Search NASA⌕ Search

SEARCH · Search NASA

Results for “electricity use”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Impact of Heaters on Molten Salt Reactors Dynamics

Recently, there has been renewed interest in the Molten Salt Reactor (MSR) concept. This interest is mainly due to its advantages over Light Water Reactors (LWRs). Among these advantages are the flexibility in the fuel choice and the possibility to burn actinides [1]. Currently, two molten research reactors are under development. A 2 MW molten salt experimental reactor has been constructed and is planned for operation in China. A 1 MW Molten Salt Research Reactor is under construction in the United States [2]. Thus, Research and Development (R&D) programs are needed for the MSR technology. Reactor dynamics studies are crucial safety evaluation studies. The development of reactor dynamics tools for MSRs started during the Molten Salt Reactor Program (MSRP) at ORNL, focusing on the Molten Salt Reactor Experiment (MSRE) [3]. Although various dynamic simulation tools are available, R&D efforts to develop specialized tools for MSRs are still ongoing [4]. The primary motivation for this interest is the goal of commercializing MSRs. Developing a dynamics tool for MSRs is crucial for analyzing their safety and supporting their demonstration efforts. A crucial safety issue in MSRs is ensuring that the fuel salt in the primary loop remains molten. This concern becomes particularly important when the reactor operates at lower power levels. Most chloride and fluoride salts used as fuel have a melting point of approximately 450 °C [5]. Therefore, maintaining fuel salt temperatures above this melting point is essential to prevent it from freezing. Freezing of the fuel salt can lead to volume expansion, potentially causing damage to reactor components [6]. Moreover, it can create local blockages within fuel salt channels, reducing cooling efficiency and resulting in hot spots [7]. Various methods can prevent fuel salt from freezing at low power levels. These include reducing the fuel mass flow rate in the primary loop, decreasing heat exchange between the primary and secondary loops in the reactor, and using electric fuel salt heaters. Reducing the mass flow rate of fuel salt in the primary loop can increase the fuel salt transit time in the core, enhancing heat production and increasing temperature. However, it can potentially lead to challenges. These include increasing the potential for fuel salt deposition on the reactor channel walls [8], affecting the overall heat transfer processes. Thus, alternative methods should be used [9]. This research investigates the effects of fuel salt heaters on the dynamics and stability during MSR operation. The investigation includes studying the reactor stability with the fuel salt heaters on and off. In addition, the reactor response to transients is compared with and without the operation of fuel salt heaters. These transients include reactivity insertion, primary and secondary pump failure, and heat sink temperature increase. The results of the reactivity insertion transient are presented in the summary.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Energy Model to Evaluate Thermal Energy Storage Integrated with Air Source Heat Pumps: Preprint

Full decarbonization in buildings requires the replacement of combustion appliances with electric ones, and air source heat pumps (ASHP) are a candidate alternative. However, technical limitations, such as the efficiency decrease when operating in cold weather, limit their adoption in the global heating market. Among several options to improve ASHP efficiency operating in colder climates, thermal energy storage (TES) has been considered, as it may provide heating when it is cold and shift ASHP operation to times when the weather is warmer. It may also take advantage of time of use electricity rates and support defrosting when necessary. The evaluation of ASHP-TES systems, however, is still limited because traditional metrics do not capture their full economic and environmental benefits. In this work, a python framework is presented to model the ASHP with and without the presence of TES. Metrics are proposed to analyze the system performance in terms of costs, equivalent CO2 emission, and efficiency metrics to evaluate and compare alternative systems. Model validation against experimental data obtained for a commercial heat pump is provided, as well as an application example using Denver, Colorado, to highlight the model capabilities.

cold climate↗
DOE OSTI2025

Demand Response in Residential Energy Code: Technical Brief

As buildings account for over 75% of U.S. electricity use, effectively managing their loads can greatly facilitate the transition towards a clean, reliable grid. Grid-interactive efficient buildings (GEBs) combine efficiency and demand flexibility with smart technologies and communication to provide occupant comfort and productivity while serving the grid as a distributed energy resource (DER). In turn, GEBs can play a key role in ensuring access to an affordable, reliable, sustainable, and modern U.S. electric power system. Their national adoption could provide $\$$100-200 billion in U.S. electric power system cost savings over the next two decades. The associated reduction in CO 2 emissions is estimated at 6% per year by 2030 (DOE 2021). Building codes represent standard design practice in the construction industry and continually evolve to include advanced technologies and innovative practices. Historically, national model energy codes establish minimum efficiency requirements for new construction (ICC 2020). Expanding codes to support GEB capabilities is a pivotal step towards realizing demand flexibility in support of a clean grid by addressing capabilities to improve interoperability between smart building systems, the grid, and renewable energy resources. Realizing GEBs requires buildings with automated demand response (DR) capabilities that enable standardized communication with or control of, subject to explicit consumer consent, energy smart appliances or home energy management systems. This is achieved through direct or indirect (i.e., via an aggregator) communication between appliances and the electric grid. Energy codes can also support DR communication standardization and advance the deployment of building-integrated DERs such as energy storage, generation, and electric vehicles (EVs). Incorporating automated DR capabilities in energy codes provides many benefits to the consumers. Specifically, it aligns building electric load demand with intermittent renewable energy source availability, decreases peak load on the electric grid, allows buildings to respond to utility price signals, supports electrical network reliability and market growth of products and processes aligned with clean economic growth. The incorporation of DR into the model residential energy codes was considered for both the 2021 and 2024 International Energy Conservation Code (IECC) code development cycles. The approved DR measures in the 2021 cycle were removed in response to appeals (ICC 2020). Updated language was presented for consideration again for the 2024 IECC, where it was negotiated and again approved, and again removed in response to appeals (ICC 2024). This resulted in many sections, including sections on demand responsive controls, being moved to the credits options or an appendix as a voluntary application. This technical brief updates the proposed DR components such that they can be considered by states and local governments for direct incorporation into their codes, as well as for future IECC energy code development. The proposal refinements are intended to support consistency in approach and provide a degree of certainty for building owners, designers, contractors, manufacturers, and building and fire safety professionals. The scope of this technical brief includes three strategies for DR in residential buildings: 1) smart thermostats with demand-responsive control, 2) electric water heating incorporating demand-responsive controls and communication and 3) grid Integrated solar and energy storage systems.

2021 IECC↗
DOE OSTI2021

World Record Demonstration of > 30% Thermophotovoltaic Conversion Efficiency

Thermophotovoltaic (TPV) devices are solid-state heat engines that convert thermal radiation into electricity using semiconductor diodes, and have applications in energy storage, primary power conversion, and waste heat recovery. Higher temperature emitters and wider band gap photovoltaics (PVs) generally afford higher TPV efficiencies. High temperature emitters are accessible in thermal energy storage applications. We have demonstrated a world record 31% +/- 2% TPV conversion efficiency with a 0.9 cm2 GaAs-based PV device under a 2430 °C thermal emitter, producing an electrical output power of 2.23 W. Critical to the result was the cell’s high reflectance of photon energies below the device band gap. Unlike solar PV, for which sub-band gap (SBG) light is lost, a TPV cell can reflect and recycle SBG light to the thermal emitter. The demonstration was made on a custom-built measurement platform in which a ~100 cm2 graphite thermal emitter was heated under vacuum up to 2430 °C. The TPV efficiency was evaluated by comparing the measured electrical output power of the TPV with the calorimetrically measured total net power incident on the cell. The measured TPV efficiency as a function of thermal emitter temperature was corroborated by our full system modeling predictions. As far as the authors are aware, this is the highest TPV conversion efficiency ever measured, and device improvements should yield > 40% efficiency in the near future.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2023

Enrichment of H2 to CO2 ratio using formic acid as a hydrogen carrier - CRADA 581 (Abstract)

The collaboration between PNNL and OCOchem will investigate approaches to enhance the hydrogen (H2) content from a stream of H2 and carbon dioxide (CO2) gases produced in the catalytic decomposition of aqueous formic acid (FA85). OCOchem uses an electrochemical process to generate FA85 from captured CO2 using electricity from renewable resources. The FA85 is a liquid organic hydrogen carrier (LOHC) that provides the opportunity to transport and store hydrogen, in liquid form, at volumetric densities significantly greater than compressed H2 gas, i.e., 50 grams H2/liter FA. PNNL has developed and tested catalytic reactors to release H2 from LOHCs like FA and aqueous formate salts (FS). The H2 released from the LOHC can be oxidized in a proton-exchange membrane fuel cell (PEM-FC) to generate electricity with water as the only by-product. The purpose of the proposed project is to increase the purity of the hydrogen released from the LOHC to enhance the operation efficiency of the PEM FC. The PEM FC in combination with the LOHC provides an approach to demonstrate a portable generator that utilizes hydrogen as the energy carrier instead of conventional diesel generator. The ‘hydrogen generator’ can be used to supply emergency backup power and significantly reduce CO2 emissions relative to a diesel generator.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

An optoelectronic heterostructure for neuromorphic computing: CdS/V 3 O 5

Nonvolatile resistive switching is one of the key phenomena for emerging applications in optoelectronics and neuromorphic computing. In most of the cases, an electric field is applied to a two terminal dielectric material device and leads to the formation of a low resistance filament due to ion migration. However, the stochastic nature of the ion migration can be an impediment for the device robustness and controllability, with uncontrolled variations of high and low resistance states or threshold voltages. Here, we report an optically induced resistive switching based on a CdS/V 3 O 5 heterostructure which can overcome this issue. V 3 O 5 is known to have a second order insulator to metal transition around T c ≈ 415 K, with an electrically induced threshold switching at room temperature. Upon illumination, the direct transfer of the photoinduced carriers from the CdS into V 3 O 5 produces a nonvolatile resistive switching at room temperature. The initial high resistance can be recovered by reaching the high temperature metallic phase, i.e., temperatures above T c . Interestingly, this resistive switching becomes volatile around the T c . In conclusion, by locally manipulating the volatile and nonvolatile resistive switching using electric field and light, this system is a promising platform for hardware based neuromorphic computing implementations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Pinhole electrical conductivity in polycrystalline Si on locally etched SiN$_y$/SiO$_x$ passivating contacts for Si solar cells

State-of-the-art monocrystalline Si (c-Si) solar cells require passivating contacts to achieve a high degree of charge-carrier separation and collection. In this work, we focus on boron-doped polycrystalline Si on locally etched silicon nitride/silicon oxide (PLENO) passivating contacts. In PLENO contacts, excellent surface passivation is provided by the ~10 nm dielectric bilayer, while pinholes in the dielectric bilayer, that are filled with doped polycrystalline Si, provide charge-carrier selectivity and transport. During PLENO fabrication, etch undercut in the dielectric bilayer occurs. Here, using electrical characterization and microscopies, we show that undercut causes pinholes to be electrically resistive in PLENO. A processing sequence that eliminates the undercut in the final PLENO structure results in electrically conductive pinholes with low contact resistivity.

14 SOLAR ENERGY↗
DOE OSTI2026

AutoBEM: A scalable framework for nationwide building energy simulation and retrofit evaluation in the United States

This paper presents AutoBEM, an integrated, automated framework for nationwide building energy modeling and retrofit evaluation in the United States. Unlike prior UBEM platforms that either rely primarily on representative stock sampling or operate at city scale, AutoBEM automates the generation of building-resolved, physics-based EnergyPlus/OpenStudio simulation models at national scale using GIS-derived geometry, prototype-based assumptions, and standardized scalable workflows. Leveraging the Model America dataset and high-performance computing, AutoBEM generates and simulates energy models for 122.9 million buildings, representing 97.8% of the U.S. building stock. These models are being made publicly and freely available as the Model America v1.0 (MAv1) dataset. AutoBEM supports detailed, building-level assessments of energy consumption, CO2 emissions, and post-processed anthropogenic heat emissions (AHE), and evaluates 151 energy conservation measures (ECMs) using localized utility pricing and building characteristics. In addition, AutoBEM incorporates both typical and future climate conditions through integration with Typical Meteorological Year (TMY) and Future TMY (fTMY) weather data derived from IPCC scenarios. In a case study of Phoenix, Arizona, AutoBEM identified several high-efficiency HVAC upgrades and selected envelope measures with short modeled payback periods (1.5 years) for certain building types and standards. Simulations under future climate scenarios (SSP5–RCP8.5) project an 11.3% increase in electricity use and a 32% reduction in natural gas demand by 2100, underscoring the need for climate-adaptive retrofit planning. By enabling reproducible, bottom-up, and location-specific analysis at scale, AutoBEM provides a step toward a national digital twin of the built environment and supports data-driven screening and planning for decarbonization, resilience, and energy equity.

Li, Hang [ORNL] (ORCID:0000000306001920)↗
DOE OSTI2022

Long-run Marginal Emission Rates for Electricity - Workbooks for 2021 Cambium Data

These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. The LRMER is an estimate of the rate of emissions that would be either induced or avoided by a long-term (i.e., more than several years) change in electrical demand. It incorporates both the projected changes to the electric grid, as well as the potential for an incremental change in electrical demand to influence the structural evolution of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treats grid assets as fixed. The Levelized LRMER worksheet within each workbook is set up to produce a levelized long-run marginal emission rate based on user-provided inputs. These levelized LRMER values are intended for analysts to use when estimating the emissions induced (or avoided) by a long-term change in end-use electricity demand. There are two workbooks that supply the data at two different geographic resolutions: states and GEA regions (20 regions that are similar to, but not exactly the same as, the US EPA's eGRID regions). For more data underlying these emissions factors, see the Cambium 2021 project at https://cambium.nrel.gov/. For more details on the inputs into the scenarios available in the workbooks, see the Standard Scenarios 2021 Report (https://www.nrel.gov/docs/fy22osti/80641.pdf). This data was produced as part of the Cambium project. For more details about the methodology, see the Cambium Documentation: Version 2021 (https://www.nrel.gov/docs/fy22osti/81611.pdf). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nrel.gov/analysis/cambium.html.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Nonnuclear Experimental Capabilities to Support Design, Development, and Demonstration of Microreactors

This work provides a summary of selected experimental capabilities being developed to support nonnuclear testing and demonstration of technology in support of microreactors under the U.S. Department of Energy’s (DOE’s) Microreactor Program. Major capabilities include the Single Primary Heat Extraction and Removal Emulator (SPHERE) and the Microreactor Agile Non-nuclear Experimental Test Bed (MAGNET). The SPHERE facility allows for controlled testing of the steady-state and transient heat rejection capabilities of a single heat pipe using electrical heaters that simulate nuclear heating. The facility is capable of monitoring axial temperature profiles along the heat pipe and surrounding test articles during startup, steady-state operation, and transients. Instrumentation includes noncontact infrared thermal imaging, surface thermocouples, spatially distributed fiber optic temperature and strain sensors, electrical power meters, and a water-cooled, gas-gap calorimeter for quantifying heat rejection from the heat pipe. The facility can be operated under both vacuum and inert-gas conditions. The MAGNET facility is a large-scale, 250-kW electrically heated microreactor test bed to enable nonnuclear experimental evaluation of a variety of microreactor concepts. It can be supplied to electrically heat a scaled section of a microreactor and further test the capabilities of heat rejection systems. The initial MAGNET experiments will support technology maturation and reduce uncertainty and risk associated with the design, operation, and deployment of monolithic heat pipe–based reactors. However, this test bed can broadly be applied to multiple microreactor concepts to evaluate a wide range of thermal-hydraulic and structural phenomena such as interface coupling with power conversion units and other collocated systems. MAGNET can evaluate integral thermomechanical effects during electrical heating of an array of heat pipes in a larger test article. Examples of initial testing will include thermal stresses in the monolith and the impact of debonding of a heat pipe from the core block and how that failure could impact surrounding heat pipes, i.e., understanding the potential for cascading failure. This work also discusses some modeling capabilities that can support experiment design, analysis, and interpretation, including the heat pipe code Sockeye and a comparison of thermal-structural simulations performed using ABAQUS and STAR-CCM+.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Techno-economic and environmental life cycle assessment of next-generation fiber-encapsulated nanoscale hybrid materials for direct air carbon capture

A techno-economic and environmental life cycle assessment was conducted for manufacturing two novel types of solid sorbents used for direct air CO 2 capture (DAC). One type of solid sorbent comprised Polyacrylonitrile (PAN)/Organopolysilizane (OPSZ)/nanoscale organic hybrid materials (NOHM)(, and the other comprised polymers of intrinsic microporosity (PIM)/NOHM. These solid sorbents were assumed to be manufactured via an unconventional route: electrospinning as fibers. Economic analyses revealed that manufacturing costs for a 0.73 MT/year capacity plant was $\$$831 and $\$$1398/kg of electrospun fiber in the case ofPAN/OPSZ/NOHM and PIM/NOHM, respectively. The costs were higher for PIM/NOHM owing to high raw material prices (Spirobisindane and tetrafluoroterephthalonitrile), that are used only in manufacturing PIM. Scaling up the plant capacity from 0.73 to 30 MT/year plant decreased the manufacturing cost by nearly half from $\$$831 to $\$$379 in the case of PAN/OPSZ/NOHM, depicting the economies of scale of effect. An LCA of both types of solid sorbent was performed using OpenLCA software, the Ecoinvent v 3.5 database, and the TRACI LCI assessment method. The Global Warming Potential (GWP) ofPAN/OPSZ/NOHM and PIM/NOHM was observed to be 151 × 10 -3 and 166 × 10 -3 kg CO 2 per kg CO 2 captured in the DAC plant, respectively. In conclusion, these GWP values were nearly five times as high as those observed for other solid sorbents existing in the literature such as PEI on silica gel, carbonate on silica, and carbonate on activated carbon owing to large amounts of electricity used in electrospinning unit operation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Long-run Marginal CO2 Emission Rates Workbooks for 2020 Standard Scenarios Cambium Data

This dataset has been superseded by a new set of workbooks that can be found here: https://data.nlr.gov/submissions/183 These workbooks contain modeled estimates of long-run marginal CO2 emission rates (LRMER) for the contiguous United States. The LRMER is an estimate of the rate of emissions that would be either induced or avoided by a long-term (i.e., more than several years) change in electrical demand. It incorporates both the projected changes to the electric grid, as well as the potential for an incremental change in electrical demand to influence the structural evolution of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treats grid assets as fixed. In addition to year-over-year data, the Levelized LRMER worksheet within each workbook is set up to produce a levelized long-run marginal emission rate based on user-provided inputs. These levelized LRMER values are intended for analysts to use when estimating the emissions induced (or avoided) by a long-term change in end-use electricity demand. Three future scenarios are provided in separate workbooks: A Mid-case (i.e., business-as-usual), and two scenarios with relatively higher or lower renewable energy costs. For more details on these scenarios, see the Standard Scenarios 2020 Report ( https://www.nlr.gov/docs/fy21osti/77442.pdf ). For more data underlying each scenario, see the Standard Scenarios 2020 project (Cambium data) at https://cambium.nlr.gov/ . This data was produced as part of the Cambium project. For more details about the methodology, see the Cambium Documentation: Version 2020 ( https://www.nlr.gov/docs/fy21osti/78239.pdf ). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nlr.gov/analysis/cambium.html .

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A life cycle assessment of e-hydrogen production using proton-exchange membrane water electrolysis coupled with desalination in Saudi Arabia

Hydrogen, considered a crucial element in the transition towards a sustainable energy future, offers the potential to mitigate greenhouse gas (GHG) emissions and reduce reliance on fossil fuels. Here, this study explores the viability of hydrogen production using proton exchange membrane water electrolysis (PEMWE) as a key driver of decarbonization within the Vision 2030 framework in the Kingdom of Saudi Arabia. A first-of-a-kind life cycle assessment (LCA) of electrolytic hydrogen (e-hydrogen) production using PEMWE in the Kingdom is performed. As the hydrogen will be produced in a freshwater scarce region, the inclusion of water desalination processes adds an important dimension to the assessment, reflecting the local context and resource availability. Two main renewable energy scenarios are assessed: solar energy through photovoltaics (PV) and wind energy through onshore turbines. The global warming potential (GWP) results indicate a GHG emissions reduction of up to 95 % compared to the state-of-the-art steam methane reforming process if the electrolysis process is powered exclusively by renewable electricity. The scenarios powered by solar and wind energy result in 3.66 and 0.76 kg CO 2 eq/kg H 2 , respectively. The metal depletion is assessed to consider the requirement of rare materials, with a 7.19 × 10 −2 kg Cu eq/kg H 2 for the solar scenario and 2.82 × 10 −2 kg Cu eq/kg H 2 for the wind scenario. A contribution analysis reveals that the majority of emissions in both scenarios originate from the electricity used for electrolysis, with the electrolyser itself contributing minimally. The absolute impact of the water desalination process is the same in both scenarios; however, it appears more prominent in the wind-powered case due to the significantly lower overall emissions in that scenario. The findings underscore the importance of renewable energy integration and process optimization in minimizing environmental impacts and advancing the sustainability of e-hydrogen production.

08 HYDROGEN↗
DOE OSTI2024

Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning

For decades, materials scientists have relied on the process-structure–property paradigm to guide investigations into material behaviors. Traditional studies often examine a limited number of process-structure–property variables, striving to elucidate mechanisms governing material response. However, this approach is time consuming and can limit exploration, as well as the discovery of process-structure–property relations in novel materials. In this paper, we combined combinatorial sputter deposition and multi-modal high-throughput materials characterization with feedforward neural networks to establish high-dimensional process-structure–property relations in Pt-Au alloys, yielding nanocrystalline alloys with high hardness and low resistivity relevant to electrical contact switch applications. We mapped three indicators of process conditions (composition and two atomic deposition characteristics) onto four indicators of material structure (X-ray diffraction, film thickness, density, and surface roughness) and two indicators of material properties (hardness and resistivity), resulting in 784 unique combinations evaluated over a 13-dimensional space. The neural networks predicted Pt-Au alloys with 18–24 at.% Au, when deposited at specific conditions, to have a nanoindentation hardness up to 7.2 GPa. This high hardness value, comparable to some steels, represents a 3-fold improvement in hardness over “hard gold”, a commonly used electrical contact alloy, while maintaining requisite electrical conductivity. The neural network models provide an avenue to identify expected process windows capable of maximizing material performance.

Electrical contact materials↗
DOE OSTI2020

Sensitivity and Uncertainty Quantification of Transition Scenario Simulations

This report documents the first collective attempt at developing and applying capabilities to quantify uncertainties, assess parametric sensitivities, and optimize multiple parameters and metrics in fuel cycle simulations generated by the SA&I Campaign. To do this, external codes that were designed to perform sensitivity analysis and uncertainty quantification (SA&UQ) needed to be coupled to the SA&I Campaign’s nuclear fuel cycle simulators (NFCS). In FY20, two approaches were pursued: 1) coupling Cyclus to an ORNL-internal code called MOT (Metaheuristic Optimization Tool) and 2) coupling DYMOND to the opensource SA&UQ tool kit Dakota. The primary objective of having these NFCS/SA&UQ coupled capabilities is to better inform DOE-NE and other stakeholders on the results generated from the NFCS. For a given set of fuel cycle strategies, policies, and technology assumptions that make up a fuel cycle scenario, these NFCS have traditionally been used by the SA&I Campaign to provide quantitative answers in terms of year-by-year mass flows, infrastructure requirements, costs, etc. With these newly developed coupled capabilities, the SA&I Campaign can now efficiently simulate hundreds or thousands of these scenarios, sample large ranges of parameters and assumptions, and use the unique features of the SA&UQ tools to process the data. This enables providing answers with known and propagated uncertainties, determining the sensitivity of important metrics to different parameters and assumptions, quantifying how much fuel cycle and technology parameters impact each other, and producing optimized fuel cycle strategies for single and multiple variables. To demonstrate these new capabilities, the Cyclus/MOT was used to model several scenarios ranging from simple fleet retirements to transitions to advanced reactors. Specifically, for a transition scenario from LWRs to SFRs and advanced LWRs, uncertainty quantification, sensitivity analysis, and optimization studies were applied to cases involving single and multiple parameter (input) and single and multiple metric (output) variations. In addition, a similar transition scenario was modeled to demonstrate how to optimize the reprocessing capacity parameter to minimize two performance metrics while taking into account uncertainties from two other parameters. Lastly, a depletion module based on SCALE/ORIGEN was added in Cyclus to simulate the third scenario that was designed to quantify the impact of the modeling assumption that all LWR used nuclear fuel have the same burnup. The newly developed DYMOND/Dakota capability was also applied to a transition scenario from the existing fleet to small modular reactors and fast reactors. This particular scenario involves not only explicit isotopic depletion via ORIGEN-2, but also includes multirecycling and utilizing the criticality search feature to determine the fresh fuel composition of recycled fuel, a feature unique to the DYMOND NFCS. A large database of simulations were run with 4 main parameters that were sampled: start date of reprocessing, reprocessing capacity, energy demand growth rate, and advanced reactor share of the fleet. The 4 main metrics were uranium consumption, enrichment requirements, waste generation, and levelized cost of electricity using data from the Cost Basis Report. The demonstrated SA&UQ results include those that inform on how to choose parameters to avoid “failed” scenarios, Sobol’ indices that inform on the importance of various parameters individually and synergistically, and Analysis of Variance (ANOVA) studies that decompose parameter ranges into groups and informs on whether variations are statistically significant.

Feng, B.↗
DOE OSTI2022

Deconstructing proton transport through atomically thin monolayer CVD graphene membranes

Selective proton (H + ) permeation through the atomically thin lattice of graphene and other 2D materials offers new opportunities for energy conversion/storage and novel separations. Practical applications necessitate scalable synthesis via approaches such as chemical vapor deposition (CVD) that inevitably introduce sub-nanometer defects, grain boundaries and wrinkles, and understanding their influence on H + transport and selectivity for large-area membranes is imperative but remains elusive. Using electrically driven transport of H + and potassium ions (K + ) we probe the influence of intrinsic sub-nanometer defects in monolayer CVD graphene across length-scales for the first time. At the micron scale, the areal H + conductance of CVD graphene (~4.5-6 mS cm -2 ) is comparable to that of mechanically exfoliated graphene indicating similarly high crystalline quality within a domain, albeit with K + transport (~1.7 mS cm -2 ). However, centimeter-scale Nafion|graphene|Nafion devices with several graphene domains show areal H + conductance of ~339 mS cm -2 and K + conductance of ~23.8 mS cm -2 (graphene conductance for H + is ~1735 mS cm -2 and for K + it is ~47.6 mS cm -2 ). Using a mathematical-transport-model and Nafion filled polycarbonate track etched supports, we systematically deconstruct the observed orders of magnitude increase in H + conductance for centimeter-scale CVD graphene. The mitigation of defects (>1.6 nm), wrinkles and tears via interfacial polymerization results in a conductance of ~1848 mS cm -2 for H + and ~75.3 mS cm -2 for K + (H + /K + selectivity of ~24.5) via intrinsic sub-nanometer proton selective defects in CVD graphene. We demonstrate atomically thin membranes with significantly higher ionic selectivity than state-of-the-art proton exchange membranes while maintaining comparable H + conductance. Our work provides a new framework to assess H + conductance and selectivity of large-area 2D membranes and highlights the role of intrinsic sub-nanometer proton selective defects for practical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Extreme-scale EV charging infrastructure planning for last-mile delivery using high-performance parallel computing

Here, this paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.

Capacity allocation↗
DOE OSTI2023

A Lattice Boltzmann Method for Predicting Porous Transport Layer Performance During Electrolysis

Electrolysis, the splitting of water into oxygen and hydrogen using electricity, is a sustainable way to produce green hydrogen for energy storage. In polymer electrolyte membrane (PEM) water electrolysis, water is brought into contact with charged catalyst layers and electrochemically separated into oxygen and hydrogen. The hydrogen product formed at the cathode is carried through the catalyst layer for eventual collection, while the oxygen by-product formed at the anode is removed from the surface via a multiphase interaction with circulating water and a solid porous transport layer (PTL). The design of this PTL aids in the detachment and advection of the oxygen by-product and thereby plays a role in the overall efficacy of the catalyst. In this presentation, we present our initial results modeling this multiphase system using a single-component, multiphase lattice Boltzmann method. We use the Shan-Chen model describing inter-particle forces to capture both the cohesion of the water (liquid) and oxygen (gas) phases and their interaction with the PTL (solid) (Shan and Chen, 1993). We use a Carnahan-Starling equation of state to model the effective density governing these inter-particle interactions which allows us to model this relatively high density ratio system (Carnahan and Starling, 1969). With these simulations, we show that the geometry and heterogeneity of the PTL geometry plays a large role in its ability to move oxygen away from the catalyst layer and the resulting bubble structures that are formed within the PTL. The current work demonstrates these effects using synthesized PTL geometries and 2D physics, which will be extended to experimentally-imaged PTL sections and 3D algorithms in the near future.

Boltzmann↗