The HPC4Mtls Program is sponsored by the DOE Office of Fossil Energy (FE) to enhance the U.S. materials-development, fabrication, and manufacturing industry to investigate, improve, and scale methods that will accelerate the development and deployment of materials that perform well in severe and complex energy application environments. This solicitation is aimed at demonstrating the benefit of HPC toward these goals within one year.
The program seeks proposals that will address key challenges in developing, modifying, and/or qualifying new or modified materials that perform well in severe and complex energy application environments through the use of HPC modeling, simulation, and data analysis. For each of the program offices supporting this solicitation, we provide a brief description of their mission and the topics of interest to them.
The Office of Fossil Energy
FE 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. Fossil energy sources constitute over 80% of the country’s total energy use and are critical to the nation’s security, economic prosperity, and growth. It partners with industry, academia, 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 detailed processes in critical focus areas such as oxidation, corrosion, and electrochemical interactions
- Use computational databases and machine learning for catalyst development to synthesis, 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 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:
Existing and New Power Plant Applications
- Reducing the cost of ingot production for nickel superalloys suitable for fossil energy applications
- Improved 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)
- Developing modeling and simulation tools that will reduce the time to qualification and certification of materials (e.g., American Society of Mechanical Engineers code materials), including but not limited to novel manufacturing processes such as chemical etching, diffusion bonding, and additive manufacturing
- Improve 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
- Predicting material behavior in specific severe environments, such as high-temperature, cyclic, or oxidative/corrosive environments, found in fossil power plants
- 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.
- Improve reliability of dissimilar welds between ferritic and stainless steels or 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