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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Fermilab PIP-II machine protection system digitized data noise elimination scheme and its FPGA implementation

In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can contaminate the data. To suppress noise across these ranges—especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies—advanced digital processing beyond standard filtering is required. Several real-time functional blocks were simulated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid baseline by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance and were successfully implemented on a low-cost FPGA.

Wu, Jinyuan [Fermilab]↗
DOE OSTI2025

Millimeter-Wave Superconducting Qubit

Manipulating the electromagnetic spectrum at the single-photon level is fundamental for quantum experiments. In the visible and infrared ranges, this can be accomplished with atomic quantum emitters, and with superconducting qubits such control is extended to the microwave range (below 10 GHz). Meanwhile, the region between these two energy ranges presents an unexplored opportunity for innovation. We bridge this gap by scaling up a superconducting qubit to the millimeter-wave range (near 100 GHz). Working in this energy range greatly reduces sensitivity to thermal noise compared to microwave devices, enabling operation at significantly higher temperatures, up to 1 K. This has many advantages by removing the dependence on rare 3⁢ He for refrigeration, simplifying cryogenic systems, and providing orders-of-magnitude higher cooling power, lending the flexibility needed for novel quantum sensing and hybrid experiments. Using low-loss niobium trilayer junctions, we realize a qubit at 72 GHz cooled to 0.87 K using only 4 ⁢He. We perform Rabi oscillations to establish control over the qubit state, and measure relaxation and dephasing times of 15.8 and 17.4 ns, respectively. This demonstration of a millimeter-wave quantum emitter offers exciting prospects for enhanced sensitivity thresholds in high-frequency photon detection, provides new options for quantum transduction and for scaling up and speeding up quantum computing, enables integration of quantum systems where 3 ⁢He refrigeration units are impractical, and, importantly, paves the way for quantum experiments exploring a novel energy range.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Wildfire-Power Grid Interactions: Feedback, Impacts, Monitoring, Modeling, and Mitigation Strategies

Wildfires are increasingly interacting with electric power systems through a two-way hazard chain: fires damage grid assets and trigger cascading outages, while grid faults can ignite new fires under hot, dry, and windy conditions. This review synthesizes the state of knowledge across five domains: (i) physical impacts of flames, heat, and smoke on lines, towers, insulators, and substations; (ii) power-infrastructure-initiated ignitions via conductor clash, high-impedance faults, and corona discharge; (iii) widespread blackouts and disproportionate societal impacts; (iv) multi-scale monitoring spanning laboratory tests, in-situ and grid-integrated sensors, and Earth observation; (v) coupled modeling that links fire behavior with grid operations; and (vi) technological and strategic mitigation pathways spanning prevention, response, and recovery. We integrate these domains into a novel 'feedback-aware' socio-technical framework. Through a longitudinal analysis (2005-2025) of global incidents, we identify that while vegetation contact remains the most frequent ignition source, aging infrastructure failure has emerged as a critical driver of catastrophic 'mega-fires'. We further identify persistent gaps, including limited interoperability of high-frequency grid and environmental data, scarce real-time data assimilation, and under-developed equity metrics for outage management. We conclude by outlining a research agenda to (1) deploy interoperable sensing architectures, (2) advance feedback-coupled fire-grid simulations, and (3) evaluate mitigation portfolios through techno-economic and fairness lenses. Recognizing wildfire-grid interactions as coupled socio-technical systems is essential for protecting infrastructure and communities and for ensuring reliable, sustainable electricity in a changing world.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Persistence of deuterium and tritium nuclear spin-polarization in presence of high-frequency plasma waves

We present first-principles numerical calculations of the depolarization rate of spin-polarized deuterium and tritium nuclei in realistic tokamak plasmas, driven by resonant interactions with plasma waves. Backed up by first-of-a-kind linear and nonlinear simulations, we find that alpha particle-driven Alfvénic modes cause only negligible depolarization, which is contrary to expectations in prior literature. Other Alfvénic instabilities can in principle degrade polarization, but only under conditions unlikely to be realized on transport timescales. By combining full-orbit particle tracing with a dedicated depolarization solver, we demonstrate that wave-driven depolarization is surprisingly weak in SPARC and ITER-scale devices. These results provide strong evidence that spin-polarized fuel can maintain its polarization long enough to boost fusion reactivity, opening a viable path toward substantially enhanced performance in magnetic confinement fusion power plants.

Cook, James William Setchfield [UK Atomic Energy A↗
DOE OSTI2026

Fermilab PIP II machine protection system digitized data noise elimination scheme and its FPGA implementation

In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can contaminate the data. To suppress noise across these ranges especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies advanced digital processing beyond standard filtering is required. Several real-time functional blocks were simulated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid baseline by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance.

Wu, J. [Fermilab] (ORCID:0000000344329521)↗
DOE OSTI2025

Fermilab PIP II Machine Protection System Digitized Data Noise Elimination Scheme and Its FPGA Implementation

In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can con-taminate the data. To suppress noise across these ranges especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies advanced digital processing beyond standard filtering is re-quired. Several real-time functional blocks were simu-lated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid base-line by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance.

Wu, Jinyuan [Fermilab] (ORCID:0000000344329521)↗
DOE OSTI2026

Cosmic axions revealed via amplified modulation of the ellipticity of a laser

We propose a new axion dark matter detection strategy that employs optical readout of laser beam ellipticity modulations caused by axion-induced electric fields in a microwave cavity, using electro-optic (EO) crystals, enhanced by externally injected radio-frequency (rf) power. Building upon the variance-based probing method [Phys. Rev. D 107, 103005 (2023).], we extend this concept to the optical domain: A weak probe laser interacts with an EO crystal coupled to the resonant microwave cavity field at cryogenic temperatures, and the axion-induced electric field is revealed through induced ellipticity. The injected rf signal coherently interferes with that of the axion field, amplifying the optical response and significantly improving sensitivity. While our EO-based method employs a Fabry-Pérot resonator, we do not require Michelson interferometers. Our method, hence, enables compact, high-frequency axion searches, across the 0.5–50 GHz range. Operating at cryogenic temperatures not only suppresses thermal backgrounds, but, critically, allows the probing method to mitigate the quantum noise. This approach offers a scalable path forward for axion detection over the ∼(few−200) μ⁢eV mass range—covering the preferred parameter space for the postinflationary Peccei-Quinn axion dark matter—using compact, tunable systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Temporal Convolutional Network Using Empirical Mode Decomposition to Detect Faults in Grid Connected Systems

Grid-connected power electronic systems require timely and reliable fault detection to prevent equipment damage and reduce downtime. This paper presents a forecasting-based anomaly detection pipeline that decomposes voltage and current measurements into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), then trains a causal temporal convolutional network (TCN) on normal-operation IMF data to predict short-horizon future dynamics. Deviations between forecasts and observations are summarized as reliability-weighted residual scores and thresholded per sensor using robust statistics with temporal persistence constraints to suppress false positives. To reduce runtime, EMD is performed on downsampled signals for detection, while raw-rate EMD is applied only within a short region of interest for high-frequency interpretability near detected events. Results on a simulated grid-connected converter system demonstrate that IMF-domain forecasting improves anomaly separability relative to raw-signal forecasting and provides interpretable evidence of faults across decomposition channels.

Sutton, Elizabeth [ORNL] (ORCID:0009000078885935)↗
DOE OSTI2026

Conductivity Spectroscopy for Investigation and Discovery of Photovoltaic Materials

Conductivity spectroscopy is an extremely powerful set of methods for probing the properties of optoelectronic materials, especially photovoltaics, where photoconductivity is one of the best spectroscopic proxies for performance. Despite this power, they are substantially less commonly used than time-resolved photoluminescence (for instance) because they tend to be more expensive to implement (THz) and/or require specialized knowledge (GHz) to construct instruments, which are not widely available. The goal of this review is to illustrate the utility of these experiments in the discovery and study of photovoltaic absorber materials and simultaneously make them more accessible to the community by providing a central tutorial resource. We provide a comprehensive review of how conductivity spectroscopy has developed over the past decade and been applied in the discovery and development of photovoltaic materials, with a primary focus on emerging solution-processable technologies. Along the way we aim to demystify conductivity spectroscopy with focused tutorial sections that explain the physical models used to fit the data and illustrate how to think about “high-frequency conductivity”.

14 SOLAR ENERGY↗
DOE OSTI2025

Real-time steerable frequency-stepped Doppler backscattering (DBS) system for local helicon wave electric field measurements on the DIII-D tokamak

A new frequency-stepped Doppler backscattering (DBS) system has been integrated into a real-time steerable electron cyclotron heating launcher system to simultaneously probe local background turbulence (f < 10 MHz) and high-frequency (20–550 MHz) density fluctuations in the DIII-D tokamak. The launcher allows for 2D steering (horizontally and vertically) over wide angular ranges to optimize probe location and wavenumber response. The vertical steering can be optimized during a discharge in real time. The new DBS system employs a programmable frequency synthesizer with adjustable dwell time as a source to launch either O or X-mode polarized millimeter waves. This system can step in real-time over the entire E-band frequency range (60–90 GHz). This combination of capabilities allows for the diagnosis of the complex internal spatial structure of high power (>200 kW) helicon waves (476 MHz) injected from an external antenna during helicon current drive experiments in DIII-D. Broadband density fluctuations around the helicon frequency are observed during real-time scans of measurement location and wavenumber during these experiments. Analysis indicates that these broadband high-frequency fluctuations are a result of backscattering of the DBS millimeter-wave probe beam from plasma turbulence modulated by the helicon wave. It is observed that background turbulence is effectively locally “tagged” with the helicon wave electric field, forming images of the turbulent spectrum in the overall density fluctuation spectrum that appear as high-frequency sidebands of the turbulence. These observations of background turbulence and high-frequency fluctuations open up the possibility of monitoring local helicon wave amplitude by comparing the high-frequency signal amplitude to the simultaneously measured background turbulence. In combination with the real-time measurement location and wavenumber scanning capabilities (offered by real-time frequency-stepping and steering), this allows rapid determination of the spatial distribution of the helicon wave power during steady-state plasma operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Durability of PGM catalyst MEAs of polymer electrolyte membrane fuel cells for heavy-duty vehicles

Polymer electrolyte membrane fuel cells (PEMFCs) are promising power sources for heavy-duty vehicles (HDVs) owing to cleanliness and efficiency. However, the degradation of membrane electrode assemblies (MEAs) under HDV conditions remains a huge challenge. Here, this work investigated MEA durability under HDV conditions using a US Department of Energy standard accelerated stress test for 180,000 cycles (equivalent to 1 million miles of HDV operation). Effects of catalyst Pt content on MEA durability were examined using homemade 30% Pt/C (H-Pt/C) and commercial 46% Pt/C (C-Pt/C) catalysts. Both MEAs experienced H 2 /air and H 2 /O 2 performance loss over cycles. Analysis with scanning transmission electron microscopy, X-ray diffraction, inductively coupled plasma mass spectrometry, and mercury intrusion porosimetry revealed severe degradation of Pt nanoparticles (NPs), support structures, and the catalyst layer. Two degradation stages for NPs were proposed: Ostwald ripening dominated the initial 60,000 cycles, followed by combined Ostwald ripening and particle migration. Measurements with ion chromatography, high-frequency resistance, and oxygen-diffusion resistance revealed degradation of membrane and ionomer, respectively.

25 ENERGY STORAGE↗
DOE OSTI2025

Multicarrier Spread Spectrum Communications With Noncontiguous Subcarrier Bands for HF Skywave Links

Existing high-frequency (HF) radio platforms offer robust performance against the volatile HF propagation channel. However, the growing traffic across the band contests the reliability of these systems. While techniques to mitigate the effects of narrowband interference have been thoroughly explored, they are insufficient against wideband interference or when the transmission band is occupied by numerous scattered users. To improve reliability in these congested channel conditions, we propose a filter-bank based multicarrier spread-spectrum waveform with noncontiguous subcarrier bands. Using noncontiguous subcarrier bands enables the system to at once leverage the robustness of a wideband system while retaining the frequency agility of a narrowband system. In this study, we modify a filter-bank transmitter structure to accommodate noncontiguous subcarrier bands and consider several immediate impacts of this change, such as elevated peak-to-average-power ratios (PAPRs). A receiver architecture to process the noncontiguous spread-spectrum signal is also introduced, along with details regarding wideband channel estimation. Finally, we develop efficient transmitter and receiver structures to support practical system implementations. We conclude by comparing the performance of contiguous and noncontiguous systems through both simulation and over-the-air testing. The results show that the noncontiguous system remains robust in typical HF channels while significantly outperforming the contiguous system in congested spectral conditions.

(PAPR↗
DOE OSTI2026

Automated Classification of Vehicle Movements at Signalized Intersections Using Vehicle Trajectories

Accurate vehicle movement classification through signalized intersections is of paramount importance to the analysis of intersection performance and the optimization of traffic control strategies. Conventional techniques for tracking vehicle turning movements depend on infrastructure-based strategies like human counts, loop detectors, and video analytics, all of which are costly, prone to errors, and spatially constrained. High-frequency trajectory data can be utilized to determine vehicle movement patterns in a scalable and infrastructure-independent method due to the adoption of connected vehicles (CVs). In recent years, several studies have utilized connected vehicle data to generate performance measures. Most of the trajectory-based performance measures approaches, however, require map matching-i.e., extracting geospatial references from maps to identify the movements that individual vehicles make at a signalized intersection. These approaches are often time-consuming and hinder scalability since geographic features need to be provided for an analysis to be conducted. Map matching methods are prone to errors as different map versions change these geographic features. This research presents a novel automatic classification pipeline that uses CV trajectory data to classify vehicle movements at signalized crossings, specifically pass-through left-turn and right-turn maneuvers. The process starts by filtering trips that cross a spatial bounding box that has been defined at the target intersection. Approach and departure headings for each trajectory crossing the boundary are computed and are clustered together to identify dominant movements. The proposed algorithm is used to classify the movement of vehicles at 10 intersections in the state of California, and the results indicate that the algorithm can classify movements at these intersections with varying traffic volumes and road network configurations, all in a map-less framework with no need for conflation of vehicle trajectories to a digital base map.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Predicting critical heat flux using localized sensing at invisible vapor-liquid interfaces

Predicting critical heat flux (CHF) in two-phase electronics cooling systems remains a significant challenge due to the sudden onset of boiling crisis and the difficulty in directly visualizing vapor-liquid interfaces. Existing sensing methods rely on lagging temperature measurements, optically accessible systems, or spatially averaged signals that cannot pinpoint CHF initiation at localized high-heat-flux regions. Here, we report a planar capacitive sensing approach that enables real-time, localized detection of vapor-liquid interface dynamics for CHF prediction in boiling heat transfer. The capacitive sensor exploits the dielectric constant difference between liquid and vapor phases to capture bubble nucleation, growth, and departure dynamics with a temporal resolution down to 2 ms. The capacitive sensing reveals distinct signals across boiling regimes: from high-frequency fluctuations during strong nucleate boiling to low-frequency fluctuations with increased amplitudes when approaching CHF. The multi-sensor array experiments demonstrate real-time localized sensing, where each sensor responds exclusively to boiling in its immediate vicinity without crosstalk from neighboring regions. This non-intrusive sensing approach provides predictive rather than lagging sensing signals of CHF occurrence, offering predictive diagnosis of two-phase liquid cooling for the thermal management of high-power-density electronics.

CHF↗
DOE OSTI2025

Comprehensive defect evaluation of advanced nuclear fuels using high-resolution acoustic signals and optimized sensor separation

Graphite pebble composite structures based on TRistructural-ISOtropic (TRISO) particles are being developed as core nuclear fuels in advanced power reactors, promising safe operation at increased temperatures. Ensuring the structural integrity of these nuclear fuels requires comprehensive and accurate non-destructive evaluation (NDE) techniques to characterize defects and damage in the pebbles. However, traditional acoustic evaluation methods face limitations in defect characterization due to the highly attenuative, and geometrically and compositionally complex nature of these structures. This study proposes an improved acoustic NDE technique for accurate detection and classification of anticipated relevant defects and damage in graphite pebbles using high-resolution acoustic signals and optimized transmit-receive sensor networks. The proposed approach utilizes a triangular three-sensor network as the base unit, comprising three transmit-receive sensors. The sensor separation distance, as well as acoustic excitation center frequency, pulse-width, and bandwidth are optimized to enhance spatial resolution and improve signal-to-noise ratio, enabling effective characterization of the smallest size and widest range of defects in pebbles. Furthermore, the use of the triangular sensor configuration instead of a more conventional transmit-receive sensor pair expands the inspection region from a one-dimensional linear path to a two-dimensional area, increasing spatial coverage. To mitigate challenges associated with processing of complex acoustic signals arising from high-frequency, high-bandwidth excitation in these structures, a machine-learning-based signal processing algorithm is integrated with the sensor network. In the machine-learning-based algorithm, multi-domain features are extracted from the acoustic signals to capture intricate signal characteristics, significantly improving defect identification and classification compared to traditional approaches. The proposed acoustic NDE technique offers considerable promise for practical and reliable defect/damage diagnostics of advanced nuclear pebble fuels.

42 ENGINEERING↗
DOE OSTI2026

Switching speed limits in electrically driven VO 2 structural Mott–Peierls transition

Mott materials are archetypal quantum systems actively explored as next-generation electronic and photonic platforms, with potential applications spanning non-Von Neumann computing, robotics, energy storage, and microwave technologies. Among these, vanadium dioxide (VO 2 ) has emerged as one of the most intensively studied compounds, owing to its sharp, near-room-temperature insulator-to-metal phase transition. VO 2 also serves as a benchmark system for testing cutting-edge theories and experimental techniques. Here, we directly visualize the electrically driven transition dynamics in VO 2 using a microwave-driven, frequency-tunable pulsed transmission electron microscope that combines nanometer spatial and picosecond temporal resolution. Under high-frequency (MHz–GHz) excitation, we capture the ultrafast nucleation, propagation, and dissolution of metallic domains within an operating device over millions of reversible cycles. We observe the ultrafast formation of consistent metallic nuclei beneath the electrodes, followed by the propagation of a structural phase front at 4.54 nm/ns. Our experiments show that phonon-mediated structural recovery ultimately limits reversible switching of VO 2 at GHz frequencies, and that a tunable regime for reversible operation spans from kHz to GHz through device engineering. Beyond VO 2 , our approach provides a powerful framework for probing non-equilibrium structural transformations in correlated and functional materials under realistic electrical stimuli.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine learning for seismic low-frequency extrapolation

The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.

58 GEOSCIENCES↗
DOE OSTI2026

Machine Learning Enabled Position Detection for 6.78 MHz UAV Wireless Power Transfer System

This paper presents a novel supervised machine learning (SML) approach for accurate position detection of the receiver coil in wireless power transfer (WPT) systems using only secondary-side electrical measurements, with applications in autonomous unmanned aerial vehicle (UAV) charging. The proposed method trains a supervised learning model to map measured secondary-side voltage and current features to the receiver’s spatial position with high precision. This enables an autonomous UAV to determine its location relative to the primary coil center, the optimal position for maximizing wireless charging efficiency. The sensing method is fully integrated into a standard WPT system, utilizing the same primary and secondary coils for both power transfer and position detection, thereby eliminating additional sensing hardware. The use of a 6.78 MHz operating frequency enhances positional sensitivity, as high-frequency near-field electromagnetic fields respond strongly to small spatial variations. Experimental validation is performed on a 30 W scaled prototype featuring a 210 mm × 140 mm primary coil, a 50 mm × 80 mm receiver coil, and a 15 mm air gap. Results demonstrate reliable position estimation and a strong correlation between predicted position and optimal coil alignment. This integrated framework unifying position detection and wireless charging offers a promising foundation for future autonomous electric vertical takeoff and landing (eVTOL) systems, enabling compact, hardware-efficient, and high-accuracy charging solutions.

Colak, Kerim [New York University]↗