The U.S. Department of Energy has granted almost $3.8 million for 13 industry projects in the High Performance Computing for Manufacturing Program, which operates in various national labs like LLNL, LBNL, Oak Ridge, and Sandia. The program combines DOE supercomputing capabilities and expertise with American manufacturers to improve innovation and industrial processes. Robin Miles, acting director of the HPC4 Manufacturing program, answered some questions about the projects, and Brandon Wood answered the ones related to KeraCel for CleanTechnica.
As the size of computational resources increase and the modeling software is better able to more accurately simulate the physics of a given industrial process or the performance of products in use the better able engineers and scientists can evaluate improvements in those products and processes prior to expensive experimental evaluation. The more quickly innovations can be introduced into the marketplace the more companies can stay competitive while providing higher quality products to their customers. The more efficient the processes become the less material and energy are used in their manufacture. High performance computing can capture effects at many scales from the atomistic level to the macro level. For example, in additive manufacture you will be able to predict the crystal structure when metals are melted together to form parts. This affects the strength of the part and you will be able to predict the performance of critical parts such as light-weight additively manufactured turbine blades for in use in jet engines. Understanding how carbon fiber structures perform in accident scenarios will enable automobile manufacturers to more confidently adapt this technology to lightweight vehicles and save significant fuel costs.
What can this type of computing do for the optimizing of reheat furnace efficiency in steel manufacturing?
The steel industry uses about 5% of the total energy used in US manufacturing. Reheat furnaces are used in steel rolling mills to take slabs of semi-finished steel and transform them into steel sheets with the desired material properties for strength and ductility. Each reheat furnace consumes over 1014 BTU/year in natural gas alone according to Arcellor Mittal. The geometries of the furnaces are complex and there are multiple heating profile scenarios for heat cycles depending on the desired steel properties. By modeling this process in detail using high performance computing we are expecting to find ways to make this process more flexible to create better steel products and to increase the overall efficiency of the process to yield significant energy savings.
How did the HPC4Mfg Program help the reliability and lifetime of wind turbines?
It is estimated that wind plant operation and maintenance consumes about a third of the revenue of wind turbines and that bearing failures constitute about 60% of wind turbine gearbox failures. White etch cracking (WEC) is a significant cause of bearing failure but the exact cause of white etch cracking is still under debate. Using High Performance Computing, we can investigate how the material transforms at the atomic scale under the cyclical loading experienced in wind turbines so that we can better understand how white etch cracks are initiated and grow. With this understanding we can better design bearings which will mitigate this kind of failure. Work at Timken and ORNL is underway to understand the strength of bearing material as a function of phase transformation in bearing materials.
How did it help reduce emissions from semiconductor processing that could potentially harm the ozone layer?
Fluorinated gases are critical to a number of high technology industries including semiconductor manufacturing and related processes such as flat panel display, LED, and photovoltaic manufacturing. These fluorinated gases have very high global warming potentials (GWPs) and, unfortunately are chemically very stable, in some cases lasting for thousands of years in the atmosphere. For environmental reasons, it is imperative that the release of these gases be minimized, but destroying these gases using existing methods requires a significant amount of energy. ALZETA is building a chemical combustion reactor to break down these gasses in a more energy-efficient reactor. Researchers at LBNL successfully modeled the combustor using high performance computers and then provided ALZETA with a set of reduced order models based on the most significant physics of the process so that ALZETA could continue to optimize its combustor post-project.
How will LLNL work with KeraCel on manufacturing solid-state lithium-ion batteries?
One of the biggest challenges for manufacturing solid-state lithium-ion batteries is the difficulty in processing the ceramic electrolyte components, which requires high temperatures that lead to materials incompatibilities at electrode-electrolyte junctions. LLNL researchers will leverage high-performance computing facilities to optimize composition and microstructure of ceramic electrolyte materials using advanced first-principles simulations, with the goal of lowering the processing temperature. The simulation results will be calibrated and validated based on input from materials testing at KeraCel.
How long might this relationship last?
The simulations will be performed, analyzed, and validated over the course of a two-year period. If the project is successful, LLNL and KeraCel have made plans for a second phase of the project, focused on understanding chemical reactions at the interface between the electrode and electrolyte.
What are some of the goals for the work?
The ultimate goal of the partnership is to identify and develop specific modifications to the solid-state electrolyte that can lower the processing temperature, thereby circumventing current incompatibilities with high-capacity electrode materials. This will result in easier, more robust, and more cost-effective processing. At the same time, the team will also work towards understanding how the intrinsically favorable ion transport properties of electrolytes might be affected by these same modifications.
Image Credit: LLNL/Public domain
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