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DOE OSTI2024

Topical Analyses Related to Co-located Industrial Facilities at Nuclear Power Plants

This report investigates topics of interest with regard to Nuclear Power Plants (NPPs) utilizing flexible plant operations and generation (FPOG). Previous reports have identified the risk associated with co-located hydrogen generation facilities. This report evaluates special topics with regard to co-location of both hydrogen and syngas production facilities. A literature review was conducted to evaluate overpressure mitigation techniques that may be available to the NPP to reduce the consequence of an overpressure event. Also, the overpressure consequence of a catastrophic hydrogen storage tank failure event was analyzed. A comparison of the similarities and differences between the methodology utilized in HyRAM+ and Regulatory Guide 1.91 (R.G. 1.91) was performed for overpressure analysis. Also, the trinitrotoluene (TNT) equivalency methodology was utilized to evaluate an overpressure event at a Syngas production facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

The Energy in Modular (EMOD) Buildings Method: A Guide to Energy-Efficient Design for Industrialized Construction of Modular Buildings

Industrialized construction has immense potential to address the growing need globally to build and upgrade the building stock to be affordable, energy-efficient, and resilient. It can also help achieve the United States' goal of a 50% reduction in U.S. greenhouse gas (GHG) emissions by 2030. Despite this potential, and the ever-increasing push for electrification and decarbonization of households in the United States, industrialized construction has not yet been leveraged specifically to help address these challenges and accelerate the pathway to meet these goals. The National Renewable Energy Laboratory (NREL) aims to claim this missed opportunity by focusing on delivering affordable, grid-efficient net-zero energy (NZE) modular buildings for underserved communities to ensure an equitable transition to the future of clean energy, accelerate decarbonization of the built environment, and support the development of a high-productivity construction and energy efficiency workforce. The Energy in Modular (EMOD) method is our approach to designing, producing, and delivering affordable, net-zero energy, low-carbon, and healthier buildings at scale. The following energy efficiency strategies are part of the scope of this guide: envelope thermal control, envelope infiltration control, mechanical, electrical, and plumbing systems, smart controls, and solar plus storage. We draw synergies between design for manufacturing and assembly, process optimization, retrofit technologies, and digitization. Our goal is to influence the improvement and production of buildings to increase performance, enhance energy efficiency, and reduce GHG emissions. This guide documents the research and development efforts initiated by a set of design objectives to "modularize" a set of energy efficiency and low-carbon strategies into a housing unit while preserving and enhancing energy efficiency benefits and decarbonization pathways. This guide is intended to serve as a framework for housing developers, housing agencies, architects, energy experts, and process engineers or factory operator personnel who are critical to today's modular builder teams. This guide focuses on specific energy efficiency strategies, decarbonization pathways, and associated processes as part of NREL's research efforts. Stakeholders may substitute other means, methods, and technologies for the ones evaluated in this study.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Estimate of Gamma Dose Rates from Arrays of Fermi-1 Blanket Elements During the MEDE Process

The Enrico Fermi Atomic Power Plant Unit 1 (Fermi-1) was a sodium-cooled fast breeder reactor located in Monroe County, Michigan. The reactor was powered by a core of enriched uranium metal alloy driver fuel, which was enveloped by an axial and radial blanket material consisting of depleted uranium metal alloyed with 3 wt.% molybdenum. There are 406 axial and 559 radial irradiated sodium-bonded Fermi-1 blanket assemblies in storage at INL, totaling 34 metric tons of heavy metal. Disposal of the Fermi-1 blanket material directly into a geological repository is prohibited due to the reactive characteristic of its bond sodium. A melt drain evaporate (MEDE) process can effectively remove bond sodium from Fermi-1 blanket material. Consequently, planning is underway to apply a MEDE process to treat the 34 metric tons of heavy metal of irradiated Fermi-1 blanket material. Given the irradiated Fermi-1 blanket material’s relatively low power history and 50+ years of decay, it can be treated in a shielded glovebox. To assess the requisite shielding, the dose rates of the Fermi-1 blanket elements in various process configurations are needed. The method to perform the dose rate calculations is to first generate an average source term and associated photon source spectra for the Fermi-1 blanket material in SCALE and then use the associated photon spectra to calculate dose rates using MCNP6.2 in seven representative geometries. Dose rates in rem/h were calculated on contact (1 cm from outer geometry surface), 30 cm away from outer geometry surface, and 1 m from outer geometry surface at axial heights spanning the length of the blanket material within the geometry. The maximum average dose rate for the single Fermi-1 radial blanket element is ~0.60 rem/h, for the Fermi-1 radial blanket assembly ~1.10 rem/h, for the single Fermi-1 axial blanket element ~0.5 rem/h, for the Fermi-1 axial blanket assembly ~1.01 rem/h, for the MEDE can ~ 0.98 rem/h, for the Fermi Storage Canister ~ 0.83 rem/h, and for the MEDE cans in the DOE Standard Canister ~0.51 rem/h. Based on these maximum average values, the bounding dose rates are assumed to be 3× the average dose rates calculated for the axial blanket material and 7.6× the average dose rates calculated for the radial blanket material, based on the distribution of Fermi-1 blanket material assembly burnup.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

A Hydrogen Iron Flow Battery with High Current Density and Long Cyclability Enabled Through Circular Water Management

The hydrogen‐iron (HyFe) flow cell has great potential for long‐duration energy storage by capitalizing on the advantages of both electrolyzers and flow batteries. However, its operation at high current density (high power) and over continuous cycling testing has yet to be demonstrated. In this article, we discuss our design and demonstration of a water‐management strategy that supports high current and long‐cycling performance of a HyFe flow cell. Water molecules associated with the movement of protons from the iron electrode to the hydrogen electrode are sufficient to hydrate the membrane and electrode at a low current density of 100 mA cm −2 during the charge process. At higher charge current density, more aggressive measures must be taken to counter back‐diffusion driven by the acid concentration gradient between the iron and hydrogen electrodes. Our water‐management approach is based on water vapor feeding in the hydrogen electrode and water evaporation in the iron electrode, thus enabling high current density operation of 300 mA cm −2 .

Yan, Litao↗
DOE OSTI2021

Carbon content of soil fractions varies with season, rainfall, and soil fertility across a lowland tropical moist forest gradient

Tropical forests contain some of the largest soil carbon (C) stocks on Earth, making them broadly relevant to terrestrial-climate feedbacks, yet our understanding of how their soil organic C (SOC) fractions vary over space and time is limited. We studied effects of season, fertility, and mean annual precipitation (MAP) on the C contents of soil fractions across 14 lowland forests in Panama. We measured free-debris, occluded-debris, and mineral-associated SOC fractions, as well as soluble C associated with each fraction. We hypothesized that mineral-associated SOC would be greatest in infertile, strongly weathered soils with large reactive mineral contents. We also hypothesized that the debris SOC fractions would accumulate during the dry season, reflecting seasonal increases in litterfall. To address this, we compared soil fractions in wet and dry seasons from fertile and infertile soils across a range of 1809 – 2864 mm MAP. The C content (mg C / g soil) of all soil fractions varied with fertility and MAP: specifically, free-debris SOC was greatest in wet, high-fertility soils, and occluded-debris SOC was greater in high-fertility than low-fertility soils. The mineral-associated SOC fraction, which contained the majority of bulk soil C, showed increasing C content with greater MAP in infertile sites, presumably driving similar spatial patterns in the bulk soil. Only the free-debris SOC fraction showed strong seasonal variation, increasing in mass during the dry season. Nitrogen behaved similarly to C. In summary, soil C contents increased with MAP in infertile sites but not fertile sites, driven by the mineral-associated SOC fraction. The dry season had greater free-debris SOC, but this seasonal trend was not apparent in bulk soil C, likely because of the small size of the free-debris SOC fraction. Furthermore, changes in the quantity and seasonality of precipitation, which are projected for tropical forests, might shift spatial and temporal patterns of soil C storage, which would in turn influence forest-climate feedbacks for this C-rich biome.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗
DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗
DOE OSTI2025

Accelerators for the Future: R&D at the Fermilab FAST Facility

High energy physics in the U.S. has ambitious plans requiring new and improved accelerator technologies, including an upgraded complex at Fermilab for DUNE, next-generation light sources, and the potential of a future collider to be built on U.S. soil. To address these requirements, Fermilab operates the Fermilab Accelerator Science and Technology (FAST) facility, dedicated to accelerator R&D. FAST includes an electron gun and superconducting RF linac (up to 300 MeV), a storage ring, and an upcoming proton source and injector line (up to 2.5 MeV). In addition to the future of Fermilab accelerators, the broad physics program includes general R&D with potential impact across the DOE science program. This colloquium will provide a basic introduction to accelerator technologies and describe the principles of proton and electron accelerators, including the challenges associated with next-generation operations. We will discuss the exciting R&D ongoing at FAST to address the required technological advancements, focusing on Non-Linear Integrable Optics (NIO) for improved beam intensity and Optical Stochastic Cooling (OSC) for improved beam quality, and touching on a wide range of additional ongoing research. I intend to make the content interesting and accessible to those who have never taken any formal courses in accelerator physics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

DER Cybersecurity Standards: Assessment and Gap Analysis

The purpose of this report is to share the comprehensive gap analysis of existing cybersecurity standards applicable to Distributed Energy Resources (DERs) within the electric power sector. This analysis aims to identify critical deficiencies in current standards, assess their alignment with industry needs, and provide actionable recommendations for enhancing cybersecurity measures. The scope encompasses various DER technologies, including solar, wind, energy storage, and hydrogen fuel cells, and emphasizes the significance of establishing robust cybersecurity frameworks and standards to safeguard these increasingly integrated systems. The report provides valuable insights for stakeholders in the DER ecosystem, including manufacturers, utilities, and regulators. It underscores the importance of continued development and refinement of cybersecurity standards to keep up with the technical advances in DERs and associated cybersecurity challenges. The analysis evaluated IEC, IEEE, ISA, ISO, and UL standards relevant to DER cybersecurity. Standards were assessed on their coverage of key requirements including data availability, integrity, confidentiality, access control, authentication, encryption, and system hardening. For each standard, the analysis assessed its alignment with current industry practices, regulatory compliance, effectiveness in addressing known risks, coverage of emerging risks, and how it promotes interoperability. The evaluation also considered potential integration challenges and barriers to adoption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Decarbonization of the power sector with CCS: Case study in two regions in the U.S. and lessons for Latin America

This paper estimates potential changes in the total systems cost (TSC) of two RTOs of the United States (U.S.), MISO-N and SPP RTO West, under the traditional decarbonization pathway of replacement of fossil-power plants with variable renewable energy (VRE) and the less traditional pathway of retrofitting fossil fuel units with carbon capture and storage (CCS). Although the power mixes of MISO-N and SPP RTO West are particular to those regions, the results can apply to other regions, including Latin American countries that are planning to decarbonize their power sectors. This case study serves to highlight lessons on the difference of technology costs between these two pathways, as well as the cost associated with decarbonization rates close to 100%.

Pena-Cabra, Ivonne A.↗
DOE OSTI2020

Robust foil regenerator flow loss and heat transfer tests under oscillating flow condition

A regenerator works as an energy storage component in a Stirling engine. Thus, it is one of the most critical components that makes the Stirling engine highly efficient. Traditionally, the regenerators are made of wire media such as woven screens and random fibers. These wire regenerators experience considerably high flow losses through the regenerator due to the flow separation and the resulting vortices. The high flow losses associated with the flow separation and vortices result in a lower engine efficiency. This research is to test a new robust foil regenerator to investigate its feasibility for free-piston Stirling engine applications. The robust foil regenerator is configured with parallel flow channels and stiffening ribs. Comparing to complex flows patterns in woven screen and random fiber regenerators, the flows in the parallel plates are simple and the flow losses are considerably small, and the heat transfer coefficient is also relatively small. To test the regenerator experimentally, a regenerator test rig was manufactured. To verify the accuracy of the test rig, a woven screen regenerator was made and tested prior to the robust foil regenerator test. The friction factor correlation of the woven screen regenerator shows a good agreement with the published regenerator correlations. The robust foil regenerator tests were conducted under the oscillating flow condition with different Reynolds numbers. The flow losses were measured with dynamic pressure transducers and the friction coefficient was derived from measured pressure drop. The measured temperature difference over the regenerator was used to calculate the heat transfer coefficient and Nusselt number. The figure of merit with two methods of NPH/NTU ratio and j/f ratio was calculated to evaluate the suitability of the robust foil regenerator. The results show that the robust foil regenerator has considerably higher figure of merit and therefore, better performance than other types of regenerator.

42 ENGINEERING↗
DOE OSTI2024

Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs

Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Interpreting pre-vegetation landscape dynamics: The Cambrian Lower Mount Simon Sandstone, Illinois, U.S.A.

ABSTRACT The Cambrian Mount Simon Sandstone has been the subject of extensive study and multiple industrial-scale carbon storage demonstrations at Decatur, Illinois, USA. The development of a reliable paleoenvironmental model is critical to successful large-scale carbon dioxide (CO2) storage, but is complicated by the need to interpret pre-vegetation sedimentation processes. The present study presents a paleoenvironmental model of the Lower Mount Simon Sandstone, based on analysis of primary sedimentary structures in two cores and four complete high-resolution resistivity logs (FMI). The Lower Mount Simon Sandstone represents a vertical “drying-up” sequence composed of three associated depositional units: a north–south oriented coastal system at the base, an eastward-directed fluvial unit in the middle, and a westward-directed eolian system at the top that recycled medium- and fine-grained sand in the basin. Quantitative analysis of fluvial cross-strata indicates that the perennial river system was shallow (c. 1 m deep) with relatively narrow channel belts (c. 1 km). Adjacent sandy eolian-floodplain deposits contain abundant thin, crinkly planar laminae that are enriched in fines and are interpreted as cementation surfaces, likely of biological origin. Deflation lags and wind-ripple strata are commonly interbedded with the crinkly strata, suggesting that the recurrence of erosion and deposition that controlled sedimentary preservation on the floodplain were dominated by eolian transport, re-wetting, and (bio-) cementation. Such a prominent role of exposure to the wind, basin-scale sediment recycling, and eolian removal of fine-grained sediment would have ceased to exist for most climates after the development of vegetation on land, yet, may well be key to understanding the environmental context for early life on Earth.

Geology↗
DOE OSTI2020

Definition of Redox Centers in Reactions of Lithium Intercalation in Li 3 RuO 4 Polymorphs

Cathodes based on layered LiMO 2 are the limiting components in the path toward Li-ion batteries with energy densities suitable for electric vehicles. Introducing an over-stoichiometry of Li increases storage capacity beyond a conventional mechanism of formal transition metal redox. However, the role and fate of the oxide ligands in such intriguing additional capacity remain unclear. This reactivity was predicted in Li 3 RuO 4 , making it a valuable model system. For this study, a comprehensive analysis of the redox activity of both Ru and O under different electrochemical conditions was carried out, and the effect of Li/Ru ordering was evaluated. Li 3 RuO 4 displays highly reversible Li intercalation to Li 4 RuO 4 below 2.5 V vs Li + /Li 0 , with conventional reactivity through the formal Ru 5+ -Ru 4+ couple. In turn, it can also undergo anodic Li extraction at 3.9 V, which involves O states to a much greater extent than Ru. This reaction competes with side processes such as electrolyte decomposition and, to a much lesser extent, oxygen loss. Although the associated capacity is reversible, reintercalation unlocks a different, conventional pathway also involving the formal Ru 5+ -Ru 4+ couple despite operating above 2.5 V, leading to chemical hysteresis. This new pathway is both chemically and electrochemically reversible in subsequent cycles. This work exemplifies both the challenge of stabilizing highly depleted O states, even with 4d metals, and the ability of solids to access the same redox couple at two very different potential windows depending on the underlying structural changes. It highlights the importance of properly defining the covalency of oxides when defining charge compensation in view of the design of materials with high capacity for Li storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Cations Control Lipid Bilayer Memcapacitance Associated with Long-Term Potentiation

Phospholipid bilayers can be described as capacitors whose capacitance per unit area (specific capacitance, C m ) is determined by their thickness and dielectric constant–independent of applied voltage. It is also widely assumed that the C m of membranes can be treated as a “biological constant”. Recently, using droplet interface bilayers (DIBs), it was shown that zwitterionic phosphatidylcholine (PC) lipid bilayers can act as voltage-dependent, nonlinear memory capacitors, or memcapacitors. When exposed to an electrical “training” stimulation protocol, capacitive energy storage in lipid membranes was enhanced in the form of long-term potentiation (LTP), which enables biological learning and long-term memory. LTP was the result of membrane restructuring and the progressive asymmetric distribution of ions across the lipid bilayer during training, which is analogous, for example, to exponential capacitive energy harvesting from self-powered nanogenerators. Here, we describe how LTP could be produced from a membrane that is continuously pumped into a nonequilibrium steady state, altering its dielectric properties. During this time, the membrane undergoes static and dynamic changes that are fed back to the system’s potential energy, ultimately resulting in a membrane whose modified molecular structure supports long-term memory storage and LTP. Here, we also show that LTP is very sensitive to different salts (KCl, NaCl, LiCl, and TmCl 3 ), with LiCl and TmCl 3 having the most profound effect in depressing LTP, relative to KCl. This effect is related to how the different cations interact with the bilayer zwitterionic PC lipid headgroups primarily through electric-field-induced changes to the statistically averaged orientations of water dipoles at the bilayer headgroup interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Probabilistic feasibility assessment of sequestration reliance for climate targets

Countries worldwide are transforming their energy systems to achieve Carbon-Neutrality. Investing in renewable resources-based technologies and implementing Carbon Capture and Storage (CCS) are common strategies to achieve higher sequestration levels. Negative emissions through Bioenergy with CCS are expected to play an essential role in the transition to full decarbonization. On top of that, biomass is a limited resource that depends on environmental factors, which create uncertainties related to the amount that can be sustainably provided to the energy system. Here this study emphasizes the relevance of variability in carbon sequestration for achieving climate targets by 2050. Here this paper proposes a probabilistic approach that integrates the Global Change Analysis Model for Latin America (GCAM-LA) with a chance constraint approach. GCAM-LA is used to assess the impact on the energy sector of different limits of sustainable biomass and carbon budget scenarios. The risk associated with exceeding the sequestration capacity of a given region is modeled via Chance Constraint. Results show that electrification is an appropriate long-term decarbonization strategy. It smoothes the effects of uncertainty in sequestration capacity and responds to end-user demands. For this case study, higher levels of electrification are obtained at likelihood levels >66% for end-use sectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Emerging multiscale insights on microbial carbon use efficiency in the land carbon cycle

Microbial carbon use efficiency (CUE) affects the fate and storage of carbon in terrestrial ecosystems, but its global importance remains uncertain. Accurately modeling and predicting CUE on a global scale is challenging due to inconsistencies in measurement techniques and the complex interactions of climatic, edaphic, and biological factors across scales. The link between microbial CUE and soil organic carbon relies on the stabilization of microbial necromass within soil aggregates or its association with minerals, necessitating an integration of microbial and stabilization processes in modeling approaches. In this perspective, we propose a comprehensive framework that integrates diverse data sources, ranging from genomic information to traditional soil carbon assessments, to refine carbon cycle models by incorporating variations in CUE, thereby enhancing our understanding of the microbial contribution to carbon cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Voltage Probability Density Function Shaping Control Strategy Considering Grid Operational Uncertainties

It is well-known that power systems operation always affected by various uncertainties which make the bus voltage a random process that can be characterized by its probability density function (PDF) at any time instant. In this context, this paper presents a novel PDF-based voltage control framework for power systems. By modeling voltage as a stochastic process, we formulate a stochastic differential equationthat captures grid uncertainties. The associated Fokker-Planck-Kolmogorov equation is derived to describe the evolution of the voltage PDF, which enables the formulation of a PDF-shaping control strategy. To simplify the PDF control formulation, a B-spline neural network is introduced for real-time estimation and regulation of the voltage distribution. The proposed PDF control law updates voltage references for energy storage systems and synchronous generators using real-time PDF measurements and feedback signals. The proposed method is validated on a modified Kundur’s two-area system. Simulation results demonstrate that the controller can significantly improve the voltage stability under stochastic conditions, highlighting its effectiveness in modern inverter-rich grids.

Gui, Yonghao [ORNL] (ORCID:0000000250435534)↗