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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Neutrino Oscillations at the NOvA Experiment

NOvA is a long-baseline neutrino oscillation experiment located at Fermi National Accelerator Laboratory (Fermilab) in Batavia, IL, USA. Its main goal is to extract neutrino oscillation parameters by examining the disappearance of $\nu_\mu \ (\bar{\nu}_\mu)$ and the appearance of $\nu_e \ (\bar{\nu}_e)$ at its far detector. The experiment uses the NuMI beamline at Fermilab, which delivers a high-purity neutrino and antineutrino beam. NOvA consists of two functionally similar, high-resolution liquid scintillator tracking calorimeters, both placed 14.6 milliradians off the beam axis. The near detector, positioned 100 m underground and 1 km from the beam's origin, captures unoscillated $\nu_\mu (\bar{\nu}_\mu)$ and instrinsic beam $\nu_e \ (\bar{\nu}_e)$ events. The far detector, located 810 km away from the beam source in Ash River, Minnesota, measures both unoscillated $\nu_\mu \ (\bar{\nu}_\mu)$ and those that have oscillated into $\nu_\mu \ (\bar{\nu}_\mu) \to \nu_e \ (\bar{\nu}_e)$. This poster will cover the latest results on three-flavor neutrino oscillation parameters, derived from an exposure of neutrino beam of $26.60 \times 10^{20}$ POT and anti-neutrino beam exposure of $12.50\times 10^{20}$ POT including an additional low energy $\nu_e$ sample.

Singh, Ishwar [Delhi U.]↗

DOE OSTI2026

Continuing Analysis of Charge Current Interactions in ANNIE

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a gadolinium-loaded water Cherenkov detector on the Fermilab Booster Neutrino Beam (BNB).Using νμ in the energy range of 500 to 1000 MeV, ANNIE is designed to measure final-state neutrons.In this poster we will cover the ongoing analysis work exploring Charged-Current neutrino interactions. Charged-Current (CC) νμ interactions in this regime have uncertainties in the relative contributions of quasielastic and res- onance production. Together with intranuclear final-state interactions (FSI) and missing hadronic energy, these interactions drive important biases in neutrino energy reconstruction. ANNIE mit- igates these effects by combining muon kinematics from the downstream Muon Range Detector (MRD) with neutron identification via delayed gamma cascades from thermal captures on gadolin- ium. We characterize CC0π and Δ samples by measuring neutron multiplicity versus event topology and reconstructed kinematics, comparing neutron-tagged data to interaction-model predictions to probe resonance production and pion FSI/absorption

Fleming, Dylon [UC, Davis; Fermilab] (ORCID:000000↗

DOE OSTI2020

High-Frequency Signature-Based Fault Detection for Future MV Distribution Grids: Preprint

Increased deployment of Distributed Energy Resources (DERs) and microgrids are significantly changing the operation of distribution grid. Increasing penetration of power electronics based DERs also raise several operational problems such as protection coordination issues, voltage raise, recloser synchronization, stability, and frequency control. Legacy systems are designed for unidirectional power flow. Bidirectional power flows, and varying short circuit levels are some of the major challenges in implementing these legacy protection schemes for future power grid with high penetration of power electronics based DERs. Inverter based DER’s can contribute to a maximum of 2 p.u. fault current. Interconnection standards might require even lower fault current contribution. If these protection related challenges are not addressed, it will result in limiting the penetration of DERs in distribution feeders. In this paper, the feasibility of high frequency signature generated by transients is evaluated to identify and locate faults in a distribution system. The paper will discuss the need for an accurate line modeling approach to accurately capture the high frequency signatures. These high frequency signatures are naturally occurring electromagnetic transients for events such as switching events in the network, faults events, etc. This is different than that implemented in transmission systems as the proposed approach utilizes advanced signal processing techniques to retrieve valuable information from the high frequency signatures generated during faults and does not just rely on time delay measurements between reflections of the high frequency signals. In the full paper, information of a modified IEEE distribution feeder test system to accurately model the high frequency signatures will be provided. The distribution lines will be modeled using universal line modeling (ULM) technique in an electromagnetic transients program (EMTP) modeling platform. Different test cases will be simulated and these test cases will include different fault types, multiple fault locations, capacitor bank switching, etc. The feeder system modeled for these simulations will also include power electronics based DERs. Additionally, experimental results demonstrating the high frequency signatures generated due to a transient event in a medium voltage (13.8 kV) distribution line will also be presented in the full paper. Furthermore, the analysis combined with simulation and experimental results in the full paper will help demonstrate the feasibility and efficacy of the proposed protection scheme. The developed protection scheme will not just help solve problems generated by bidirectional current flow, varying short-circuit current capacity, but also help in proliferation of DERs in distribution systems.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Exploiting Isospin Symmetry to Study the Role of Isomers in Stellar Environments

Proton capture on the excited isomeric state of 26 Al strongly influences the abundance of 26 Mg ejected in explosive astronomical events and, as such, plays a critical role in determining the initial content of radiogenic 26 Al in presolar grains. This reaction also affects the temperature range for thermal equilibrium between the ground and isomeric levels. We present a novel technique, that exploits the isospin symmetry of the nuclear force, to address the long-standing challenge of determining proton-capture rates on excited nuclear levels. Such a technique has in-built tests that strongly support its veracity and, for the first time, we have experimentally constrained the strengths of resonances that dominate the astrophysical 26m Al(p, γ) 27 Si reaction. Furthermore, these constraints demonstrate that the rate is at least a factor ~ 8 lower than previously expected, indicating an increase in the stellar production of 26 Mg and a possible need to reinvestigate sensitivity studies involving the thermal equilibration of 26 Al.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Results of the 2018 Wood Stove Design Challenge

The 2018 Wood Stove Design Challenge was an international design competition which sought to identify top performing residential wood stoves based on automation. To prepare for the event, the Northeast States for Coordinated Air Use Management (NESCAUM) developed a testing protocol that challenged the stoves by testing them in more field-like conditions—capturing emissions from start-up, reloading a stove, and the use of larger piece sizes. Flue gas emissions were measured using a combination of in-stack and novel dilution sampling methods, as the event occurred on the National Mall in Washington D.C. in a non-laboratory setting. Particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), and methane (CH4) were measured from three stoves in real-time. A combustion efficiency was also calculated for each stove in real-time. Measured PM emission rates ranged between 1.8 and 8.0 g/hr for all stoves and operating conditions, with the highest emissions being measured during cold start in all cases. The test average PM emission rates were 2.4, 4.0, and 2.4 g/hr for stoves A, B, and C, respectively. Results showed emissions measured during transient operations, which are often excluded in current certification methods, can be significantly higher than steady-state periods—echoing other studies and highlighting the importance of testing in various operational modes for more realistic emission estimates. Additionally, the stoves had overall CO emission rates of 48.8, 284.8, and 100.8 g/hr, for stoves A, B and C, respectively. Calculated PM and CO emission factors overall were low considering the testing protocol sought to follow more challenging yet realistic practices in terms of fuel loading and operating procedures. The overall estimated combustion efficiency for stoves A, B, and C was 85%, 78%, 76%, respectively and in all cases was lowest during the cold start period.This report details the successfully proposed and assembled instrumentation that was relatively portable for field-site testing and the results of the emissions measurements for the 2018 Wood Stove Design Challenge competition. Overall, the repeatability of each stove was favorable with coefficients of variation (COV) ranging from 7 to 15% for measured PM concentrations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Global mean frequency increases of daily and sub-daily heavy precipitation in ERA5

Abstract Changes in heavy precipitation frequency can be viewed as a change in the event return period, a common metric of risk. Compared with intensity, frequency changes are less well-studied and past work has largely been constrained to analysis of well-instrumented regions. We exploit the latest ERA5 reanalysis and its global hourly accumulations at 1/4° spatial resolution and apply a metric that captures frequency changes across both wet and dry regions. According to ERA5 and in a global average sense, during 1989–2018, hourly events that occurred once per year in 1979–1988 increased in frequency by 71 (53–93, 95% range) %, while the one day per year heavy event frequency increased by 44 (37–54) %. Thus, hourly events that occurred once per year in the baseline decade are on track to double in frequency by 2021–2030, and the daily events by 2047–2056. Furthermore, our results replicate prior findings that relative frequency increases are larger for increasingly rare events, and for the first time we quantify that mean frequency increases have been greater over ocean than land. Ocean increases are larger by factors of 3.0 and 2.1 for the hourly and daily events that occurred once per year in 1979–1988, respectively.

Environmental Sciences & Ecology↗

DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Increased frequency of planetary wave resonance events over the past half-century

We demonstrate a tripling in the frequency of planetary wave resonance events over the past halfcentury, coinciding with the rise in persistent boreal summer weather extremes. This increase aligns with changes in the underlying climate conditions favoring these events, including amplified Arctic warming and land-sea thermal contrast. We also observe increased prevalence of resonant amplification events following the mature phase of strong El Niño events, suggesting that such events may precondition the mean state conditions in ways that favor large-scale quasi-stationary wave patterns and quasi-resonant wave amplification. Since the impact of anthropogenic warming on quasi-resonant amplification is not well captured by current-generation climate models, it is likely that models are underpredicting the potential increase, indicating even greater risk of persistent extreme summer weather events with ongoing warming.

Arctic amplification↗

DOE OSTI2025

First measurement of neutron capture multiplicity in neutrino-oxygen neutral-current quasielasticlike interactions using an accelerator neutrino beam

We report the first measurement of neutron capture multiplicity in neutrino-oxygen neutral-current quasielasticlike interactions at the gadolinium-loaded Super-Kamiokande detector using the T2K neutrino beam, which has a peak energy of about 0.6 GeV. A total of 30 neutral-current quasielasticlike event candidates were selected from T2K data corresponding to an exposure of 1.76 × 10 20 protons on target. The 𝛾 ray signals resulting from neutron captures were identified using a neural network. The flux-averaged mean neutron capture multiplicity was measured to be 1.37 ± 0.33 (stat.)$^{+0.17}_{−0.27}$ (syst.), which is compatible within 2.3 sigma than predictions obtained using our nominal simulation. We discuss potential sources of systematic uncertainty in the prediction and demonstrate that a significant portion of this discrepancy arises from the modeling of hadron-nucleus interactions in the detector medium.

particle interactions↗

DOE OSTI2024

Quantile regression-enriched event modeling framework for dropout analysis in high-temperature superconductor manufacturing

High-temperature superconductor (HTS) tapes have shown promising characteristics of high critical current, which are prerequisites for applications in high-field magnets. Due to the unstable growth conditions in the HTS manufacturing process, however, the frequent occurrences of dropouts in the critical current impede the consistent performance of HTS tapes. To manufacture HTS tapes with large scale, high yield, and uniform performance, it is essential to develop novel data analysis approaches for modeling the dropouts and identifying the related important process parameters. Conventional methods for modeling recurrent events, such as the point process, require the extraction of events from quality measurements. As the critical current is a continuous process, it may not comprehensively represent the drop patterns by transforming the time-series measurements into a set of events. Here, to solve this issue, we develop a novel quantile regression-enriched event modeling (QREM) framework that integrates the non-homogeneous Poisson process for modeling the occurrence of dropouts and the quantile regression for capturing the drop patterns. By incorporating the feature selection and regularization, the proposed framework identifies a set of significant process parameters that can potentially cause the dropouts of HTS tapes. The proposed method is tested on real HTS tapes produced using an advanced manufacturing process, successfully identifying important parameters that influence dropout events including the substrate temperature and voltage. The results demonstrate that the proposed QREM method outperforms the standard point process in predicting the occurrence of dropouts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Online Event Detection in Synchrophasor Data with Graph Signal Processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

Event detection↗

DOE OSTI2020

Online Event Detection in Synchrophasor Data with Graph Signal Processing

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Chapter 6: Abuse Response of Batteries Subjected to Mechanical Impact

Electrochemical and thermal models to simulate nominal performance and abuse response of lithium-ion cells and batteries have been reported widely in the literature. Studies on mechanical failure of cell components and how such events interact with the electrochemical and thermal response are relatively less common. This chapter outlines a framework developed under the Computer Aided Engineering for Batteries program to couple failure modes resulting from external mechanical loading to the onset and propagation of electrochemical and thermal events that follow. Starting with a scalable approach to implement failure criteria based on thermal, mechanical, and electrochemical thresholds, we highlight the practical importance of these models using case studies at the cell and module level. The chapter also highlights a few gaps in our understanding of the comprehensive response of batteries subjected to mechanical crash events, the stochastic nature of some of these failure events, and our approach to build safety maps that help improve robustness of battery design by capturing the sensitivity of some key design parameters to heat generation rates under different mitigation strategies.

abuse simulations↗

DOE OSTI2020

Assessment of Low-Altitude Atmospheric Turbulence Models for Aircraft Aeroelasticity

We investigate the dynamic aeroelastic response of large but slow aircraft in low-altitude atmospheric turbulence. To this end, three turbulence models of increasing fidelity, namely, the one-dimensional von Kármán model, the two-dimensional Kaimal model and full three-dimensional wind fields extracted from large-eddy simulations (LES) are used to simulate ambient turbulence near the ground. Load calculations and flight trajectory predictions are conducted for a representative high-aspect-ratio wing aircraft, using a fully coupled nonlinear flight dynamics/aeroelastic model, when it operates in background atmospheric turbulence generated by the aforementioned models. Comparison of load envelopes and spectral content, on vehicles of varying flexibility, shows strong dependency between the selected turbulence model and aircraft aeroelastic response (e.g. 58% difference in the predicted magnitude of the wing root bending moment between LES and von Kármán models). This is mainly due to the presence of large flow structures at low altitudes that have comparable dimensions to the vehicle, and which despite the relatively small wind speeds within the Earth boundary layer, result in overall high load events for slow-moving vehicles. Results show that one-dimensional models that do not capture those effects provide fairly non-conservative load estimates and are unsuitable for very flexible airframe design.

atmospheric turbulence↗

DOE OSTI2026

Substrate-Mediated Evaporation and Stochastic Evolution of Supported Au Nanoparticles

Here, we use in situ transmission electron microscopy with automated tracking to study supported gold nanoparticles (NPs) during high-temperature vacuum annealing. The average mass loss per NP is governed by a flat, nearly size-independent substrate-mediated evaporation profile. On top of this mean shrinkage, individual NPs show significant fluctuations in apparent growth or shrinkage, and NP volume follows a random-walk-like trajectory. To rationalize both the ensemble-mean behavior and the particle-resolved variability, we develop a self-consistent theory that couples substrate-mediated evaporation to collective 2D Ostwald-type mass exchange through a shared adatom field, described in terms of a renormalized screening length and background concentration. In the experimentally relevant regime, the theory predicts an approximately size-independent mean shrinkage rate and clarifies how net mass loss suppresses classical coarsening. Superimposed on this deterministic drift, we quantify stochastic volume trajectories and capture their fluctuation spectrum with a minimal Langevin description consistent with intermittent adatom attachment and detachment events. In addition, we characterize the lateral diffusive motion of NPs, which is responsible for their coalescence. Altogether, our results highlight that stochasticity is intrinsic at the nanoscale and that predicting the evolution of supported NPs at early and intermediate times requires a unified framework combining substrate-mediated evaporation, collective mass exchange, and stochastic fluctuations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

The study of C-13(a, n)O-16 reactions in the Majorana Demonstrator calibration data

Neutron captures and delayed decays of reaction products are common sources of backgrounds in ultra-rare event searches. In this work, we studied 13C(a, n)1640 O reactions induced by a-particles emitted within the calibration sources of the Majorana Demonstrator. These sources are thorium-based calibration standards enclosed in carbon-rich materials. The reaction rate was estimated by using the 6129 keV ?-rays emitted from the excited 1643 O states that are populated when the incoming a-particles have enough energy. Thanks to the excellent energy performance of the Demonstrator’s germanium detectors, these characteristic photons can be clearly observed in the calibration data. Facilitated by Geant4 simulations, a comparison between the observed 6129 keV photon rates and predictions by a TALYS-based software was performed. The measurements and predictions were found to be consistent with each other, albeit with large statistical uncertainties. This agreement provides supports for further background predictions for (a, n)-reactions in future double-beta decay search efforts.

6 ≤ A ≤ 19↗

DOE OSTI2022

Multiplicative Shot-Noise: A New Route to Stability of Plastic Networks

Fluctuations of synaptic weights, among many other physical, biological, and ecological quantities, are driven by coincident events of two “parent” processes. Here we propose a multiplicative shot-noise model that can capture the behaviors of a broad range of such natural phenomena, and analytically derive an approximation that accurately predicts its statistics. We apply our results to study the effects of a multiplicative synaptic plasticity rule that was recently extracted from measurements in physiological conditions. Using mean-field theory analysis and network simulations, we investigate how this rule shapes the connectivity and dynamics of recurrent spiking neural networks. The multiplicative plasticity rule is shown to support efficient learning of input stimuli, and it gives a stable, unimodal synaptic-weight distribution with a large fraction of strong synapses. The strong synapses remain stable over long times but do not “run away.” Our results suggest that the multiplicative shot-noise offers a new route to understand the tradeoff between flexibility and stability in neural circuits and other dynamic networks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Northern Region Coal Prospects

This presentation was delivered to the audience attending the U.S. DOE/USEA Northern Regional Coal Workshop in Bismarck, ND on March 11, 2026. The presentation highlights the opportunity for coal plants in the region to provide power to military installations and data centers. There is also an analysis on how coal-fired power stations maintain regional supply during extreme cold weather events. Finally, a summary slide is presented that highlights a recent business case analysis on diverting captured carbon dioxide for use in enhanced oil recovery in the Bakken.

Bakken↗