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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fast quantum interferometry at the nanometer and attosecond scales with energy-entangled photons

In classical optical interferometry, loss and background complicate achieving fast nanometer-resolution measurements with illumination at low light levels. Conversely, quantum two-photon interference is unaffected by loss and background, but nanometer-scale resolution is physically difficult to realize. As a solution, we enhance two-photon interference with highly nondegenerate energy entanglement featuring photon frequencies separated by 177 THz. We observe measurement resolution at the nanometer (attosecond) scale with only O(10 4 ) photon pairs, despite the presence of background and loss. Our nondestructive thickness measurement of a metallic thin film agrees with atomic force microscopy, which often achieves better resolution via destructive means. With contactless, nondestructive measurements in seconds or faster, our instrument enables metrological studies in optically challenging contexts where background, loss, or photosensitivity are factors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Observation of microwave shielding of ultracold molecules

Harnessing the potential wide-ranging quantum science applications of molecules will require control of their interactions. Here, we used microwave radiation to directly engineer and tune the interaction potentials between ultracold calcium monofluoride (CaF) molecules. By merging two optical tweezers, each containing a single molecule, we probed collisions in three dimensions. The correct combination of microwave frequency and power created an effective repulsive shield, which suppressed the inelastic loss rate by a factor of six, in agreement with theoretical calculations. The demonstrated microwave shielding shows a general route to the creation of long-lived, dense samples of ultracold polar molecules and evaporative cooling.

Anderegg, Loïc↗
DOE OSTI2020

Addressing Soiling: From Interface Chemistry to Practicality

Natural soiling has reduced the energy output of photovoltaic (PV) systems since the technology was first used. Projecting even a small average annual soiling loss translates to billions of dollars in annual lost revenue worldwide. Production losses due to soiling can be very high in some locations, substantially increasing the levelized cost of electricity (LCOE) due to lost power production, increased operating and maintenance costs, and/or increased finance cost due to the uncertainty. Furthermore, although soiling has been discussed in the literature for more than 70 years, solutions to many problems are still needed. The National Renewable Energy Laboratory (NREL) is working with the PV industry to develop the tools/knowledge so that the effects of soiling can be predicted for different environmental conditions and cost-effective mitigation can be implemented. For this project, NREL performed a number of research and development tasks/subtasks in the following general areas to 1) predict PV module soiling losses based on environmental factors at a PV installation and from its energy production data, 2) quantitatively measure the adhesion forces to understand the physics enabling soiling, and 3) develop related standards on PV module coatings and artificial soiling. At the inception of the project, the PV industry considered these efforts to be the most important and immediate soiling issues that we could address to have the highest impact on LCOE. These focused efforts have led to outstanding accomplishments that have been communicated and very well received by the community.

14 SOLAR ENERGY↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of machine learning (ML), specifically neural networks, has grown significantly and has spurred research in its applicability to digital instrumentation and control systems (DI&C). While ML models have shown promise in operational contexts, the trustworthiness of using such algorithms has not been adequately assessed. Failures of ML integrated systems are not well understood, and the lack of comprehensive risk modeling can degrade the trustworthiness in these systems. In recent reports by the National Institute for Standards and Technology (NIST) [1] and the Nuclear Regulatory Commission (NRC) [2], they indicate that trustworthiness in ML is a critical barrier and will play a vital role in the safe, accountable, and secure operation of intelligent systems. Thus, in this work, we demonstrate a dynamic model-agnostic method to quantify the relative reliability of AI/ML predictions by incorporating out-of-distribution (OOD) detection on the training dataset. It is well documented that most ML algorithms excel at interpolation (or near-interpolation) tasks but experience significant performance degradation at extrapolation. The method, referenced as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets which can used to the relative reliability of AI/ML predictions. LADDR is then demonstrated on a feedforward neural network based digital twin used for the prediction of safety significant factors during a loss-of-flow transient. LADDR is used to demonstrate how training data can be used as evidence to support the relative reliability of ML/AI predictions enhancing the overall trustworthiness of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of data-driven neural network-based machine learning (ML) algorithms has grown significantly and spurred research in its applicability to instrumentation and control systems. While they are promising in operational contexts, the trustworthiness of such algorithms is not adequately assessed. Failures of ML-integrated systems are poorly understood; the lack of comprehensive risk modeling can degrade the trustworthiness of these systems. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption and will play a vital role in intelligent systems' safe and accountable operation. Thus, in this work, we demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset. It is well documented that ML algorithms excel at interpolation (or near-interpolation) tasks but significantly degrade at extrapolation. This occurs when new samples are "far" from training samples. The method, referred to as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets, which is used to calculate a prediction's relative reliability. LADDR is demonstrated on a feedforward neural network-based model used to predict safety significant factors during different loss-of-flow transients. LADDR is intended as a "data supervisor" and determines the appropriateness of well-trained ML models in the context of operational conditions. Ultimately, LADDR illustrates how training data can be used as evidence to support the trustworthiness of ML predictions when utilized for conventional interpolation tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

On Performance Prediction of Big Data Transfer in High-performance Networks

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

Liu, Wuji↗
DOE OSTI2026

Precise 2D electric field density simulations for superconducting quantum devices

Dielectric loss due to two-level systems is a limiting factor for superconducting qubit relaxation times. These losses arise mostly from nanometer-scale interfacial defect regions in superconducting devices with planar dimensions of microns to millimeters, thus making it resource intensive to accurately simulate the electric field density in these regions with traditional electromagnetic solvers. In this work, we demonstrate a fast boundary integral equation solver that allows precise simulation of electric field density in these thin regions, showing a speedup of around two orders of magnitude over traditional solvers, with relative errors around $10^{-7}$ for a ten-minute solution runtime. By computing participation ratios through Green's first identity without squaring the electric field, our approach is less susceptible to the field singularities near conductor corners. We apply this solver to a basic untrenched coplanar waveguide cross-section, showing that the common assumption of participation ratio linearity with dielectric constant holds well for some interfaces and not others; in particular, while the metal-air (MA) top and corner follow this linear relationship strongly, the MA sidewall does not. We then compare isotropic and anisotropic etching, showing that the MA sidewall and the metal-air-substrate triple junction are the most strongly affected. We are currently leveraging this solver to explore geometries that will uniquely isolate the participation ratios of the different dielectrics. Finally, we are working to combine this solver framework with a full 3D microwave solver to accurately calculate participation ratios for the thin dielectrics that are known sources of loss in superconducting qubits.

Gimbutas, Z. [NIST, Boulder] (ORCID:00000003320982↗
DOE OSTI2022

Wildfire exacerbates high-latitude soil carbon losses from climate warming

Abstract Arctic and boreal permafrost soil organic carbon (SOC) decomposition has been slower than carbon inputs from plant growth since the last glaciation. Anthropogenic climate warming has threatened this historical trend by accelerating SOC decomposition and altering wildfire regimes. We accurately modeled observed plant biomass and carbon emissions from wildfires in Alaskan ecosystems under current climate conditions. In projections to 2300 under the RCP8.5 climate scenario, we found that warming and increased atmospheric CO 2 will result in plant biomass gains and higher litterfall. However, increased carbon losses from (a) wildfire combustion and (b) rapid SOC decomposition driven by increased deciduous litter production, root exudation, and active layer depth will lead to about 4.4 PgC of soil carbon losses from Alaska by 2300 and most (88%) of these loses will be from the top 1 m of soil. These SOC losses offset plant carbon gains, causing the ecosystem to transition to a net carbon source after 2200. Simulations excluding wildfire increases yielded about a factor of four lower SOC losses by 2300. Our results show that projected wildfire and its direct and indirect effects on plant and soil carbon may accelerate high-latitude soil carbon losses, resulting in a positive feedback to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Advanced Signal Decomposition Analysis and Anomaly Detection in Photovoltaic Systems

With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This work introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate (PLR), recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.

14 SOLAR ENERGY↗
DOE OSTI2020

Spatially Resolved Performance Analysis for Perovskite Solar Cells

This review explores the current state of the art in spatially resolved characterization of mixed-halide perovskite solar cells. As the size of perovskite cells and modules continues to grow, quantification of the spatial distribution of key cell parameters will become increasingly valuable in predicting ultimate cell-level performance and tracking process homogeneity. Here, both high resolution microscopic approaches using scanning techniques and camera-based methods for full-area cell and/or module analysis are discussed. The value of this local data in predicting performance losses at the cell level is particularly emphasized. Measurable physical parameters sensitive to losses of voltage, current, fill factor, and efficiency are discussed together with selected experimental results. It is demonstrated that a combination of spatially resolved cell parameter mapping/imaging can be used to quantitatively discriminate various loss contributions at high resolution. The impact and control of inhomogeneities become particularly important when upscaling from small devices to large formats compatible with industrial mass production.

14 SOLAR ENERGY↗
DOE OSTI2025

Preliminary Look at the LBEG & MPEG Beam Transmissions between HPSim and Operation Data

This report summarizes recent work on estimating the transmission of LANSCE H- beams from the end of the present DTL to both the PSR stripper foil (LBEG) and WNR target 4 (MPEG). The LBEG beam might be considered a more typical LINAC beam and lends itself to continuous monitoring of transmission through the various stages of the accelerator. For the MPEG, however, the widely-spaced micropulses and lack of the requisite sensitivity current monitors throughout the accelerator make these measurements extremely difficult and more uncertain. Therefore, to assist in estimating the MPEG beam transmission, beam-dynamics simulations using HPSim were employed. These beam transmission estimates from the end of the Drift Tube Linac (DTL) to Target 4 (MPEG) and the PSR stripper foil (LBEG) are vital to determine the charge requirements for LAMP’s front end. In this technote, we demonstrate three major efforts in determining the transmission: (1) Convert the WNR beamline lattice from TRANSPORT to HPSim for use in the simulation; (2) Simulate the optimization process in the Central Control Room (CCR) that brings down the losses by up to a factor of 4000 between the end of the initial physics-based tuneup phase and production beam operation; (3) Analyze operational data to deduce measured transmissions. Table 1 shows the estimated losses with HPSim and operational analysis. Finally, a better measurement and other improvements to refine the results are also proposed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Low-loss millimeter-wave resonators with an improved coupling structure

Millimeter-wave superconducting resonators are a useful tool for studying quantum device coherence in a new frequency domain. However, improving resonators is difficult without a robust and reliable method for coupling millimeter-wave signals to 2D structures. We develop and characterize a tapered transition structure coupling a rectangular waveguide to a planar slotline waveguide with better than 0.5 dB efficiency over 14 GHz, and use it to measure ground-shielded resonators in the W band (75–110 GHz). Having decoupled the resonators from radiative losses, we consistently achieve single-photon quality factors above 10 5 , with a two-level-system loss limit above 10 6 , and verify the effectiveness of oxide removal treatments to reduce loss. These values are 4–5 times higher than those previously reported in the W band, and much closer to typical planar microwave resonators. The improved losses demonstrated by these on-chip millimeter-wave devices shed new light on quantum decoherence in a different frequency regime, offer increased selectivity for high-frequency detectors, and enables new possibilities for hybrid quantum experiments integrating millimeter-wave frequencies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Magnetic field sensors for detection of trapped flux in superconducting radio frequency cavities

Superconducting radio-frequency (SRF) cavities are fundamental building block of modern particle accelerators. They operate at liquid helium temperatures 2-4 K to achieve very high quality factors 1e10-1e11. Trapping of magnetic flux within the superconductor is a significant contribution to the residual RF losses which limit the achievable quality factor. Suitable diagnostic tools are in high demand to understand the mechanisms of flux trapping in technical superconductors and the fundamental components of such diagnostic tool are magnetic field sensors. We have studied the performance of commercially available Hall probes, anisotropic magnetoresistive sensors and flux-gate magnetometers with respect to their sensitivity and capability to detect localized, low magnetic flux amplitudes, of the order of few tens of magnetic flux quantum at liquid helium temperature. Although Hall probes have the lowest magnetic field sensitivity (~96$ nV$/microT at 2 K), their physical dimensions are such that they have the ability to detect the lowest number of trapped vortices among the three type of sensors. Hall probes and anisotropic magnetoresistive sensors have been selected to be used in a setup to map regions of trapped flux on the surface of a single-cell SRF cavity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A valuation framework for customers impacted by extreme temperature-related outages

Extreme temperature outages can lead to not just economic losses but also various non-energy impacts (NEI), such as increased mortality rates, property damage, and reduced productivity, due to significant degradation of indoor operating conditions caused by service disruptions. However, existing resilience assessment approaches lack specificity for extreme temperature conditions. They often overlook temperature-related mortality and neglect the customer characteristics and grid response in the calculation, despite the significant influence of these factors on NEI-related economic losses. This paper aims to address these gaps by introducing a comprehensive framework to estimate the impact of resilience enhancement not only on the direct economic losses incurred by customers but also on potential NEI, including mortality and the value of statistical life during extreme temperature-related outages. The proposed resilience valuation integrates customer characteristics and grid response variables based on a scalable grid simulation environment. This study adopts a holistic approach to quantify customer-oriented economic impacts, utilizing probabilistic loss scenarios that incorporate health-related factors and damage/loss models as a function of exposure for valuation. The proposed methodology is demonstrated through comparative resilient outage planning, using grid response models emulating a Texas weather zone during the 2021 winter storm Uri. The case study results show that enhanced outage planning with hardened infrastructure can improve the system resilience and thereby reduce the relative risk of mortality by 16% and save the total costs related to non-energy impacts by 74%. In conclusion, these findings underscore the efficacy of the framework by assessing the financial implications of each case, providing valuable insights for decision-makers and stakeholders involved in extreme-weather related resilience planning for risk management and mitigation strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Collective Analysis of Alpha Particle Losses Due to Self-Absorption by Mass Loading on Radioactive Particulate Glass Fiber Filters

In this study, we derived a relationship between filter mass loading and the percent loss during analysis using the mass loading data collected from six previous studies of self-absorption. Components of mass loading include particulate dust, radioactive particulates, and filter material. In a research report published in 1984, Higby calculated a minimum burial depth for an alpha particle to be lost due to absorption (100% loss) of about 3.7 mg/cm 2 based on calculations for the range of 239-Pu alpha particles in glass fiber filters. From there, Higby concluded that a correction factor of 0.85 assumes approximately 15% losses in the count rate of both alpha and beta particles. In 2000, Luetzelschwab et al. recommended assuming a 40% loss at a loading of 3.3 mg/cm 2 and a 28% loss for a loading of 2.3 mg/cm 2 which included the frontal face mass of the filter. More recently, the 100% losses due to absorption were reported to be in the 10 mg/cm 2 range. Presented here is a trinomial relationship method of relating percent loss due to self-absorption to filter mass loading, based on data reported by Higby, Luetzelschwab et al., Huang et al., Barnett et al., Smith et al., and Hogue et al. Under normal operating conditions at the stacks monitored by Effluent Management, the mass loading of sample filters averages 0.09 ± 0.12 (2s) mg/cm 2 (excluding negative values and outliers) and ranges from 0 mg/cm 2 to 0.24 mg/cm 2 . Based on current mass loading results for Effluent Management stack sample filters, the forced-zero trinomial relationship method estimated self-absorption losses of less than 5%. Because American National Standards Institute/Health Physics Society N13.1-2011 guidelines indicate a correction factor should be used when the penetration of radioactive material into the collection media or self-absorption of radiation by the material collected would reduce the count rate by more than 5%, it is possible continued application of a correction factor to the Effluent Management stack samples is no longer necessary. Nevertheless, continuing to assign a correction factor at the 5% threshold (i.e., 0.95) would be a conservative approach.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Impact of absorption due to zero-field splitting on loss in dielectrics: A case study in sapphire

The coherence times of superconducting qubits are limited by loss mechanisms, whose microscopic origins have remained elusive. We propose a mechanism caused by transitions between zero-field-split states of paramagnetic impurities or defects. We derive the absorption cross section for a magnetic dipole transition and apply it to calculate the bulk loss tangent. For Cr, Fe, and V impurities in sapphire, we find loss tangents at 4.5 GHz in the range of 10 −9 –10 −8 , comparable to the loss measured in experiments. This value suggests that magnetic loss may be a limiting factor in the coherence times of superconducting qubits.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Lower limits for non-radiative recombination loss in organic donor/acceptor complexes

Understanding the factors controlling radiative and non-radiative transition rates for charge transfer states in organic systems is important for applications ranging from organic photovoltaics (OPV) to lasers and LEDs. Here we explore the role of charge-transfer (CT) energetics, lifetimes, and photovoltaic properties in the limit of very slow non-radiative rates by using a model donor/acceptor system with photoluminescence dominated by thermally activated delayed fluorescence (TADF). This blend exhibits an extremely high photoluminescence quantum efficiency (PLQY = ~22%) and comparatively long PL lifetime, while simultaneously yielding appreciable amounts of free charge generation (photocurrent external quantum efficiency EQE of 24%). In solar cells, this blend exhibits non-radiative voltage losses of only ~0.1 V, among the lowest reported for an organic system. Notably, we find that the non-radiative decay rate, k nr , is on the order of 10 5 s -1 , approximately 4–5 orders of magnitude slower than typical OPV blends, thereby confirming that high radiative efficiency and low non-radiative voltage losses are achievable by reducing k nr . Furthermore, despite the high radiative efficiency and already comparatively slow k nr , we find that k nr is nevertheless much faster than predicted by Marcus–Levich–Jortner two-state theory and we conclude that CT-local exciton (LE) hybridization is present. Our findings highlight that it is crucial to evaluate how radiative and non-radiative rates of the LE states individually influence the PLQY of charge-transfer states, rather than solely focusing on the PLQY of the LE. This conclusion will guide material selection in achieving low non-radiative voltage loss in organic solar cells and high luminescence efficiency in organic LEDs.

36 MATERIALS SCIENCE↗