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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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332 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Entanglement Structure of Non-Gaussian States and How to Measure It

Rapidly growing capabilities of quantum simulators to probe quantum many-body phenomena require new methods to characterize increasingly complex states. Here, we present a protocol that constrains quantum states using experimentally measured correlation functions. This method enables measurement of a quantum state’s entanglement structure, opening a new route to study entanglement-related phenomena. Our approach extends Gaussian state parameterizations by systematically incorporating higher-order correlations. We show the protocol’s usefulness in conjunction with current and forthcoming experimental capabilities, focusing on weakly interacting fermions as a proof of concept. Here, the lowest nontrivial expansion quantitatively predicts early time thermalization dynamics, including signaling the onset of quantum chaos indicated by the entanglement Hamiltonian.

Fermi gases↗
DOE OSTI2026

ResStock Measure Documentation: Residential Two-Stage Geothermal Heat Pump (4.0 COP, 20.5 EER) With Light Envelope Improvements

This report is part of series describing a variety of different ResStock(TM) measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Residential Two-Stage Geothermal Heat Pump (4.0 COP, 20.5 EER) With Light Envelope Improvements" measure upgrade methodology and briefly discusses key results. All results can be accessed on the ResStock Open Energy Data Initiative "End-Use Load Profiles for the U.S. Building Stock" data lake and on the data viewer at resstock.nlr.gov.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

ResStock Measure Documentation: Residential Variable-Speed Geothermal Heat Pump (4.4 COP, 30.9 EER) With Light Envelope Improvements

This report is part of series describing a variety of different ResStock™ measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Residential Variable-Speed Geothermal Heat Pump (4.4 COP, 30.9 EER) with Light Envelope Improvements" measure upgrade methodology and briefly discusses key results. All results can be accessed on the ResStock Open Energy Data Initiative "End-Use Load Profiles for the U.S. Building Stock" data lake and on the data viewer at resstock.nlr.gov.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Development and Evaluation of a Cost-Effective Behind-the-Meter Synchronized Measurement Unit for Enhanced Grid Integration

This paper presents the development of the Inverter Based Resource Monitor (IBRM), an innovative behind-the-meter synchronized measurement unit (SMU) tailored for integration with inverter-based resources (IBRs). The IBRM distinguishes itself as a highly accurate and cost-effective SMU, offering facile deployment and connectivity to IBRs. It is equipped to conduct real-time voltage and current waveform analyses, serving as a phasor measurement unit (PMU) with exceptionally rapid synchrophasor transmission capabilities. The device incorporates a cutting-edge dual-core architecture designed to minimize sampling delays inherent to its microprocessor, thereby enhancing the precision of synchronized waveform measurements. Moreover, the IBRM is adept at recording high-fidelity waveform data, capturing nuances such as waveform distortions, high-order harmonics, and wide-band oscillations prevalent in power grids with substantial IBR presence. A prototype of the IBRM has been constructed and subjected to rigorous testing to assess its functional capabilities and measurement precision, utilizing both idealized signal generators and a real-world off-grid inverter setup as benchmarks.

Wu, Ori [ORNL] (ORCID:0000000326723410)↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
DOE OSTI2024

Elastic Modulus Measurement at High Temperatures for Miniature Ceramic Samples Using Laser Micro-Machining and Thermal Mechanical Analyzer

In this paper, we demonstrate a method of measuring the flexural elastic modulus of ceramics at an intermediate (~millimeter) scale at high temperatures. We used a picosecond laser to precisely cut microbeams from the location of interest in a bulk ceramic. They had a cross-section of approximately 100 μm × 300 μm and a length of ~1 cm. They were then tested in a thermal mechanical analyzer at room temperature, 500 °C, 800 °C, and 1100 °C using the four-point flexural testing method. We compared the elastic moduli of high-purity Al2O3 and AlN measured by our method with the reported values in the literature and found that the difference was less than 5% for both materials. This paper provides a new and accurate method of characterizing the high-temperature elastic modulus of miniature samples extracted from representative/selected areas of bulk materials.

Chemistry↗
DOE OSTI2025

AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks

We propose the artificial intelligence velocimetry-thermometry (AIVT) method to reconstruct a continuous and differentiable representation of the temperature and velocity in turbulent convection from measured three-dimensional (3D) velocity data. AIVT is based on physics-informed Kolmogorov-Arnold networks and trained by optimizing a loss function that minimizes residuals of the velocity data, boundary conditions, and governing equations. We apply AIVT to a set of simultaneously measured 3D temperature and velocity data of Rayleigh-Bénard convection, obtained by combining particle image thermometry and Lagrangian particle tracking. This enables us to directly compare machine learning results to true volumetric, simultaneous temperature and velocity measurements. We demonstrate that AIVT can reconstruct and infer continuous, instantaneous velocity and temperature fields and their gradients from sparse experimental data at a high resolution, providing an additional approach for understanding thermal turbulence.

Science & Technology - Other Topics↗
DOE OSTI2026

Optimal binning of correlated measurements

Experimental measurements are commonly represented on a discrete grid, requiring a balance between granularity and statistical noise. Two strategies have traditionally been used to improve such representations: selecting an appropriate bin width to control discretization error and applying kernel-based smoothing to suppress fluctuations. Despite their shared goal, these approaches have largely developed independently, without a unified statistical description of how discretization and correlation jointly determine measurement precision. Here, we extend the discussion of optimal interval averaging to a correlation-aware setting by Gaussian process regression, which explicitly accounts for correlations among neighboring bins. Starting from first principles, we derive the mean-squared error of discretized measurements and obtain closed-form asymptotic expressions for the optimal bin width and correlation length. When recast in reduced variables, the theory reveals distinct universal scaling laws governing the error in the correlation-free and correlation-controlled regimes. Characterized by intrinsically smooth intensity profiles and counting-based statistics, neutron scattering measurements are well suited for demonstrating the enhanced error contraction enabled by inter-bin correlations. We show that such improvement is achievable over the experimentally accessible Q-range and across multiple instruments and material systems. These results show that explicitly accounting for correlations systematically reshapes the limits of precision in discretized, noise-limited measurements. More broadly, the framework provides a transferable statistical foundation for optimizing data representation, inference, and experimental design across the physical and data sciences.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗
DOE OSTI2026

Multiple shape coexistence near {sup 118}Sn: First 0+ 3 lifetime measurement

The intruder bands in Sn isotopes, built on the 2p-2h excitation across the Z = 50 proton shell gap, are well-known examples of shape coexistence near the neutron mid-shell region. Spectroscopic signatures for shape coexistence include enhanced E0 transitions between the 0+ band heads. However, the underlying shape coexistence and mixing has been unclear because lifetime information for the excited 0+ states was incomplete in 118Sn. We thus present here the first measurement of the 0+ 3 lifetime in 118Sn using the fast-timing technique following thermal-neutron capture. The observed enhancement in ρ2(E0;0+ 3 → 0+ 2 ) of 150(30) milliunits provides compelling indications for multiple shape coexistence in 118Sn. Additionally, three distinct shapes in 116,118,120Sn naturally emerged in theoretical calculations based on the quantum-number-projected generator coordinate method employing a relativistic energy density functional.

Wu, F.↗
DOE OSTI2026

Emissivity measurements of CuCrZr alloy

The Facility for Rare Isotope Beams (FRIB) heavy-ion accelerator, in user operation since 2022, produces rare isotope beams via interactions of high-intensity stable ion beams with a graphite production target. Approximately 20–40% of the primary beam power is deposited in the target, while the remaining 60–80% is absorbed by the beam dump. The minichannel beam dump (MCBD), currently operated at 20 kW and designed for operation up to 50 kW, uses CuCrZr alloy absorber plates. Thermal validation and thermal cycling tests of the MCBD were conducted at the Applied Research Laboratory (ARL) at Pennsylvania State University. Temperature measurements were obtained from an infrared (IR) camera. Since accurate temperature determination requires reliable emissivity values, the emissivity of CuCrZr was measured using the IR camera validated against thermocouple reference temperatures up to approximately 650 °C. The measurements were conducted under a vacuum level of approximately 10 -5 torr to minimize emissivity variations due to surface oxidation. The emissivity of CuCrZr was determined to be 0.057 ± 0.009 using a constant fit to the measured data over the surface temperature range from 100–650 °C.

Accelerator Subsystems and Technologies↗
DOE OSTI2026

Polarized target nuclear magnetic resonance measurements with deep neural networks

Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and higher precision in subsequent offline analysis. By reducing analysis-induced uncertainty, the resulting methodology can improve the effective figure of merit for scattering experiments employing dynamically polarized targets and provides a new toolset for NMR-based polarimetry in high-energy and nuclear physics.

Metrology↗
DOE OSTI2026

The Reaction of Atomic Hydrogen with an Imine: Direct Kinetic Measurements and Computational Studies

Imines are of interest in the combustion modeling of nitrogen-based fuels such as ammonia, as intermediates in atmospheric oxidation of amines, and may offer a new class of environmentally benign substitutes for perhalogenated reagents. Elementary gas-phase rate constants for the consumption of H atoms by 1,1,1,3,3,3-hexafluoro-2-propanimine were measured over 294–736 K using the laser flash photolysis/atomic resonance fluorescence technique. The results are summarized as 3.0 × 10 –11 exp(−14.5 kJ mol –1 /RT) cm 3 molecule –1 s –1 with a confidence interval of ±13%. These are the first temperature-dependent kinetic experiments on an imine of the form R 2 C═NH and thereby provide the only validation of transition-state modeling for such systems. Several pathways are feasible; comparison of theory and experiment suggests the dominant channel is addition of H atoms to the nitrogen atom. On this basis, the observations are rationalized quantitatively. Computations also yield the thermochemistry of (CF 3 ) 2 CNH, with Δ f H 298 = −1232 kJ mol –1 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗