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DOE OSTI2023

Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML)

Although the interest in the use of artificial intelligence (AI) and machine learning (ML) in nuclear energy is increasing rapidly, at present their implementation is limited. This rapid increase in interest is not surprising considering that implementing AI and ML technology would allow for continuous monitoring, facilitate the implementation of predictive maintenance with optimized staffing plans, enable automation and autonomy opportunities that could drastically reduce fixed operation and maintenance costs, and provide training for operations and maintenance. Other industries are using AI for construction, and in the nuclear arena AI could provide great benefit in decommissioning activities. The ability of AI and ML to operate in real time vastly increases their potential impact. Before AI can be used in design, operations, or as a regulatory tool, the specifics on the regulations applicable to the use of AI for nuclear power applications need to be established. The difficulty is that the specific use cases will dictate the applicability of regulations. For example, even within the application domain associated with operations, the regulations might vary if the AI is used to create a virtual reference for plant operations or is used for training, optimization of maintenance intervals, prioritization of maintenance activities, etc. Different still is if the AI is to be used for design or setting technical specifications, which will introduce additional requirements. US Nuclear Regulatory Commission (NRC) licensing reviews are based on an applicant’s design meeting its performance assessment based on (1) safety goals and objectives, (2) deterministic and/or probabilistic analysis of accident scenarios, and (3) quantitative assessment of design alternatives against the safety goals and objectives using accepted engineering tools, methodologies, and performance criteria. The current regulatory framework does not explicitly address AI or autonomous control. However, as implementing AI technology will require the use of a digital platform, it must meet the requirements of an instrumentation and control (I&C) system. The regulatory requirements for AI, which will be incorporated into the I&C system, will be very dependent on how it is used (i.e., its functionality, safety classification, etc.). The licensing process is primarily risk-based with the identification of components and systems as nonsafety, important to safety, or safety related. A risk-informed approach allows further gradation of components and systems based on risk metrics such as core damage frequency or large early release fractions. Thus, the use cases and the risk categorization of impacted systems and components will determine the regulatory requirements. Regardless of how AI is used it presents new opportunities for risk-informing operating, maintenance, and regulatory decisions. Trustworthiness, transparency, and the ability to validate and verify the results will be paramount in showing that the systems and plant still meet their performance requirements. This report describes the results of research to identify regulatory implications of AI technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of AI for design (including design changes or new designs including advanced reactors), construction, operations, training, maintenance, research, testing, and as a regulatory tool. AI can be automated at different levels from purely informative purposes to autonomous controls. The focus of this review included determination of constraints on the application of AI technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. Currently, any use of AI at nuclear power plants is focused on nonsafety-related applications. The NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than create new regulations to address the use of AI and ML. This approach seems to be the best to encourage AI development without adding regulatory uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Bridging the gap: adapting advanced display technologies for use in hybrid control rooms

The Institute for Energy Technology (IFE), runs the OECD Halden Reactor Project (HRP), featuring a state-of-the-art research simulator facility in Halden, Norway, called HAMMLAB. HAMMLAB serves two main purposes: the study of human behaviour in interaction with complex process systems; and the development, test and evaluation of prototype control centres and their individual systems. By studying operator performance in HAMMLAB and integrating the knowledge gained into new designs, the HRP contributes to improving operational safety, reliability, efficiency and productivity. The U.S. Department of Energy’s (DOE) Light Water Reactor Sustainability (LWRS) Program has contracted IFE to assist DOE national laboratory staff at Idaho National Laboratory (INL) in adapting HAMMLAB design concepts for the purpose of control room modernization at nuclear power plants in the U.S. In support of this effort, the DOE has built a simulator research facility at INL called the Human Systems Simulation Laboratory (HSSL). The HSSL is centered on control room modernization, in which industry provided plant instrumentation and controls are modified for upgrade opportunities. The HSSL houses the LWRS simulator, which is a reconfigurable full-scale and full-scope control room simulator. Consisting of 45 large touchscreens on 15 panels, the LWRS simulator is currently using this glass top technology to digitally represent and replicate the functionality of the analog I&C systems in existing control rooms. The LWRS simulator is reconfigurable in that different plant training simulator models obtained from the utilities can be run on the panels, and the panels can be physically moved and arranged to mimic the layout of those control rooms. The glass top technology and reconfigurability capabilities allow the LWRS simulator to be the research platform that is necessary to design, prototype, and validate human-system interface (HSI) technologies that can replace existing analog I&C. IFE has recently assisted INL in establishing the technical infrastructure for implementation of HSI prototypes from HAMMLAB into the HSSL to demonstrate relevant control room replacement systems in support of the LWRS program. In March, 2014, IFE delivered the first HSI prototype utilizing this infrastructure — a large screen overview display for INL's simulator. The co-operation now continues by developing Procedure Support Displays targeted for operators in hybrid control room settings. These prototypes are being validated with U.S. reactor operators in the HSSL and optimized to enhance their performance. This research serves as a crucial stepping stone toward incorporation of advanced display technologies into conventional main control rooms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Vattenfall Decommissioning Planning in Sweden - 17544

A rapid decrease in the power prices in the Nordic market for the last three years has drastically reduced the profitability of the nuclear power plants in Sweden. This has resulted in a very quick re-evaluation of the optimal life-term of the older generation, from the 1970's, nuclear units in Sweden. Hence, it was decided last year to shut-down the Vattenfall Ringhals-1 BWR and the Ringhals-2 PWR by 2020 and 2019, respectively, for financial reasons. Before that decision, the planning had been to operate the units until 50 years life-time, i.e. 2024 and 2025. This decision resulted in a prompt start of the planning for decommissioning of the two units. The planning started with a project, called AVANS, to ensure that the overall planning and to-be-developed organisations had well defined requirements and a common platform. Another project, STURE, was started to ensure the continued operation and in some case movement of common site functions to the two younger units on the Ringhals site, i.e. the PWRs Ringhals-3 and Ringhals-4. This project also has the responsibility to optimise operation of Ringhals-1 and Ringhals-2 to EOL and the subsequent removal of the nuclear fuel from the units after necessary cool-down. A third project started is the actual decommissioning project, TYKO, benefiting from experience gained and staff involved in still ongoing decommissioning of a legacy materials reactor, i.e. the Studsvik R2/R2-0 reactor. The planning and experience to date for the three projects will be presented in the paper, as well as lessons-learned and future plans, including some critical items identified wrt dismantling and site function transfer. Half of the nuclear units in Sweden, six units, and one legacy power reactor, will be starting decommissioning within the next few years. Impacts on the back-end system; the financial situation, with respect to the state-controlled financial funding; and the supplier market are discussed in the paper. Furthermore, the planning process and the planned management of the interfaces between the involved organisations is described. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Partially cracked ammonia mixture combustion in gas turbine combustors

Ammonia is gaining popularity for single fuel use in existing gas turbines, as it is carbonless, has higher hydrogen content per unit volume than liquid hydrogen, and facilitates easy storage and handling. However, the low flame speed of ammonia makes it prone to flame blow off and remains the chief concern for its use in power generation. Following previous studies that have shown improved combustion performance by partially cracking the fuel, in this computational study, efficacy of partially cracked ammonia as a replacement fuel for natural gas is evaluated. A high-pressure optically accessible combustor operated at ~10 bar pressure with non-vitiated heated air was used as the test platform. This combustor was equipped with a novel multifuel multi-tube micromixing injector (M3 injector). High resolution simulations of the flow fields both in the injector as well as the combustor were performed to understand the fuel mixing in the injector and in the inlet region of the combustor. These modeling results showed that the injector was highly effective for achieving mixture homogeneity at the entrance of the combustor for ammonia and natural gas injection, whereas, for hydrogen an earlier injection facilitating longer residence time would be beneficial. Also, these simulations show that while harnessing waste heat the ideal fuel dissociation fraction is between 0.4 and 0.55 for optimal operation.

Ammonia↗
DOE OSTI2025

Exceptional points in a passive strip waveguide

Abstract Exceptional points (EPs) in non‐Hermitian systems have attracted significant interest due to their unique behaviors, including novel wave propagation and radiation. While EPs have been explored in various photonic systems, their integration into standard photonic platforms can expand their applicability to broader technological domains. In this work, we propose and experimentally demonstrate EPs in an integrated photonic strip waveguide configuration, exhibiting unique deep wave penetration and uniform‐intensity radiation profiles. By introducing the second‐order grating on one side of the waveguide, forward and backward propagating modes are coupled both directly through second‐order coupling and indirectly through first‐order coupling via a radiative intermediate mode. To describe the EP behavior in a strip configuration, we introduce modified coupled‐mode equations that account for both transverse and longitudinal components. These coupled‐mode formulas reveal the formation of EPs in bandgap closure, achieved by numerically optimizing the grating’s duty cycle to manipulate the first‐ and second‐order couplings simultaneously. Experimental observations, consistent with simulations, confirm the EP behavior, with symmetric transmission spectra and constant radiation profiles at the EP wavelength, in contrast to conventional exponential decay observed at detuned wavelengths. These results demonstrate the realization of EPs in a widely applicable strip waveguide configuration, paving the way for advanced EP applications in nonlinear and ultrafast photonics, as well as advanced sensing technologies.

Materials Science↗
DOE OSTI2017

A Unified Data-Driven Approach for Programming In Situ Analysis and Visualization: An Interim Report of Sandia Sub-Team Contributions

As we look ahead to next generation high performance computing platforms, the placement and movement of data is becoming the key-limiting factor on both performance and energy efficiency. Furthermore, the increased quantities of data the systems are capable of generating, in conjunction with the insufficient rate of improvements in the supporting I/0 infrastructure, is forcing applications away from the off-line post-processing of data towards techniques based on in ,situ analysis and visualization. Together, these challenges are shaping how we will both design and develop effective, performant and energy-efficient software. In particular, the challenges highlight the need for data and data-centric operations to be fundamental in the reasoning about, and optimization of, scientific workflows on extreme-scale architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Ultra-Low SWaP CO 2 Sensing for Demand Control Ventilation (Final Report)

In this project PARC and Energy ETC aimed to develop an ultra-low cost, size, weight, and power (SWaP) printed CO 2 sensor system for occupancy detection to enable Demand Control Ventilation (DCV) on a per-room basis. The CO 2 sensor technology is based on the temperature variation when CO 2 reversibly physisorbs to a highly conductive and high surface area sorbent surface, and is compatible with integration with PARC innovations in printed sensors and flexible electronics, for which PARC is a globally leading research center. The printed CO 2 sensor itself is designed to be compatible with PARC’s “peel-and-stick” platform of ultra-low power, low-cost, distributed sensors, and to facilitate real-time DCV based on overall indoor air quality (IAQ). Previously, PARC has developed flexible hybrid electronics (FHE) compatible materials to measure humidity, temperature, light, strain, and gases such as carbon monoxide, methane, ammonia, and hydrogen sulfide. Through this project, PARC developed FHE-compatible materials to measure CO 2 . Thus, with one <$15 FHE “peel-and-stick” based sensor node, a building management system (BMS) will be able to capture a complete picture of the indoor environment. This includes IAQ, light, temperature, and other comfort factors that impact building operations. Combined with optimized DCV, this low-cost sensor capability can be a key enabler of annual primary energy savings of ~ 0.3-0.4 Quad in commercial buildings while ensuring healthy IAQ. Energy ETC is a leader in supplier-agnostic BMS deployments and will design the commissioning and deployment procedures to maximize system interoperability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Butyric Acid Production from Delignified Corn Stover Using Thermophilic Bacterial Co-Cultures

Butyric acid (BA) is a valuable platform chemical in the food and pharmaceutical industries, and it is also a potential precursor for the production of biobutanol and sustainable aviation fuels (SAFs). BA is mainly synthesized from petroleum; thus, cost-effective, and sustainable alternatives for its production are attracting the interest of several sectors. The present work proposes a solids-to-acids bioprocess to produce BA from corn stover utilizing a co-culture that consists of two thermophilic bacteria, Clostridium thermocellum, a well-known efficient degrader of insoluble and oligomeric cellulosic substrates, and Clostridium thermobutyricum, a highly efficient BA producer from monomeric sugars. After initial proof of concept experiments, a series of optimization studies were carried out to evaluate the process limits of this co-culture. First, the co-cultivation of both microorganisms at different inoculum sizes in deacetylated and mechanically refined corn stover (DMR) was evaluated. No significant differences were found on the solids deconstruction and carbohydrates utilization among all the treatments. In addition, BA production was similar under all conditions, ranging between 2.1 and 2.4 g/L. Next, the deconstruction, and BA production capabilities of the co-culture at increased DMR solids contents (3, 4.5, and 6% (w/v)) were tested. Although no difference was observed in the solids deconstruction and carbohydrates utilization among treatments, BA production increased concomitantly to solids loading; the maximum values observed were 2.8, 3.6, and 6.2 g/L in the 3, 4.5, and 6% treatments, respectively. In fact, BA production did not reach its absolute maximum in the 6% treatment after 160 h of fermentation. Lastly, a fed-batch experiment was performed to investigate the co-culture capabilities and possible system constraints to achieve higher BA titers. Data on substrate modifications and product formation as well as on the growth of each microorganism will be shown. Results from this work demonstrate a promising bioprocess approach for the production of BA from lignocellulosic biomass using a thermophilic bacterial co-culture.

bacteria↗
DOE OSTI2022

SOMAS: a platform for data-driven material discovery in redox flow battery development

Abstract Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe route to large-scale energy storage. The energy density is one of the key performance parameters of organic redox flow batteries, which critically depends on the solubility of the redox-active molecule in water. Prediction of aqueous solubility remains a challenge in chemistry. Recently, machine learning models have been developed for molecular properties prediction in chemistry and material science. The fidelity of a machine learning model critically depends on the diversity, accuracy, and abundancy of the training datasets. We build a comprehensive open access organic molecular database “Solubility of Organic Molecules in Aqueous Solution” (SOMAS) containing about 12,000 molecules that covers wider chemical and solubility regimes suitable for aqueous organic redox flow battery development efforts. In addition to experimental solubility, we also provide eight distinctive quantum descriptors including optimized geometry derived from high-throughput density functional theory calculations along with six molecular descriptors for each molecule. SOMAS builds a critical foundation for future efforts in artificial intelligence-based solubility prediction models.

25 ENERGY STORAGE↗
DOE OSTI2026

From Cell to System: Accelerated hpc Simulations of BESS Aging under Frequency Regulation and Arbitrage use cases

Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.

Ayalasomayajula, Surya [ORNL] (ORCID:0009000860788↗
DOE OSTI2024

Virtual Resource Management Framework (CRADA Final Report)

There is a need for advanced and widespread business automation within the nuclear industry to drive down operational costs while sustaining or improving safe operations. While there are many business process automation platforms commercially available, the difficulty is that business processes typically rely on a mixture of resource types to accomplish the desired activities and there is no universal software framework virtualizing diverse resource types for the purpose of automation. In this context, we are referring to any capability, physical or intangible, that can be used by an organization to achieve its objectives as a resource. To deploy business automation broadly and enable integrated operations for nuclear (ION), a framework is needed to represent all resource types and their associated disparate data within a plant and to enable seamless flow of resource information to the technologies used for process automation and resource optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Anaerobic Fungal Mevalonate Pathway Genomic Biases Lead to Heterologous Toxicity Underpredicted by Codon Adaptation Indices

Anaerobic fungi are emerging biotechnology platforms with genomes rich in biosynthetic potential. Yet, the heterologous expression of their biosynthetic pathways has had limited success in model hosts like E. coli. We find one reason for this is that the genome composition of anaerobic fungi like P. indianae are extremely AT-biased with a particular preference for rare and semi-rare AT-rich tRNAs in E coli, which are not explicitly predicted by standard codon adaptation indices (CAI). Native P. indianae genes with these extreme biases create drastic growth defects in E. coli (up to 69% reduction in growth), which is not seen in genes from other organisms with similar CAIs. However, codon optimization rescues growth, allowing for gene evaluation. In this manner, we demonstrate that anaerobic fungal homologs such as PI.atoB are more active than S. cerevisiae homologs in a hybrid pathway, increasing the production of mevalonate up to 2.5 g/L (more than two-fold) and reducing waste carbon to acetate by ~90% under the conditions tested. This work demonstrates the bioproduction potential of anaerobic fungal enzyme homologs and how the analysis of codon utilization enables the study of otherwise difficult to express genes that have applications in biocatalysis and natural product discovery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

High-order algorithmic developments and optimizations for large-scale GPU-accelerated simulations (Milestone CEED-MS36)

The goal of this milestone was to improve the high-order software ecosystem for CEED-enabled ECP applications by making progress on efficient matrix-free kernels targeting forthcoming ECP architectures. These kernels included matrix-free preconditioning and the development of new set of CEED solver bake-off problems. As part of this milestone, we also released the next version of the CEED software stack, CEED-4.0, reported on results from several application collaborations, and documented the efforts of porting to AMD GPUs for Frontier and other modern architectures, such as Fugaku. The specific tasks addressed in this milestone were: (1) Port and run CEED benchmarks/miniapps on Frontier EA systems; (2) Demonstrate performant libCEED integration in MFEM, Nek and applications; (3) Matrix-free preconditioning of high-order operators; (4) Benchmark problems for fast high-order solvers on GPU platforms; and (5) Public release of CEED-4.0. The artifacts delivered include the next version of the CEED software stack, CEED-4.0, the next libCEED release, libCEED-0.8, and a number of developments integrated within applications to improve their GPU and CPU performance and capabilities. See the CEED website, https://ceed.exascaleproject.org and the CEED GitHub organization, https://github.com/ceed for more details.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Simulating non-native cubic interactions on noisy quantum machines

As a milestone for general-purpose computing machines, we demonstrate that quantum processors can be programed to efficiently simulate dynamics that are not native to the hardware. Moreover, on noisy devices without error correction, we show that simulation results are significantly improved when the quantum program is compiled using modular gates instead of a restricted set of standard gates. We demonstrate the general methodology by solving a cubic interaction problem, which appears in nonlinear optics, gauge theories, as well as plasma and fluid dynamics. To encode the non-native Hamiltonian evolution, we decompose the Hilbert space into a direct sum of invariant subspaces in which the nonlinear problem is mapped to a finite-dimensional Hamiltonian simulation problem. Furthermore, in a three-states example, the resultant unitary evolution is realized by a product of approximately 20 standard gates, using which approximately ten simulation steps can be carried out on state-of-the-art quantum hardware before results are corrupted by decoherence. In comparison, the simulation depth is improved by more than an order of magnitude when the unitary evolution is realized as a single cubic gate, which is compiled directly using optimal control. Alternatively, parametric gates may also be compiled by interpolating control pulses. Modular gates thus obtained provide high-fidelity building blocks for quantum Hamiltonian simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗
DOE OSTI2023

FORCE Integration with DRAFT and IDAES

Integrated energy systems (IES) combine, in mutually beneficial ways, power from variable renewable energy sources and nuclear power plants (NPP) to produce multiple commodities and improve economic viability under uncertain market conditions. Technical and economic analysis of IES requires modeling of complex processes with software models having enough fidelity to capture real-world dynamics while still being capable of running using reasonable computing resources. The open-source Framework for Optimization of Resources and Economics (FORCE) tool suite, developed at Idaho National Laboratory (INL), has enabled comprehensive modeling and simulation of IES. The capabilities within FORCE include grid portfolio optimization through the Holistic Energy Resource Optimization Network (HERON) and the transient process model analysis library HYBRID, among others. Recent developments for the FORCE toolset have centered on strengthening the flexibility and modularity to link with external models and other available software to enhance IES simulations. Adding versatility to the FORCE toolset improves capability resulting in better techno-economic simulation of nuclear and IES components (both in potential higher fidelity and accuracy to expected performance after deployment). It also helps leverage existing work in the IES field and improve efficiency in national code development. This report focuses on two such endeavors: integration of the Dynamic Reliability Analysis Framework Tool (DRAFT) and the Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) software packages into FORCE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cooperative and bifunctional Ga-Ca-Cr 2 O 3 @CaO structured monoliths as versatile platform for reactive capture of CO 2 and its subsequent conversion to ethylene

Cooperative and bifunctional materials (BFMs) that integrate adsorbents and catalysts offer a promising strategy for the reactive capture of CO 2 to produce valuable fuels and chemicals. In this study, we developed structured BFMs via 3D printing that combine CaO as an adsorbent with Ga–Ca–Cr 2 O 3 metal oxides as the catalyst for the reactive capture of CO 2 and its subsequent conversion to C 2 H 4 via the oxidative dehydrogenation of C 2 H 6 (CO 2 -ODHE). Three different Ga–Ca compositions were used to modify the catalyst surface characteristics and enhance C 2 H 4 selectivity. In these formulations, Ga ions stabilize the oxygen lattice of the BFM, while Ca ions interact strongly with Cr to form CaCrO 4 , thereby altering the oxygen species and enhancing the material’s basic properties. Under adsorption–reaction conditions at 600–650 °C, the optimal BFM achieved an excellent C 2 H 4 selectivity of 96.4 %, attributed to a balanced redox process and improved basicity that facilitate efficient C 2 H 6 conversion and rapid desorption of C 2 H 4 without excessive oxidation. Overall, this work provides new insights into the formulation of BFMs monoliths and highlights the critical role of catalytic surface modification in enhancing C 2 H 4 selectivity in the CO 2 -ODHE reactive capture process.

C2H4 production↗