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Exascale for Energy


Nuclear Power

Nuclear fission plays a significant and growing role in world energy production. Currently, 436 nuclear power plants in 30 countries produce about 15% of the electrical energy used worldwide. Sixteen countries depend on nuclear power for at least a quarter of their electricity. The USA is the world’s largest producer of nuclear power, with more than 30% of worldwide nuclear generation of electricity. America’s 104 nuclear reactors produced 809 billion kWh in 2008, almost 20% of total electrical output.


By enabling the high-fidelity modeling and simulation of complete nuclear power systems, exascale computing could reduce design, construction, and operation costs while also reducing uncertainty and risk.

Despite more than 30 years with almost no new construction, U.S. reliance on nuclear power has continued to grow. The U.S. nuclear industry has maximized power plant utilization through improved refueling, operating efficiency, maintenance, and safety systems at existing plants. There is growing interest in operating existing reactors beyond their original design lifetimes.

The Energy Policy Act of 2005 has stimulated investment in a broad range of electricity infrastructure, including nuclear power. Over the last few years more than a dozen utility companies have announced their intentions to build a total of 27 new nuclear reactors. In addition, to meet the rising demand for carbon-free energy, a new generation of advanced nuclear energy systems is under development that would be capable of consuming transuranic elements from recycled spent fuel (see reactor sidebar, page 5). These advanced reactors would extract the full energy value of the fuel, produce waste that does not create long-term hazards, and reduce proliferation of nuclear weapons materials.

Exascale computing is poised to play a major role in the development of next-generation nuclear plants. By enabling the high-fidelity modeling and simulation of complete nuclear power systems, exascale computing would change nuclear engineering from a test-based to a science-based discipline. Such modeling can benefit nuclear energy in several ways:

  • Accelerate the iteration cycle of technology evaluation, design, engineering, and testing to optimize existing and new nuclear energy applications.
  • Shorten the licensing process by providing reliably predictive integrated performance models that reduce uncertainties.
  • Reduce construction and operations costs while also reducing uncertainty and risk.

Modeling has always played a key role in nuclear engineering, design, and safety analysis. Computational analyses based on large experimental databases have been used to analyze material properties, fuel performance, reactor design, safety scenarios, and waste storage. But legacy applications do not provide the high fidelity required to understand fundamental processes that affect facility efficiency, safety, and cost, including:

  • Determination of material properties of fuels and structural materials under both static and dynamic conditions, including nuclear (e.g., neutron and gamma reactions), thermophysical (e.g., thermal conductivity), mechanical (e.g., fracture toughness), and chemical (e.g., corrosion rates). Nuclear fuel assemblies must perform in extreme environments where they are subjected to stress, heat, corrosion, and irradiation, all of which can lead to progressive degradation of the fuel cladding materials and other structural components. Researchers hope that simulations will help them discover new ways of preventing or mitigating material degradation.

  • Spent fuel reprocessing is an option that was abandoned in the 1970s but is now looked on more favorably. Reprocessing involves dissolving the spent fuel in acid, treating the fuel in a series of solvent extraction processes, and fabricating it into fuel or waste forms. Current models provide only qualitative descriptions of process behavior and are unable to answer many key questions. To support the detailed design and safe operation of reprocessing plants, advanced reprocessing models require improved chemistry, fluid dynamics, interfaces with nuclear criticality calculations, and whole-plant modeling.

  • Fuel development and performance evaluation is currently an empirical process that takes decades. New fuels must be fabricated, be tested in a test reactor under multiple accident scenarios, undergo post-irradiation examinations, and finally be placed in an operational reactor for several cycles. Fuel performance simulation tools could reduce the current 10- to 15-year qualification time by a factor of three. But these tools must be comprehensive enough to predict the thermal, mechanical, and chemical response of the fuel rod throughout its irradiation lifetime.

  • Reactor design and safety simulation tools need improved physical, numerical, and geometric fidelity.

There are multiple challenges in modeling the design, performance, and safety of a nuclear reactor. Figure 3 illustrates the multiscale physical challenges that a nuclear reactor faces in size (length) and time.

Length and time scales

Figure 3. Individual simulation tools and integrated performance and safety codes (IPSCs) involve different physical phenomena at varying scales of interest. Source: Science Based Nuclear Energy Systems Enabled by Advanced Modeling and Simulation at the Extreme Scale workshop

 

A May 2009 workshop sponsored by the DOE Office of Science and Office of Nuclear Energy developed a detailed picture of the computational requirements for nuclear energy modeling, culminating in a 10 exaflop requirement by the year 1024. The timeline in Figure 4 includes nuclear energy drivers, science simulations to resolve science unknowns, and engineering simulations that use integrated codes. The workshop participants estimated that it will take nearly 15 years to resolve many of the scientific questions identified in Figure 4 and to establish fully predictive, integrated codes that can quantify uncertainties. But they also estimated that exascale modeling could reduce the construction cost of a large-scale nuclear plant by 20%, saving as much as $3 billion on a $15 billion plant.

Computational requirements for nuclear energy modeling

Figure 4. Computational requirements for nuclear energy modeling: PF = petaflops, EF = exaflops. Source: Science Based Nuclear Energy Systems Enabled by Advanced Modeling and Simulation at the Extreme Scale workshop

 

simulations of fuel pin behavior
Figure 5. Three-dimensional predictive simulations of fuel pin behavior from microstructure evolution will require exascale resources. Source: DOE Exascale Initiative

Exascale resources will improve the geometric, numerical, and physics fidelity in modeling key phenomena such as the evolution of fuel pin microstructure and behavior (Figure 5):

  • Improved geometric fidelity will extend lower-length-scale fidelity to sub-10 mm resolution of three-dimensional phenomena such as microstructure evolution and material failure, validated with uncertainty quantification methodologies.
  • Improved numerical fidelity will involve bridging vastly different time and length scales with multi-physics phenomena, such as bubble–fission fragment interactions (including molecular dynamics), and scaling up oxide and metal models into pellet simulations.
  • Improved physics fidelity will apply to modeling phenomena such as fission gas bubble formation, transport, and release; fuel chemistry and phase stability; fuel–cladding mechanical interactions; and thermal hydraulics, turbulence, and coolant flow in pin assemblies, and their effect on fuel and cladding evolution.

The creation of integrated performance and safety codes faces considerable technical challenges that range from improvements in software engineering and numerical methods to the development of more fully integrated physics models. But if these challenges are met with a sustained and focused effort, exascale computing can revolutionize the modeling and design of nuclear energy systems.

 


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