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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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DOE OSTI2025

Early Research in Load-Following Management for HPC-Nuclear Integration

With the rising demand for high performance computing (HPC) and artificial intelligence (AI) systems, maintaining a stable and efficient power supply is increasingly critical. The HPC team at Idaho National Laboratory is spearheading efforts to seamlessly integrate HPC systems with nuclear reactors. This lightning talk explores one early strategy for managing power fluctuations using software-defined controls. To effectively harness nuclear reactors for power generation, control mechanisms are essential to address the slow load-following capabilities of reactors, which are typically around 5% per minute. While this rate is sufficient for many uses, large HPC systems can experience rapid power consumption changes by tens of megawatts when jobs start or stop running. A reactor could overproduce power and match the peak power rating for the HPC system, however when the system is not running a job or a job unexpectedly stops, the load-following of the system would be affected leading to power being wasted and the likelihood of power transient occurrences increases. Controlling the increase or decrease of power consumption on these systems at the same rate as the load-following of reactors is one piece of the puzzle to properly utilizing nuclear reactors as a power source for HPC systems.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Roadmap and Benchmarking: Privacy in Federated Load Forecasting

Data-driven techniques for energy demand forecasting continue to emerge with promising impacts on distribution grid planning. However, the development of robust and generalizable machine learning models requires that representative high quality training data are available. Distributed energy resources have begun to embed intelligence, gathering large amounts of data on customer demand, behavior, and household devices that are connected to the grid. Though utilities aggregate meter-level demand data for load shaping, demand response, outage management, reliability planning, and billing applications, there lies an inherent privacy concern in sharing consumption data that may identify individual consumer behavioral patterns. Hence, while sharing the data is crucial, the private sensitive customer data must be safeguarded from being exposed or manipulated. In this study, we propose a roadmap for implementing a based privacy preserving framework to support the advancement of data-driven analytics in data-sensitive distributed energy resources environments. The roadmap incorporates federated learning–a distributed training framework, differential privacy–a statistical framework that provides guarantees to safeguard the leakage of sensitive data, secure multiparty computation and homomorphic encryption– techniques for encrypting model gradients and applying secure aggregation on the server. Moreover, we perform baseline experiments on the federated short-term load forecasting (STLF) task using open-source residential load profile datasets, offering insights into the challenges of integrating differential privacy into federated learning.

Abebe, Waqwoya [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

Horizontal 1 K refrigerator with novel loading mechanism for polarized solid targets

We describe a helium evaporation refrigerator used to cool dynamically polarized proton and deuteron targets for electron-scattering experiments using the CEBAF Large Acceptance Spectrometer CLAS12 at Jefferson Lab. The geometry of the CLAS12 detector systems places severe design and construction constraints on the refrigerator and its ancillary equipment, resulting in a horizontal cryostat with a length of 4 m. The 16 cm 3 target samples, consisting of frozen ammonia (NH 3 or ND 3 ), are loaded at the upstream end of the cryostat and moved to the beam-interaction region using a novel transport mechanism. At this location they are cooled with superfluid helium and polarized via dynamic nuclear polarization at 1 K and 5 T. In this manner samples can be replaced and cooled to 1 K in about 30 minutes without disturbing any elements of the electron beam line or particle detection system. In conclusion, we estimate that this method saved 18 days of valuable beam time over the course of a recent, 88-day long experiment.

Evaporation refrigerator↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2024

An electrochemical generator for the continual supply of 213 Bi from 225 Ac for use in targeted alpha therapy applications

Bismuth-213 is a radionuclide of interest for targeted alpha therapy and is supplied via a radiochemical generator system through the decay of 225 Ac. Radionuclide generators employ longer lived “parent” radionuclides to routinely supply shorter-lived “daughter” radionuclides. The traditional 225 Ac/ 213 Bi radiochemical generator relies on an organic cation exchange resin where 225 Ac binds to the resin and 213 Bi is routinely eluted. These resins degrade when they absorb large doses of ionizing radiation (>1 × 10 6 Gy/mg), which has been observed when the loading activity of 225 Ac exceeds 2.59*10 9 Bq (70 mCi). Herein we report the development of an electrochemical generator for the supply of 213Bi that has the potential to overcome this limitation. Bismuth-213 spontaneously electrodeposits onto nickel foils in 0.1 M hydrochloric acid at 70 °C. Using this method, we were able to plate an average of 73 ± 4 % of the 213 Bi in solution and obtain a final 213 Bi recovery of 65 ± 8 % in 0.1 M citrate pH 4.5 via reverse electrolysis using titanium as the cathode. The recovered 213Bi had an average radiochemical purity of >99.8 % and was successfully used to radiolabel DOTATATE with an average radiochemical yield of 85.1 % (not optimized).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The Electron Thermal Conductivity of Pu and Zr Substituted $\mathcal{γ}$-U

Uranium alloys are attractive recycled nuclear fuels because of their high thermal conductivity (𝑘) and fissile density. Limited experimental studies of the 𝑘 of U-Pu-Zr alloys in the range of 15 to 20 wt% Pu and 6 to 15 wt% Zr indicate that increasing the content of either Zr or Pu tends to lower 𝑘. However, which element has the greater effect on 𝑘, and the associated mechanisms, remains unclear. Here, in this study, the electron thermal conductivity (𝑘 𝑒 ) of U-Pu-Zr compositions are calculated using density functional theory. The electronic structure is evaluated to understand the effects of plutonium (Pu) and zirconium (Zr) substitution on the 𝑘 𝑒 of 𝛾-U. Alloys of up to 37.5 at. % Pu and 37.5 at. % Zr are examined. Two methods are applied to calculate 𝑘 𝑒 ; we find that the accuracy of each method depends on the electronic and mass similarities between the solute and solvent atoms. Specifically, when the solute atom is similar in electronic structure and mass, the more accurate method is that which employs the electron relaxation time of 𝛾-U, while if the elements are dissimilar, a mixed method that mixes several parameters associated with JNW_S⁢3033426825100132 from each element in the alloy is best. The introduction of all alloying elements decreases 𝑘 𝑒 ; however, in binary compounds, Pu and Zr have different effects. Pu flattens the electronic bands but compensates for this deleterious effect by increasing electron density near the Fermi level. Zr flattens the electronic bands more severely without adding electron density near the Fermi level. Therefore, Zr decreases 𝑘 𝑒 more than Pu in binary compounds. In ternary compounds, the difference between Pu and Zr is minimal due to the phononic change from the large mass change of Zr substitution, even at 12.5 at. %. Thus, we predict that higher loadings of Pu, and potentially other actinides, can be added to U-Pu-Zr compositions for faster recycling of spent fuel without sacrificing 𝑘. We also note that these 𝑘 𝑒 calculation methods can be applied to non-fuel alloys that require 𝑘 𝑒 predictions, such as cladding, heat exchanger, and structural materials.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A method for modeling battery-temperature-aware EV power profiles utilizing Next-Gen Profile data

With the expected increase in the number of electric vehicles (EVs) on the road in the coming years, it is important that analysis tools are capable of modeling and predicting the expected load on the power grid due to both individual EV charging sessions as well as large populations of vehicles. To do this accurately, the power profile of an EV charge session must be accurately modeled, including for scenarios where the temperature is above or below the ideal, and also take into account the nuances of manufacturer charging preferences. This paper introduces a method that utilizes the data in the Next-Gen Profile (NGP) data collection project to build a model of EV charging that takes into account the variations in charging power that occur due to off-nominal battery temperature and manufacturer preferences that limit power due to cold temperatures or high battery state-of-charge.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

The Electron Thermal Conductivity of Pu and Zr Substituted Gamma-Uranium

Uranium alloys are attractive recycled nuclear fuels because of their high thermal conductivity (k) and fissile density; however, the effects of alloying elements on k remain unclear. Here, the electron thermal conductivity (k_e) of U-Pu-Zr compositions are calculated using density functional theory. The electronic structure is evaluated to understand the effects of plutonium (Pu) and zirconium (Zr) substitution on the k_e of ?-U. Alloys of up to 37.5 at. % Pu and 37.5 at. % Zr are examined. Two methods are applied to calculate k_e; we find that the accuracy of each method depends on the electronic and mass similarities between the solute and solvent atoms. Specifically, when the solute atom is similar in electronic structure and mass, the method that applies the electron relaxation time of ?-U is best, while if the elements are dissimilar, a mixed method that mixes several parameters associated with k_e from each element in the alloy is best. The introduction of all alloying elements decreases k_e; however, in binary compounds, Pu and Zr have different effects. Pu generally flattens the electronic bands but compensates for this deleterious effect by increasing electron density near the Fermi level. Zr flattens the electronic bands more severely without adding electron density near the Fermi level. Therefore, Zr decreases the k_e more than Pu in binary compounds. In ternary compounds, the difference between Pu and Zr is minimal due to the phononic change from the large mass change of Zr substitution, even at 12.5 at. %. Thus, we predict that higher loadings of Pu, and potentially other actinides, can be added to U-Pu-Zr compositions for faster recycling of spent fuel with without sacrificing k. We also note that these k_e calculation methods can be applied to non-fuel alloys that require k_e predictions, such as cladding, heat exchanger, and structural materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Drop Analysis of Department of Energy Standard Canister with Fort Saint Vrain SNF

DOE manages over 300 types of SNF, most of which are located at the INL site. The Idaho Cleanup Project and INL are collaborating on the Road-Ready Capability Demonstration Project, which will develop and demonstrate the designs, technology, processes, and regulatory framework for packaging DOE-managed SNF at the INL site for ?road-ready dry storage?. ?Road-ready dry storage? is a SNF management concept where SNF is packaged into dry and sealed canisters, which are then placed in on-site storage in anticipation of later transportation. The forward-looking goal of the Demonstration is establishing the foundation for a large-scale road-ready dry storage program at the INL site. The Demonstration will first package Fort Saint Vrain SNF currently stored at INL into several DOE Standard Canisters. These Standard Canisters will then be loaded into another commercial transportation or storage containment system (e.g., storage cask or transportation package). The Standard Canister is a class of standardized canisters designed for containing the large variety of DOE-managed SNF during interim storage, transportation and/or disposal at a geological repository. One critical aspect of road-ready dry storage is the ability to license the DOE Standard Canisters and its associated transportation package to 10 CFR 71. Depending on the SNF and transportation strategy, the Standard Canisters may have to maintain structural integrity under normal conditions of transport and hypothetical accident scenarios (i.e., drop events). The Standard Canisters have been tested and analyzed under various SNF loading configurations and accident drop events in support of the Idaho Spent Fuel Facility and other DOE programs. However, no analysis has been completed to support the recent Demonstration. This analysis will consider the Ø0.5 m × 5.1 m Standard Canister under drop scenario(s) considered in previous INL tests and analyses, including the 9 m drop at 80 degree off vertical. However, this analysis will consider the more recent Fort Saint Vrain loading configurations proposed for the Demonstration. This analysis will performed using strain-based acceptance criteria established by the American Society of Mechanical Engineers Boiler and Pressure Vessel Code, Section III, Division 3. It will be compared to previous analyses and form the foundation of further formal calculations that will be used to support licensing efforts of the road-ready dry storage system at INL.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

To What Extent Will Decarbonization Deepen the Conversation Between Industry and the Grid?

Decarbonization - the transition away from un-mitigated fossil fuel combustion throughout the economy - requires big changes from both power and process systems. On the power system side, those changes are expected to include large increases in variable generation, e.g., from wind and solar, which has near-zero marginal costs and at large shares can produce infrequent but consequential energy droughts. On the process systems side, industries are investigating their options for direct and indirect electrification, the latter exemplified by replacing fossil fuel inputs with zero-carbon, energy-carrying chemicals like hydrogen and ammonia produced via electrochemical processes. The economic features of these changes within the larger context of power and process systems suggest that their realization could be accompanied by a paradigm shift in how industrial facilities interact with the grid. For example, the dominant type of demand participation in power markets could change from today's focus on load reductions at peak times to a new focus on shifting electricity use, enabled in part by large-scale product storage, to take advantage of renewable energy that would otherwise be curtailed and to avoid consumption during high-price energy droughts. This talk will describe these and other possible design and operational approaches from grid and industrial economic perspectives, culminating in an enumeration of open problems that lie at the interface of today and tomorrow's power and process systems.

co-design↗
DOE OSTI2024

Parallel computing for power system climate resiliency: Solving a large-scale stochastic capacity expansion problem with mpi-sppy

Here we propose a nodal stochastic generation and transmission expansion planning model that incorporates the output from high-resolution global climate models through load and generation availability scenarios. We implement our model in Pyomo and perform computational studies on a realistically-sized test case of the California electric grid in a high performance computing environment. We propose model reformulations and algorithm tuning to efficiently solve this large problem using a variant of the Progressive Hedging Algorithm. We utilize the parallelization capabilities and overall versatility of mpi-sppy, exploiting its hub-and-spoke architecture to concurrently obtain inner and outer bounds on an optimal expansion plan. Initial results show that instances with 360 representative days on a system with over 8,000 buses can be solved to within 5% of optimality in under 4 h of wall clock time, a first step towards solving a large-scale power system expansion planning problem across a wide range of climate-informed operational scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Coupled Aerodynamic and Hydrodynamic Hybrid Simulation of Floating Offshore Wind Turbines

The development and innovation of floating offshore wind energy in the U.S. requires detailed high-fidelity observations and measurements of turbine and platform loading due to wind, waves, and currents. However, full-scale and quasi-full-scale experiments require significant financial and temporal investments for construction, experimental testing, and long-term field campaigns. To support the commercial advancement of the offshore wind energy industry, specialized wind tunnel and wave basin experimental facilities are critical to be able to test FOWT designs at small scale under controlled conditions prior to full-scale deployment. Oregon State University (OSU) is internationally known as a leader in water and energy research, development, and testing. The O.H. Hinsdale Wave Research Laboratory (HWRL) and the Wallace Energy Systems and Renewables Facility (WESRF) at OSU have extensive experience building, modeling, monitoring, controlling, and actuating scaled systems. Experiments on wave-structure interaction have been performed at the HWRL since its establishment in 1972. Studies have included the interaction of waves with coastal structures (breakwaters, seawalls, buildings, cylinders, bridges, fixed foundations of offshore wind turbines, etc.) and with floating structures (e.g., wave energy converters, maneuvering of vessels, etc.). Hinsdale is actively used by marine energy technology developers, both for private testing and OSU-collaborative research projects. However, despite the availability of several large-scale facilities for hydrodynamic testing (at OSU and elsewhere in the U.S.), existing experimental laboratories are generally limited in their ability to accurately generate combined wind and wave conditions. The simulation of both wind and waves in experimental testing is complicated due to a number of constraints, including: [i] incompatible similitude laws governing the wind and waves for scaled experiments, [ii] producing accurate wind over a large enough control volume via fans, and [iii] generating wind that reasonably represents the atmospheric boundary layer in existing wave basins/flumes. Hence, physical test data providing insight into the simultaneous wave- and wind-structure response of floating offshore wind components can be difficult to generate. Given the aforementioned challenges in classic hydrodynamic experiments, the motivation of this project is to establish a real-time hybrid simulation (RTHS) approach that can apply aero- and hydro-dynamic loading by augmenting wave-only experimental facilities with virtual aerodynamic forces through numerical models representing the remaining dynamic forces. RTHS is a physical-numerical approach that partitions a prototype system into physical and numerical sub-assemblies that interact with each other through actuators and sensors in real time. In coupling physical and numerical models, the hybrid simulation approach applied herein is ideal for problems with: (1) structures subjected to different scaling laws, such as floating offshore wind turbines subjected to combined aero/hydro-dynamic loading, (2) structures that are too large or complex to be tested entirely in a laboratory setting, such as deep-water mooring applications, and (3) component testing, where the behavior of a portion of the assembly is uncertain but still interacts with other portions of the structure, such as testing the fatigue life of turbine blades. Few U.S. experimental facilities are able to test simultaneous aero- and hydro-dynamic loading and none can accurately produce aero/hydro-dynamic response on scaled FOWT models due to conflicting similitude laws between the wind (commonly Reynolds) and the waves (commonly Froude). To aid in accelerating the development of the U.S. floating offshore industry, there is a significant need to develop a flexible, modular framework that can expand the capacities of existing wave-only laboratories. The project goal is to demonstrate a hydrodynamic real-time hybrid simulation (hydro-RTHS) framework that couples numerical wind and physical waves acting on a FOWT, thus representing simultaneous aero/hydro-dynamic loading. The FOWT is partitioned into a full-scale numerical sub-assembly associated with the aerodynamics and a model-scale physical sub-assembly associated with the hydrodynamics. The numerical-physical partition associated with hydro-RTHS mitigates scaling constraints by supplying different scaling laws to the physical and numerical sub-assemblies. Herein, length, force, and time are scaled and exchanged between the sub-assemblies using Froude scaling to represent the open-channel flow in the physical sub-assembly. Other similitude laws could also be utilized depending on the problem definition. It is envisioned that the ability to model FOWTs under waves and wind, with mitigation of similitude distortions, would result in reduced development costs (currently, FOWT concept development is performed with full-size pro- totypes at enormous expense and risk) and increase the reliability of the FOWT industry (since extreme wave and wind conditions and contingency events can be tested safely in a controlled environment).

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Structure of Iridium Oxides and Their Oxygen Evolution Electrocatalysis in Acidic Media

Proton exchange membrane water electrolyzers (PEMWEs) have emerged as one of the most promising technologies for the large-scale production of clean hydrogen. Gigawatt scale deployment of PEMWEs requires substantial reduction in the loading of iridium (Ir), which is one of the most expensive and rarest elements. Substantial reduction in Ir loading calls for the development of innovative Ir-based anodes, which requires a clear understanding of how iridium oxides accelerate the sluggish oxygen evolution reaction (OER) in acidic media. Herein, we studied the structure and OER electrocatalysis of three representative iridium oxides ─ hydrous, amorphous, and rutile ─ by employing a combination of physicochemical and electrochemical characterization. Additionally, we found that the hydrous iridium oxide had a different local structure of IrO 6 octahedra and a superior OER intrinsic activity compared with the other two, and that the OER activities of all three types decreased with decreasing pH of acidic solution. We proposed that the OER process of these iridium oxides is limited by water nucleophilic attack on the OER intermediate oxygenated adsorbates. Based on this mechanism, we attributed the superior OER activity of hydrous iridium oxides to their longer Ir–O bonds and the pH-dependent OER activity of iridium oxides to the pH-dependent oxidation of Ir.

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

Architecting the Third Dimension of Electrochemical Energy Storage

Three-dimensional (3D) architectural design has emerged as a powerful strategy to push electrochemical energy storage (EES) devices beyond the intrinsic limitations of conventional two-dimensional (2D) electrodes. While planar architectures enable high packing density and mature manufacturing, they suffer from limited ion transport and low active-material loading. In contrast, 3D architectures introduce low-tortuosity networks and high surface area that enhance charge and mass transport while supporting thick, high mass-loading electrodes. However, their practicality remains hindered by challenges in volumetric density, mechanical stability, and large-scale manufacturability. Here, this Perspective examines the key evaluation and design principles that govern 3D device performance. We discuss the fundamental trade-offs between porosity, volumetric density, and mechanical stability that shape 3D design and highlight emerging strategies for integrating materials engineering, structural optimization, device integration, computational modeling, and scalable manufacturing. By aligning structural functionality with manufacturability, 3D architectures can evolve from laboratory prototypes to commercially viable energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2026

Atomistic Mechanisms of Stress-Dependent Molten Salt Corrosion in NiCr Alloys

Ni-based structural alloys in molten salt environments often experience simultaneous mechanical loading and corrosive attack, yet the mechanisms governing stress-corrosion interactions remain unclear. Prior studies largely emphasize tensile stress, while the role of compressive stress has received limited attention. Here, reactive molecular dynamics simulations are used to investigate the coupled effects of applied strain and corrosion in Ni 0.75 Cr 0.25 exposed to molten FLiNaK at 800 °C. A Σ5(210) grain boundary model is subjected to tensile (+4%) to compressive (−4%) uniaxial strains, and corrosion behavior is evaluated through fluorine adsorption, charge redistribution, and grain boundary evolution. Tensile strain accelerates intergranular corrosion susceptibility by reducing local atomic packing through elastic dilation and increasing excess free volume at the grain boundary, which enhances atomic mobility and salt infiltration. In contrast, compressive strain can suppress corrosion by promoting the formation of a ridge-like surface layer along the grain boundary, limiting salt access to the underlying alloy. These results provide atomistic insight into how stress states influence grain boundary corrosion in molten salts.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Field Test Report Neutron Scintillator Array Dry Storage Cask Scanner FY2024

During two weeks of Field Testing at the Idaho National Laboratory INTEC Cask Farm in July and August 2024, the LLNL Dry Storage Cask Scanner Array was lifted on top of an MC-10 dry storage fuel cask and operated to acquire neutron and gamma-ray data from the 24 fuel bundle positions. Neutron and gamma-ray data acquisition scans across the top of the cask of varying dwell times were performed July 15-18, 2024 and August 19-22, 2024 to evaluate the ability of the scanner data to reveal asymmetries in the fuel positions that reflect asymmetries in the MC-10 cask fuel bundle loading. The MC-10 cask 24 position fuel bundle loading at the INTEC Cask Farm is well documented, including the locations of six empty fuel bundle positions. This loading presents an opportunity to test the ability of the scanner system to detect diversion of spent fuel bundles as well as to validate the MC-10 cask MCNP modeling. The cask scanner array consists of six Stilbene crystal scintillator detectors and a linear actuator frame that moves the six detectors across the MC-10 dry storage cask to obtain data above each of the 24 fuel bundle positions. The detectors are connected to a pulse-shape discrimination data acquisition system capable of generating separate neutron and gamma-ray spectra for each detector and for each scan position. From the prior single detector Field Test in 2021 and iteration with MCNP modeling, the neutron and gamma-ray data were analyzed in multiple energy regions to identify an analysis method that would provide the strongest and most consistent signature of the asymmetric MC-10 cask fuel loading1 . From both the 2021 Field Test and the current Field Test results, the neutron capture gamma-ray count rate around 2.2 MeV provides the strongest signature of the asymmetric MC-10 cask fuel loading and has qualitative agreement with MCNP calculations. Counting all gamma-rays produces a similar signature. Neutrons emerging from the cask top are moderated and captured by the hydrogen in the polyethylene moderator and scintillator detector, producing a 2.2 MeV gamma ray which is seen in the scintillator gamma-ray spectrum. The count rate in the 2.2 MeV gamma-ray region is ~50 c/s, which is ~1000x higher than the ~0.05 n/s rate in the > 4MeV neutron region, and ~50x greater than the ~1 n/s rate in the neutrons > 500 keV region. Analysis of the 2.2 MeV neutron-capture Compton-scattered gamma-rays produces a statistically significant signature of the INTEC Cask Farm MC-10 asymmetric fuel loading. MCNP simulations indicate that the average neutron energy spectrum offers the potential to detect a large asymmetry from several missing bundles as well as individual missing fuel bundles. Testing this feature will require measurements on a cask with single missing elements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Distribution Grid Model Publication Investigation

Interest in the external exchange of distribution grid model data is growing around the world, driven largely by the challenges and opportunities presented by the increasing amount of generation, storage, and flexible load being embedded within the distribution grid. This report provides an overview of the current state of distribution grid model data sharing, with a focus on the industry-leading activities currently underway in Great Britain (GB). A second report will explore opportunities for external distribution grid model sharing in the United States.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Gateway Cities Unplugged: (em)Powering Affordable Housing (Final Technical Report)

The Gateway Cities Unplugged: (em)Powering Affordable Housing (the “Project”) was launched to address a critical challenge in the U.S. building and energy sectors: enabling multifamily buildings especially those serving low and moderate‑income (LMI) communities to actively support grid modernization through Grid‑Interactive Efficient Buildings (GEB). Multifamily housing represents a large share of national building energy consumption, yet the sector faces persistent barriers to adopting advanced load flexibility technologies, including capital constraints, aging infrastructure, and limited access to demand‑side management tools. This Project sought to overcome those barriers by defining, evaluating, and designing commercially viable GEB technology packages for six representative multifamily properties across Massachusetts, Connecticut, and New York.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗