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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 OSTI2022

A Simple Condition for Sustained Super-Eddington Black Hole Growth

One of the most pressing questions in cosmology is how the black holes (BHs) powering quasars at high redshift grow to supermassive scales within a billion years of the Big Bang. Here we show that sustained super-Eddington accretion can be achieved for BHs with Eddington fractions f Edd ≳ 2/ϵ, where ϵ is the efficiency with which radiation is generated in the accretion process. In this regime, the radiation carries too little momentum to halt the accretion flow and the infalling gas traps the radiation. The BH growth then proceeds unimpeded until the gas supply is exhausted, in contrast to accretion at lower rates, which is limited by the radiation generated in the accretion process. The large gas supply available in massive high-redshift quasar host galaxies may be readily accreted onto seed BHs via this supply-limited mode of accretion, providing an explanation for how such supermassive BHs are assembled in the early universe. This sustained super-Eddington growth may also explain the short lifetimes inferred for the H ii regions surrounding high-redshift quasars, if the bulk of the BH growth occurs without the associated radiation escaping to ionize the intergalactic medium. It furthermore implies that a population of obscured rapidly growing BHs may be difficult to detect, perhaps explaining why so few quasars with Eddington fractions higher than a few have been observed. Finally, this simple condition for sustained super-Eddington growth can easily be implemented in cosmological simulations that can be used to assess in which environments it occurs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Enzyme interactions on lignocellulosic biomass structure

This chapter shows that, although well studied over the years, the cellulose enzymatic hydrolysis process has still many obstacles to be overcome or minimized. High enzyme loading and relatively long incubation periods are generally required for effective cellulose saccharification to be achieved. These limitations make the process unattractive from an economic perspective. Most studies show that the conversion rate of cellulose hydrolysis decreases over reaction time due to several factors that may inhibit, deactivate, or slow the work of enzymes. Some of these factors are inhibition by the product generated; inhibition/deactivation of enzymes by by-products generated during pretreatment (PT); limitation of mass transfer; shear stress by high stirring; air–liquid interfacial area; and unproductive adsorption on lignin or other compounds present in the biomass. The interaction between cellulases and the lignin present in these biomasses has a major negative impact on the process, with the source of the raw material and the type of PT used having a strong influence on it. Finally, the use of nonenzymatic proteins seems to be the best way to mitigate this problem.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Influence of Control and Limiter Schemes on Sequence-Domain Fault Models of Grid-Forming Inverter-Interfaced Distributed Generators

Unlike synchronous generators, the fault response of grid-forming (GFM) inverter-interfaced distributed generators (IIDGs) is notably governed by the selection of control and current limiting strategies rather than inherent physical traits. While recent research has focused on the sequence domain fault model of GFM IIDGs, a research gap exists in elucidating the influence of control and current limiting schemes on this model's characteristics. This article aims to fill this void by examining how different control and current limiting schemes influence the positive and negative sequence impedances in the phasor-domain fault model of GFM IIDGs. This investigation encompasses droop-based, virtual synchronous machine-based, and virtual oscillator-based reference generation controls alongside rotating and stationary reference-frame-based voltage controls. Furthermore, saturation-based, latching-based, circular and virtual impedance-based current limiting schemes are analyzed. To achieve this goal, a thorough numerical simulation study is conducted. Findings indicate that outer reference generation controls exhibit minimal impact. Conversely, the choice of voltage control and various current limiting schemes emerge as the predominant factors shaping the sequence models of GFM IIDGs. These analyses and results are instrumental in devising reliable protection strategies within inverter-based grids, as a comprehensive understanding of electrical elements in the sequence domain is imperative for effective protective measures.

current limiters↗
DOE OSTI2020

Radial radio frequency (RF) electron guns

Radial radio frequency (RF) electron guns and radial RF electron gun systems are provided that are capable of generating an electron beam that can propagate either radially inward, towards the axis of a cylinder, or radially outward from the axis. A beam source capable of generating a radially inwardly propagating electron beam, while perhaps not particularly useful as a source for a higher-energy accelerator, offers potential advantages for materials processing, as the geometry allows irradiation from all sides of an enclosed material flow with a single structure. Other potential applications include, but are not limited to, atmospheric plasma generation, radiation damage testing, and possibly, novel electron lens-type devices for hadron accelerators.

Lewellen, John↗
DOE OSTI2022

Improving Seasonal Forecast Using Probabilistic Deep Learning

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Sunsetting coal power in China

Reducing CO 2 emissions from coal-fired electricity generation in China is critical to limit global warming. Long-term projections of China's electricity supply tend to assume that coal generation will be a mainstay of China's electricity system through 2050, due to limitations in the scalability of hydropower, nuclear, and natural gas generation and the commercial availability of carbon capture and storage. This paper examines the resource, economic, and institutional implications of reducing and replacing coal generation in China with mostly renewable energy and energy storage by 2040. We find that the scale of solar, wind, and storage resources needed to do so is on the order of 100–150 GW/year of solar and wind capacity and 15 GW/year of energy storage from 2020 to 2025, growing to 250 GW/year and 90 GW/year, respectively, from 2025 to 2040. We then also evaluate the sensitivities if coal plants are retired by 2050.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Examination of Ac-225 production from Ra-226 using fast reactor JOYO

In this study, the authors investigated the method of producing the radionuclide Ac{sup 225} used for targeted alpha therapy (TAT). Currently, Ac{sup 225} is mainly generated from ORNL's Th{sup 229} generator, and the annual production amount is limited to about 63 GBq, and methods for generating it using accelerators are under development in each country. The method using an accelerator has the advantage of being able to generate Ac{sup 225} from a small amount of target nuclides with high efficiency but has the disadvantage of not being able to irradiate a large amount of target nuclides at once due to the small irradiation area. Therefore, the authors investigated a method to generate Ac{sup 225} by neutron irradiation of Ra{sup 226} as a target nuclide using the experimental fast reactor JOYO, which has abundant neutrons and a large loading region. Irradiation of Ra{sup 226} with fast neutrons causes a (n, 2n) reaction to generate Ra{sup 225}, and then decay to produce Ac{sup 225}. In addition, although harmful Ac{sup 227} is also produced at the same time by the (n,γ) reaction, first of all the actinium isotope is chemically separated and eliminated. Since the remaining Ra{sup 225} collapses and Ac{sup 225} is produced, pure Ac{sup 225} can be extracted by performing chemical separation again. As a result of the analysis, 1 g of Ra{sup 226} is irradiated with JOYO for 60 days, and milking is performed 4 times every 17.5 days. By doing these three times a year, it was found that about 50 GBq of Ac{sup 225} was generated. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Small Hydropower Plant Response Improvement Using Energy Storage

Small hydropower plants contribute significantly to global power generation. However, due to limited storage, these can have low ramping capacity and poor load-frequency regulation. These can prevent small hydropower plants from being used in isolated grids as backup power to critical loads and rural areas. In this paper, a control architecture for frequency control is proposed that facilitates the use of energy storage to improve the response of standalone small hydropower plants. The frequency controller generates power commands using proportional control on frequency deviation and a user-defined power setpoint, and this incorporates with a current controller that facilitates power injection. The distinctive feature of the controller design is that it takes into account frequency thresholds and storage constraints to enable power injections, and it provides the capability to perform automated and manual recharge of the energy storage. Simulations using detailed nonlinear models demonstrate the improved performance of the hydropower with energy storage using the proposed controller. Automated and manual recharge and the impact of the energy storage constraints on the performance of the hydropower plant are demonstrated.

13 HYDRO ENERGY↗
DOE OSTI2023

Transmission risk of Oropouche fever across the Americas

Abstract Background Vector-borne diseases (VBDs) are important contributors to the global burden of infectious diseases due to their epidemic potential, which can result in significant population and economic impacts. Oropouche fever, caused by Oropouche virus (OROV), is an understudied zoonotic VBD febrile illness reported in Central and South America. The epidemic potential and areas of likely OROV spread remain unexplored, limiting capacities to improve epidemiological surveillance. Methods To better understand the capacity for spread of OROV, we developed spatial epidemiology models using human outbreaks as OROV transmission-locality data, coupled with high-resolution satellite-derived vegetation phenology. Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas. Results Models based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of different parameters such as different study areas and environmental predictors. Models estimate that up to 5 million people are at risk of exposure to OROV. Nevertheless, the limited epidemiological data available generates uncertainty in projections. For example, some outbreaks have occurred under climatic conditions outside those where most transmission events occur. The distribution models also revealed that landscape variation, expressed as vegetation loss, is linked to OROV outbreaks. Conclusions Hotspots of OROV transmission risk were detected along the tropics of South America. Vegetation loss might be a driver of Oropouche fever emergence. Modeling based on hypervolumes in spatial epidemiology might be considered an exploratory tool for analyzing data-limited emerging infectious diseases for which little understanding exists on their sylvatic cycles. OROV transmission risk maps can be used to improve surveillance, investigate OROV ecology and epidemiology, and inform early detection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Neutron Transmission Imaging with a Portable D-T Neutron Generator

Fast-neutron transmission imaging provides complementary information to x-ray transmission imaging. While fast neutron imaging resolution is generally below x-ray imaging, 14-MeV neutrons have an advantage over portable x-ray systems. Neutrons have higher transmission through high-Z materials due to a more uniform attenuation as a function of material atomic number Z compared to X-rays, and can therefore image low-Z materials inside high-Z materials. As a result, portable neutron transmission imaging has many applications, including inspection of concrete and welds for corrosion in vehicles, bridges, and other infrastructure, measurement of material levels in containers, and inspection of suspicious packages. Fast-neutron imaging is also more practical for field use than thermal-neutron imaging due to the size and shielding requirements typical of thermal-imaging systems compared to the availability of small 14.1 MeV D-T neutron generators. However, there are limitations in portable fast-neutron imaging systems, including limited neutron output, limited light produced by neutron scintillators, and lower resolution due to neutron source spot size and 2-3 mm scintillator thickness. In addition, digital-panel dark-noise is roughly 100x higher than neutron scintillator light, and variations in noise across the panel and in time is comparable to the imaging signal. Here we discuss recent efforts in developing a portable fast-neutron radiography system, including an improved neutron scintillator, mitigation of panel noise, and new commercial portable D-T neutron generators. We also present MCNP efforts to model neutron imaging, including scintillator resolution and the effects of neutron scattering from the object and surrounding materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Generalizing the Gurson model using symbolic regression and transfer learning to relax inherent assumptions

Abstract To generate material models with fewer limiting assumptions while maintaining closed-form, interpretable solutions, we propose using genetic programming based symbolic regression (GPSR), a machine learning (ML) approach that describes data using free-form symbolic expressions. To maximize interpretability, we start from an analytical, derived material model, the Gurson model for porous ductile metals, and systematically relax inherent assumptions made in its derivation to understand each assumption’s contribution to the GPSR model forms. We incorporate transfer learning methods into the GPSR training process to increase GPSR efficiency and generate models that abide by known mechanics of the system. The results show that regularizing the GPSR fitness function is critical for generating physically valid models and illustrate how GPSR allows a high level of interpretability compared with other ML approaches. The method of systematic assumption relaxation allows the generation of models that address limiting assumptions found in the Gurson model, and the symbolic forms allow conjecture of decreased material strength due to void interaction and non-symmetric void shapes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning to generate gridded extreme precipitation data sets for global land areas with limited in situ measurements

There is a strong need for gridded observational data sets that describe the climatology of extreme precipitation across the globe; such data sets are critical for quantifying precipitation extremes and their corresponding influence on large perturbations on surface and groundwater systems and flooding. Machine learning (ML) methods can be used to generate pseudo in situ measurements of the climatology of extreme precipitation for land regions with poor sampling by matching relevant geographic (e.g., orography) and atmospheric (e.g., surface temperature, precipitation, and pressure) variables for densely sampled regions and those with limited geographic sampling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

On the Radiation Effects of Strontium Ions on Satellite Solar Cells in Low Earth Orbits

This study focuses on the radiation effects 19 of Sr + ions—generated from high-altitude nuclear explosions (HANE)—on satellite solar cells in low-Earth orbits (LEO). Along four selected satellite orbits, ion fluences are sampled inside the evolving Sr + distributions for days, determined from our newly developed HANE environment model. These fluences, along with the help of radiation transport codes including the MULASSIS and SRIM models, enable us to quantify the radiation damages by determining the values of total ionizing doses and the equivalent 1 MeV electron fluences for displacement damages. Comparing the dose values to existing experimental data, we conclude that HANE-generated Sr + ions have limited darkening effects to quartz solar cell coverglasses in LEO with apogees of 100s to 1000 km. In addition, with the extremely high equivalent fluences, we also conclude that these Sr+ ions may cause severe or even fatal displacement damage to exposed solar photovoltaic (PV) cells on satellites in LEO. The radiation effects of Sr + ions are much less significant for the orbits with high apogees beyond ten thousand km. We also conducted model parameter sensitivity studies on the charge exchange cross-sections, neutral atmosphere density profiles and explosion local time positions, and the above conclusions stay unchanged. The methodology developed in this study can be extended to other HANE-generated heavy ion species in the future.

14 SOLAR ENERGY↗
DOE OSTI2020

A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing

Data-driven algorithms have been widely used as effective tools to mimic hydrologic systems. Unlike black-box models, decision tree algorithms offer transparent representations of systems and reveal useful information about the underlying process. A popular class of decision tree models is model tree (MT), which is designed for predicting continuous variables. Most MT algorithms employ an exhaustive search mechanism and a pre-defined splitting criterion to generate a piecewise linear model. However, this approach is computationally intensive, and the selection of the splitting criterion can significantly affect the performance of the generated model. These drawbacks can limit the application of MTs to large datasets. To overcome these shortcomings, a new flexible Model Tree Generator (MTG) framework is introduced here. MTG is equipped with several modules to provide a flexible, efficient, and effective tool for generating MTs. The application of the algorithm is demonstrated through simulation of controlled discharge from several reservoirs across the Contiguous United States (CONUS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Learning turbulent flows with generative models for super resolution and sparse flow reconstruction

Neural operators are promising surrogates for dynamical systems but when trained with standard L 2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that combining operator learning with generative modeling overcomes this limitation. We consider three practical turbulent-flow challenges where conventional neural operators fail: spatio-temporal super-resolution, forecasting, and sparse flow reconstruction. For Schlieren jet super-resolution, an adversarially trained neural operator (adv-NO) reduces the energy-spectrum error by 15 × while preserving sharp gradients at neural operator-like inference cost. For 3D homogeneous isotropic turbulence, adv-NO trained on only 160 timesteps from a single trajectory forecasts accurately for five eddy-turnover times and offers 114 × wall-clock speed-up at inference than the baseline diffusion-based forecasters, enabling near-real-time rollouts. For reconstructing cylinder wake flows from highly sparse Particle Tracking Velocimetry-like inputs, a conditional generative model infers full 3D velocity and pressure fields with correct phase alignment and statistics. These advances enable accurate reconstruction and forecasting at low compute cost, bringing near-real-time analysis and control within reach in experimental and computational fluid mechanics.

Fluid dynamics↗
DOE OSTI2023

Characterization of the In Vivo Deuteration of Native Phospholipids by Mass Spectrometry Yields Guidelines for Their Regiospecific Customization

Customization of deuterated biomolecules is vital for many advanced biological experiments including neutron scattering. However, because it is challenging to control the proportion and regiospecificity of deuterium incorporation in live systems, often only two or three synthetic lipids are mixed together to form simplistic model membranes. This limits the applicability and biological accuracy of the results generated with these synthetic membranes. Despite some limited prior examination of deuterating Escherichia coli lipids in vivo, this approach has not been widely implemented. In this report an extensive mass spectrometry-based profiling of E. coli phospholipid deuteration states with several different growth media was performed, and a computational method to describe deuterium distributions with a one-number summary is introduced. The deuteration states of 36 lipid species were quantitatively profiled in 15 different growth conditions, and tandem mass spectrometry was used to reveal deuterium localization. Regressions were employed to enable the prediction of lipid deuteration for untested conditions. Small-angle neutron scattering was performed on select deuterated lipid samples, which validated the deuteration states calculated from the mass spectral data. Based on these experiments, guidelines for the design of specifically deuterated phospholipids are described. This unlocks even greater capabilities from neutron-based techniques, enabling experiments that were formerly impossible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Neutrino point source searches for dark matter spikes

Any dark matter spikes surrounding black holes in our Galaxy are sites of significant dark matter annihilation, leading to a potentially detectable neutrino signal. In this paper we examine 10-10 5 M ⊙ black holes associated with dark matter spikes that formed in early minihalos and still exist in our Milky Way Galaxy today, in light of neutrino data from the ANTARES and IceCube detectors. In various regions of the sky, we determine the minimum distance away from the solar system that a dark matter spike must be in order to have not been detected as a neutrino point source for a variety of representative dark matter annihilation channels. Given these constraints on the distribution of dark matter spikes in the Galaxy, we place significant limits on the formation of the first generation of stars in early minihalos — stronger than previous limits from gamma-ray searches in Fermi Gamma-Ray Space Telescope data. The larger black holes considered in this paper may arise as the remnants of Dark Stars after the dark matter fuel is exhausted; thus neutrino observations may be used to constrain the properties of Dark Stars. The limits are particularly strong for heavier WIMPs. For WIMP masses ~ 5TeV, we show that ≲ 10 % of minihalos can host first stars that collapse into BHs larger than 10 3 M ⊙ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Designer Metasurfaces for Antireflective Applications Enabled by Advanced Nanoparticle Technology

Optic processing advances have shifted system limitations, with current constraints consisting of antireflective (AR) coatings applied to optics and the inability to reduce size, weight, and price. Metasurfaces, and more broadly metaoptics, provide an avenue to overcome these limitations. The ability to generate substrate-engraved metasurfaces that can be tailored is demonstrated within a broad design space to satisfy large-area AR applications for bandpass or broadband needs, demonstrating an optically equivalent surface roughness standard deviation of only 5 Å. Using an advanced nanoparticle masking approach, two metasurfaces are fabricated: 1) vertical sidewall nanofeature metasurface (MS) for AR applications at 351 nm, and 2) sloped sidewall MS designed for broadband AR applications. For the 351 nm MS, the reflectance is measured to be 0.127%. In the case of the sloped sidewall MS, the reflectance is measured to be <0.25% over 400–700 nm and <1% over 400–1100 nm. To the best of the authors’ knowledge, this is the first demonstration of a broadband AR capable of this performance. The tunability of these structures, with a parameter space spanning even more remarkable designs such as broader bands and acceptance angles, coupled with high laser-induced damage durability and mechanical robustness, displays the strength of metasurfaces for AR applications.

36 MATERIALS SCIENCE↗