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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Asymmetrical cavity design that bypasses mode mixings in axion haloscope experiments

Microwave cavities used in axion haloscope experiments typically employ a tuning rod as a means to widen the range of resonance frequencies at which it is sensitive to axion-to-photon conversion. A realistic tuning mechanism requires a gap between the cavity end caps and the tuning rod to ensure movement, and causes some modes to hybridize with the resonant mode that is being tracked for the experiment. These so-called mode mixings lead to gaps in the frequency range that practically lose sensitivity to axions. Here, to solve this problem, we present a cavity design which, for two tuning rod configurations corresponding to a lower and higher frequency range, have a dielectric rod inserted at a specific location that makes the cavity asymmetrical. Moving the tuning rod closer to the dielectric insert changes the location and frequency of the mode mixing compared to when it is farther away from it. This design is easily realizable in practical experiments and makes possible an axion dark matter search with minimal loss in sensitivity due to mode mixings. We also show that the same design has the same desired effect when cavity dimensions are scaled down to be smaller and are at higher resonance frequencies.

Axion↗
DOE OSTI2026

Experiments at Jefferson Lab

This chapter presents experiments conducted at Thomas Jefferson National Accelerator Facility (Jefferson Lab), a U.S. Department of Energy national laboratory in Newport News, Virginia. There, physicists exploring the nature of matter make use of the Continuous Electron Beam Accelerator Facility (CEBAF), a DOE Office of Science user facility that enables the research of more than 1,650 scientists worldwide. CEBAF’s precise electron beams can reach energies up to 12 billion electron-volts and exhibit high degrees of polarization. Jefferson Lab’s first experiment began taking data in 1995. Since then, the facility has become a world leader in the study of quantum chromodynamics. Today, experiments are carried out simultaneously in four experimental halls, each with specialized capabilities. The primary instruments in use are focusing or large-acceptance magnetic spectrometers, many of which feature superconducting elements. Jefferson Lab’s physics program provides unprecedented insight into the particles and forces that shape the visible universe.

Achenbach, Patrick [Thomas Jefferson National Acce↗
DOE OSTI2025

Phase-field modeling and experiments of dynamic fracture in single crystal quartz

Predicting the onset and characteristics of brittle fracture is important for a wide range of engineering and geological material applications. In this paper, we study important aspects of brittle fracture in α-quartz by phase-field modeling and experiments using a top-down approach. In the modeling framework, the work term in the Griffith energy balance is replaced with internal energy contributions that represent surface energy, thermal energy, and elastic strain energy stored in defects. This allows parametrization of individual energy contributions in terms of internal state variables and keeps track of energy partitioning after the onset of fracture. The path and history dependence of fracture is included in evolution laws for internal state variables, e.g., entropy evolution, while the energy remains a true potential. In the experimental part, dynamic compression experiments coupled with X-ray phase contrast imaging are performed on cube-like samples with a hole. In the top-down analysis, dynamic compression and three point bending experiments from the literature are simulated with the developed phase-field damage model. In conclusion, the fitted model highlights the strain rate, size, and stress state dependence of damage nucleation and evolution in single crystal α-quartz.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High strain-rate strength response of single crystal tantalum through in-situ hole closure imaging experiments

The properties of crystalline materials often depend on directionality and operating conditions. Specifically, the strength of materials can depend anisotropically on crystal direction and the loading condition. To probe these effects, a preliminary series of high strain-rate (> 105/s) strength plate-impact hole closure experiments were performed on high purity single crystal Tantalum cubes. The orientation of the single crystals with respect to impact/loading were varied to provide data to inform crystal plasticity modeling efforts. The experiments consist of in-situ high-resolution X-ray radiographic imaging of the hole collapse under dynamic compression conditions to infer the material strength via its resistance to closure at increasing levels of plastic strain. The experiments are compared against hydrocode simulation predictions. Here, a comparison with simple elastic perfectly plastic strength model predictions is presented to elucidate the response of the different crystal orientations at high strain-rate and large plastic strains.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Leveraging the High Flux Isotope Reactor for nuclear fuel development: a review of experiments, facilities, and capabilities

Materials testing reactors (MTRs) have been used to develop in-core nuclear fuels and materials since the outset of the nuclear power industry. However, the closure of prominent MTRs worldwide and protracted construction timelines for new facilities have increased reliance on existing infrastructure for near-term irradiation testing needs. One facility that can support these needs is the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. HFIR boasts the highest steady-state neutron flux in the Western Hemisphere and, among other roles, has been used to rapidly administer high fluences on fuels and materials for fission and fusion reactor applications. This paper reviews HFIR facilities and infrastructure, fuel-bearing irradiation experiments conducted in HFIR, and select nonfueled experiments that demonstrate advanced techniques transferable to fuels experiments. Collectively, these examples underscore HFIR's potential role as a nuclear fuels testbed supporting both the existing reactor fleet and advanced reactor fuel development.

Fuel qualification↗
DOE OSTI2026

Experiments and simulations on the Richtmyer-Meshkov instability with a thin intermediate layer

Experiments and simulations of the Richtmyer-Meshkov instability (RMI) in two- and three-layer configurations are presented. The two-layer case utilizes a light-over-heavy configuration and consists of air as the light gas and sulfur hexafluoride (SF 6 ) as the heavy gas. The three-layer case utilizes a light-intermediate-heavy configuration, with helium (He) as the light gas, air as the intermediate gas, and SF 6 as the heavy gas. Statistically significant differences in the mixing layer width of the lower interface are not observed between the two cases. This differs from the experiments of Schalles et al., where a small, though statistically significant, difference in mixing layer growth was observed between the two- and three-layer cases with a nominally two-dimensional, single mode perturbation. Notably, the perturbations on the lower interface in the present work do not grow large enough to significantly interact with the upper interface during the duration of the experiments. In conclusion, this suggests that the differences in mixing layer growth observed by Schalles et al. may be due to interactions of the perturbations on one interface with the other interface rather than being inherent to the three-layer problem.

42 ENGINEERING↗
DOE OSTI2024

Interactions between phosphate and arsenic in iron/biochar-treated groundwater: Corrosion control insights from column experiments

An increasing number of studies have reported the coexistence of arsenic (As) and phosphorus at high concentrations in groundwater, which threatens human health and increases the complexity of groundwater remediation. However, limited work has been done regarding As interception in the presence of phosphate in flowing systems. In this study, a series of experiments were conducted to evaluate the interactions between phosphate and As during As removal by iron (Fe)-based biochar (FeBC). The addition of phosphate promoted As removal by FeBC in the batch and column experiments. X-ray absorption near edge structure (XANES) analysis provided evidence of simultaneous oxidation and reduction of trivalent arsenic in the FeBC column experiment, accompanied by corrosive Fe oxidation. However, the addition of phosphate enhanced As stabilization, attributed to the As-incorporated Fe-Ca-phosphates precipitates. The involvement of phosphate decelerated the Fe corrosion and the formation of secondary minerals in the column, mediating the risk of passivation and clogging. The As retained by Fe-Ca-phosphate precipitates was more readily oxidized, resulting in higher proportions of pentavalent arsenic. In conclusion, the results of this work identify the corrosion control and sustained-release roles of phosphate in FeBC application, informing the perspective of FeBC in As-contaminated groundwater remediation and providing new insights into the interactions between phosphate and As.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Shining Light on Halide Perovskites: Teaching Analytical Chemistry Using Flexible, Inquiry-Based Experiments

Two-dimensional (2D) metal halide perovskites are promising next generation semiconducting materials at the forefront of research in solar cells, LEDs, and other devices. Here, we report on an undergraduate intermediate analytical chemistry laboratory experience where students were taught fundamental chemistry concepts, including solubility, complexation, spectroscopy, and microscopy, through the introduction and study of 2D halide perovskite materials. Students explore multiple facets of perovskite synthesis, structure, and properties through a modular set of experiments that students used to form a holistic picture of this material. Importantly, this inquiry-based lab supports students through a guided research process, and students report high interest and learning gains from an end of the semester survey. We further discuss ways to adapt this lab to course, student, equipment, and budget needs. Overall, this laboratory experience teaches and applies the fundamental concepts and tools of analytical chemistry to the contemporary materials research field.

Analytical Chemistry↗
DOE OSTI2024

Time‐And‐Space Averaging Applied to Intermittent Multiphase Flow Experiments

Abstract Various researchers have studied fluctuations in pore‐scale phase occupancy during multiphase flow in porous media using synchrotron‐based X‐ray microcomputed tomography (micro‐CT). However, the impact of these fluctuations on the concept of a representative volume is not yet fully understood. In this study, we performed spatial and temporal averaging of multiphase flow experiments visualized with synchrotron‐based micro‐CT, focusing on oil saturation as the key parameter to determine a representative time‐and‐space average. Our findings revealed that a saturation value representative of both time and space was achieved during fractional flow experiments in drainage mode with fractional flows of 0.8, 0.5, and 0.3. Furthermore, we computed a range of relative permeabilities on the basis of whether momentaneous saturation or time‐and‐space averaged saturation was utilized for direct simulation. Our results highlighted the importance of time‐and‐space averaging in determining a representative relative permeability and indicated that the temporal and spatial scales covered in a typical micro‐CT flow experiment were sufficient to obtain a representative saturation value for sandstone rock under intermittent flow conditions.

Environmental Sciences & Ecology↗
DOE OSTI2026

Relative Source Time Functions, Spectral Ratios, and Near‐Source Spallation in the Source Physics Experiment Phase I Chemical Explosions

The Source Physics Experiment (SPE) Phase I was composed of six chemical explosions at the Nevada National Security Sites (NNSS) between 2011 and 2016. The experiment provided a robust set of dense, local to regional distance observations suitable for characterizing shallow chemical explosions located within the same borehole. We investigate the time-dependent source processes of each chemical explosion using Relative Source Time Function (RSTF) estimation, derived from five linear seismometer profiles located between 100 and 2000 m of the shot location. The RSTF estimate provide a detailed characterization of each chemical explosion's time-history. Subsequent modeling efforts suggest these measurements may be useful for precise characterization of explosion processes and spallation. RSTF estimation allows us to expand our understanding of the SPE Phase I chemical explosion series. Application of these techniques contributes to our understanding of explosion source physics and facilitates future applications to additional seismic source experiments and characterization of explosion phenomenology.

58 GEOSCIENCES↗
DOE OSTI2025

Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM’s ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Resolving root causes of experiment discrepancies guided by machine learning

Abstract Scientists rely on accurate experimental data to explain nature and then harness this knowledge for applications addressing human needs. However, discrepancies between experiments of the same observable can impede scientific progress if one does not understand the underlying causes. Here, we developed a process that unravels data discrepancies by first using Bayesian machine learning to relate discrepancies to few of many, potentially biasing metadata features that encode experiment procedures. This machine learning output guides human experts to study discrepancy causes by simulating suspicious aspects of historical experiments or designing modern ones to address open questions. The study findings then lead to rejecting or correcting historical data on firm scientific bases. This process is demonstrated for the energy spectrum of neutrons emitted promptly (<1 ns) after fission of 252 Cf, a trusted nuclear physics Standard. It reduces the spread in experimental 252 Cf spectra by up to a factor of 6.

Neudecker, D. (ORCID:0000000339200627)↗
DOE OSTI2025

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Analysis of neoclassical tearing mode stabilization experiment by electron cyclotron injection in KSTAR

We report the neoclassical tearing mode (NTM) stabilization experimental results by injection of the electron cyclotron (EC) beam in KSTAR, and its analysis with integrated modelling. In the KSTAR experiment, NTM was intentionally induced and the EC beam was injected by the plasma control system (PCS) to stabilize it. Here, the EC angle was controlled based on the minimum seeking algorithm for the island growth rate. Although the EC angle was not changed significantly, the island width significantly decreased towards the end of the experiment followed by the recovery of plasma performance. To assess the NTM stabilization experiment from a stability perspective, we performed integrated modelling with TRIASSIC utilizing the reconstructed equilibrium with magnetic diagnostics and motional stark effect diagnostics. As a result, we observed that the NTM is stabilized by EC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Latest results of the Muon g-2 experiment at Fermilab

The muon magnetic anomaly, aμ = g - 2/ 2, is a low-energy observable which can be both measured and computed to high precision, making it a sensitive test of the Standard Model and a probe for new physics. The Muon g − 2 experiment at Fermilab aims to measure aμ with a final accuracy of 140 parts per billion (ppb). The experiment is based on the measurement of the muon spin anomalous precession frequency, ωa, in a magnetic field. The first result of the experiment, based on the 2018 data-taking campaign, was published in 2021 and it confirmed the previous result obtained at Brookhaven National Laboratory with a similar sensitivity of 460 ppb. In this proceeding, the result based on the 2019 and 2020 datasets is presented and the improvement in the accuracy with respect to the 2018 dataset are discussed.

Sorbara, Matteo [INFN, Rome2; Rome U., Tor Vergata↗
DOE OSTI2025

Real-Time event reconstruction for Nuclear Physics Experiments using Artificial Intelligence

Charged track reconstruction is a critical task in nuclear physics experiments, enabling the identification and analysis of particles produced in high-energy collisions. Machine learning (ML) has emerged as a powerful tool for this purpose, addressing the challenges posed by complex detector geometries, high event multiplicities, and noisy data. Traditional methods rely on pattern recognition algorithms like the Kalman filter, but ML techniques, such as neural networks, graph neural networks (GNNs), and recurrent neural networks (RNNs), offer improved accuracy and scalability. By learning from simulated and real detector data, ML models can identify and classify tracks, predict trajectories, and handle ambiguities caused by overlapping or missing hits. Moreover, ML-based approaches can process data in near-real-time, enhancing the efficiency of experiments at large-scale facilities like the Large Hadron Collider (LHC) and Jefferson Lab (JLAB). As detector technologies and computational resources evolve, ML-driven charged track reconstruction continues to push the boundaries of precision and discovery in nuclear physics. In these proceedings, we highlight advancements in charged track identification leveraging Artificial Intelligence within the CLAS12 detector, achieving a notable enhancement in experimental statistics compared to traditional methods. Additionally, we showcase real-time event reconstruction capabilities, including the inference of charged particle properties, such as momentum, direction, and species identification, at speeds matching data acquisition rates. These innovations enable the extraction of physics observables directly from the experiment in real-time.

Gavalian, Gagik (ORCID:0000000267385457)↗
DOE OSTI2026

DIII-D High Field Side Lower Hybrid Current Drive Experiment Overview

In preparation for high field side lower hybrid current drive (HFS LHCD) experiments in DIII-D, the HFS LHCD was to be commissioned and physics experiments commence once the system operated up to 300 kW for 0.5 s. The initial physics experiments sought to characterize coupling, wave propagation, and driven current measurements. In HFS LHCD first campaign, the maximum power was limited to <200 kW due to waveguide pressure leaks limiting the number of available modules and power per module. Here we summarize commissioning progress and initial physics observations. The HFS LHCD coupler was optimized for high qmin, DIII-D discharges where efficient off-axis current at r/a~0.6-0.8 is desired. The coupler n|| spectrum is peaked at 2.7 and is predicted to generate ~0.14 MA/MW coupled for 1.6 MW injected. In preparation, the HFS scrape-off layer density profile was characterized and found to have steeper profiles and lower fluctuation levels than the low field side. Furthermore, the HFS SOL density profile can be accurately predicted using global plasma quantities using machine learning. Thus far, one module has injected ~100 kW for 0.5 s with <5% reflected power. Nonthermal electrons have been observed on lower frequency channels of the electron cyclotron emission radiometer correlated with the LH power indicating core wave absorption. To avoid 30R neutral beam heat flux, a split launcher is proposed to avoid the high heat flux region while maintaining power spectrum and directivity.

Wukitch, Stephen J. [Massachusetts Inst. of Techno↗
DOE OSTI2024

Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments

We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.

97 MATHEMATICS AND COMPUTING↗