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HPC4Manufacturing Program names four awardees for $1.2M in DOE funding; steelmaking and aluminum production

The High Performance Computing for Manufacturing Program (HPC4Mfg) announced the recipients of $1.2 million in federal funding for four public/private projects aimed at solving key manufacturing challenges in steelmaking and aluminum production through supercomputing.

Arcelor

Source: ArcelorMittal.

The summer 2018 HPC4Mfg call for proposals, the sixth overall for the program, had a special focus—applying the high-performance computing capabilities of the Department of Energy’s (DOE) national laboratories to steelmaking and aluminum production processes. Under the program, each selected industry partner will have access to the national labs’ HPC machines and expertise to help these industries become more competitive, boost productivity and support American manufacturing jobs.

Primary metals industries are significant energy users, so opportunities to reduce energy consumption in this area is of great interest to our sponsors. Additionally, this program is helping US steelmakers produce the higher strength steels vital to lightweighting the next generation of automobiles.

—HPC4Mfg Director Robin Miles of Lawrence Livermore National Laboratory (LLNL)

Funded by DOE’s Advanced Manufacturing Office, the HPC4Mfg Program is administered by LLNL along with managing principal laboratories Lawrence Berkeley National Laboratory (LBNL) and Oak Ridge National Laboratory (ORNL).

The four projects receiving funding are:

  • LLNL is working with US Steel Corporation on a hot strip mill simulation model that will provide predictions of through-thickness temperature, deformation behavior and associated microstructure.

    USS developed a hot strip mill simulation model that provides predictions of through-thickness temperature, deformation behavior, and associated microstructure at a selected single location along the slab length throughout the hot rolling process. Expansion of the thermomechanical profile across the strip width is desired to improve the model’s predictive capability.

    Potential impacts include cost reductions by optimizing rolling operations and reductions in unforeseen metallurgical changes by decreasing the number of required trials to develop robust AHSS rolling practices.

  • ORNL collaborating with Alcoa USA Corporation to use HPC simulations to understand and optimize the performance of Alcoa’s new advanced aluminum smelting cell.

    Alcoa is developing a novel hybrid advanced smelting process to increase productivity and cell performance while minimizing emission of greenhouse gases. The design utilizes novel materials and a unique anode-cathode combination.

    Computational tools and pilot prototypes are being developed to address materials, process, and design for this technology. Alumina and the bath ratio in the process both must be maintained at the desired levels for the new smelting process, which is quite challenging due to the unique flow field and limited area of the narrow space between the anode and cathode pair.

    Sophisticated computational tools are required to understand the alumina dissolution and bath ratio distribution while optimizing the number of feed, feed rates, and feeder locations. This work proposes application of large eddy simulations (LESs) to understand alumina dissolution and bath ratio distribution in the proposed cell design. Optimized feeder operation is necessary to achieve the target 15% energy efficiency improvement over the conventional Hall-Héroult process.

  • LLNL and AK Steel Corporation working to demonstrate real-time modeling of hot strip milling for next-generation steels. The steel industry needs a near real-time model to be applicable for actual production and calculate the results along the entire length of a coil. The model is expected to predict with a reasonable accuracy the mechanical properties through the entire length of the hot rolled coil.

    The benefits of such a tool for the steel industry are: 1) Produce products with consistent properties i.e. reduce variation and non-conformance; 2) Develop new products with new required properties; 3) Save time, money, and energy by reducing the number of expensive industrial trials; 4) Reduce both thermal and electrical energy using an optimized, fast hot rolling model in real time.

  • A collaboration by LLNL and ArcelorMittal USA to apply HPC and machine learning to enable more energy-efficient, defect-free manufacturing of steel slabs.

    The iron and steel industry is the fourth-largest energy-consuming industry in the US. About 80% of the total energy consumed in the steel industry is used to produce steel slabs via the continuous casting route (96% of steel in the US today is produced via this route). Therefore, being able to produce defect free slabs (by making it right, first time, every time) would lead to a huge benefit in terms of energy savings and reduction of CO2 emissions during the steelmaking process by minimizing wastes and increasing quality and yield. Therefore, as the most recycled material on earth (more than all other materials combined), a slight improvement to steel production would have a lasting positive impact on the environment and on the US energy landscape.

DOE’s Advanced Manufacturing Office, within the Office of Energy Efficiency and Renewable Energy, is providing $300,000 for each project. Participating companies are required to contribute in-kind funds of at least 20% of DOE’s funding for the project. Each project is funded for one year.

To date, the HPC4Mfg program has granted nearly $20 million for 64 projects. Recent projects include efforts to improve the manufacturing of solid-state lithium-ion batteries, increasing the efficiency of gas turbine combustors, optimizing laser-based metal 3D printing platforms, improving energy efficiency and reducing waste during the papermaking process and reducing emissions in diesel engines.

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