SPECIAL NOTICE
99 -- Notice of Intent to Issue a Funding Opportunity from U.S. Department of Energy�s (DOE) High Performance Computing for Energy Innovation (HPC4EI) Initiative
- Notice Date
- 11/3/2020 8:54:19 AM
- Notice Type
- Special Notice
- Contracting Office
- LLNS � DOE CONTRACTOR Livermore CA 94551 USA
- ZIP Code
- 94551
- Solicitation Number
- FBO497-21
- Response Due
- 11/19/2020 9:00:00 PM
- Archive Date
- 11/21/2020
- Point of Contact
- Connie Pitcock, Phone: 9254221072, Robin Miles, Phone: 9254228872
- E-Mail Address
-
pitcock1@llnl.gov, miles7@llnl.gov
(pitcock1@llnl.gov, miles7@llnl.gov)
- Description
- The U.S. Department of Energy's (DOE) High Performance Computing for Energy Innovation Initiative will issue a Fall 2020 solicitation in November 2020, covering the High Performance Computing for Manufacturing (HPC4Mfg) and High Performance Computing for Materials (HPC4Mtls) programs. HPC4Mfg and HPC4Mtls programs are funded with support from the Office of Energy Efficiency and Renewable Energy�s Advanced Manufacturing Office and the Office of Fossil Energy. HPC4EI programs are designed to spur the use of national lab supercomputing resources and expertise for high performance computing projects that improve manufacturing processes, address products� lifecycle energy consumption, and increase the efficiency of energy conversion and storage technologies. HPC4EI conducts two regular solicitations annually, one in the fall and one in the spring. The fall solicitation will target qualified industry partners to participate in short-term, collaborative projects with DOE National Laboratories that address key manufacturing challenges and the development of new materials by applying modeling, simulation, and data analysis to solutions impacting the energy agenda. The solicitation will encourage applicants to partner with universities and non-profit organizations located within federally designated Opportunity Zones and/or Historically Black Colleges and Universities (HBCUs). Eligibility for the program is limited to entities that manufacture products or operate systems in the U.S. for commercial applications and organizations that support them. Selected projects will be awarded up to $300,000 to support computing cycles and work performed by DOE National Laboratories, universities, and non-profit partners. All DOE National Laboratories are eligible to participate. The industry partner must provide a participant contribution of at least 20% of the total project funding. DOE�s Advanced Manufacturing Office (AMO), within the Office of Energy Efficiency and Renewable Energy, is the primary sponsor of the High Performance Computing for Manufacturing program. AMO partners with private and public stakeholders to advance innovation in U.S. manufacturing and promote American economic growth and energy security. The Office of Fossil Energy is the primary sponsor for the High Performance Computing for Materials program. FE supports cost?shared research, development, and demonstration activities in support of crosscutting next-generation fossil technologies. Learn more about the HPC4EI initiative. Topics of interest specific to the offices supporting this solicitation are below. HPC4Manufacturing DOE�s Advanced Manufacturing Office within the Office of Energy Efficiency and Renewable Energy is the primary sponsor of the HPC4Mfg Program. The Office of Fossil Energy and EERE�s other Technology Offices may also sponsor select projects in this portfolio. AMO partners with private and public stakeholders to support the research, development, and deployment of innovative technologies that can improve U.S. competitiveness, save energy, and ensure global leadership in advanced manufacturing. AMO supports cost-shared research, development, and demonstration activities in support of crosscutting next-generation technologies and processes that hold high potential to significantly improve energy efficiency and reduce energy-related emissions, industrial waste, and the life?cycle energy consumption of manufactured products. Improved energy efficiency across the manufacturing industry is one of the primary goals of the HPC4Mfg Program. The program solicits proposals that require HPC modeling and simulation to overcome impactful manufacturing process challenges resulting in reduced energy consumption and/or increased productivity. Proposals should provide a realistic assessment of the energy impact, the improvement in U.S. manufacturing competitiveness, and the increase in U.S. manufacturing jobs that a successful outcome of the project could have across the industrial sector. Of particular interest to AMO are: Improvements in manufacturing processes which result in significant national energy savings. Examples include Process improvements in high-energy consuming industries such as paper and pulp, primary metal manufacturing, water and wastewater, glass and chemical industries; Improvements in material performance in harsh service environments such as very high temperature or highly corrosive processes; Integration of advanced object recognition and other machine learning algorithms (e.g. sortation, defect detection) into high throughput industrial processes; Improvements in modeling prediction and closed-loop control for smart manufacturing systems (e.g. advanced sensors and process controls); and Improvements in separation and processing for critical materials (e.g. rare earth elements). Improvements in the lifecycle energy consumption of products of interest to AMO. Examples include Improvement in jet engine efficiency could save significant energy over the lifecycle of the engine; Improved materials and shape optimization for light-weighting in transport technologies; Semiconductor electrical efficiency; and Increased recycling and reuse of end-of-life and waste associated with industrial-scale materials production and processing. Efficiency improvements in energy conversion and storage technologies. Examples include Improvements in combined heat and power units which save significant energy; Novel energy storage and energy conversion techniques; and Improvements in waste heat recovery. HPC4Materials DOE�s Office of Fossil Energy� is the primary sponsor for this HPC4Mtls Program. FE plays a key role in helping the United States meet its continually growing need for secure, reasonably priced, and environmentally sound energy from our abundant fossil energy resources. The Office of Fossil Energy Research and Development (FER&D) Program advances transformative science and innovative technologies that enable the reliable, efficient, affordable, and environmentally sound use of fossil fuels. Decarbonization of the power and industrial sectors is of renewed interest, and hydrogen is expected to play a role in decarbonizing these sectors. As fossil energy is the source of >95% of hydrogen worldwide and in the U.S., FE technologies in hydrogen production and utilization will play a major role. FE partners with industry, academia, national labs, and research facilities in transformative science and innovative technologies that enable the reliable, efficient, affordable, and environmentally sound use of fossil fuels. FE supports cost?shared research, development, and demonstration activities in support of crosscutting next-generation technologies and processes that further the development of advanced fossil technologies. Proposals should provide a realistic assessment of the benefits to the domestic materials supply chain and/or fossil energy application (e.g. power plant).� Of particular interest to FE in this solicitation are: Improving the understanding of the materials impacts including corrosion and erosion effects of gasification of blends of coal, biomass and waste plastics on materials in high temperature regions of a gasifier, including sensitivity analysis of blend percentages and types of coal, biomass and waste plastics in the process feed Improving the understanding of the material impacts including hydrogen embrittlement effects of blends of natural gas and hydrogen on materials in pipelines, welded joints or compressors, including sensitivity analysis of blend percentages Use of computational databases and machine learning for thermal barrier coating (TBC) development for hot gas path components of combustion turbines firing natural gas-hydrogen blends or 100% hydrogen Improving the understanding of detailed processes in critical focus areas such as oxidation, corrosion, and electrochemical interactions in Creep Strength Enhanced Ferritic (CSEF) alloys, austenitic alloys and high nickel superalloys Use of computational databases and machine learning for catalyst development to synthesize, test, characterize, and scale materials which convert carbon oxides into value-added products with increased energy efficiency, higher selectivity, and lower environmental impacts based on a lifecycle analysis relative to conventional products Developing machine learning capabilities to predict composition, thermal performance, and mechanical properties of new materials for energy storage Developing the capability to predict the mechanical behavior and properties of additively manufactured components for use in advanced power cycles such as supercritical carbon dioxide cycles Materials Supply Chain for Fossil Energy Applications: Reducing the cost of ingot production for nickel superalloys suitable for fossil energy applications Improving high-temperature mechanical performance for lower-cost alloys as compared with more costly, high nickel/cobalt alloys Overcoming barriers to scale up new material production from grams to kilograms, and from kilograms to tonnes Overcoming barriers to the manufacture of components with High Entropy Alloys (HEA) Improving speed and quality of welding and other advanced joining methods for nickel superalloys Advanced manufacturing of components for fossil energy applications, particularly for repair of existing plant components and modular fabrication of new plants Machine learning within the supply chain to lower costs and improve productivity � � � 2.� Existing and New Power Plant Applications: Predicting material behavior in specific severe environments, such as high-temperature, cyclic, or oxidative/corrosive, erosive environments, found in coal gasification systems Development of coatings, claddings, and other surface treatments to mitigate oxidation, corrosion, and erosion of high-temperature components AI applications for monitoring and diagnostics of power plants focused on materials failures such as calculating remaining useful life of components or pattern recognition Analysis of thermal fatigue-driven failures, particularly in coal-fired boilers and natural gas combined cycle heat recovery steam generators, to develop and/or validate remaining life predictive tools. Improving reliability of dissimilar welds between CSEF alloys, austenitic alloys and/or high nickel superalloys Overcoming barriers to the manufacture of components for fuel cells Developing machine learning capabilities to identify promising new materials for non-battery energy storage technologies that can integrate with fossil energy power generating units
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