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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Atomistic Simulations for Thermophysical Properties of Uranium-Containing Halide Molten Salts

Characterizing the thermophysical properties in both fuel and coolant salts are critical in modeling, developing, process optimizing and utilizing molten salt reactors (MSRs), as these properties directly relate to operation metrics and can inform on the selection of candidate salts. The demand for consistent, accurate and publicly available thermophysical property data has become more apparent in recent years as interests have increased from molten salt reactor developers. There are a number of challenges in experimentally measuring properties such as thermal conductivity, viscosity, density and heat capacity , which have led to sparse and often times conflicting data points or molten salts in general. Additionally, there are a number of hazards to consider when synthesizing, storing, using, treating and disposing of molten salts. With the advances in computational capabilities over the last 10 years, the use of atomistic simulations can be implemented to support these efforts. The primary objective of this work is characterize the thermophysical transport properties in a number of molten chloride salts, and in particular NaCl-UCl 3 using ab-initio molecular dynamic (AIMD) simulations. In this binary salt the UCl 3 acts as the primary fissile material and NaCl acts as a carrier salt due with its’ high solubility for actinides A number of studies on the thermophysical properties of NaCl-UCl 3 have been published but there is not a vast amount of viscosity data for this system. In 1975, Desyatnik, et al published a study reporting dynamic viscosities that were calculated from kinematic viscosity measurements, and using the coefficients provided the viscosity in a 70:30 NaCl:UCl 3 mixture is 2.29 cP and 2.88 for a 60:40 mixture. Termini et al. recently reported viscosities in the range of 2.75 – 3 cP for the 63:37 NaCl-UCl 3 mixture in the same temperature range using rolling ball viscosity measurements. Computational viscosity of a similar mixture (64:36) can be obtained from the work Andersson et al. using the reported diffusion coefficients, and the hydrodynamic radius from the pair-radial distribution functions (RDFs). Using Eq (1) (vida infra), the viscosity would be 2.50 cP at 1100K. This is not to say that these values are incorrect due to the varying reported values, but aims to highlight the necessity of this work. The data reported in this ongoing work are computations on a 64:36 mixture of NaCl-UCl 3 at 987K. This work is likely to be expanded into varying concentrations of this mixture along with the inclusion of other salt candidate mixtures.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Photonic circuits for laser stabilization with integrated ultra-high Q and Brillouin laser resonators

The integration of stabilized lasers, sources that generate spectrally pure light, will provide compact, low-cost solutions for applications including quantum information sciences, precision navigation and timing, metrology, and high-capacity fiber communications. We report a significant advancement in this field, demonstrating stabilization of an integrated waveguide Brillouin laser to an integrated waveguide reference cavity, where both resonators are fabricated using the same CMOS-compatible integration platform. We demonstrate reduction of the free running Brillouin laser linewidth to a 292 Hz integral linewidth and carrier stabilization to a 4.9 × 10 –13 fractional frequency at 8 ms reaching the cavity-intrinsic thermorefractive noise limit for frequencies down to 80 Hz. We achieve this level of performance using a pair of 56.4 x 10 6 quality factor Si 3 N 4 waveguide ring-resonators that reduce the high-frequency noise by the nonlinear Brillouin process and the low-frequency noise by Pound–Drever–Hall locking to the ultra-low loss resonator. These results represent an important step toward integrated stabilized lasers with reduced sensitivity to environmental disturbances for atomic, molecular, and optical physics (AMO), quantum information processing and sensing, and other precision scientific, sensing, and communications applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Effects of noise on the overparametrization of quantum neural networks

Overparametrization is one of the most surprising and notorious phenomena in machine learning. Recently, there have been several efforts to study if, and how, quantum neural networks (QNNs) acting in the absence of hardware noise can be overparametrized. In particular, it has been proposed that a QNN can be defined as overparametrized if it has enough parameters to explore all available directions in state space. That is, if the rank of the quantum Fisher information matrix (QFIM) for the QNN's output state is saturated. Here, we explore how the presence of noise affects the overparametrization phenomenon. Our results show that noise can “turn on” previously zero eigenvalues of the QFIM. This enables the parametrized state to explore directions that were otherwise inaccessible, thus potentially turning an overparametrized QNN into an underparametrized one. For small noise levels, the QNN is quasioverparametrized, as large eigenvalues coexists with small ones. Then, we prove that as the magnitude of noise increases all the eigenvalues of the QFIM become exponentially suppressed, indicating that the state becomes insensitive to any change in the parameters. As such, there is a pull-and-tug effect where noise can enable new directions but also suppress the sensitivity to parameter updates. Finally, our results imply that current QNN capacity measures are ill-defined when hardware noise is present. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hydropower flexibility valuation tool for flow requirement evaluation

Timing of generation is becoming more and more valuable. This creates greater potential tension between environmental and power system objectives since both systems require their own flow patterns. Identifying win–win outcomes in this context requires being able to discuss the value of flexibility across stakeholder groups. This research proposes a two-stage optimization method to understand hydropower flexibility to meet both environmental and power system requirements. The tool simulates the two-settlement market process in the U.S. by maximizing revenues from both the day-ahead and real-time markets, subject to plant operational limits, regulatory flow and ramping requirements, and uncertainties associated with water availability and market prices. The model is formulated as linear programming problems and solved using IBM ILOG CPLEX optimizer. By examining a range of flow requirements, ramping constraints, and storage capacities, the proposed tool shows how to make more informed decisions to weigh the cost of specific flow requirements in the context of the overall license requirements. Results from the case study show that revenue is more sensitive to the ramping constraints than the minimum flow constraints. We also demonstrate that removing flow constraints in a dry month increases monthly revenue by up to 118%, as opposed to only 1% in a wet month. In addition, our results suggest that using a learning-based water flow forecast results in an increase of monthly revenue up to 6.4% compared with persistence forecast.

13 HYDRO ENERGY↗
DOE OSTI2025

Passive Environment-Assisted Quantum Communication with GKP States

Bosonic pure-loss channel, which represents the process of photons decaying into a vacuum environment, has zero quantum capacity when the channel’s transmissivity is less than 50%. Modeled as a beam splitter interaction between the system and its environment, the performance of bosonic pure-loss channel can be enhanced by controlling the environment state. We show that by choosing the ideal Gottesman-Kitaev-Preskill (GKP) states for the system and its environment, perfect transmission of quantum information through a beam splitter is achievable at arbitrarily low transmissivities. Our explicit constructions allow for experimental demonstration of the improved performance of a quantum channel through passive environment assistance, which is potentially useful for quantum transduction where the environment state can be naturally controlled. In practice, it is crucial to consider finite-energy constraints, and high-fidelity quantum communication through a beam splitter remains achievable with GKP states at the few-photon level.

Wang, Zhaoyou [Univ. of Chicago, IL (United States↗
DOE OSTI2022

What you get is not always what you see—pitfalls in solar array assessment using overhead imagery

Effective integration planning for small, distributed solar photovoltaic (PV) arrays into electric power grids requires access to high quality data: the location and power capacity of individual solar PV arrays. Unfortunately, national databases of small-scale solar PV do not exist; those that do are limited in their spatial resolution, typically aggregated up to state or national levels. While several promising approaches for solar PV detection have been published, strategies for evaluating the performance of these models are often highly heterogeneous from study to study. The resulting comparison of these methods for practical applications for energy assessments becomes challenging and may imply that the reported performance evaluations overly optimistic. The heterogeneity comes in many forms, each of which we explore in this work: the degree of diversity of the locations and sensors (e.g. different satellites, aerial photography) from which the training and validation data originate, the validation of ground truth (manual annotation of imagery vs known solar PV locations), the level of spatial aggregation (e.g. array-level vs regional estimates), and inconsistencies in the training and validation datasets (e.g. different datasets are used for each study and those data are not always made accessible). For each, we discuss emerging practices from the literature to address them or suggest directions of future research. As part of our investigation, we evaluate solar PV identification performance in two large regions: the entire state of Connecticut and the city of San Diego, CA. In Connecticut, we also use 33,114 known parcel-level solar PV installations from Berkeley Lab’s Tracking the Sun dataset to evaluate parcel-level performance and evaluate capacity estimates using 169 municipalities. We also make our code (which we call SolarMapper), pre-trained models, training data, and predictions publicly available and provide a web portal for interactively inspecting each prediction that was made. Here our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process. The takeaways from this work are intended to inform and catalyze the large-scale practical application of automated solar PV assessment techniques by energy researchers and professionals.

14 SOLAR ENERGY↗
DOE OSTI2021

Oxygen redox chemistry in P2-Na 0.6 Li 0.11 Fe 0.27 Mn 0.62 O 2 cathode for high-energy Na-ion batteries

Owing to the abundance of raw material reserves and low cost, Na-ion batteries (NIBs) have successfully gained widespread attention from academic and industrial communities in the past few decades. However, the insufficient cathode energy density is still one of the critical bottlenecks restricting the development of NIBs. Following a strategy of introducing Li+ into the transition-metal (TM) layer to enhance the oxygen redox reaction, a novel layered cathode material P2-Na 0.6 Li 0.11 Fe 0.27 Mn 0.62 O 2 (NLFMO) was designed and successfully synthesized. This NLFMO cathode not only delivers a large initial reversible capacity of 207.3 mA h g -1 , but also shows a good cycling performance (104.2 mA h g -1 after 80 cycles) and rate capability (126.2 mA h g -1 at 1C). The ultrahigh capacity is contributed by both cationic (Fe 3+ /Fe 4+ and Mn 3+ /Mn 4+ ) and partially reversible anionic redox (O 2- /O n- ) reactions, revealed by in situ X-ray absorption spectroscopy (XAS) and X-ray photoelectron spectroscopy (XPS) techniques. Moreover, no detrimental P2–O2 phase transition was observed in ex situ X-ray diffraction (XRD) patterns, confirming the high structural stability during Na + deintercalation/intercalation processes. These results provide valuable information about the high-energy density layered cathode materials based on anionic redox reactions for NIBs.

25 ENERGY STORAGE↗
DOE OSTI2020

A database of battery materials auto-generated using ChemDataExtractor

A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.

25 ENERGY STORAGE↗
DOE OSTI2020

Deconstructing the Soil Microbiome into Reduced-Complexity Functional Modules

The soil microbiome is an invaluable component of the biosphere and critical for ecosystem functions, including biogeochemical cycling, soil-atmosphere gas exchange, degradation of toxic compounds, and promotion of plant growth and stress resistance/resilience. An improved understanding of the soil microbiome will help with predicting how these processes respond to external perturbations, and with harnessing beneficial aspects of the soil microbiome for agronomic applications such as crop amendments. However, the extensive taxonomic and functional diversity inherent within the soil microbiome hinders efficient analysis of this system. Microbial biodiversity in soils is orders of magnitude greater than other commonly studied systems such as the human gut microbiome (Blum, Zechmeister-Boltenstern, and Keiblinger 2019; Berendsen, Pieterse, and Bakker 2012). Thousands of microbial taxa may be found in a single gram of soil (Roesch et al. 2007), and high rates of gene flow and mutation further promote microbial diversification (Sergaki et al. 2018). Concomitantly, functional diversity in soil is similarly extensive. Soil is a heterogeneous mixture of microenvironments with defined physical and chemical attributes (Bach et al. 2018), within which numerous microbial guilds of distinct life-strategies and metabolic capacities can be found (Perez-Garcia, Lear, and Singhal 2016; H.-S. Song et al. 2014). Furthermore, the soil microbiome harbors a significant fraction of rare and/or quiescent members (Blagodatskaya and Kuzyakov 2013) that exist below the threshold of detection of current technologies. Such rare taxa may make significant contributions to process rates (Shade and Gilbert 2015; Dawson et al. 2017), but their scarcity complicates their identification and analysis. Finally, the vast amounts of information generated through holistic analyses of soil communities represent an intense computational burden (Scholz, Lo, and Chain 2012; Prosser 2015) that precludes assessment of the complete functional and taxonomic diversity contained in this ecosystem.

Naylor, Dan T.↗
DOE OSTI2026

Predicting Li-Ion Battery Capacity Fade Using Early-Life Data and a Hybrid Data-Driven Gaussian Process-Bayesian Regression Approach

Accurately predicting Li-ion battery capacity trajectories using early-life data can dramatically improve battery-life understandings and be used to rapidly evaluate design/cost/performance trade-offs when developing new battery materials. Accurate early-life predictions enable researchers to quickly iterate over cell designs and material precursor properties without consistently cycling cells to failure. To this end, we present a toolbox that uses a combined Gaussian Process and Bayesian regression approach that capitalizes on signals other than just capacity (e.g., dQ/dV, voltage drops) to rapidly predict capacity-fade trajectories. The prediction tool uses Bayesian regression to fit functional forms, e.g., power law, sigmoids, etc., to predict capacity-fade dynamics. By fitting functional forms, the capacity fade can be interrogated at any point in the future, allowing for early cell-failure prediction. Additionally, Bayesian regression allows for accurate uncertainty estimates that account for cell-to-cell variability (aleatoric uncertainty) and the lack of observation data (epistemic uncertainty). By only using early cycle data to predict the capacity fade trajectory, uncertainty bounds at end-of-life can be extremely large. The large uncertainty bounds are further exacerbated because there is no systematic way to define the prior distribution of the functional forms' parameters. We improve our the predicted trajectory confidence interval of our predicted trajectory using two methods. First, we shows that a small amount of held-out cycling data is sufficientuse some train cells, that have been cycled to failure to derive information regarding the appropriate prior distributions for the functional forms' parameters of the functional form, effectively leading to data-driven priors.. We propose constructing the data-driven priors by first running a Bayesian regression starting with uninformed priors to generate intermediate cell-specific posterior parameter distributions. These posterior distributions are combined using a Ggaussian mixture model for each parameter to create the data-driven priors. These mixture models serve as the data-driven prior distributions for the parameters for. Second, we derive multiple features, e.g., C_dchg 0.5 DoD 0.5, log (|mean(dQ/dV_(w_3-w_0 ) (V)|), etc., from the train cellsheld-out cycling data, identify which the features are that best predicting capacity at early/mid-life cycles, and then create Ggaussian process regression models that are used for predicting capacity at early/mid-life cycles for the test cells (see blue dots with error bars in Fig 1b). Finally, these predicted data-points are used in addition to the actual early cycle data capacity fade to construct the Bayesian regression trajectory for the test cell s. Notably. We note that these two methods are complementary and can be combined with each other. We evaluate the performance of our proposed method on an testing open-source dataset from Iowa State University and Iowa Lakes Community College (ISU-ILCC). This dataset comprises of 251 nickel-manganese-cobalt/graphite Lithium-ion cells that are cycled under 63 different conditions. We compute the mean average percentage error (MAPE) and negative log predictive density (NLPD) to quantify the efficacy of our method. Our initial findings suggest that, when only few observations are available, for test cells, when using only Bayesian regression with uninformed priors, a power law functional provides the most accurate predictions. with very few data points. However, asHowever, a the number of data points increases, a twin sigmoidal function becomes more accurate as the number of observations further increases. We also find that using as little as 10% of the data set towards generating data-driven priors can lead to significant improvement in prediction accuracy when using early cycle data. Lastly, we found that augmenting early-cycle data with Gaussian process-predicted capacity data for Bayesian regression greatly improves the prediction accuracy. We will present a comprehensive comparison of our methods to other methods available in the literature and apply this method to additional battery datasets.

42 ENGINEERING↗
DOE OSTI2024

Grid connection barriers to renewable energy deployment in the United States

Bulk-power grid connection is an emerging bottleneck to the entry of wind, solar, and storage but has been understudied due to a lack of data. We create and analyze two novel interconnection datasets with more than 38,000 project-level observations that provide new information documenting interconnection challenges in the United States. Active grid connection requests are more than double the total installed capacity of the US power plant fleet (2,600 vs. 1,280 GW). The time required to secure a connection has increased by 70% over the last decade, and withdrawal rates remain high at 80%, suggesting a constrained transmission system that jeopardizes energy transition targets. Wide distributions of interconnection costs indicate the inherent uncertainty of the interconnection process. Interconnection requests that identify large transmission upgrades tend to withdraw from the process. These findings suggest the need for interconnection reforms, tighter links between long-term transmission planning and project-level interconnection processes, and more interconnection outcome transparency.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Collaborative Design of Robust Adaptation Pathways for Urban Flooding Resilience in Baltimore

Urban pluvial flooding poses a growing threat to the city of Baltimore, driven by changes in climate and rainfall, increased impervious area, aging infrastructure, and severe social inequity. Adaptations to urban flooding should strengthen community-scale resilience while navigating the uncertainties associated with future actions. However, the highly uncertain future climate and conflicting stakeholder objectives complicate the selection of effective and socially acceptable strategies. This study addresses these challenges through a community-informed Multi Objective Robust Decision Making (MORDM) process, coupled with Dynamic Adaptation Policy Pathways (DAPP). Community stakeholders are engaged on a large scale through community partnerships, including priority identification sessions, focus groups, and metric identification workshops. The combination of community partnership, MORDM, and DAPP will create an original set of adaptation pathways which are aligned with community goals and values, and furthermore are adjustable under changing future constraints and conditions. This framework will enhance the city's capacity to respond to evolving pluvial flood conditions while accounting for diverse stakeholder interests and will provide guidance for addressing both the immediate and long-term challenges of urban flooding in Baltimore.

BSEC↗
DOE OSTI2026

Greenhouse Gas Life Cycle Emissions Assessment Model (GLEAM) Model Documentation

The Greenhouse gas Life cycle Emissions Assessment Model (GLEAM) estimates life cycle greenhouse gas emissions from future scenarios of electricity generation considering a wide range of generation technologies. Building on the National Laboratory of the Rockies longstanding effort to quantify life cycle emissions by electricity generation technology under the LCA Harmonization Project, GLEAM streamlines the process of estimating cumulative greenhouse gas emissions on a life cycle basis. Given a set of inputs regarding annual installed and decommissioned generation capacity, as well as generation, GLEAM estimates the carbon dioxide equivalent emissions by year. The model also offers optional modules to decompose carbon dioxide equivalent emissions into constituent greenhouse gases (e.g., carbon dioxide, methane, and nitrous oxide) as well as estimate hydrogen leakage from relevant technologies. The results from GLEAM can be used to inform future electricity planning scenarios as well as investment or regulatory decisions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Mapping Support for Targeted Critical Minerals Exploration and Extraction

The United States’ dependency on imported minerals poses significant risks to economic stability and national security due to potential supply disruptions. Recognizing the strategic importance of critical minerals, the Department of Energy (DOE) emphasizes the need for a secure and resilient supply chain to support emissions reduction, technology development, and capitalization on clean energy opportunities. The DOE’s Office of Manufacturing and Energy Supply Chains (MESC), in collaboration with the Office of Policy (OP), addresses these vulnerabilities by focusing on upstream domestic critical minerals production, balancing extraction with social and environmental goals, including conservation, environmental justice, and respect for Tribal sovereignty. This report showcases a collaborative effort involving Idaho National Laboratory (INL), Argonne National Laboratory (Argonne), National Renewable Energy Laboratory (NREL), and the U.S. Geological Survey (USGS) to map mineral development potential along with key social and environmental datasets. A geographical information system (GIS)-based web map application was developed as a preliminary tool for environmental analysis, integrating 158 geospatial data layers such as critical habitat, land ownership, economic indicators, and environmental concerns. Data were sourced from agencies like the Bureau of Land Management (BLM) and USGS and processed using GIS technology to enhance visualization and analysis. The proposed analysis framework categorizes areas into high, mid, and low concern based on withdrawn lands, special status species, the Economic Development Capacity Index (EDCI) Mining Composite Index, and the Climate and Economic Justice Screening Tool (CEJST). While the application provides broad visualizations, it is not a substitute for detailed environmental reviews required under the National Environmental Policy Act (NEPA). Users must conduct further analyses and engage with tribal entities and other stakeholders for comprehensive planning. A case study of the Idaho Cobalt Belt (ICB) in Lemhi County, Idaho, has been provided in the report to illustrate the tool's practical use. This report introduces a GIS application and framework to support stakeholders in identifying and prioritizing areas for critical mineral exploration, promoting secure supply chains, and advancing the nation's energy independence through responsible resource stewardship.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Carbon Capture Pilot at Vicksburg Containerboard Mill (Final Technical Report)

This is the final technical report for the DOE-OCED project DE-CD0000051. The report provides the progress at the close of the project. The objective of the project is to design and build a large pilot plant for carbon dioxide (CO 2 ) capture from a pulp and paper (P&P) mill using RTI’s non-aqueous solvent (NAS) technology at a capacity of 120,000 t-CO 2 /year, with >90% CO 2 captured. The pilot plant will be used for testing and evaluating the NAS capture process using real flue gas from the P&P mill’s power boiler for a minimum of 1 year of parametric testing and a minimum of 2,000 hours of continuous long-term testing. The testing will provide data for process optimization and scale-up for the P&P industry, provide information on interaction of flue gas contaminants on solvent performance and degradation, and inform strategies for emission control. At the end of the project, the pilot plant will be managed by International Paper (IP), the host site owner, which will continue to capture CO 2 , sequester the captured CO 2 , and be eligible for the 45Q credit.

42 ENGINEERING↗
DOE OSTI2025

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗
DOE OSTI2024

Hydrogen Storage for Load-Following and Clean Power: Duct-firing of Hydrogen to Improve the Capacity Factor of NGCC Plants (Final Report, Phase II Pre-Front End Engineering Design Study)

GTI Energy (GTI) and team members Southern Company Services (SCS), Pacific Gas & Electric (PG&E) and the Electric Power Research Institute (EPRI) performed a Phase II Pre-Feed Study under contract DE-FE0032008 for Hydrogen Storage for Load-Following and Clean Power. The configuration of the proposed system consists of subsystems for on-site H 2 production, on-site H 2 storage (up to 54 MWth in commercial vessels) and H 2 combustion in a duct burner in a Heat Recovery Steam Generator (HRSG) integrated with an existing fossil asset. Here, the firing rate of the duct burner is varied to let the plant respond to fluctuations of electrical load and H 2 production is relatively constant by storing H 2 . The proposed system is an improvement over alternate low carbon dispatchable power options. Technoeconomic analyses show H 2 produced with GTI’s patented Compact Hydrogen Generator (CHG) with inherent carbon capture will be lower cost (Levelized Cost of Hydrogen, LCOH) relative to hydrogen produced with a Steam Methane Reformer (SMR) with an amine system for carbon capture. This lower cost hydrogen enables our integrated system to deliver electricity (Levelized Cost of Electricity, LCOE) at 17.4% lower cost relative to a SMR sourced H 2 -fired HRSG (with an amine system for carbon capture) – Steam Turbine Generator (STG). Our analysis using EPRI’s US REGEN macroeconomic model shows our system will have significant demand in the power market and therefore require significant capacity expansion (CHG plants built to deliver hydrogen) to deliver low cost, low carbon power. In Phase II, our Team has completed a detailed system definition including the development of process models, definition of battery limits, Process Flow Diagrams (PFDs), Piping and Instrumentation Drawings (P&IDs), and a plant layout. A preliminary design for the key components of the CHG was developed including component lists & specifications. An evaluation of environmental and permitting considerations was completed and included the development of an Environmental Information Volume (EIV). The duct burners, which are flexible and can burn hydrogen and/or natural gas, were defined and initial CFD analyses were completed.

03 NATURAL GAS↗
DOE OSTI2020

Finite Element Analysis and Techno-economic Modeling of Solar Silicon Molten Salt Electrolysis

A new process is presented for low-cost one-step production of pure solid silicon from natural quartzite by molten salt electrolysis. At a process temperature of 1100°C, a techno-economic model including detailed mass and energy balances estimates energy consumption below 15 kWh/kg, with operating cost of $\$1.74$/kg and capital cost around $\$10,500$ per t/a (tonne annually) of production capacity for a 160,000 t/a plant. Use of an inert solid oxide membrane anode results in a pure oxygen by-product and no direct emissions. As a result, finite element analysis estimates the current density distribution and total current to inform the design of slab-shaped solid silicon cathodes.

14 SOLAR ENERGY↗