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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Welding of Haynes 282 to Steels to Enable Modular Rotors for Advanced Ultra Super-Critical Steam Turbines

Steam turbines for an Advanced Ultra Super Critical (AUSC) fossil fired power plant will operate at temperatures well above those of current commercial steam cycles with inlet temperatures more than 760 C , which is beyond the capabilities of alloy steels presently used for steam turbine applications and requires advanced materials. Large components such as steam turbine rotors may be made of nickel based super alloys such as Haynes 282 (H282). However, monolithic forgings of superalloys in the sizes required for large steam turbines rotors can be prohibitively expensive besides many technical challenges. To minimize cost and alleviate the related technical challenges, superalloy use needs to be limited to locations on the steam turbine rotor where strength and temperature requirements cannot be met by conventional steels. This is possible if nickel-based superalloys can be successfully welded to steels and the related technical challenges - machining parts made of dissimilar welded materials, non-destructive examination of such welds for flaw detection; and, material properties of such hybrid components – are sufficiently addressed. In this technology development project, we successfully welded H282 to plates up to ~ 75 mm (~ 3 inches) to a 3.5NiCrMoV steel of similar thickness. Advanced ultrasonic inspection technique called Phased Array Ultrasonic Testing (PAUT) was employed to examine the dissimilar H282-Steel welds, into which flat bottomed side drilled holes (SDH) of various diameters were introduced, to determine the minimum detectable feature sizes; it was shown that with PAUT SDH of dia. down to 0.5 mm could be detected in the base alloys and SDH with dia. down to 2.4 mm could be detected in the weld metal under multiple orientations successfully. An autonomous machining process monitoring system was developed and demonstrated whereby the forces acting on the cutting tool could be actively monitored as the cutting tool transitioned from H282 to Steel across the weld using which the machining parameters can be potentially altered without interruption to extend tool life. This project successfully achieved its objectives of - i. Developing a welding methodology and viable welding geometries, to successfully join H282 to steel 3.5CrMoNiV steel to enable manufacture of modular steam turbine rotors for AUSC applications (conditions of at least 760 °Celsius and 3,100 psia (pounds per square inch absolute pressure) and evaluate the material properties of the welded specimen. ii. Employ the advanced ultrasonic inspection technique to the dissimilar weld metal joint and determine the minimum detectable feature sizes. iii. Develop effective machining techniques to machine such hybrid structures with online tool force monitoring and effect machine state metrics for optimal results.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Interior Athabascan Energy Capacity Building Network (Final Report)

Tanana Chiefs Conference (TCC) is the traditional tribal consortium for the 37 federally recognized Tribes and villages of Interior Alaska. TCC is based on a belief in tribal self-determination and the need for regional Native unity. TCC is a nonprofit organization that works toward meeting the needs and challenges for more than 10,000 Alaska Natives (mostly Alaskan Athabaskans) in Interior Alaska. With support from DOE-OIE’s Inter-Tribal Technical Assistance grant, TCC’s goal was to expand and build on the solid foundation of work that the existing TCC Energy Program established through the creation of the Interior Athabascan Energy Network (IAEN). More specifically, the IAEN was designed to create a network of village-based energy champions across the interior and hold quarterly teleconferences and annual inperson meetings. Among the greatest impacts of the IAEN were the creation and support of realistic, communitybased energy projects that produced tangible results such as fuel savings and skills and capacity development among local staff as well as establishment of an energy team that included TCC staff, Tribal participants, technical consultants, and support agencies. The DOE-TA funding allowed us to provide technical assistance to our Tribal communities to develop energy projects, and to foster a network consisting of Tribal community members and energy experts that has strengthened the ability in the region to troubleshoot energy issues, develop meaningful projects, envision a clean energy future, and to share project successes and challenges. The funding from DOE was a catalyst for growth, has created opportunities for learning and knowledge sharing, and has led to the development of cost saving energy projects. Most importantly, the DOE project allowed energy champions around the region to share their knowledge, experiences, and expertise to create place-based solutions and has forged inter-Tribal relationships that will continue to be an asset for years to come. Through information exchange that occurred at out IAEN annual meetings and other events, this technical assistance grant award has spurred a spinoff project that is focused on establishing a collaborative of independent utilities that work together to solve energy problems and share services to operate and maintain their electric utilities. We have indentified at least 10 and possibly 12 communities that are interested in collaborating in managing their utilities to reduce costs and increase reliability. As well, by identifying and supporting Community Energy Champions in the Interior villages and adding capacity to the TCC Energy Program, we were able to create actionable community energy plans, develop specific projects, and increase local capacity, skills, and literacy around clean energy initiatives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Discovery of Signatures, Anomalies, and Precursors in Synchrophasor Data with Matrix Profile and Deep Recurrent Neural Networks (Final Project Report)

The widespread deployment of phasor measurement unit (PMU) across the U.S. together with the burgeoning machine learning technology made it possible to develop data-driven PMU data analytics to improve grid security and reliability in a more insightful and effective manner. Although PMU applications have been explored for over a decade, the representative PMU usage is limited to the bulk power system monitoring mainly due to the data integrity issues associated with PMUs (typically missing, fragmented, and wrongly amplified data). To forge a breakthrough on this stalemate and embrace PMUs for power system control and protection as well, we applied various advanced machine learning and big data analysis technology to the power system event detection and classification as the first step toward the power system control and protection pertaining to grid security enhancement.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Predictive Tools for Customizing Heat Treatment of Additively Manufactured Aerospace Components

Laser-bed powder fusion (LBPF) additive manufacturing is increasingly being used to produce components of complex geometries using the Ni-base superalloy Inconel 718. The composition and the microstructure of the alloy are currently well optimized for wrought components made using conventional manufacturing processes such as rolling, forging, extrusion, etc. The attractive mechanical properties of the alloy result from the underlying austenitic matrix with fine equiaxed grains, and a high density and uniform distribution of the precipitation hardening phase, γ". Heat treatment steps such as homogenization, solutioning and aging are well documented for the wrought alloy. However, when the same wrought alloy compositions are used for the additive manufacturing (AM) processes, the asprocessed microstructure is significantly different, because of the different thermal history associated with LBPF, including rapid solidification and multiple temperature excursions that lead to multiple re-melting and reheating in the solid state. Rapid solidification introduces potential non-equilibrium effects at the moving solid-liquid interfaces that impact the extent of solute segregation, as well as the morphology of the dendritic grains that form. In order to recover the target mechanical properties, AM components have to undergo post-process heat treatments. However, such heat treatments have to be custom designed for the AM process and the component geometry because of the expected vast differences in the microstructure at various locations of a component with complex geometry. The homogenization and precipitation steps should be optimized for the component so that target mechanical properties can be obtained throughout the part. The objective of this research is to utilize High Performance Computing in phase field simulations of microstructure evolution during post-processing of AM components. The physics-based modeling will be beneficial in reducing the experimental effort required for heat treatment process selection, optimization, and certification, thus leading to a significant reduction in energy consumption for AM and post-processing heat treatment. The optimization study will help identify heat treatments steps that are critical for development of a final desired microstructure with the minimum energy input. This combined with shortening of the production cycle (time-to-market) by reducing the number of failed parts (property targets), and reduction in the number of iterations for process optimization, will enable 30-40% savings in the energy costs. Phase field simulations of the degree of homogenization and the effect of local matrix composition on the nucleation and growth of competing precipitating phases were performed using the Microstructure Evolution Using Massively Parallel Phase Field Simulations code developed in-house at the Oak Ridge National Laboratory. The simulations were able to successfully capture the kinetics of nucleation and growth, and morphologies of various precipitating phases as a function of local matrix compositions and composition gradients characteristic of local microstructures arising from location-dependent variations in the thermal conditions. Future work will involve extending the simulations to a length scale consisting of multiple dendrites, so that the effect of homogenization on the coarsening of the dendrites can be simulated and used as an additional input to the optimization of the heat treatment process.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Radionuclide Waste Disposal: Development of Multi-scale Experimental and Modeling Capabilities (Final Report)

The DOE EPSCoR Implementation project “Radioactive waste management: Development of multi-scale experimental and modeling capabilities” helped to develop a team of scientists and engineers from Clemson University, South Carolina State University, and the University of South Carolina to address the disposition of nuclear wastes and study the transport of radioisotopes from a waste repository in the near and far field. The project involved 20 faculty from the three institutions as well as 13 postdoctoral fellows, 32 graduate students, and 32 undergraduate students and was active from 2014-2019. Additionally, we forged new collaborations with nine researchers from DOE laboratories SRNL, LLNL, and ANL as well as the University of Manchester and the China Academy of Engineering Physics. The overarching goal of the project was to understand the conditions under which important classes of co-reactants, ranging from counter ions in crystal lattices to dissolved oxygen in pores, control the chemistry and transport characteristics of radionuclides in engineered waste forms and natural soils. Our approach was to characterize the time and length scales over which non-equilibrium states are maintained by rate-limiting, or rate-enhancing, reactions between radionuclides and co-reactants due to interactions between physical mass-transfer processes (i.e., advection, diffusion) and (biogeo) chemical reactions. We have focused our project on three specific classes of reactions relevant to radionuclide transport at DOE legacy sites: ion exchange/substitution, ligand complexation, and redox-mediated reactions. Understanding radionuclide migration requires detailed knowledge of how changes to a system – whether engineered or natural – drive the behavior of co-reactants, which in turn provide the geochemical context controlling radionuclide transport. Student engagement and training were a primary focus of the project in order to create a pipeline of researchers who could work in the area of nuclear waste disposition to support the state and the nation. Over the duration of the project we worked with 32 undergraduate, graduated 18 M.S. students and 14 Ph.D. students, and advised 13 postdoctoral fellows. The Ph.D students and postdocs have primarily taken positions at DOE laboratories, academia, and industry. Through our collaborative team, numerous follow on projects have been started with over $5M in sponsored research. Additionally, thus far the team has published 53 peer reviewed papers and given over 75 technical presentations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Natural Language Processing for Text Based Event Extraction: Identifying Events of Interest Related to Worldwide State-Sponsored Civil Nuclear Power

Beginning in FY20, SRNL was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development to develop a prototype natural language processing/natural language understating machine learning-based modeling and analysis pipeline to extract and forecast events of interest from massive open data sources. The working hypothesis within the approach is that contextual shifts in key words and phrases act as indicators of events of interest over time. Therefore, by identifying points in time where contextual shifts occur, events of interest can be extracted along with explicit and implicit connections of entities and activities. The development of the preliminary prototype pipeline proved successful, meriting further testing of the pipeline on more broad topical domains and in a worldwide data environment. Therefore, SRNL, in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, have continued development with a test case of identifying events of interest related to worldwide state-sponsored civil nuclear power in open data sources. In the first year of this follow-on effort, the team has curated domain-specific data corpuses using an automated scheme and applied the modeling and analysis pipeline. This robust, focused, and efficient approach consists of an ensemble of analyses applied to time dependent word embedding models that are trained on the data corpuses. In this report, the team has demonstrated the capability of the existing pipeline (as development has continued in parallel) by exploring several specific case-studies centered around Rosatom’s international activities regarding the planning, construction, operation, and/or shutdown of nuclear reactors. A basic timeline events has been generated by manually cataloging known “milestone” events that have occurred at reactors in Turkey, Finland, Hungary, and Egypt and compared with the output of the modeling pipeline. In this approach, the team has characterized the lead time using the prototype pipeline, as well as the ability to capture relevant information, which proved 100% successful. A deep dive example of the Akkuyu reactor (Turkey) is presented that shows the breadth of information that can be captured using the approach. In this case study, events were extracted pertaining to the planning/construction of Akkuyu including protests from the population, information campaigns in response to the protests, forged regulatory documents and lawsuits, budgetary/shareholder information, geopolitical tensions, and the various construction milestones. This has demonstrated the pipeline’s utility as a research aid or real-time event extraction tool, where summary-level information and detailed text extractions from millions of articles or Tweets across long time periods can be generated with significantly less effort than current techniques.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Manufacturing Dissolvable Alloy Components using ShAPE: A Study on Solid Phase Processing of Mg-W Composites and WJ11 Alloys

Dissolvable alloys and composites are an emerging class of materials that demonstrate tailored sorptivity in corrosive environments. This work explored the viability of solid phase processing (SPP) techniques, namely the shear assisted processing and extrusion (ShAPETM) method, to manufacture dissolvable magnesium composites and alloys components such as rods and tubes. The study also determined the effects of material composition and manufacturing process conditions on material microstructures developed during SPP and the resulting performance of the components. Explicit properties of interest included ultimate tensile strength, ultimate compressive strength, and yield strength. The application fields of interest for dissolvable alloys were identified as oil-and-gas operations and biomedical implants. Accordingly, PNNL collaborated with industrial and academia partners, Fortek Industries, Houston and University of Pittsburgh, during the course of this project for material and ShAPE process development. PNNL determined the viability of the using ShAPE to process the custom-designed materials provided by the project partners. This was done by identifying process parameters such as tool rotation rate, tool plunge rate, and process cooling optimal for manufacturing components with consolidated microstructures. Subsequently, PNNL also performed microstructural characterization on unprocessed and processed samples and mechanical property testing. One of the major findings from this work is that ShAPE was able to manufacture dissolvable magnesium composite and alloy rods and tubes with consolidated homogeneous microstructures and minimal macroscale defects on component surfaces. It was observed that samples processed via ShAPE demonstrated an average grain size < 2 - 5 µm, which was markedly lower than that of the precursors with 8 – 13 µm grains. Results showed that the ShAPE synthesized dissolvable magnesium composite and alloy strength was on par with or better than those components that were manufactured using traditional manufacturing processes such as forging and extrusion. It is noteworthy that the ShAPE samples were made with considerably lower number of process steps.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Solid Phase Gradient Alloying Method via ShAPE

Alloying has been used to confer desirable properties to various metallic systems since the bronze age. For example, Ni, Mn, and Cr are alloyed into an Fe matrix to make steel, which has improved strength and corrosion resistance (as displayed in Fig. 1). In addition to the type of alloying elements, the amount of alloying elements added into a matrix is also very critical for a material’s mechanical property, physical property, machinability, and cost. Conventionally, new alloys are discovered by combining alloy precursors and elemental constituents into a mixture and melting them together. The solidified product or ingot represents a usually non-homogeneous combination of the chemistry with a microstructure dictated by the physics of solidification. It is time and energy intensive to optimize the alloying amounts via the casting process and hence, economically not conducive to rapid alloy discovery. In addition, the cast microstructure seen in the as-cast ingot is often not the ideal, nor even desired structure for optimized performance. Cast materials often need further processing such as homogenization, annealing, and deformation work put in through rolling, forging, extruding, etc. to have favorable microstructures and properties. A faster method to discover new alloy combinations and evaluate them in their worked or processed form is needed. Recently, Laser Engineered Net Shaping was applied to fabricate gradient compositional material for alloy designing. However, the mechanical properties of this gradient alloy were poor due to the existence of impurities, oxidation and cracks that are inherent in the melt-solidification process. Because of these limitations, we propose to use a solid-phase process (ShAPE) to create bulk materials (extrudates) that vary in composition from one end of the extruded solid to the other. Various manufacturing methods for alloy design including conventional casting method, laser based combinatorial method and current solid phase gradient alloying method are displayed and compared in Fig. 2. ShAPE machine and a schematic of ShAPE process are displayed in Fig. 3. Starting material is processed by a rotating and plunging die to form an extrudate. We are proposing through this methodology to invent a new, bulk-scale combinatorial technique. With this novel technique, alloying element can be dissolved into the matrix with a continued gradient without bulk melting. Formation of inter-metallics and defects originating from melt processing can be avoided. In addition, the ShAPE process creates the mixed alloy chemistry, and meanwhile subjects the material to severe plastic strain, which induces the favorable “worked” microstructure. Products from the ShAPE process can be tested in hardness directly providing a path to rapid evaluation of mechanical properties. The ability to create graded structures using friction stir processing (FSP) has been demonstrated, however using the ShAPE process we believe will lead to much faster and higher fidelity results. When combined with high throughput screening methods of physical, mechanical and microstructural property characterization, efficiency and accuracy of alloy design can be significantly improved.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Development of Novel Ferritic-Martensitic Steels with Superior Creep Properties for Power Plant Applications

A creep resistant martensitic steel, CPJ-7, was developed with an operating temperature approaching 650°C. Subsequently, another creep resistant martensitic steel, JMP, was designed with the potential to operate at, or slightly above, 650°C. This report describes the development of these alloys from early iterations of CPJ steels to the final CPJ-7 formulation as well as the more heat resistant JMP steel formulation. The design originated from computational modeling for phase stability and precipitate strengthening using fifteen constituent elements. Approximately forty heats of CPJ and ten of JMP, each weighing ~7 kg, were vacuum induction melted. A computationally optimized heat treatment schedule was developed to homogenize the ingots prior to hot forging and rolling prior to final normalization and tempering. Overall, wrought and cast versions of CPJ-7 present superior creep properties when compared to wrought and cast versions of COST alloys for steam turbine and wrought and cast versions of P91/92 for boiler applications. For instance, the Larson Miller Parameter curve for CPJ-7 at 650°C almost coincides with that of COST E at 620°C. The prolonged creep life was attributed to slowing down the process of the destabilization of the MX and M 23 C 6 precipitates at 650°C. On the other hand, the cast version of CPJ-7 also revealed superior mechanical performance, well above commercially available cast 9% Cr martensitic steel or derivatives, especially for creep life at 650°C. The casting process employed slow cooling to simulate the conditions of a thick wall full-size steam turbine casing but utilized a separate homogenization step prior to final normalization and tempering. To advance the development of CPJ-7 for commercial applications, a process was used to scale up the production of the alloy using vacuum induction melting (VIM) and electroslag remelting (ESR), which underlined the importance of melt processing control of intentionally designed minor and trace elements in these advanced alloys. Following the work on CPJ-7, the JMP steels were designed with higher Co for increased solid solution strengthening, Si for oxidation resistance and increased W (with low Mo content) for matrix strength and stability as well as solid solution strengthening. The JMP steels showed increases in creep life compared to CPJ-7 between 118 to 150% at 650°C for testing at various stresses between 138 MPa and 207 MPa. On a Larson-Miller plot, the performance of the JMP steels surpasses that of state-of-the-art MARBN and other MARBN-type steels. The influence of various elements within the composition of the alloys on the microstructure and mechanical properties are discussed. This report presents approximately 420,000 h of in-house creep testing, the equivalent of almost 50 years of cumulative creep tests.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Research Plan and Preliminary Results in Developing the Fabrication Parameters for Alloy 709 in Different Product Forms―Grain Coarsening Temperature Evaluation

The Advanced Reactor Technologies (ART) Program has established a multi-year plan to develop Alloy 709 advanced stainless steel (A709), generate the data package and develop material-specific design parameters in qualifying it as a new structural material for Class A component design in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code, Section III, Division 5, High Temperature Reactors. In collaboration with material vendors, the Advanced Materials Development activities under ART have successfully scaled the A709 plate form production from a laboratory heat of 500 pounds to commercial heats totaling 133,000 pounds of A709 plate fabricated from three heats. The goal of the overall A709 development program is to establish the necessary microstructural and mechanical properties relationship for A709 to ultimately develop fabrication parameters for other product forms such as bars, piping and forging using the available ART A709 materials. The objective of this A709 development work at ORNL in FY2023 is to experimentally determine grain coarsening behavior for the A709 heats and to experimentally generate the continuous cooling precipitation (CCP) diagram for A709 using the as-rolled commercial heat plate materials. Integral to this work is the characterization of the as-rolled materials and the determination of an effective solution annealing process. This report summarizes the work performed to identify the grain coarsening temperature for commercial heat 58776-3RB fabricated by G. O. Carlson and heat 529900-02 fabricated by Allegheny Technologies Incorporated (ATI) Flat Rolled Products.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior

Our goal is to develop, apply and validate a holistic thermal, hydrologic, mechanical, and chemical (THMC) workflow that includes evaluation of induced seismic slip in EGS reservoirs. We will integrate experimental and modeling approaches to reduce parameter uncertainty and better predict/mitigate seismic hazard at EGS sites. Our novel approach couples 3D physics-based earthquake simulations with THMC models (THMc+E). This capability will enable improve engineering decisions at Utah-FORGE and move EGS operations toward repeatable, robust, economically viable, and socially accepted development. For example, our THMC+E models will predict circulation scenarios and related seismic hazard for a suite of flow rates and under uncertainty, thus enabling evaluation of optimal circulation strategy. Laboratory experiments will be performed to constrain key model parameters and Bayesian techniques will provide a probabilistic evaluation of parameters used in models. THMC+E simulations will enable exploration various circumstances that may hinder EGS success and develop mitigation strategies.

58 GEOSCIENCES↗
DOE OSTI2023

ESnet Requirements Review Program Through the IRI Lens: A Meta-Analysis of Workflow Patterns Across DOE Office of Science Programs (Final Report)

The Department of Energy (DOE) ensures America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. The DOE’s Office of Science (SC) delivers groundbreaking scientific discoveries and major scientific tools that transform our understanding of nature and advance the energy, economic, and national security of the United States. The SC’s programs advance DOE mission science across a wide range of disciplines and have developed the research infrastructure needed to remain at the forefront of scientific discovery. The DOE SC’s world-class research infrastructure — exemplified by the 28 SC scientific user facilities — provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance core DOE mission science for its sponsor SC program and to stimulate a rich discovery and innovation ecosystem. Research communities gather and flourish around each user facility, bringing together diverse perspectives. A hallmark of many facilities is the large population of students, postdoctoral researchers, and early-career scientists who contribute as full-fledged users. These facility staff and users collaborate over years to devise new approaches to utilizing the user facility’s core capabilities. The history of the SC user facilities has many examples of wildly inventive researchers challenging operational orthodoxy to pioneer new vistas of discovery; for example, the use of the synchrotron X-ray light sources for study of proteins and other large biological molecules. This continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high performance data, networking, and computing solutions. The DOE SC’s three HPC user facilities and the Energy Sciences Network (ESnet) high-performance research network serve all of the SC’s programs as well as the global research community. Argonne Leadership Computing Facility (ALCF), the National Energy Research Scientific Computing Center (NERSC), and Oak Ridge Leadership Computing Facility (OLCF) conceive, build, and provide access to a range of supercomputing, advanced computing, and large-scale data-infrastructure platforms, while ESnet interconnects DOE SC research infrastructure and enables seamless exchange of scientific data. All four facilities operate testbeds to expand the frontiers of computing and networking research. Together, the ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements reviews (RRs) methodology. In recent years, the research communities around the SC user facilities have begun experimenting with and demanding solutions integrated with HPC and data infrastructure. This rise of integrated-science approaches is documented in many community and high-level government reports. At the dawn of the era of exascale science and the acceleration of artificial intelligence (AI) innovation, there is a broad need for integrated computational, data, and networking solutions. In response to these drivers, DOE has developed a vision for an Integrated Research Infrastructure (IRI): To empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation.

42 ENGINEERING↗
DOE OSTI2024

Biomolecular and Characterization Imaging Science Program: 2023 Principal Investigator Meeting Proceedings

The 2023 U.S. Department of Energy (DOE) Biological and Environmental Research (BER) program’s Biomolecular Characterization and Imaging Science (BCIS) Principal Investigator Meeting expanded in scope from previous Bioimaging Science Program (BSP) meetings to include BER Structural Biology and Imaging Resources, which are located largely at DOE Office of Science national laboratories. The BCIS meeting was part of BER’s Biological Systems Science Division (BSSD) annual PI meeting, which was held April 17–19, 2023, and featured parallel meetings of the BCIS and Genomic Science programs (GSP). The meetings were held together to encourage networking and idea exchange across technologies and biological application areas, forging new multidisciplinary collaborations among researchers from adjacent BSSD programmatic areas. Two joint BCIS-GSP sessions were held: “BCIS Technologies for Investigating the Rhizosphere” and “Joint Emerging Topics and Technologies.” The rhizosphere session focused on scientific findings from BCIS and GSP PIs, including national laboratory collaborations. The intent was to identify new opportunities to measure and understand the complex community of microbes, roots, and soils that support plant growth under challenging environmental conditions. The emerging technologies session highlighted forward-looking approaches and tools to tackle challenges within the scope of BSSD research on investigating and modifying genomic and molecular function. A final interactive discussion of the BCIS program was led by plenary session chairs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Bioimaging Science Program Principal Investigator Meetings Proceedings (2021)

As part of the 2021 Biological Systems Science Division (BSSD) Principal Investigator (PI) Meeting, the Bioimaging Science program (BSP), within BSSD's Biomolecular Characterization and Imaging Science portfolio, held its annual PI meeting virtually February 22–23. BSP's mission is to understand the translation of genomic information into the mechanisms that power living cells, communities of cells, and whole organisms. The goal of BSP is to develop new imaging and measurement technologies to visualize the spatial and temporal relationships of key metabolic processes governing phenotypic expression in plants and microbes. BSP convenes annual PI meetings to bring together its contributing investigators to review progress and current state-of-the-art bioimaging research. Holding the BSP meeting as part of the broader BSSD PI meeting allowed researchers to interact with the extended Genomic Science program community. This convergence provided a platform for networking and exchange of ideas, helping to forge new multidisciplinary collaborations among investigators from the two sister programs. An important highlight of the BSP meeting was the keynote presentation “Enhancing Fluorescence Microscopy with Computation” by Dr. Hari Shroff of the NIH National Institute of Biomedical Imaging and Bioengineering. All the BSP PIs made presentations describing their research focus and progress, and these were followed by roundtable discussions of each project. The meeting’s proceedings provide an outline of the program’s current state and potential future directions and opportunities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Bioimaging Science Program: 2022 Principal Investigator Meetings Proceedings

The mission of the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program’s Bioimaging Science Program (BSP) is to understand the translation of genomic information into the mechanisms that power living cells, communities of cells, and whole organisms. The goal of BSP is to develop new imaging and measurement technologies to visualize the spatial and temporal relationships of key metabolic processes governing phenotypic expression in plants and microbes. The extended goal of dynamic imaging is to functionally connect cellular components and interdependent organisms. Information about the time and place of chemical reactions in situ can identify causal relationships between biological activators and downstream effectors. BSP held its annual PI meeting virtually February 28–March 1. Contributing investigators are convened to review progress and current state-of-the-art bioimaging research. Holding the 2022 BSP meeting as part of the broader Genomic Science Program (GSP) PI meeting allowed researchers to interact with the extended GSP community. This convergence provided a platform for networking and exchange of ideas with experts in other technologies and in target BSP application areas, helping to forge new multidisciplinary collaborations among investigators from the sister programmatic areas within BER’s Biological Systems Science Division. An important highlight of the BSP meeting was the keynote presentation by Nobel Laureate Dr. Joachim Frank on Time-Resolved Macromolecular Imaging using Cryo-EM. He discussed microfluidic mixing and fast freezing to capture nonequilibrium intermediate states during molecular binding and conformational changes. The action of molecular machines can be captured at nanometer resolution and millisecond discrimination. BSP PIs made presentations describing their research focus and progress in plenary sessions on bioimaging science and on quantum-enabled bioimaging science research projects. BSP research at universities and DOE laboratories is presented in this report. A final discussion of the BSP was organized by meeting plenary session chairs, who prepared the following Executive Summary of current BSP research, research challenges, future opportunities, and potential ideas for expanding the BSP’s impact and interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quarterly Research Performance Progress Report (Q8)

As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. Once satisfied with the results we have started by targeting the modeling of the DFITs at conducted at well 58-32. We have a identified a specific test (cycle 4 in zone 2) as the most interesting to be model with GEOS hydraulic fracturing module. Thus, we have first produced results with an isothermal model and adjusted model parameters to get a satisfying match with field pressure data. The, we have added thermal effects and compared the modeling results with and without thermal effects to estimate how thermal effects may influence test interpretation. Models seem to suggest that, for small volumes of fluid, thermal effects are moderate. In Task 2, we have adapted GEOS phase-field formulation to be able to simulate near-wellbore hydraulic fracture nucleation and propagation. We have devised a novel formulation that, compared to other existing ones, incorporates rock strengths. We have submitted a journal publication about our work. We are currently employing this phase-field formulation to model the experiments taking place at U Pitt and help us understand the effect of various parameters. In Task 3, we have built a model of the region surround well 16A and have started modeling stage 3 stimulation because of its simpler planar geometry. After calibrating simulation parameters using known analytical solutions, we have simulated the stage 3 stimulation using our isothermal hydraulic fracturing module, varying the permeability field, the stress conditions including different physics to get a better understanding of the numerical challenges and of the effects of varying these parameters on the simulation results. In Task 4 laboratory experimentation, a set of specialized drilling and injection tools has been customized and constructed to accommodate an inclined well with an orientation of up to 30 degrees relative to material anisotropy or principal stress axes. These inclined samples have also undergone thermal stress and hydraulic fracturing at a temperature of 190 degrees Celsius. Furthermore, both vertical and deviated sampling testing setups enable an extended analysis of post-peak pressure behaviors, facilitating post-test pressure analyses such as the G-function, step rate, and fracture reopening measurements. Thus, the key components of in-situ stress estimation can be extracted and validated through our experiment, providing a solid foundation for validating existing in-situ stress estimation theories or proposing new ones. Simultaneously, we are integrating computer vision techniques with traditional experimental fracture observation methods such as multi-overcore/slicing and water-penetration fracture observation. This combination will prove beneficial in populating the hydraulic fracture patterns database, generated under challenging EGS conditions. This approach aims to deepen our understanding of the complexities in EGS reservoirs and pave the way for future data-driven investigations. Additionally, PITT has also equipped the ELE International compression machine, which is now prepared for conducting indirect tensile and fracture toughness tests. These tests will aid in characterizing how rock fabrics influence the resulting fracture patterns. Additionally, we have completed the required personnel training and gained access to Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) for conducting more detailed characterization and analysis of rock fabrics, as well as the examination of thermal and hydraulically induced fracture patterns. Thus, the PITT team has effectively demonstrated the capabilities of our experimental apparatuses in exploring the thermal effects, well deviation angles, material anisotropy, and operational choices (such as circulation rate, injection fluid viscosity, and injection rate) and their impact on pressure responses and fracture trajectories under the Utah FORGE conditions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Near-Net-Shape Hot Isostatic Press Manufacturing Modality for sCO2 CSP Capital Cost Reduction

Through this DOE funded 3.5-year research project, feasibility of fabricating supercritical carbon dioxide (sCO2) turbine components by powder metallurgy (PM) based near-net-shape (NNS) hot isostatic pressing (HIP) was demonstrated in a turbine nozzle ring, a turbine casing with Haynes 282 powder, and a bimetallic pipe with Haynes 282 and SS415 powder. As-HIP microstructure of various powders and HIP processing conditions was studied to downselect a condition for the prototype components. Tensile strength and low cycle fatigue (LCF) capability of PM HIP 282 was found to be superior to cast 282, despite a debit in creep stress capability that could be mitigated by component design modification. Near-Net-shape with minimal post machining was achieved by modeling the non-uniform shrinkage during HIP cycle and designing the HIP tooling to meet dimensional targets. The prototypes of turbine nozzle ring and bimetallic pipe were successful in achieving overall dimension, microstructure, and properties, despite dimensional tolerance affected by chemical milling rate. The prototype of a 1700lbs turbine casing was partially successful in achieving dimension, microstructure in as-HIP state, and providing consistent mechanical properties, however, cracking issue during post heat treatment required further investigation. The estimated manufacturing cost using NNS HIP was a ~50% reduction compared to forging with extensive machining, which translated into ~$100/kWe CAPEX cost reduction for concentrated solar power (CSP) power block.

14 SOLAR ENERGY↗
DOE OSTI2023

Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense (Final)

In the world of ever-advancing technology, Autonomous Systems (AS) find extensive application, bolstering functionalities of critical infrastructures such as nuclear power plants. These systems, however, are increasingly becoming a target for nefarious activities, namely through inference attacks, trojan attacks, and adversarial reprogramming. This paper delves into a comprehensive exploration of machine learning (ML)-driven autonomous control systems within advanced nuclear reactor designs, revealing the vulnerabilities and proposing strategies for defense against potential cyber-attacks. Advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)- based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber-physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems. As global reliance on generation III reactors begins to be critically assessed, the evolution towards advanced reactor systems utilizing digital instrumentation and controls (I&C) becomes not merely preferable, but essential. The integration of semi and fully autonomous control systems (ACS), powered by digital I&C and machine learning (ML)-based digital twinning (DT) technologies, emerges as a potent strategy to mitigate operations and maintenance costs, thereby enhancing the economic feasibility of novel reactor designs. However, with a staggering 500% and 380% increase in cyber-attacks reported against the energy sector by the United States Department of Energy (DoE) and the European Union respectively, a surge in cyber vulnerabilities specifically targeting the nuclear industry has been 2 markedly observed. Notable incidents, such as the W32.Ramnit spyware infiltration at the Gundremmingen nuclear power plant in Germany and the Dtrack spyware intrusion at the Kudankulam nuclear power plant in India, while not directly compromising core industrial control systems (ICS), underscore a compelling necessity to fortify cybersecurity protocols in safeguarding reactor systems against increasingly adept digital adversaries. In light of this, our investigation extends beyond conventional cybersecurity parameters, diving into the intricate web of potential vulnerabilities woven into ML-based DTs and ACS in advanced reactor systems. A crafted cyber-physical testbed and preliminary ACS were devised to act as a mirror, reflecting potential configurations of advanced reactor control designs. Moreover, this study is intertwined with a scrutinization of ML models, developed either through conventional, manually tuned methodologies or via automated means through AutoML, probing into their cyber-risk profiles within operational technology (OT) environments. Expanding on this, two distinct ACS blueprints were forged – one navigating through the corridors of traditional ML and the other traversing the path of AutoML – in an effort to holistically encapsulate the considerations pivotal to ML-based DT control system design. Employing the SANS Institute Industrial Control System (ICS) Kill Chain and the MITRE ATT&CK Tactics, Techniques, and Procedures (TTP) framework, a structured analysis was conducted, launching three targeted attacks against the training dataset, real-time dataset, and ML models, therein dissecting the potential cyber-attack implications against both ML frameworks within an ACS milieu. It is essential to note that three distinct categories of attacks were conducted against both ACS configurations, each encompassing three distinct ML-based DTs, cumulating in a total of 18 varied attacks. This exploration extends into the realms of Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense, unraveling vulnerabilities, and opportunities for fortified defenses against such intrusions, particularly where ML-driven technologies, and by extension, ACS, are deployed. Final recommendations, articulated through a lens of security, safeguard, and implementation considerations, are presented for both traditional and AutoML models, anchoring upon the existing knowledge landscape and ML-based DT modeling for ACS, and are offered as a beacon to guide the nuclear industry through the intricate cybersecurity challenges that lie ahead.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗