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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Electronic energy loss and ion velocity correlation effects in track production in swift-ion-irradiated LiNbO3: A quantitative assessment between structural damage morphology and energy deposition

The primary motivation for studying how irradiation modifies the structures and properties of solid materials involves the understanding of undesirable phenomena, including irradiation-induced degradation of components in nuclear reactors and space exploration, and beneficial applications, including material performance tailoring through ion beam modification and defect engineering. In this work, the formation mechanism of latent tracks with different damage morphologies in LiNbO 3 crystals under 0.09–6.17 MeV/u ion irradiation with an electronic energy loss from 2.6–13.2 keV/nm is analyzed by experimental characterizations and numerical calculations. Irradiation-induced damage is preliminarily evaluated via the prism coupling technique to analyze the correlation between the dark-mode spectra and energy loss profiles of irradiated regions. Under the irradiation conditions of different ion velocities and electronic energy losses, different damage morphologies, from individual spherical defects to discontinuous and continuous tracks, are experimentally characterized. During ion penetration process, the ion velocity determines the spatiotemporal distribution of deposited irradiation energy induced by electronic energy loss, meaning that the two essential factors including electronic energy loss and ion velocity co-affect the track damage. The inelastic thermal spike model is used to numerically calculate the spatiotemporal evolutions of energy deposition and the corresponding atomic temperature under different irradiation conditions, and a quantitative relationship is proposed by comparison with corresponding experimentally observed track damage morphologies. Additionally, the obtained quantitative relationship between irradiation conditions and track damage provides deep insight and guidance for understanding the damage behavior of crystal materials in extreme radiation environments and selecting irradiation parameters, including ion species and energies, for ion beam technique application in atomic-level defect manipulation, material modification, and micro/nanofabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The performance evaluation of direct detection electron energy-loss spectroscopy at 200 kV and 80 kV accelerating voltages

Direct electron detectors (DeDs) have been widely used for imaging studies because of their higher beam sensitivity, lower noise, improved pixel resolution, etc. However, there have been limited studies related to the performance in spectroscopic applications for the direct electron detection. Hereby, taking the advantage of the DeD installed on a high-performance electron energy-loss spectrometer, we systematically studied the performance of a DeD (Gatan's K2 IS) fitted on an aberration-corrected transmission electron microscope (TEM) equipped with an electron monochromator. Using SrTiO3 as the model system, the point spread function in the zero-loss region of the spectrum and the performance for core loss spectroscopy have been investigated under both 200 kV and 80 kV operating conditions. We demonstrate that the K2 detector can achieve an overall better performance at 200 kV than a charge coupled device (CCD) detector. At 80 kV, the K2 DeD is still better than a CCD, except for the relative broad tails of the zero-loss peak. The signal-to-noise ratio is very close for DeD and CCD under 80 kV. Based on our data obtained at different operating voltages, it is clear that DeD will benefit the microscopy community and boost the development of cutting-edge materials science studies by pushing the frontiers in electron energy-loss spectroscopy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

A large net carbon loss attributed to anthropogenic and natural disturbances in the Amazon Arc of Deforestation

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km 2 ), we found that 24.1, 24.2, and 14.5 Tg C y −1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y −1 ) and other disturbances (50.3 Tg C y −1 ) were partially counterbalanced by forest growth (44.1 Tg C y −1 ). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y −1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effects of collisional ion orbit loss on neoclassical tokamak radial electric fields

We report that ion orbit loss is considered important for generating the radially inward electric field E r in a tokamak edge plasma. In particular, this effect is emphasized in diverted tokamaks with a magnetic X point. In neoclassical equilibria, Coulomb collisions can scatter ions onto loss orbits and generate a radially outward current, which in steady state is balanced by the radially inward current from viscosity. To quantitatively measure this loss-orbit current in an edge pedestal, an ion-orbit-flux diagnostic has been implemented in the axisymmetric version of the gyrokinetic particle-in-cell code XGC. As the first application of this diagnostic, a neoclassical DIII-D H-mode plasma is studied using gyrokinetic ions and adiabatic electrons. The validity of the diagnostic is demonstrated by studying the collisional relaxation of E r in the core. After this demonstration, the loss-orbit current is numerically measured in the edge pedestal in quasisteady state. In this plasma, it is found that the radial electric force on ions from E r approximately balances the ion radial pressure gradient in the edge pedestal, with the radial force from the plasma flow term being a minor component. The effect of orbit loss on E r is found to be only mild.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Effect of resonant magnetic perturbations including toroidal sidebands on magnetic footprints and fast ion losses in HL-2M

Externally applied resonant magnetic perturbations (RMPs), generated by magnetic coils located outside the plasma (referred to as RMP coils), provide an effective way to control the edge localized mode (ELM) in tokamak devices. Due to the discrete nature of the toroidal distribution of these window-frame coils, toroidal sidebands always exist together with the fundamental harmonics designed for ELM control. In this work, the MARS-F code (Liu et al 2000 Phys. Plasmas7 3681) is applied to investigate the detailed features of the RMP spectra considering both the dominant harmonic (n = 2) and the associated sideband (n = 6), and the impact of the combined fields on magnetic footprints as well as on the fast ion losses for a reference double-null scenario in the HL-2M device. It is found that the sum of the n = 2 and n = 6 RMP fields splits the footprint and widens the footprint area, as compared to the single-n (n = 2) harmonic case. The resistive plasma response breaks the up–down symmetry of the footprint pattern on the outer divertor plates, which is otherwise symmetric assuming vacuum RMP fields. Considering fast ion losses, a threshold value exists for the initially launched radial position of test particles, as well as for the RMP coil current, before the loss occurs. When the threshold criterion is satisfied, the combined n = 2 and n = 6 RMP fields enhance the fast ion loss rate by , as compared to that of the n = 2 component alone. These results illustrate the important role of the sideband of RMP fields on the magnetic footprints and fast ion losses in tokamak plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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 OSTI2024

Entropy-Infused Deep Learning Loss Function for Capturing Extreme Values in Wind Power Forecasting

Extreme scenarios in wind power generation occur with higher frequency and larger magnitude in the recent years due to the ever-increasing extreme meteorological factors. Accurate forecasting of the occurrence of extreme values in wind power generation is of great concern to ensure reliable power system operation. Recently, deep learning models have surged in popularity for wind power forecasting, with the mean squared error (MSE) loss function being commonly used. However, the MSE loss function, being sensitive to extreme values, disproportionately penalizes larger errors, cannot adequately capture the extreme values present in wind energy data, and novel loss functions have seldom been tailored for wind power forecasting. To this end, in this paper, we introduce a novel loss function specifically crafted to capture extreme values in wind power forecasting. The experimental results with four fundamental deep learning methods on open source wind power dataset validate that the new loss function is efficient and superior in all cases compared to MSE in capturing extreme values while maintaining forecasting performance.

17 WIND ENERGY↗
DOE OSTI2021

Extremum Seeking-Based Control System for Particle Accelerator Beam Loss Minimization

Particle accelerators throughout the world vary widely in terms of age and availability of advanced noninvasive diagnostics that provide varying levels of detail about the accelerated beams. Additionally, beam loss measurements and current monitors are ubiquitous in the accelerator community, they are noninvasive, and they are some of the most important metrics in terms of preventing damage or irradiation of beam pipes and equipment for high-energy machines. However, beam loss and current measurements are difficult to use for feedback tuning because of a lack of a known analytic relationship between scalar loss measurements throughout an accelerator and the hundreds of thousands of parameters that influence the beam and time variation of the beam source itself. In this work, we present a model-independent extremum seeking (ES) controller, which has been implemented for automated tuning and optimization of the Los Alamos Neutron Science Center (LANSCE) linear ion accelerator based only on scalar quantities, such as beam loss or flux measurements. We demonstrate the approach on various accelerator subsystems, including groups of radio frequency (RF) accelerating cavities, quadrupole magnets, bending magnets, and steering magnets. This tool is now available as a use graphical user interface (GUI) in the LANSCE control room, allowing beam physicists to choose arbitrary groups of parameters and beam loss or current monitors for adaptive tuning.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Conceptual Design of a Scintillator-Based Fast-Ion Loss Detector for the Wendelstein 7-X Stellarator

Here, a conceptual design of a scintillator-based fast-ion loss detector (FILD) has been developed for the Wendelstein 7-X stellarator (W7-X). Simulations using the Monte Carlo codes ASCOT5 and BEAMS3D have been performed to calculate the expected flux of neutral beam injection (NBI)-generated fast hydrogen ions onto the conceptual detector probe head. These fast-ion loss fluxes have been calculated for several magnetic field configurations as well as probe insertion positions. At the maximum insertion position, both co-and counter-going losses with high incident pitch angles are observed; however, at retracted positions, only co-going fast ions reach the probe head. The FILDSIM code has been used to optimize the geometry of the detector entrance and collimating elements to achieve a wide velocity space coverage as well as a high velocity–space resolution. A synthetic FILD signal is calculated for the expected loss distribution via forward modeling using the instrument response function. The synthetic signal is found to largely retain the velocity space features of the loss distribution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study

Abstract Large‐scale estimations of flood losses are often based on spatially aggregated inputs. This makes risk assessments vulnerable to aggregation bias, a well‐studied, sometimes substantial outcome in analyses that model fine‐grained spatial phenomena at coarse spatial units. To evaluate this potential in the context of large‐scale flood risk assessments, we use data from a high‐resolution flood hazard model and structure inventory for over 1.3 million properties in Massachusetts and examine how prominent data aggregation approaches affect the magnitude and spatial distribution of flood loss estimates. All considered aggregation approaches rely on aggregate structure inventories but differ in whether flood hazard is also aggregated. We find that aggregating only structure inventories slightly underestimates overall losses (−10% bias), and when flood hazard data is spatially aggregated to even relatively small spatial units (census block), statewide aggregation bias can reach +366%. All aggregation‐based procedures fail to capture the spatial covariation of inputs distributions in the upper tails that disproportionately generate total expected losses. Our findings are robust to several key assumptions, add important context to published risk assessments and highlight opportunities to improve flood loss estimation uncertainty quantification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effects of collisional ion orbit loss on neoclassical tokamak radial electric fields

Ion orbit loss is considered important for generating the radially inward electric field Er in a tokamak edge plasma. In particular, this effect is emphasized in diverted tokamaks with a magnetic X point. In neoclassical equilibria, Coulomb collisions can scatter ions onto loss orbits and generate a radially outward current, which in steady state is balanced by the radially inward current from viscosity. To quantitatively measure this loss-orbit current in an edge pedestal, an ion-orbit-flux diagnostic has been implemented in the axisymmetric version of the gyrokinetic particle-in-cell code XGC. As the first application of this diagnostic, a neoclassical DIII-D H-mode plasma is studied using gyrokinetic ions and adiabatic electrons. The validity of the diagnostic is demonstrated by studying the collisional relaxation of Er in the core. After this demonstration, the loss-orbit current is numerically measured in the edge pedestal in quasisteady state. In this plasma, it is found that the radial electric force on ions from Er approximately balances the ion radial pressure gradient in the edge pedestal, with the radial force from the plasma flow term being a minor component. The effect of orbit loss on Er is found to be only mild.

gyrokinetic simulations↗
DOE OSTI2026

Loss Factors for Small Distributed Wind Turbines Based on Field Data in the United States

While wind energy production loss due to unavailability, environmental impacts, curtailment, and other causes has been studied and characterized at the utility-scale wind farm level, observation-based characterization of project loss is lacking for distributed wind energy, particularly for projects involving small wind turbines. Contemporary tools and research that support pre-construction distributed wind energy characterization present a wide range of default loss factors to convert gross energy estimates to net: 7-18%. We hypothesize that we can use generation observations from operational distributed wind projects to develop more accurate representations of loss. Using a density-based filtering technique on distributed wind power generation timeseries, we determine periods of typical performance and use them with regression algorithms in a measure-correlate-predict fashion to simulate what the generation would have been during periods of atypical or unreported performance. From there, the actual versus predicted generation leads to the establishment of observation-informed loss factors (median = 17%) for small, single turbine installation distributed wind projects.

17 WIND ENERGY↗
DOE OSTI2023

Estimating the Performance Loss Rate of Photovoltaic Systems Using Time Series Change Point Analysis

The accurate quantification of the performance loss rate of photovoltaic systems is critical for project economics. Following the current research activities in the photovoltaic performance and reliability field, this work presents a comparative assessment between common change point methods for performance loss rate estimation of fielded photovoltaic installations. An extensive testing campaign was thus performed to evaluate time series analysis approaches for performance loss rate evaluation of photovoltaic systems. Historical electrical data from eleven photovoltaic systems installed in Nicosia, Cyprus, and the locations’ meteorological measurements over a period of 8 years were used for this investigation. The application of change point detection algorithms on the constructed monthly photovoltaic performance ratio series revealed that the obtained trend might not always be linear. Specifically, thin film photovoltaic systems showed nonlinear behavior, while nonlinearities were also detected for some crystalline silicon photovoltaic systems. When applying several change point techniques, different numbers and locations of changes were detected, resulting in different performance loss rate values (varying by up to 0.85%/year even for the same number of change points). The results highlighted the importance of the application of nonlinear techniques and the need to extract a robust nonlinear model for detecting significant changes in time series data and estimating accurately the performance loss rate of photovoltaic installations.

photovoltaics↗
DOE OSTI2023

Critical Metallicity of Cool Supergiant Formation. I. Effects on Stellar-mass Loss and Feedback

This paper systematically studies the relation between metallicity and mass loss of massive stars. We perform one-dimensional stellar evolution simulations and build a grid of ~2000 models with initial masses ranging between 11 and 60 M ⊙ and absolute metallicities Z between 0.00001 and 0.02. Steady-state winds, comprising hot main-sequence winds and cool supergiant winds, are the main drivers of the mass loss of massive stars in our models. We calculate the total mass loss over the stellar lifetime for each model. Our results reveal the existence of a critical metallicity Z c at Z ~ 10 –3 , where the mass loss exhibits a dramatic jump. If Z > Z c , massive stars tend to evolve into cool supergiants, and a robust cool wind is operational. In contrast, if Z < Z c , massive stars usually remain as blue supergiants, wherein the cool wind is not activated and the mass loss is generally weak. Moreover, we calculate the wind feedback in a 10 5 M ⊙ star cluster with the Salpeter initial mass function. The kinetic energy released by winds does not exhibit any significant transition at Z c because the wind velocity of a cool supergiant wind is low and contributes little to the kinetic energy. The effects of critical metallicity provide implications for the fates of metal-poor stars in the early universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

In silico evaluation of plant nitrification suppression effects on agroecosystem nitrogen loss

Abstract Nitrification regulates the potential for nitrogen (N) loss from ecosystems because it converts ammonium to nitrate, which is susceptible to leaching and gaseous emissions. Crops can suppress the microbes that perform nitrification by exuding nitrification‐inhibiting compounds from their roots and taking up available ammonium. However, the effect of nitrification suppression on agroecosystem N losses remains poorly characterized, and a lack of temporal synchrony between nitrification, N losses, and nitrification suppression by plants could limit the effect of nitrification suppression. We used the DayCent‐CABBI model to evaluate the effectiveness of the suppression of nitrification by sorghum to reduce N 2 O emissions and nitrate leaching in an energy sorghum/soybean rotation at the Energy Farm in Urbana‐Champaign, IL. We simulated nitrification suppression at the measured levels (MeasNS) and at the maximum measured level applied to the entire growing season (MaxNS), and we also explored ways to better utilize nitrification suppression by altering the timing of urea–ammonium–nitrate fertilizer applications. Model experiments showed that most nitrification occurred immediately after fertilizer was applied, whereas nitrification suppression began to ramp up more than a month after planting. On an annual basis, MeasNS experiments showed a 1%–2% reduction in annual N 2 O emissions relative to no nitrification suppression (NoNS), and MaxNS experiments showed a 4%–9% reduction in annual N 2 O emissions relative to NoNS. Both nitrification suppression levels showed <1% reduction in nitrate leaching. Altering the timing of fertilizer applications to better synchronize nitrification suppression with high soil ammonium levels had mixed effects on annual N 2 O emissions and nitrate leaching and sometimes resulted in increased N losses. The timing of simulated N 2 O emissions shifted with the timing of fertilization, and N 2 O emissions from denitrification increased when N 2 O emissions from nitrification decreased. Increasing N retention during the non‐growing season may be more effective than growing‐season nitrification suppression for reducing annual N losses in the rainfed Midwest.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Jensen–Shannon divergence based novel loss functions for Bayesian neural networks

Bayesian neural networks (BNNs) are state-of-the-art machine learning methods that can naturally regularize and systematically quantify uncertainties using their stochastic parameters. Kullback–Leibler (KL) divergence-based variational inference used in BNNs suffer from unstable optimization and challenges in approximating light-tailed posteriors due to the unbounded nature of the KL divergence. To resolve these issues, we formulate a novel loss function for BNNs based on a new modification to the generalized Jensen–Shannon (JS) divergence, which is bounded. In addition, we propose a Geometric JS divergence-based loss, which is computationally efficient since it can be evaluated analytically. We found that the JS divergence-based variational inference is intractable, and hence employed a constrained optimization framework to formulate these losses. Our theoretical analysis and empirical experiments on multiple regression and classification data sets suggest that the proposed losses perform better than the KL divergence-based loss, especially when the data sets are noisy or biased. Specifically, there are approximately 5% and 8% improvements in accuracy for a noise-added CIFAR-10 dataset and a regression dataset, respectively. There is about 13% reduction in false negative predictions of a biased histopathology dataset. Additionally, we quantify and compare the uncertainty metrics for the regression and classification tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Heat Loss Effects on Emissions in an NH 3 RRQL Combustor

Ammonia (NH 3 ) is a carbon-free energy carrier with an infrastructure for production, storage, and distribution. There is interest in direct NH 3 combustion, but managing pollutant emissions is a key challenge, particularly nitric oxides (NO x ) due to the fuel-bound nitrogen atom, nitrous oxide (N 2 O), which is a potent greenhouse gas, and unburned NH 3 , which is harmful to humans and the environment. Rich staged combustor concepts with extended primary residence times (τ res,primary ), like Rich-Relax- Quick-mix-Lean (RRQL), offer a viable pathway for direct NH 3 combustion with low levels of NO x formation. However, minimizing secondary emissions such as N 2 O and unburned NH 3 and hydrogen (H 2 ) remains a critical challenge. Prior atmospheric-pressure studies have demonstrated that RRQL operation with sufficiently long τ res,primary enables substantial NO x relaxation and promotes NH 3 cracking to H 2 , if heat losses from the relaxation stage are limited. However, the combined influence of elevated pressure and long residence time on RRQL performance has not been explored. The present work examines RRQL operation at pressures up to 5 bar and elevated τ res,primary . Exhaust measurements of NO x , NH 3 , and N 2 O are used to quantify the extent of NO x relaxation and NH 3 cracking under nonadiabatic conditions. To contextualize and quantify the effects of heat losses in the experimental data, chemical reactor networks (CRNs) incorporating prescribed heat loss rates are employed to assess the sensitivity of emissions to thermal losses in the relaxation stage. Collectively, the results demonstrate that management and quantification of heat losses are essential to preserve NO x relaxation and limit NH 3 and N 2 O emissions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗