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DOE OSTI1995

Nuclear Safety [Vol. 36, No. 2, July-December 1995]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. THE CHORNOBYL ACCIDENT: 195 The Chornobyl Accident Revisited, Part III: Chernobyl Source Term Release Dynamics and Reconstruction of Events During the Active Phase, A. R. Sich; GENERAL SAFETY CONSIDERATIONS: 218 Second ANS Workshop on the Safety of Soviet-Designed Nuclear Power Plants, R. A. Bari; 234 Elements of a Nuclear Criticality Safety Program, C. M. Hopper; 243 Rickover, Excellence, and Criticality Safety Programs, R. E. Wilson; ACCIDENT ANALYSIS: 249 Transient Analysis of the PIUS Advanced Reactor Design with the TRAC-PF1/MOD2 Code, B. E. Boyack, J. L Steiner, S. C. Harmony, H. J. Stumpf, and J. F. Lime; 278 The Hierarchy-By-Interval Approach to Identifying Important Models that Need Improvement in Severe-Accident Simulation Codes, T. J. Heames, M. Khatib-Rahbar, J. E. Kelly, R. P. Jenks-Johnson, and Y.-S. Chen; 290 RELAP5/MOD3 Code Coupling Model, R. P. Martin; 299 Missiles Caused by Severe Pressurized-Water Reactor Accidents, R. Krieg; DESIGN FEATURES: 310 Validation of COMMIX with Westinghouse AP-600 PCCS Test Data, J. G. Sun, T. H. Chien, J. Ding, and W T. Sha; ENVIRONMENTAL EFFECTS: 321 Spent Nuclear Fuel Characterization for a Bounding Reference Assembly for the Receiving Basin for Off-Site Fuel, S. D. Kahook, R. L. Garrett, L. R. Canas, and M J. Beckum; OPERATING EXPERIENCES: 332 Reactor Shutdown Experience, Compiled by J. W. Cletcher; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 335 Reactor Coolant System Blowdown at Wolf Creek on September 17, 1994, J. V. Kauffman and S. L. Israel; RECENT DEVELOPMENTS: 344 Reports, Standards, and Safety Guides, D. S. Oueener; 349 Proposed Rule Changes as of June 30, 1995; ANNOUNCEMENTS: 320 Symposium on Acceptability of Risk From Radiation—Application to Manned Space Flight; 320 24th DOE/NRC Nuclear Air Cleaning and Treatment Conference; 361 Radiation Biology and Radiation Protection— Modern Developments and Tendencies in Radiation Biology; 362 1997 IEEE Sixth Conference on Human Factors and Power Plants; 354 The Authors; 360 Reviewers of Nuclear Safety, Vol. 36.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Development of a strip-shaped X-ray mapping system for 9-cell superconducting cavities

Electrons emitted via field emission during superconducting (SC) radio-frequency (RF) cavity tests at vertical test stands often collide with the iris region inside the cavity, generating X-rays at these locations. In 1.3 GHz 9-cell SC RF cavities designed for the International Linear Collider (ILC), stiffener rings located outside the iris region between cells can interfere with X-ray detection, complicating the precise identification of field emission sites. Hence, in this study, we developed a high-density strip X-ray mapping systems (sX-map) that can be inserted into the iris region of ILC-type 9-cell SC RF cavities. This sX-map facilitates efficient and accurate detection of X-rays generated near the irises, unaffected by the presence of stiffener rings. The sX-map consisted of 32 sensors per strip, with sensors spaced approximately 10 mm apart. It was deployed in every iris of the 9-cell cavity, using a total of 320 sensors. A multiplexer was employed to facilitate the readout of a large number of detectors using a minimal number of signal lines, connecting the strips inside within the vertical test cryostat. In a vertical test conducted at Jefferson Lab (JLab), we demonstrated the capability of sX-map to detect X-rays despite the presence of a stiffener ring. This paper presents the detailed design of the sX-map and the results from the vertical test at JLab.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Experimental Characterization of High-Surface Area Thermal Energy Storage

There is growing interest in energy storage technologies due to the expansion of renewable energy sources that are inherently intermittent and the increasing frequency of extreme weather events that disturb the power grid. Power consumption in buildings makes up approximately 76% of all electricity usage on the grid and is primarily used for thermal applications such as space conditioning, hot water, and cooking. This makes thermal energy storage (TES) an ideal solution for many of these applications. Many TES technologies rely on latent energy storage, which utilizes the melting/solidification of phase change materials (PCM) to store energy. Typically, TES designs suffer from low power density due to their low inherent thermal conductivity. This limitation makes the deployment of TES in active applications difficult as the ease of access to energy is essential for effective use. Common routes for improving power density include high thermal conductivity additives or extended features such as fins that increase cost. This study presents an alternative approach to improving performance through increasing the overall surface area to volume ratio of the device, to increase the available area for convection to occur between the working fluid and PCM. In the study a commercial PCM was selected with a transition temperature ideal for space heating applications. The heat exchanger design utilizes a unique application of triply periodic minimal surfaces (TPMS) for macro-encapsulation of the PCM. The use of TPMS for heat exchangers has been growing in interest due to their high-surface area to volume ratios, which were previously unmanufacturable until the development of additive manufacturing. A modular system was designed and manufactured with a stereolithography resin printing system that is then backfilled with PCM. An experiment test set-up is designed to test the charge and discharge performance of the thermal storage using a conditioned air stream. The pressure drop of the design is tested across a variety of flow rates. When compared to existing experimental data within literature, there is excellent agreement based on the Reynolds number at similar hydraulic diameters. Several inlet temperatures are tested at consistent temperature differences from the phase change temperature for both charging and discharging. Additionally, the volumetric flow rate is varied for each temperature set point. It was found that increasing flow rate had diminishing returns in reducing the overall charge time of the TES. The temperature delta from the melting temperature was the primary contributor to the change in average heat flux with limited variation in average heat transfer rate between charging and discharging at similar inlet temperatures and flow rates. The TPMS heat exchanger design has a high air-side pressure drop but it provides high heat transfer rates. This helps maintain a high outlet temperature during discharge, which is important to thermal comfort applications. The design, manufacturing, and experimental characterization of the TES device will be presented as part of this study.

25 ENERGY STORAGE↗

DOE OSTI2025

SSAPy - Space Situational Awareness for Python

SSAPy is a fast and flexible orbit modeling and analysis tool for orbits spanning from low-Earth into the cislunar regime. Orbits can be flexibly specified from common input formats such as Keplerian elements or two-line element (TLE) data files. SSAPy allows users to model satellites and specify parameters such as satellite area, mass, and drag coefficients. SSAPy includes a customizable force-propagation with a range of Earth, Lunar, radiation, atmospheric, and maneuvering models. SSAPy makes use of various community integration methods and can calculate time-evolved orbital quantities, including satellite magnitudes and state vectors. Users can specify various space- and ground-based observation models with support for multiple coordinate and reference frames. SSAPy also supports orbit analysis and propagation methods such as multiple hypothesis tracking and has built-in uncertainty quantification. The majority of SSAPy’s methods are vectorized and parallelizable, allowing for effective use of high-performance computer (HPC) systems. Finally, SSAPy has plotting functionality, allowing users to visualize orbits and trajectories. Examples are shown in Figure 1 and Figure 2.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

CCDC 2383610: Experimental Crystal Structure Determination

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

bis(pyrrolidin-1-ium) bis(tetrabutylammonium) trid↗

DOE OSTI2025

A Mössbauer Spectroscopy Investigation of Nickel‐Zinc Ferrites Synthesized by a Self‐Combustion Method for Soft Magnetic Core Applications

Soft ferrites are materials of interest for magnetic cores, as used for wireless charging transformers. Their low permeabilities, high resistivity, and magnetic polarization make them interesting for high-power electric vehicle charging and drive systems. The nickel-zinc-doped ferrites are of particular interest; however, the compositional space is quite large with respect to dopant concentrations, stoichiometric ratios and synthesis technique. Nickel-zinc spinel ferrites with varying nickel-zinc ratios prepared by a self-combustion reaction followed by heat treatment exhibit good crystallinity, and their low-temperature Mössbauer spectra show local magnetism and site occupation in agreement with materials prepared by solid-state reaction. Thus, the combustion synthesis method offers a facile tunability of compositions, which, combined with the possibility of rapid characterization of atomic-scale magnetism by Mössbauer spectroscopy, enables advances in the compositional and processing space at a fast pace. Low-temperature Mössbauer spectroscopy data for samples with increasing nickel content reveals a systematic increase in average hyperfine field (2.8 T/Ni) and decrease in average isomer shift (−0.036 mm/s/Ni) that can determine the nickel/zinc content, even in the absence of applied magnetic field data. Furthermore, a gradual evolution of color is also observed with increasing nickel content, albeit trends in color depend on sintering conditions.

Mössbauer spectroscopy↗

DOE OSTI2025

A Climatology and Life‐Cycle Characteristics of Atmospheric Fronts and Their Associated Precipitation

Abstract Atmospheric fronts are one of the main sources of mid‐latitude variability. We employ a novel method for identifying and tracking fronts and frontal precipitation. Thermal and dynamical variables are used to identify fronts as areal objects in space, which are tracked in time using the open‐source TempestExtremes software package. Precipitation objects are co‐located to identify frontal precipitation. The method is subjected to validation and sensitivity tests using manually curated data from the National Weather Service. Climatologies of fronts and frontal precipitation are computed from reanalysis and observations; fronts are present upwards of 14% of the time in the storm tracks, and represent the majority (up to 90%) of total and extreme precipitation. Novel aspects of the method are showcased through the lifetime characteristics of fronts across North America. Three sets of warm and cold fronts were discovered, and their duration, distance‐traveled, and translation velocity are examined. Plain Language Summary Mid‐latitude low‐pressure systems and weather fronts are important for our day‐to‐day experience of weather events, particularly in the mid‐latitudes. This work makes use of standardized atmospheric data and creates a method of automatically tracking these important atmospheric features and their precipitation to quantify their relative role in global precipitation. Weather fronts are persistent in the mid‐latitudes and are associated with the majority of precipitation–particularly the most intense precipitation. Trajectories of fronts over North America are categorized to create a set of archetypal fronts that occur in that region. The differences between these types of fronts are characterized. Key Points An automated, efficient, and skillful frontal detection algorithm is developed and validated Fronts contribute a larger fraction of extreme precipitation than all precipitation in mid‐latitude storm tracks Fronts across North America have substantial variation in characteristics depending on their origin location

extratropical cyclone↗

DOE OSTI2024

Novel data interpretation method for DIII-D divertor retarding field energy analyzer with 3-D particle-in-cell simulations

A novel data interpretation process that utilizes comprehensive particle-in-cell (PIC) simulations is developed for the new retarding field energy analyzer (RFEA) currently being constructed at DIII-D for the lower divertor using the Divertor Material Evaluation System. Furthermore, this probe is expected to survive a heat load of up to 100 MW/m 2 for up to 5 s and reliably measure the main ion temperature (T i ) on the divertor target ranging from 10 to 200 eV. These extreme conditions posed significant engineering limitations on the probe geometry, thus extensive validation work has been performed. The conventional fitting method for the RFEA I–V characteristics is based on a simplified 1-D model without considering the ion space charge inside the probe cavity and may not be sufficient for probes designed for the DIII-D divertor environment. In this article, a more realistic description of the particle propagation process within the RFEA cavity is achieved by including both 3-D geometric effects and ion space charge in the PIC simulations, and the capability to reconstruct the ion energy distribution functions is demonstrated with reasonable consistency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

StOKeDMD: Streaming Occupation kernel dynamic mode decomposition

Dynamic mode decomposition (DMD) has become a common technique for constructing surrogate models for dynamical systems from observed system states. The Occupation Kernel DMD (OKDMD) method proposed in (Rosenfeld et al., 2022) and (Rosenfeld et al., 2024) is a Liouville operator based method that builds surrogate models from system state trajectories. Here, this paper proposes an extension of OKDMD to the case when the system states are observed in a streaming fashion, i.e., only a small fraction of the state trajectory is available at a given time. The developed method, Streaming Occupation Kernel DMD (StOKeDMD), accommodates the streaming data input by leveraging properties of specific choices of kernel functions and occupation kernels. We apply the StoKeDMD method as a compression method for streaming data, analyze the memory complexity, and demonstrate the performance of StoKeDMD in the compression of streaming data generated from a Lorenz system and a fluid flow simulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Microscopic Scattering Approach to In-Gap States

We develop a microscopic scattering formalism to describe Yu-Shiba-Rusinov (YSR) states due to a single Cr adatom on the Bi-terminated surface of beta Bi2Pd, by combining ab initio Wannier functions with a real-space Green's function approach in the Bogoliubov-de Gennes formalism[1]. Our framework reproduces key scanning tunneling spectroscopy features, including a single particle-hole asymmetric YSR peak and isotropic dIdV maps around the impurity. Decomposing the YSR states reveals contributions from four nearly degenerate C4v representations, with energy broadening masking their individual signatures. Spin-orbit coupling induces partial spin polarization, while the spatial asymmetry between particle and hole components arises from Cr d-Bi p hybridization. These results highlight the importance of realistic band structures and microscopic modeling for interpreting STM data for magnetic in-gap states on superconductors. Further advances examining layered 2D material surfaces, such as NbSe2, will be described[2]. For this system the superconducting properties are obtained from a full anisotropic Eliashberg calculation of the superconducting order parameter along with the charge density wave gap. Additional features associated with proposals to measure the dynamics of these individual YSR states will be presented. [1] arXiv:2507.08740 [2] arXiv:2507.11856

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

A primer on forest structure measurement with lidar for ecologists

Light detection and ranging (lidar) technology has fundamentally advanced the way we measure forest structure, facilitating new insights into ecological processes. Lidar for forest ecology applications is deployed on multiple types of platforms that operate from the ground, air, or space, and each has associated strengths and limitations. Ideally, the choice of what kind of lidar to use in a particular study should be guided by the ecological question of interest; however, practical considerations of cost, data availability, and processing tools can be equally important. This synthesis is a practical introduction to how different lidar platforms characterize forest structure (e.g., tree size/location, wood volume, branching structure, aboveground biomass, leaf properties), designed for a general audience of ecologists (not remote sensing scientists) seeking an accessible introduction to the use of lidar. We also provide examples of novel ecological insights from recent lidar research and describe current limitations and areas of expected improvement. Last, we include an appendix of data collected from terrestrial, mobile, unoccupied aerial system, airplane, and satellite lidar platforms within a common temperate forest area, with associated code to allow new lidar users to visualize and manipulate data in R.

Cushman, KC [ORNL] (ORCID:0000000234641151)↗

DOE OSTI2025

Optimal Control of Differentially Private EV Charging: A Scalable Learning Approach Under Uncertainty

Internet of Things (IoT)-enabled electric vehicles (IoEVs) enable intelligent charging coordination that accounts for grid congestion. However, increased data exchange raises privacy concerns, as charging patterns can reveal sensitive driver behavior to grid operators. Here, we propose a differentially private (DP) EV charging framework that enables coordinated control while protecting driver data with theoretical privacy guarantees. Nevertheless, integrating DP inevitably introduces uncertainty into the control strategy for EVs, which can lead to infeasible solutions. To tackle this challenge, we develop a feasible and scalable control algorithm based on constrained reinforcement learning (CRL) and convex hulls. While our framework is designed to handle the uncertainty introduced by DP, it is general and also applicable to other sources of uncertainty in EV charging, such as the stochastic nature of driver behavior and renewable variability. This ensures feasible and privacy-preserving coordination of EV charging at scale. Our method constructs convex hulls within the action space to guarantee feasibility under stochastic constraints and incorporates constraint reduction techniques to improve scalability. Case studies based on IEEE benchmark systems demonstrate that the proposed approach effectively balances feasibility under uncertainty, scalability, and privacy in large-scale EV charging control.

Engineering - Power transmission and distribution↗

DOE OSTI2025

Energy gain scale calibration of the XRISM Resolve microcalorimeter spectrometer: ground calibration results and on-orbit comparison

The Resolve instrument aboard the X-ray Imaging and Spectroscopy Mission (XRISM) is a 36-pixel microcalorimeter spectrometer that provides nondispersive spectroscopy with ∼ 5 eV spectral resolution in the soft X-ray waveband. Resolve has a requirement to provide an absolute energy-scale calibration of ± 2 eV from 0.3 to 12 keV. We describe our ground calibration strategy and results of a subset of the ground calibration campaigns, including a discussion of improvements in the energy scale ground calibration compared with Hitomi’s. These improvements include calibration of the low-energy band below 4 keV with the instrument in the flight dewar and the dewar aperture door open, which was not performed for Hitomi, and thorough measurements over an extended high-energy waveband to 22 keV. We also developed an improved technique for gain calibration of “mid-res” secondary events, which have suppressed gain due to proximity to a preceding X-ray event (18 to 70 ms) on the same pixel. We provide a discussion of the on-orbit energy scale monitoring campaigns and an assessment of the Resolve energy scale uncertainties, a key parameter for astrophysics analysis. Energy-scale calibration approaches for future space-based instruments, including the X-ray Integral Field Unit on Athena and microcalorimeter spectrometers proposed or under discussion for future X-ray observatory concepts, have heritage in the calibration of XRISM. We briefly comment on lessons learned from Resolve calibration that are relevant for these future instruments.

Aluminum↗

DOE OSTI2025

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

CCDC 2391277: Experimental Crystal Structure Determination

(mu-2↗

DOE OSTI2026

Extremely Metal-poor Galaxies in DESI DR1: Connections to Galaxies in the Early Universe

Extremely metal-poor galaxies (XMPGs), defined as having metallicities below 10% of the solar value, are considered possible local analogs to primordial systems and offer a unique window into early galaxy evolution. This study presents a large-scale search for XMPGs using data from the Dark Energy Spectroscopic Instrument Data Release 1, systematically evaluating their resemblance to high-redshift galaxies. From a parent sample of more than 14 million galaxies, we identify 662 (556 new) confirmed XMPGs and 763 (666 new) high-quality candidates via the direct T$_{e}$ method. Our results reveal that XMPGs follow a distinct star-forming main sequence (SFMS) that is elevated and shallower than that of the comparing star-forming galaxies. Notably, at higher stellar masses (M$_{⋆}$ > 10$^{7.5}$M$_{⊙}$), the XMPG SFMS converges with the sequence observed in high-redshift galaxies by the James Webb Space Telescope, indicating that mature XMPGs sustain star formation rates comparable to their primordial counterparts. Furthermore, XMPGs consistently deviate below the local fundamental metallicity relation, mirroring high-redshift galaxy behavior. These findings demonstrate that XMPGs not only exhibit low metallicities but also preserve scaling relations characteristic of the early Universe, confirming their potential value as local laboratories for studying early galaxy formation processes.

Sui, Jipeng [Beijing Observ.; Beijing, GUCAS] (ORC↗

DOE OSTI2026

Short‐Term Hourly Weather Forecasting Using PredRNN With Image Preprocessing

Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a short-term hourly weather forecast framework with a wavelet transform function for data preprocessing and a spatiotemporal DL model, PredRNN, for predicting five surface atmospheric variables, including wind speed and direction, mean sea level pressure (MSLP), temperature, and precipitation. The framework demonstrated promising results. It produces global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of 1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature. Although our model does not surpass state-of-the-art AI weather forecast models across all metrics, it outperforms these models in precipitation forecasting and wind prediction at short lead times and achieves comparable accuracy for MSLP. Its native hourly forecasting capability, together with training on widely accessible GPU hardware, contributes meaningfully to the advancement of accessible DL weather forecasting methods. Our work highlights the importance of integrating temporal components and data transformation techniques to improve the predictability and accuracy of weather forecasts.

Tran, Hoang [Pacific Northwest National Laboratory↗