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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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At least 415 records · Page 23

A laser powder bed fusion system for operando synchrotron x-ray imaging and correlative diagnostic experiments at the Stanford Synchrotron Radiation Lightsource

Laser powder bed fusion (LPBF) is a highly dynamic multi-physics process used for the additive manufacturing (AM) of metal components. Improving process understanding and validating predictive computational models require high-fidelity diagnostics capable of capturing data in challenging environments. Synchrotron x-ray techniques play a vital role in the validation process as they are the only in situ diagnostic capable of imaging sub-surface melt pool dynamics and microstructure evolution during LPBF-AM. In this article, a laboratory scale system designed to mimic LPBF process conditions while operating at a synchrotron facility is described. The system is implemented with process accurate atmospheric conditions, including an air knife for active vapor plume removal. Significantly, the chamber also incorporates a diagnostic sensor suite that monitors emitted optical, acoustic, and electronic signals during laser processing with coincident x-ray imaging. The addition of the sensor suite enables validation of these industrially compatible single point sensors by detecting pore formation and spatter events and directly correlating the events with changes in the detected signal. Experiments in the Ti–6Al–4V alloy performed at the Stanford Synchrotron Radiation Lightsource using the system are detailed with sufficient sampling rates to probe melt pool dynamics. X-ray imaging captures melt pool dynamics at frame rates of 20 kHz with a 2 µm pixel resolution, and the coincident diagnostic sensor data are recorded at 470 kHz. Furthermore, this work shows that the current system enables the in situ detection of defects during the LPBF process and permits direct correlation of diagnostic signatures at the exact time of defect formation.

36 MATERIALS SCIENCE↗

A Digital Twin for an Inverter-Based Resource Power Plant: Real-time data streaming unlocks situation awareness

Here, this study presents the development and successful implementation of a digital twin specifically designed for a grid-connected IBR power plant. By integrating a reduced-order model of the IBR system and dynamically updating the grid impedance with real-time data, the digital twin effectively captures and replicates the behavior of the physical system. Its accuracy and reliability are validated through critical test scenarios, including a three-phase fault and a line-tripping event. The results confirm that the digital twin closely emulates its physical counterpart, demonstrating its strong potential for real-time analysis, system monitoring, and predictive decision making in modern power systems.

Digital twins↗

Determination of jet calibration and energy resolution in proton–proton collisions at $\sqrt{s} = 8~\hbox {TeV}$ using the ATLAS detector

The jet energy scale, jet energy resolution, and their systematic uncertainties are measured for jets reconstructed with the ATLAS detector in 2012 using proton–proton data produced at a centre-of-mass energy of 8 TeV with an integrated luminosity of 20fb -1 . Jets are reconstructed from clusters of energy depositions in the ATLAS calorimeters using the anti-k t algorithm. A jet calibration scheme is applied in multiple steps, each addressing specific effects including mitigation of contributions from additional proton–proton collisions, loss of energy in dead material, calorimeter non-compensation, angular biases and other global jet effects. The final calibration step uses several in situ techniques and corrects for residual effects not captured by the initial calibration. Furthermore, these analyses measure both the jet energy scale and resolution by exploiting the transverse momentum balance in γ + jet, Z + jet, dijet, and multijet events. A statistical combination of these measurements is performed. In the central detector region, the derived calibration has a precision better than 1% for jets with transverse momentum 150 GeV < p T < 1500 GeV, and the relative energy resolution is (8.4 ± 0.6)% for p T =100 GeV and (23 ± 2)% for p T =20 GeV. The calibration scheme for jets with radius parameter R = 1.0, for which jets receive a dedicated calibration of the jet mass, is also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Tunnel Event 2020 (G-tunnel event modeling) [Slides]

HOSS (Hybrid Optimization Software Suite) is a fracture mechanics code that properly conserves mass, energy and momentum. HOSS-FSIS was utilized to conduct exploratory calculations (2D plane strain/3D) for the G-tunnel event 2020 where a pseudo NPE 1.1kt HE equivalent source is used. The purpose of this analysis is to determine if the HOSS fluid-solid coupling solver can capture relevant down-tunnel phenomenology.

42 ENGINEERING↗

Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas

Estimation of daily suspended sediment concentration (SSC) is required for water resources and environment management. In this paper, a copula-based stochastic method was proposed for daily SSC simulation. Here, the multivariate copula function, constructed based on a bivariate copula and two bivariate conditional probability distributions, was used to model the temporal and cross dependence structures in daily SSCs. Then, the daily SSCs were generated by sampling from the multivariate conditional distribution. As a result, synthetic long-term SSCs data beyond the limited observation period can be provided for water resources managers, which plays a critical role in accurately estimating frequency and magnitude of extreme SSCs events. The proposed method was under rigorous examination by applying to a case study at Pingshan station in the Jinsha River Basin, China. Results showed that the generated daily SSC sequences not only had a high degree of accuracy in preserving the statistical characteristics of the daily SSC observations, but also captured both the temporal correlation and the cross-correlation between the daily streamflow and daily SSC. Specifically, the average daily relative error values corresponding to mean, standard deviation, skewness, lag-1 temporal correlation, and cross correlation were 0.87%, 4.24%, 7.52%, 0.51% and 2.02%, respectively. The multivariate copula framework proposed here can accurately and efficiently generate long-term daily SSC data for water resources management such as frequency analysis and risk assessment of extreme SSC events.

54 ENVIRONMENTAL SCIENCES↗

Flux Eruption Events Drive Angular Momentum Transport in Magnetically Arrested Accretion Flows

We evolve two high-resolution general relativistic magnetohydrodynamic simulations of advection-dominated accretion flows around nonspinning black holes (BHs), each over a duration ~3 × 10 5 GM BH /c 3 . One model captures the evolution of a weakly magnetized (SANE) disk and the other that of a magnetically arrested disk (MAD). Magnetic flux eruptions in the MAD model push out gas from the disk and launch strong winds with outflow efficiencies at times reaching 10% of the incoming accretion power. Despite the substantial power in these winds, average mass outflow rates remain low out to a radius ~100GM BH /c 2 , only reaching ~60%–80% of the horizon accretion rate. The average outward angular momentum transport is primarily radial in both modes of accretion, but with a clear distinction: magnetic flux eruption–driven disk winds cause a strong vertical flow of angular momentum in the MAD model, while for the SANE model, the magnetorotational instability (MRI) moves angular momentum mostly equatorially through the disk. Further, we find that the MAD state is highly transitory and nonaxisymmetric, with the accretion mode often changing to a SANE-like state following an eruption before reattaining magnetic flux saturation with time. The Reynolds stress changes directions during such transitions, with the MAD (SANE) state showing an inward (outward) stress, possibly pointing to intermittent MRI-driven accretion in MADs. Pinning down the nature of flux eruptions using next-generation telescopes will be crucial in understanding the flow of mass, magnetic flux, and angular momentum in sub-Eddington accreting BHs like M87* and Sagittarius A*.

79 ASTRONOMY AND ASTROPHYSICS↗

Optimizing infrasound observations for sample return capsule re-entry: Insights from OSIRIS-REx and Hayabusa2

The atmospheric entry of meteoroids presents a rare and unpredictable phenomenon, posing challenges for systematic observation and detailed characterization. Such events are nonetheless critical for advancing understanding of acoustic wave propagation, atmospheric structure, and entry dynamics. In contrast, sample return capsules (SRCs) from space missions follow well constrained re-entry trajectories, enabling planned observations of shock wave generation and propagation under controlled conditions. This study compares two SRC atmospheric entries, Hayabusa2 in 2020 and OSIRIS-REx in 2023, to assess how different infrasound array configurations influence shock wave detection and trajectory validation. Hayabusa2’s re-entry was monitored using a distributed network of 28 portable infrasound sensors across seven arrays in Woomera, Australia, permitting three-dimensional reconstruction of the trajectory and analysis of wave characteristics. For OSIRIS-REx, a compact four-sensor array deployed near Eureka Airport provided trajectory confirmation through arrival-time differences and back-azimuth estimates. Spectral and waveform analyses revealed differences in signal properties associated with variations in entry angle and velocity. The results illustrate both the strengths and the limitations of the deployed array configurations. The dense and distributed arrays during Hayabusa2’s re-entry enabled detailed trajectory reconstruction, whereas the compact array at Eureka primarily provided confirmation of signal coherence and back-azimuth consistency. These case studies highlight that even relatively small arrays, if located close to the predicted ground track, can still capture useful information on arrival direction and timing. Such insights provide practical guidance for planning future observational campaigns of SRC returns and other controlled atmospheric entries, and may also inform approaches to opportunistic observations of meteoroid events and other atmospheric acoustic phenomena.

celestial mechanics: orbit determination↗

Spatiotemporal forecasting of the edge localized modes in tokamak plasmas using neural networks

Artificial intelligence techniques have been increasingly adopted by the plasma and fusion science to address problems like plasma reconstruction, surrogate modeling, and tokamak/stellarator optimization. A key focus in sustained fusion research is the prediction and mitigation of edge-localized-modes (ELMs), instabilities that occur in short, periodic bursts and can cause erosion to the tokamak vessel wall. Recent research has demonstrated the power of neural networks in approximating continuous functions. In this work, we build spatiotemporal forecasting models that can predict the onset of ELMs and their evolution at early stages. We leverage recent advances in generative modeling, sequence-to-sequence modeling, and Fourier neural operators to propose architectures and training strategies that can learn to forecast short to long term dynamics of the noisy signals due to ELMs. We benchmark the developed model against a state-of-the-art foundation model using the beam emission spectroscopy (BES) data that captures the plasma fluctuations due to ELMs over a 8 x 8 spatial grid. Our models demonstrate high accuracy, outperforming the baselines, in predicting the evolution of BES signals during ELM events. Furthermore, the developed models exhibit high accuracy in predicting the rapid rise and relaxation of the signals due to ELMs within 30–80 µs.

edge localized modes↗

Convection–Kelvin Wave Coupling in a Global Convection-Permitting Model

A convectively coupled equatorial Kelvin wave (CCKW) was observed over the equatorial Indian Ocean in early November 2011 during the DYNAMO field campaign. This study examines the structure of the CCKW event using two simulations made using the MPAS model: one with 3-km grid spacing without convective parameterization and another with a 15-km grid and parameterized convection. Both simulations qualitatively capture the observed structure of the CCKW, including its vertical tilt and progression of cloud/precipitation structures. The two simulations, however, differ substantially in the amplitude of the CCKW-associated precipitation. While the 3-km run realistically captures the observed modulation of precipitation by the CCKW, the 15-km simulation severely underestimates its magnitude. To understand the difference between the two MPAS simulations regarding wave–convection coupling within the CCKW, the relationship of precipitation with convective inhibition, saturation fraction, and surface turbulent fluxes is investigated. Results show that the 15-km simulation underestimates the magnitude of the CCKW precipitation peak in association with its unrealistically linear relationship between moisture and precipitation. Precipitation, both in observations and the 3-km run, is predominantly controlled by saturation fraction and this relationship is exponential. In contrast, the parameterized convection in the 15-km run is overly sensitive to convective inhibition and not sensitive enough to environmental moisture. The implications of these results on CCKW theories are discussed.

54 ENVIRONMENTAL SCIENCES↗

DetSuM: Detector Surrogate Model for LArTPC

In large neutrino experiments such as the Deep Underground Neutrino Experiment (DUNE), estimating detector response uncertainties typically requires simulation samples that consume substantial computing resources and time. To mitigate this challenge, we present DetSuM, an uncertainty-aware surrogate model designed to capture the detector response variations with reduced computing load compared to full simulations. This poster describes the construction and evaluation of DetSuM using simulation and reconstruction datasets in a rare-event search at DUNE. We assess DetSuM's ability to predict key detector-response variations and their associated uncertainties, discuss current limitations, and outline improvements to extend its validity in systematics studies of DUNE physics.

Li, Aobo [UC, San Diego]↗

Linking classical PRA models to a dynamic PRA

Here, this paper presents a series of methods designed to incorporate classical Probabilistic Risk Assessment (PRA) models such as Event Trees (ETs) and Fault Trees (FTs) into dynamic PRA. In contrast to classical PRA, dynamic PRA couples stochastic methods with system simulators to determine the risks associated with complex systems such as nuclear power plants. Compared with classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the progression of the accident. As part of a dynamic PRA analysis, it is not uncommon that parts of the system to be analyzed might not require a computationally expensive simulation model. These parts could be in fact modeled by employing classical PRA models (e.g., a FT). Here, we present a set of methods and tools that can be used to link the most common classical PRA models (ETs, FTs, reliability block diagrams and Markov models) to simulation codes such as RELAP5-3D: creating a “hybrid PRA.” In order to show the potential of such an hybrid PRA we employ this method to verify ET modeling assumptions (e.g., success criteria) using a large break loss of coolant accident initiating event as a test case. In this respect, we link a set of FTs from the original PRA to the RELAP5-3D code and perform a hybrid PRA. The FTs are employed to model the control logic of several safety systems and to propagate component failures throughout the system. Provided the generated dynamic PRA data, we show how conservative assumptions in the original PRA can be identified and how such original PRA can be modified by updating success criteria captured by the set of RELAP5-3D simulation runs.

97 - MATHEMATICS AND COMPUTING↗

Microcanonical Kinetics of Water-Mediated Proton Transfer in 4ABAH + ·(H 2 O) n = 4–6 Clusters (ABA = Aminobenzoic Acid): A Model System for Size-Dependent Relaxation to Ergodic Behavior

Here, we leverage the unique properties of the 4ABAH + · (H 2 O) n clusters (ABA = 4-aminobenzoic acid, n = 4−6) to quantitatively address how a finite, isolated system evolves into an ergodic condition starting from localized arrangements in configuration space. This system adopts two distinct structural isomers in which water molecules cluster around the cationic centers of its two protomers with widely separated positive charge centers. These isomers arise from excess proton attachment to either the acid (O) or amino (N) group on opposite sides of the benzene ring. Both forms are captured and kinetically trapped using cryogenic ion methods and then selectively vibrationally excited through their mutually exclusive IR bands involving NH and OH stretching fundamentals. Because the IR excitation lies below the water binding energy, the system can evolve to explore slow, rare events that lead to the interconversion between the two isomers. The rates of these intracluster reactions are determined by using a pump−probe scheme involving ∼5 ns IR pump and UV probe lasers. The rates occur on the microsecond time scale, leading to steady state populations of the isomers, thus revealing the cluster size-dependent fractionation between the two species at microcanonical equilibrium. The steady state distributions are correlated with the expected trend in the cluster size-dependent reaction energetics, which are in turn consistent with changes in the relative densities of states of the two species. These results thus provide an unusually clear example in which complex, protic-solvent-mediated chemical transformations are captured within a finite system at a precisely determined internal energy.

Rana, Abhijit [Yale Univ., New Haven, CT (United S↗

Volume IV. The DUNE far detector single-phase technology

The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay -- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. DUNE is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. Central to achieving DUNE's physics program is a far detector that combines the many tens-of-kiloton fiducial mass necessary for rare event searches with sub-centimeter spatial resolution in its ability to image those events, allowing identification of the physics signatures among the numerous backgrounds. In the single-phase liquid argon time-projection chamber (LArTPC) technology, ionization charges drift horizontally in the liquid argon under the influence of an electric field towards a vertical anode, where they are read out with fine granularity. A photon detection system supplements the TPC, directly enhancing physics capabilities for all three DUNE physics drivers and opening up prospects for further physics explorations. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. Volume IV presents an overview of the basic operating principles of a single-phase LArTPC, followed by a description of the DUNE implementation. Each of the subsystems is described in detail, connecting the high-level design requirements and decisions to the overriding physics goals of DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment

This study presents a framework to quantify changes in soil saturation in response to flooding caused by extreme hydrologic perturbation on coastal ecosystems at the interfaces and transition between terrestrial and aquatic systems. Subsurface heterogeneity limits the use of in situ measurements to quantify subsurface flow during flooding due to the spatial discontinuity in the measured data. While geophysical methods, including time-lapse electrical resistivity imaging (ERI), are increasingly used to monitor soil hydrological processes, their abilities to parameterize flow models have been underutilized. This study combines background ERI, ground penetrating radar (GPR), time-lapse ERI, soil characterization, and a numerical flow model developed using an Advanced Terrestrial Simulator (ATS) code to quantify the infiltration pathway and describe the hydrological dynamics during a simulated flooding experiment. We assessed the use of two conceptual models developed using [1] ERI and GPR data that described the stratigraphic distribution, and time-lapse ERI that mapped permeability contrast, and [2] information from a national soil database for capturing changes in saturation. Combining the ERI and GPR results with soil core data revealed the stratigraphic heterogeneity at the site with a silty clay layer from 1 to 2 m between an overlying loamy topsoil and an underlying saturated silty sand. This silty clay layer could restrict deep infiltration. The time-lapse ERI showed up to a 35% decrease in resistivity, which correlated with soil moisture data (R 2 value > 0.53) and revealed preferential infiltration zones used to inform the flow model. Numerical simulation results from both the geophysics- and soil database-informed models quantified changes in soil saturation with calculated soil moistures that agreed with field data. The geophysics-informed model captured more of the system’s variability, reflective of shallow subsurface heterogeneities. The framework presented will serve as a precursor for a robust ecohydrological model that can describe the impacts of extreme events induced by climate change on coastal ecosystems.

54 ENVIRONMENTAL SCIENCES↗

Northern Hemisphere Snow Drought in Earth System Model Simulations and ERA5‐Land Data in 1980–2014

Abstract Low snow levels over the past few decades and predictions of a low‐to‐no snow future have spurred research into snow droughts, which pose a threat to water security and management. Systematic data‐model comparisons of snow drought have been lacking, hindering our understanding of the drivers of snow drought in the past. To address this gap, we analyzed snow drought events using standardized snow water equivalent index derived from monthly results of four numerical experiments using the E3SM Land Model (ELM) and ERA5‐Land data during the period of 1980–2014. Additionally, we compared snow drought duration calculated from models with those from the ERA5‐Land data during selected El Niño‐Southern Oscillation (ENSO) years. The numerical experiments were conducted with ELM driven by two prescribed atmospheric forcings, and with the coupled land‐atmosphere configuration of E3SM with and without plant hydraulics scheme feedback. Analysis reveals that 20%–30% of snow droughts occur due to factors other than above‐normal temperature and low snowfall, such as low soil moisture, warm soil temperature, and low relative humidity, etc., especially in high latitudes (50° North). Furthermore, our study highlights the exacerbating effect of ENSO events on snow drought conditions in various regions, despite some discrepancies between model and ERA5‐Land results. We also identified limitations of the coupled land‐atmosphere models in our current configuration in capturing the spatial patterns of snow droughts. This study underscores the challenge of predicting and mitigating snow drought and the need for a comprehensive understanding of the factors contributing to snow drought.

54 ENVIRONMENTAL SCIENCES↗

Human protein synthesis requires aminoacyl-tRNA pivoting during proofreading

Abstract Rigorous studies have characterized the aa-tRNA selection mechanism in bacteria, which is essential for maintaining translational fidelity. Recent investigations have identified critical distinctions in humans, such as the requirement of subunit rolling and a tenfold slower proofreading step. Although these studies captured key intermediates involved in tRNA selection, they did not elucidate the transitions of aa-tRNA between intermediates. Through diverse structure-based simulations, we simulated 1856 aa-tRNA accommodation events into the human ribosomal A site. Here we show the requirement for a distinct ~30° pivoting of aa-tRNA about the anticodon stem within the accommodation corridor. This pivoting is crucial for navigating the crowded accommodation corridor, which becomes more constrained due to subunit rolling. Subunit rolling-dependent crowding increases the steric contributions of the accommodation corridor during aa-tRNA accommodation, consistent with the tenfold reduction in the rate of proofreading. Furthermore, we show that eEF1A interacts with the accommodating aa-tRNA through conserved basic residues, limiting premature aa-tRNA dissociation from the A site. These findings provide a structural description of the human aa-tRNA selection process and demonstrate that the aa-tRNA alignment relative to the ribosomal catalytic sites is a critical determinant of translational fidelity.

Science & Technology - Other Topics↗

A consistent, Bayesian, approach to the cross section probability distribution in the unresolved resonance region

The cross sections of neutron-induced reactions can be divided into three energy ranges: the resolved resonance region (RRR), the unresolved resonance region (URR), and the fast region. In general, the cross sections in the URR show significant fluctuations that cannot be predicted and cannot be experimentally resolved, thus, it is commonly assumed that the cross section at a specific energy is given by a probability distribution function (PDF) over a range of values that can span several orders of magnitude. The current methodology used to describe such behavior is to construct the PDF by stochastically generating resonance ladders and numerically measuring the PDF. The resonance ladders are sampled using known resonance statistical properties and average resonance widths and spacings extrapolated from the RRR. Although this is a standard and widely used technique, it is computationally very expensive, therefore, an alternative, analytical, approach would be preferable due to the considerable speed up of the computational time in real life applications. Moreover, the current methodology does not take into account existing experimental data, such for total and capture cross sections, that are available for many nuclei. Finally, this approach was developed to be used in reactor-scale applications and it is not suited for use in single-event applications. In this work we will rethink the entire approach to the PDF construction using a Bayesian mindset. This will allow us to provide a different definition of the PDF that allows a much faster calculation of the higher-temperature PDFs and a proper combination of theoretical and experimental PDFs following the probability theory. We will also show that our definition is well suited for single-event applications and we will make an explicit connection between our method and the standard approach. We do this by showing that the central limit theorem applies and our method leads to the same PDF obtained with the standard methodology, for a large number of events per history.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Dataset of Generative AI Workload Power Profiles

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model . This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems. The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison. The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment. If you use this dataset, please cite the associated publication: Vercellino et al., “Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning,” arXiv:2604.07345 (2026).

97 MATHEMATICS AND COMPUTING↗