Steven Louie, professors of physics at the University of California at Berkeley, have recorded a number of firsts in this area. They address the question of how solid-state electronic and structural properties evolve from the properties of the constituent atoms. By studying the properties of combined units-- atoms, molecules, microclusters, fine particles, and bulk solids--they can explore how electronic properties change with size and complexity. Much of this research relies on calculations requiring numerical solutions to problems about strongly interacting particles in real materials. These calculations require hundreds of hours of Cray CPU cycles and megawords of computer memory. Several first- principles quantum approaches are used. The numerical algorithms include the repeated manipulation of large matrices (involving dimensions of thousands) and extensive use of three-dimensional fast Fourier transforms and Monte Carlo sampling schemes. The complexity of the calculations is usually a strong function of the number of atoms contained in a unit cell of the crystal. These methods have proven to be highly accurate and capable of predictive power. A host of solid-state properties have been obtained with these methods. Among these are structural information, surface and interface characteristics, superconducting transition temperatures, and phase stability. In addition to contributing to the understanding of existing materials, Cohen and Louie hope to successfully predict the existence of new materials not previously found in the laboratory. One proposed material is comprised of gallium and arsenide. By controlling the ratio of Ga to As they might control the degree to which the material is a conductor. The future direction of this work depends on both theoretically motivated problems and experimental discoveries. Cohen and Louie hope to respond to both and make contributions that are potentially useful to experimentalists and theorists. Their theoretical calculations can simulate conditions that are not easily obtainable. Their plans for the near future are to examine iron, iron oxides, and possibly iron hydrides under pressure. These materials are important for both solid state physics and for geophysics, but they are difficult to examine theoretically. They are also exploring the unusual properties of materials based on elements in the first row of the Periodic Table. Examples include the hardest materials known (diamond and boron nitrite). Once they compute the structural properties of known compounds, they expect to propose new compounds based on these elements. Calculations will be done of composite materials to look for supermodulus effects, which cause materials to be less compressible than their components. They have recently proposed that it may be possible to fabricate a carbon- nitrogen compound that has a hardness comparable to or greater than that of diamond. A new theoretical tool based on quantum Monte Carlo simulation has been developed by this group for looking at many electron effects. This approach goes beyond the standard self- consistent field theories for solids and opens up a brand new direction for research on the electronic properties of real materials. They plan to apply this technique to study metallic hydrogen, transition metals, and transition metal oxides. Cohen and Louie's group has been using computers since the early 1960s. Says Cohen, "the supercomputer is a wonderful tool for exploring scientific and technical problems. It is an intellectual aid. Often the simplest physical ideas elude us until we do a 'full-blown' calculation. Then, we are in the position to use hindsight to say, 'Why didn't we think of that to begin with?' So, in addition to the more obvious use of supercomputers for applying theories, number crunching, simulations, and computerexperiments, often overlooked is the use of doing some numerical calculations to 'gain insight.'" (far left) Calculated magnetization density distribution in bcc iron in a (110) plane. The atoms are located at the hollow centers. The 3D image was created using MONGO software on a VAX/VMS workstation. (left) Electron charge density distribution of the material from (above) image. (above) Ball and stick model of the structure of a proposed new material containing gallium (Ga-small spheres) and arsenide (As-large spheres). (above) and (left) Images were created with software written at UC-Berkeley. Marvin L. Cohen (seated) Steven G. Louie ". . . supercomputers allow us to take a more experimental approach to our problems and try out many ideas. This sort of exploration is very helpful in problems that are as wide open as protein folding." Cracking the protein folding code Protein tertiary structure recognition Peter G. Wolynes Department of Chemistry University of Illinois at Urbana-Champaign Almost since their invention, computers have been used to try to break codes. A grand challenge code scientists seek to understand is that which determines the folding and three- dimensional structure of a protein, given its sequence. In turn, detailed mechanisms of a protein's function can be worked out only when the structure is known. Thus a lack of understanding of the protein folding code is a major impediment to many areas of molecular biology. One of the great surprises in this area is that many, if not all, protein molecules can find an organized but complex structure spontaneously. It appears, then, that the relation between sequence and structure is a consequence of the intermolecular forces. However, fully mapping these interactions is a major challenge since the technology of finding the sequence of proteins is quite sophisticated, and determining their structures remains a difficult experimental task. Peter Wolynes, professor of chemistry at the University of Illinois at Urbana-Champaign, has developed some simple models of proteins as information processing units that provide a schematic view of folding. Experimental studies of proteins suggest that the code is very degenerate and robust--many errors can be made in a sequence pattern but the final structure remains essentially the same. This feature suggests an analogy to brains and neural networks where very fuzzy patterns can be recognized. Wolynes and his team have developed energy functions that embody the known patterns of protein structures. The energy functions are determined in a way analogous to that used in neural biology where patterns reinforce certain interactions between neurons. The associative memory polymer Hamiltonians developed by the Illinois researchers are closely related to models used for very simple neural nets. The polymers in this research have many elements of reality so that, in a schematic way, the folding process that is carried out may resemble the real thing. At the same time, because the folding patterns filter out mostly relevant information, the folding process can be greatly speeded up with the computer so that it happens in the analog of nanoseconds rather than the millisecond-to-second range of real processes. This approach should give insight into the relationship of thermodynamics to the dynamics of protein folding and may provide practical algorithms for predicting low-resolution structures of proteins when only their sequence is known. Wolynes and his group use NCSA's CRAY-2 and CRAY Y-MP high- performance computers for their folding calculations. Their algorithms are basically molecular dynamics simulations and Monte Carlo calculations. They also use methods closely tied to neural networks such as back propagation for training auxiliary networks. In all of these applications, the amount of data handled is quite large because the entire set of protein crystal structures is used to build the energy functions. And the molecular dynamics, roughly equivalent to one nanosecond of real time, translate into an hour or so of Cray time for a single trial guess at the folding code used in the energy function. These studies--requiring the continuous interplay of hypothesis and testing--would be impossible with slower computers. By focusing on determining structure from sequence alone, this approach has many connections with other problems of sequence analysis that enter into the human genome project. Recognizing protein structures from sequence would be a catalytic intermediate step between the determination of sequence and function. More abstract studies of protein folding being carried out in this context may help in understanding folding as it occurs in living cells. Defects in the folding process have been implicated in a variety of diseases, including Alzheimer's disease. Wolynes' future plans include pursuing two biologically oriented directions. One employs more sophisticated sequence comparison algorithms to screen data for the associative memory approach, and the other uses further analogies to higher order neural processing to achieve better folding predictions. Supercomputers have greatly affected how the group does research. Says Wolynes, "I still believe that analytical studies provide great insights into physical and biological processes, but to take analytical ideas and translate them into useful results often involves large-scale computations. These can only be carried out with supercomputers." The images show the overlap of a predicted protein tertiary structure with the actual x-ray crystal structure of the cytochrome proteins 351C (above) and ICCR (left). The algorithms used to predict these structures utilize analogies drawn from other complex systems, e.g., associative memory Hamiltonian neural network schemes. The computations were performed on the CRAY-2, and the images were created using Wavefront Technologies' DataVisualizer. "[Certain] problems I am working on now can only be approached by computer simulations. [High-performance] computers have provided the means to analyze problems experimentally or analytically untractable, to confirm theories, and to generate models that explain experimental results." Adrift in an electric sea Linear and circular polymer gel electrophoresis Monica Olvera de la Cruz Department of Materials Science and Engineering Northwestern University A basic question of the human genome project is how to sequence human chromosomes-- molecular complexes containing millions of precisely ordered units. Monica Olvera de la Cruz, associate professor of materials science and engineering, and Northwestern University graduate student Dilip Gersappe have been tackling this problem. In particular, they are studying gel electrophoresis, an important experimental technique widely used in modern molecular biology for separating molecules according to size. The simplest form of the technique consists of applying a constant electric field to a gel (a three-dimensional random network) that contains the charged molecules of interest. After a period of time, chains of different sizes separate physically in the gel. The shorter the chain, the faster it migrates in the applied field direction. Unfortunately, the basic technique can only separate DNA of roughly 30,000 base pairs (the molecular size unit of DNA). The rest remain clumped in a tangled web. The first approach to solving the problem is to understand the separation process and determine why DNA chain mobility becomes independent of molecular size. The equations of motions of a long chain drifting through a network are too intractable to be solved analytically. And simplified diffusion models cannot be constructed because the shape of the molecule while drifting is unknown; whether the chain is stretched or contracted in the presence of an external field has to be found by solving the dynamics. In an effort to understand the process and optimize the separation technique, the Northwestern researchers investigated the chain dynamics using a detailed off-lattice computer simulation of the process. Unlike lattice or grid models which assume the motion of polymers can be broken into discrete chunks, off-lattice is a continuum model where this assumption is not made. The team found that although the mobility of long chains is a constant that is molecular-size independent, the chains undergo cyclic oscillations from contracted-to-stretched conformations that are molecular-size dependent. The longer the chain, the larger the cycle and amplitude of the oscillations. These changes in conformations can be used to separate longer chains by using pulsed field rotations. The group examined the effects of alternating the field direction between forward and transverse (orthogonal pulsed field gel electrophoresis) as a function of molecular size. They found that mobility is reduced due to orientation effects and that the longer the chain, the larger the reduction in mobility. Therefore, many chains that are unresolved by constant field gel electrophoresis can be separated by pulsing the field. The reduction in mobility saturates for very long chains, however, suggesting a window of molecular sizes for which the resolution is maximum for a fixed pulsed rate. The agreement of this theoretical work with the experimental observations of others has produced a totally revised model for the dynamics of pulsed gel electrophoresis, leading to an increased understanding of the limitations and ways of improving the separation technique. The Northwestern group has also analyzed, in collaboration with J. M. Deutsch, professor of physics and astronomy at the University of California at Santa Cruz, the statistics of polymer chains in random environments. In a porous medium, this comprises the initial environment in a separation process such as a gel electrophoresis. In the absence of an external field the linear chains are unaffected by the presence of the random environment. However, the time average monomer density (TAMD)--a measure of the frequency at which a point is visited by a monomer--was found to have huge fluctuations from point to point. In the absence of impurities, the TAMD is a constant because all regions are equally probable to be visited by the monomers. In a random medium, however, the TAMD is a multifractal measure; i.e., fluctuations are so large and self-similar that an infinite number of exponents associated with the moments are required to characterize the distribution. The research group is currently studying gel electrophoresis of molecules with various topologies, such as circular molecules. These studies may lead to new methods for characterizing and separating polymers of various topological configurations, such as those observed in closed circular DNA. With high-performance computers such as the CRAY-2 as a basic tool for scientific development, Olvera de la Cruz and her team can continue to probe the mysteries surrounding the human genome and generate models that explain experimental results. (left, from top to bottom) Chain conformations during gel electrophoresis. (1) Initial conformation of the chain occurs in an array of obstacles. (2) In the presence of an electric field, closed J (hooks) conformations result. (3) The chain opens up when the field direction is rotated. (4) Open U shape conformations result after a period of time. (above) Time average monomer density (TAMD) in a random medium, where 20% of the lattice sites are occupied by impurities. Periodic boundary conditions are used in a box of 64 x 64 lattice sites for a chain of 120 monomers. The image was processed from CRAY-2 data using Mathematica on a Sun SPARCstation 2. The cortical connections Simulation of complex neural tissues Klaus J. Schulten Department of Physics University of Illinois at Urbana-Champaign If we are to improve our understanding of the brain, we must observe its structure and activity. Even the simplest brain functions involve the activity of thousands of neurons whose simultaneous observation is beyond our means. To cope with this immense problem, a combined research approach involving observation and simulation of complex neural tissues has emerged. An example of this new method is the study of the representation of optical images in the visual cortex of macaque monkeys by Professor Klaus Schulten and research assistant Klaus Ober-mayer at the Beckman Institute for Advanced Science and Technology at UIUC, and Professor Gary Blasdel of Harvard Medical School. Using voltage-sensitive dyes, Blasdel observed the electrical activity of hundreds of thousands of nerve cells in a contiguous brain region of a macaque monkey. He also noted that this activity in area 17 of the visual cortex depends on the type of images presented to the monkey. The conversion of such images into electrical cell activity allows for the monitoring of important elements of cortical organization--the visual map. Such maps are not genetically specified in detail, but rather are acquired by an animal during a self-organizing process involving visual experience. In the course of the development of a visual map, the cortical areas modify their connections to the retinas of the eyes. Each square millimeter of cortical area consists of 100,000 or more neurons, with each neuron having 1,000 or more connections. Schulten and coworkers are simulating the evolution of visual maps in young monkeys and young cats. Since the simulation involves the variation of 30,000,000 synaptic connections and is accompanied by millions of visual images, the computational task is enormous. They have found NCSA's Connection Machine Model 2 (CM-2), with its 32K processors, ideally suited for their simulations. Particularly useful is the DataVault of the CM- 2, which allows storage of the intermediate developmental stage data. The researchers have now obtained results of many different visual maps. The research addresses the following questions: How does nature string the millions of connections between the eyes and the brain through the optical nerve tract? And why does nature prefer the part-icular representation of images observed for cats and monkeys over other possibilities? The simulations on the CM-2 show that a small set of simple developmental rules--all well within the known property range of neural systems-- together with suitable visual experiences, suffice to develop the maps as they are observed. In fact, the simulations and observations agree so closely that it is sometimes hard to tell them apart. A more detailed analysis of the visual maps generated and observed shows that the representation of optical images achieved in the visual cortex combines a variety of image attributes: location of the stimulus in the visual field, orientation of line segments in the image, stereo information, color, and texture. Why the particular presentation shown? It appears that nature tries to combine many image attributes while at the same time preserving their continuity. To appreciate this, one should realize that the visual maps, mathematically speaking, establish a connection between a many- dimensional space of image attributes (each image attribute adding one dimension) to a merely 2D cortical area. The advantage of maximizing the conservation of continuity through the visual maps is twofold: first, a continuous change of attributes, such as those representing a moving object, is presented to the brain by a change of activity that is also nearly continuous; second, to process images for higher cognitive tasks requires that the map simplify the activities of neurons needing to compare their signals by locating them close together. Image processing in brains beyond the level of visual maps is the subject of future investigations. Observations have given strong indications that the coherences in the firing patterns of cortical neurons provide higher brain areas with information concerning which segments of an image make up a visual scene--that is, which parts of the image are objects (for example, an animal), and which belong to the background. The next generation of simulations in computational neural science promises to be even more exciting than the present since it will lead us--together with observations--to an understanding of simple cognition. Such simulations must account for the dynamics of hundreds of thousands of neurons with their millions of connections; present simulations only describe the static connectivity scheme. Schulten and his colleagues are waiting eagerly for the next generation of massively parallel machines with a thousandfold performance increase which they hope will allow them to address the cognitive processes of vision. (left) Observed (left side) and simulated (right side) brain maps are compared. There are actually two maps shown: one in color representing the sensitivity of cortical neurons to orientation, i.e., (top row) red shows cells sensitive to vertical lines, green shows cells sensitive to horizontal lines; and one in grey (bottom row) showing the sensitivity to input from the right (light) and left (dark) eye. All refer to the same brain area. (above) Simulated brain map showing cells sensitive to vertical lines (red) and those sensitive to horizontal lines (green). These simulations were run on the CM-2 frame-buffer, and the images were created using C/Paris. "It [the supercomputer] allowed me to obtain conclusive answers fairly rapidly to a specified task by using several alternative techniques." Turning up the heat Development of phased arrays for hyperthermia Emad S. Ebbini Department of Electrical Engineering and Computer Science The University of Michigan Hyperthermia cancer therapy is an experimental method that is being investigated extensively by a number of researchers. Although not new, interest in hyperthermia has been rekindled due to some new techniques--particularly tissue culture--that allow biologists to investigate how heat alone or with radiation can kill cancerous cells. It turns out that cells are quite sensitive to heat in the part of the cell cycle when they are most resistant to radiation. Hyperthermia cancer treatment depends on raising the temperature of a tumor to about 43C for an hour or more. The difficulty is heating the tumor adequately without damaging nearby healthy cells. Some tumors can be heated easily with available technology; others cannot-- because of location, because of a peculiar shape, or because they happen to be highly perfused with blood. Increased blood flow, which counteracts the desired effect by cooling the tissue, is the body's response to a rise in temperature. To get an optimum temperature distribution requires a changing energy deposition during treatment--not a trivial thing to do. Several methods are currently used to heat tissue for cancer therapy. One technique uses microwave energy, but this is not ideal for tumors deep in the body. When the microwave frequency is low enough for deep penetration, the wavelength is so long that heating is difficult to focus or control. Another technique uses ultrasound. The advantage of ultrasound is that frequencies that can penetrate deep inside the body have a relatively short wavelength that can be precisely controlled. Emad Ebbini, professor of electrical engineering and computer science at the University of Michigan, is working on a project that uses computer simulations for the analysis, design, and optimization of novel ultrasound phased-array applicators for hyperthermia cancer therapy. The parent project, headed by Charles A. Cain, director of the University of Michigan's Bioengineering Program, investigates the use of phased arrays for hyperthermia from the basic hypothesis that phased arrays are potentially capable of producing precisely defined heating patterns tailored to the tumor geometry even in the presence of tissue inhomogeneities. In fact, only phased-array applicators have the potential for dynamic and adaptive focusing deep into the body through tissue inhomogeneity. Furthermore, phased arrays offer the promise of versatile and flexible applicator systems that focus and steer the ultrasonic energy electronically without the need to mechanically move the applicator heads. By greatly simplifying the machine-patient interface, the clinical use of hyperthermia could be enhanced. Phased arrays can also directly synthesize multiple-focus heating patterns, thus tailoring hyperthermia to the tumor geometry. This research is currently moving into optimization of site- specific applicator systems to treat specific tumor types. Supercomputer simulations, using the NCSA CRAY Y-MP system, will be used with 3D datasets of patients' anatomy from computerized tomography (CT) or magnetic resonance image (MRI) scans. Geometric, acoustic, and thermal optimization computations will also be performed. In a clinical setting, most of these computations will be performed on workstations. However, in this development stage, the supercomputer will continue to be an invaluable asset to this research program. While this work is primarily concerned with the development of phased-array applicators for hyper-thermia, its results will also be applicable to coherent imaging systems, image synthesis, etc. One exciting application is the activation of certain anticancer agents by controlling the ultrasound within precise spatial and temporal boundaries. (left) Novel site-specific phased-array applicator designs with conformal geometries are optimized, both geometrically and acoustically, based on 3D data-sets of patients' anatomy from CT or MRI scans. The spherical geometry of the array shown heats a prostatic tumor. Usable array elements are shown in blue. Red indicates elements obstructed by bone, and white indicates elements obstructed by gas spaces (both bone and gas are strong scatterers of ultrasound). The images were created using SunVision on a Sun SPARCstation 2. (above) Phased arrays can be used to directly synthesize complex multiple-focus beam patterns overlaying tumor geometry. This image shows an intensity profile produced by simultaneously focusing the spherical array on 30 points of the image at left. The image was created using IDEAL on a DEC 3100 workstation. "Young people don't have scientific role models; they don't see scientists as heroes. Yet their talents may lie in scientific pursuits, and they may find tremendous personal and career satisfaction in science. Programs like SuperQuest, which allow students to witness science firsthand, are what we need to foster a future generation of scientists and engineers in our country." --David Ruzic, mentor for SuperQuest students and associate professor in the Department of Nuclear Engineering, University of Illinois at Urbana-Champaign Educating tomorrow's scientists Using high-performance computing resources to solve grand challenge problems in science and engineering has a parallel in addressing a challenge perhaps even more far-reaching in its societal impact. That challenge--to encourage high school students to pursue interests, and eventually careers, in the sciences--is addressed, in part, by SuperQuest, a national computational science competition for high school students. Fourteen students from four high schools and their teacher-coaches attended NCSA's first Super-Quest Institute in July 1991. The SuperQuest competition is based on student research proposals from participating high schools. The proposals are evaluated by a national panel of researchers on the basis of scientific merit, clarity, suitability for supercomputer solution, and the student's previous work. Winning teams spend three weeks of intensive training at a SuperQuest Center. NCSA is currently one of three places that provide such an opportunity: the other two are the Cornell Theory Center and the University of Alabama at Huntsville. During the three weeks at the SuperQuest Institute, students worked on their own projects and learned how to write code for the supercomputers. Science mentors provided additional guidance in their studies. The students also attended talks on supercomputing topics including numerical methods, parallelization, symbolic manipulation, networking, and visualization. Teacher-coaches had as much to learn as the students. They attended the same lectures and were given private sessions on networking and mathematical software. On returning to their high schools, students and their teacher- coaches continue their research by remotely accessing NCSA's supercomputers, as well as other supercomputer centers, using donated workstations and network connections. Other students and teachers will benefit as well, because the workstations will continue to be used in local computational science programs. When their yearlong projects are completed, the students compete among themselves in the Best Student Paper Competition. The students also give talks on their projects at professional scientific meetings. Details on several of the SuperQuest projects follow. When two balls collide . . . When Patrick J. Crosby plays pool, he probably has more on his mind than sinking the eight ball. Crosby, a student from Evanston Township High School in Evanston, Illinois, is using NCSA's CRAY-2 supercomputer to construct an analytical model of the collisions of spherical balls--a problem that has puzzled physicists for years. Through physical experiments and computer simulations, Crosby is analyzing the contact time of the two spheres under varying impact conditions. Crosby's computer model is based on many mass points, connected by molecular springs, in a spherical lattice structure. Using the basic laws of physics and a mathematical approximation, he determines the position, velocity, acceleration, kinetic energy, and potential energy of each mass point, and of the entire structure, as a function of time. The model is versatile enough to be used to study other aspects of the collisions of spheres, including the deformations that occur upon contact and the energy distribution during deformations. Only crude approximations could be made with the use of a personal computer, according to Crosby. Crosby's research has led him to the finals in the highly competitive Westinghouse Science Talent Search. He is one of only 40 students (chosen from 300 semifinalists nationwide) who holds the honor. The winner of the competition is awarded a $40,000 scholarship, as well as a good measure of prestige. From wave tracing to thermal images Crosby's SuperQuest teammate Doran Fink, also a student from Evanston Township High School, has already reached one of those milestones that make science exciting: he has solved his original Super-Quest problem, and moved on to another. For his first SuperQuest topic, Fink used the CRAY-2 to determine the shape and position of a wave front and the velocities of its individual points as a function of the density of the medium. This method could be used to determine the path of a seismic wave front as it propagates through the earth, a sonar wave front as it propagates through an ocean, or a light wave front as it propagates through a strand of fiber optics material. Thanks to the network connection to NCSA, Fink has begun a new research project, again using NCSA's CRAY-2. He is interested in thermal images--those images produced when a heat source of a certain shape is applied to a solid material. (Doctors use various thermal imaging methods to scan for tumors and analyze organ performance.) Fink is conducting physical experiments and computer simulations to determine the equilibrium depth of a thermal image in an insulated, homogeneous solid. He has found that the depth of the image is independent of the temperature of the impressed image, and that it can be augmented if higher conductivity materials are placed within the solid. Fink suggests that this phenomenon could be used as a nondestructive test for weld joints: any distortions in the thermal image of the joint would indicate low conductivity regions (perhaps air pockets) within the weld joint. Fink advanced to the semifinals in the Westinghouse Science Talent Search for his efforts in this project. Modeling an x-ray telescope Choosing a topic related to astronomy was perhaps natural for Tracy Speigner, a junior from the J. Oliver Johnson High School in Huntsville, Alabama. Huntsville is the site of NASA's Marshall Space Flight Center. Speigner is using NCSA's Connection Machine Model 2 (CM-2) to model a novel x-ray imaging telescope. X-rays, emitted from otherwise invisible astronomical objects, are detected through devices carried on balloons or satellites. Computer imaging techniques translate those signals into meaningful pictures. She is running Monte Carlo simulations to determine the behavior of photons in the telescope. In addition to exploring the world of computational science and supercomputing, Speigner has had some remarkable opportunities to meet new people and expand her horizons. Along with her teachers and another SuperQuest teammate, she traveled to Washington, D.C. to participate in a demonstration of her project to the National Science Foundation. Given the "returning hero" status accorded SuperQuest students by many of their peers, Speigner and the girls participating in SuperQuest are perhaps especially important as role models to other girls interested in computational science. Predicting traffic jams From Los Angeles to New York City, countless motorists stuck in traffic jams have wondered whether anything could be done about the commuter's curse. Patrick Chan, a student at the James Logan High School in Union City, California, may not be able to prevent traffic jams, but his SuperQuest project is an attempt to understand the predictability of the snarl. Using elements of network theory, Chan first constructed a mathematical model of the traffic flow on a freeway on the CRAY-2 supercomputer. He used his model to simulate traffic flow given the conditions of a traffic accident, road construction, or simply too many cars. By analyzing the data from his simulation, Chan hopes to determine the relationship between road conditions and traffic jams. He hopes that the results of his study can be used to help commuters plan their routes around traffic jams, or to assist the highway department in determining ideal times for construction projects. Modeling fractal growth Daniel Stevenson, a student at Hudson High School in Hudson, Ohio, is using the CM-2 and CRAY-2 supercomputers to study the growth processes of natural fractal structures--frost patterns, electrochemical deposits, and biological structures, to name a few. Stevenson took a simple fractal approach to modeling natural growth processes--called diffusion limited aggregation (DLA)--and added some conditions to make the model more closely approximate reality. The simple DLA approach assumes infinitely dilute concentrations of particles randomly moving around until attaching to a central cluster. Steven-son's conditions included the effects of cluster geometry on the rate of surface growth, and the diffusion of particles away from the surface. Under these extended DLA conditions, Stevenson hopes to determine characteristic geometries of computer-simulated fractal structures, and to compare them with naturally occurring fractal structures. Stevenson was chosen to represent the Super-Quest program at the Supercomputing '91 Conference in Albuquerque, New Mexico. He gave a paper and, along with his SuperQuest teammates, presented a poster. Expanding HPCC in education SuperQuest is part of a larger NCSA effort to expand the role of high-performance computing in education at local, state, and national levels. Computational science allows the students to explore real scientific problems, conduct real scientific experiments, and to discover the joy of scientific research. "One of the problems in science education is that the students are talked to about science," said Nora Sabelli, assistant director of education at NCSA. "That doesn't teach science. . . . You learn science by doing science. In the next ten years, computer modeling and mathematical experimentation will be an accepted mode of forming scientific hypotheses, along with theory and physical experimentation. The sooner we introduce young people to supercomputers, the better." (top) Hudson High School participants. (bottom) J. Oliver Johnson High School participants. (top) James Logan High School participants. (bottom) Evanston Township High School participants. Evanston Township High School Evanston, IL David A. Dannels, teacher-coach Patrick J. Crosby Doran L. Fink Sarah Hayford Paul Lewis Hudson High School Hudson, OH Vaughn D. Leigh, teacher-coach Andrew E. Goodsell Daniel Stevenson Jeremy Stone James Logan High School Union City, CA Charmaine Banther, teacher-coach Patrick Chan Francis Eymard R. Mendoza Francis Michael R. Mendoza Benjamin T. Poh J. Oliver Johnson High School Huntsville, AL Sharon Carruth, teacher-coach Melissa Chandler LaShawna Morton Tracy Speigner Executive Editor Paulette Sancken Associate Editor Mary Hoffman Managing Editor Melissa LaBorg Johnson Designer Linda Jackson Contributing Editors Fran Bond Stephanie Drake Copy Editor Virginia Hudak-David Contributing Writers Fran Bond, Jarrett Cohen, Mary Hoffman, Sara Latta, Paulette Sancken, the researchers and their assistants Photographers ThompsonoMcClellan Photography Printer University of Illinois Office of Printing Services Printing Division, March 1992 Disclaimer: Neither the National Center for Supercomputing Applications nor the United States Government nor the National Science Foundation nor any of their employees makes any warranty or assumes any legal liability of responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. 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