Industrial-Strength Computing:
ORNL’s Computational Center
for Industrial Innovation

by W. Harvey Gray


Harvey Gray shows the logos for ORNL’s Computational Center for Industrial Innovation and the Center for Computational Sciences. Photograph by Tom Cerniglio.

DOE’s Computational Center for Industrial Innovation at ORNL is helping researchers from American industrial firms, governmental agencies, and universities harness parallel computing to solve complex industrial problems. Using powerful parallel computing codes and machines at ORNL, researchers are simulating advanced aircraft, aluminum production processes, collisions of lightweight automobiles, internal combustion processes, and the buildup of ice on airplane wings. The results of this cost-effective approach to research should aid the design and manufacture of safer, more efficient products.

For some industrial firms, a new or improved product or process is not possible without solving complex problems. Sometimes these solutions can be obtained only by writing computer codes that run on parallel computers built from many nodes. To enter the esoteric world of codes, nodes, and other aspects of high-performance computing, indus-trial firms often require the aid of appropriate computer experts. Such expertise could help many companies become more competitive in the world marketplace and manufacture safer, more efficient products.

Providing support and assistance to U.S. industry to smooth its path into high-performance computing is a prime expectation of ORNL’s Center for Computational Sciences (CCS). To help meet this expectation, CCS launched the Computational Center for Industrial Innovation (CCII). This DOE national user facility, established in August 1994, hosts ORNL-industry collaborations in projects featuring high-performance computing. Thanks to our computational capabilities, CCII users are solving challenging, industrially relevant problems—problems that have previously eluded solution because of insufficient computational power or inadequate software availability.

A number of user agreements have been signed with a variety of busi-nesses, software vendors, universities, and other federal agencies. Since the inauguration of CCII, users who have taken advantage of the substantial computational environment afforded by CCS include Reynolds Metals, Lockheed Martin Skunk Works, SENES Inc., Computational Mechanics Corp., Eastman Chemical, Samsung Advanced Institute of Technology, the Department of Transportation, UES Inc., and Tennessee State University in Nashville. Consider these ORNL-industry collaborations that illustrate CCII’s impact.

Aircraft Simulation

Advanced military aircraft are being designed to take off and land quickly, avoiding the need for long runways. To explore the aerodynamic properties of generic “advanced short takeoff and vertical landing” fighter aircraft, Lockheed Martin Skunk Works scientists are using CCII facilities. Large, complex three-dimensional models of this type of aircraft are simulated using sophisticated computational fluid dynamics codes. Shortened takeoff distances and vertical landings for these aircraft are made possible by using additional jet outlets under the aircraft’s fuselage and wings to provide a large vertical thrust. Investigating the design parameters of this aircraft using conventional experimental techniques is difficult and expensive. By using the high-performance computational facilities of CCS, Skunk Works scientists can rapidly and accurately simulate many aircraft systems and flight envelopes while reducing the number of costly physical experiments that must be performed (see Fig. 1).


Fig. 1. Image of an advanced short takeoff and vertical landing fighter aircraft simulated by Lockheed Martin Skunk Works’ scientists using computers available at the Computational Center for Industrial Innovation at ORNL. The yellow strings are particle traces of the exhaust from thruster jets during a simulated landing.

Modeling Aluminum Production Processes

Reynolds Metals scientists are using CCII facilities to model industrial magnetohydrodynamic processes in which a magnetic field interacts with a conducting fluid. These processes are widely used in the aluminum industry for stirring, confining, and controlling liquid metal before and during casting operations. In addition, after the aluminum solidifies, inductive heating devices are frequently used both in the rolling of the aluminum ingots into strips and in the final heat treatment of the strips. Accurate modeling of these processes is important for both control of the existing manufacturing processes and design of future enabling technologies. This modeling, however, is computationally intense because of the strong coupling among various physical phenomena, such as heat transfer, electromagnetism, and fluid flow. Differences in magnitude between the size of the processes (typically meters) and the scale of change of the parameters that must be modeled (often millimeters) further complicate the calculations. Preliminary modeling of these complex industrial processes has been achieved by using the powerful Intel Paragon computers in CCS.

Modeling microstructural changes that accompany the deformation processing of aluminum is another important area in which Reynolds Metals and ORNL scientists are collaborating. Bulk deformation processes, such as rolling, extrusion, and forging, are routinely used in the aluminum industry to form, shape, and modify the strength of aluminum products. Of particular interest to researchers modeling these processes is the subtle change in crystal texture during deformation.

By using the Intel Paragon computers in CCS, researchers can model the micro-mechanical changes within an aluminum product and integrate them into a continuum framework to enable a simulation of bulk deformation processes that accurately reflects the metal’s prior deformation history. A hybrid finite element analysis formulation, coupled with a model for crystalline plasticity theory, is used to simulate the texture change during the metal’s plastic deformation. Preliminary results from the models of several deformation processes (see Fig. 2) agree with micrographs of aluminum products undergoing deformation.

Fig. 2. This colorful image shows how the polycrystalline microstructure of aluminum changes during deformation. By modeling the nonuniform deformation of metals using a parallel supercomputer, scientists can obtain information on how to optimize processing conditions to obtain desired microstructures—and properties.

Car Performance Simulation

An important national goal in transportation is to design an automobile that travels three times farther on a tank of fuel than the average car today, yet emits virtually no pollutants. Leaner, cleaner cars now being designed would be made of materials lighter than the steels used today—materials such as aluminum, magnesium, and plastic composites. One safety question of great interest is whether a properly designed car made of these lighter materials would be as resistant to damage in a collision as vehicles made of steel. Using CCS computers at CCII to simulate automobile performance involving cars made of various materials, scientists are addressing questions related to vehicle safety, fuel efficiency, and emissions.

Researchers at the U.S. Department of Transportation are collaborating with ORNL researchers to evaluate simulation capabilities for automobile performance modeling using high-performance parallel computers; to evaluate new algorithms and material models for advanced structural materials being developed at ORNL; and to evaluate biomechanical simulation of the effects of safety systems, such as air bags, on human models during automobile collisions. (This activity is discussed in more detail in the article “Analysis of Material Performance in Automotive Applications” by Srdan Simunovic, Gus Aramayo, and Thomas Zacharia.) Several different collision scenarios are being modeled to explore and optimize vehicle simulations. These simulations use a massively parallel, finite element analysis program. The program calculates vehicle accelerations, velocities, deformations, and forces, taking into consideration variables such as different materials, impact interactions, complex constraints, and spot welds. The results of this collaborative research will produce accurate vehicle and material models that can be used to evaluate the performance of lightweight materials in vehicles with respect to safety and fuel efficiency.

Modeling Internal Combustion Engines

ORNL scientists and collaborators are developing better computational tools for modeling the complex physical processes in internal combustion engines. These tools will help the automobile and power generation industries design engines with improved fuel efficiency and lower emissions of harmful combustion by-products. CCII users are exploring internal combustion engine simulation using models developed for the KIVA computer program. KIVA is a computer model that analyzes the coupled fluid dynamics, fuel spray dynamics, combustion and pollution formation reactions, and heat transfer in an engine cylinder. It is a widely used analysis tool within the industry. (KIVA is discussed in more detail in the article “Computational Engine Modeling” by Osman Yasar.) By applying the processing power of the Intel Paragon XP/S 150, more realistic engine models that take into account the physics of the process can be developed to explore operational regimes that may reduce emissions of nitrogen oxides. Researchers from Samsung Advanced Institute of Technology investigated computational engine modeling after being taught general parallel computational techniques during their three-month stay at CCII in the spring of 1996.

Modeling Airplane Ice Deposits

The accumulation of ice on wings has been considered a contributing cause of several crashes of commuter airplanes. Ice accretion on airfoils (surfaces of a wing, propeller blade, or rudder, whose shape and orientation control stability, direction, lift, thrust, or propulsion) is being studied by CCII users at Tennessee State University (TSU), who have been using CCS facilities to apply high-performance computing techniques to several areas of their research. TSU researchers are developing and extending a coupled computational fluid dynamics, heat transfer, and mass transfer model that simulates the air flow around an airfoil under conditions that are conducive to the formation and buildup of ice deposits. Improving models of ice buildup on airfoils may help researchers determine design and operational parameters that reduce the adverse aerodynamic effects caused by the ice.


Other projects under way at CCII involve computational chemistry, materials processing and design, engineering design, nuclear reactor modeling, and manufacturing strategies. Additional companies are joining the center, and still more, upon learning of CCII capabilities and accomplishments, are considering membership. By calling upon the capabilities and systems provided by CCS to help open doors to industry, CCII is helping to meet an important need of U.S. industry—computational tools and skills for economically designing safer and more efficient new products, improving the competitiveness of American industry in the world marketplace.


W. HARVEY GRAY, then director of the Computational Center for Industrial Innovation (CCII) at ORNL and now deputy director of the National Security Program Office for Lockheed Martin Corporation’s DOE plants in Oak Ridge, is a mechanical engineer who has a Ph.D. degree from Vanderbilt University. He has held a variety of positions at ORNL since joining the staff in 1974. His position in the Center for Computational Sciences involved managing CCII, the Department of Energy’s national user facility that serves as the focal point for industrial partnerships with ORNL in the critical area of high-performance computing. Prior to accepting this position, he was a group leader in the Computing and Telecommunications Division, where he led projects in computer-aided manufacturing, electronic medical records, and computer-aided engineering data exchange. In addition, he represented ORNL on the CAM-I Next Generation Manufacturing Systems project and on several DOE complex-wide computer-aided design, manufacturing, and engineering committees. Earlier, as a member of the Laboratory’s research staff, he used high-performance computing to design and develop advanced superconducting magnets for fusion projects.


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