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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Gaussian wave packet transform based numerical scheme for the semi-classical Schrödinger equation with random inputs

In this work, we study the semi-classical limit of the Schrödinger equation with random inputs, and show that the semi-classical Schrödinger equation produces O(ε) oscillations in the random variable space. With the Gaussian wave packet transform, the original Schrödinger equation is mapped to an ordinary differential equation (ODE) system for the wave packet parameters coupled with a partial differential equation (PDE) for the quantity w in rescaled variables. Further, we show that the w equation does not produce ε dependent oscillations, and thus it is more amenable for numerical simulations. We propose multi-level sampling strategy in implementing the Gaussian wave packet transform, where in the most costly part, i.e. simulating the w equation, it is sufficient to use ε independent samples. We also provide extensive numerical tests as well as meaningful numerical experiments to justify the properties of the numerical algorithm, and hopefully shed light on possible future directions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

GenMod: A generative modeling approach for spectral representation of PDEs with random inputs

Here, we propose a method for quantifying uncertainty in high-dimensional PDE systems with random parameters, where the number of solution evaluations is small. Parametric PDE solutions are often approximated using a spectral decomposition based on polynomial chaos expansions. For the class of systems we consider (i.e., high dimensional with limited solution evaluations) the coefficients are given by an underdetermined linear system in a regression formulation. This implies additional assumptions, such as sparsity of the coefficient vector, are needed to approximate the solution. Here, we present an approach where we assume the coefficients are close to the range of a generative model that maps from a low to a high dimensional space of coefficients. Our approach is inspired be recent work examining how generative models can be used for compressed sensing in systems with random Gaussian measurement matrices. Using results from PDE theory on coefficient decay rates, we construct an explicit generative model that predicts the polynomial chaos coefficient magnitudes. The algorithm we developed to find the coefficients, which we call GenMod, is composed of two main steps. First, we predict the coefficient signs using Orthogonal Matching Pursuit. Then, we assume the coefficients are within a sparse deviation from the range of a sign-adjusted generative model. This allows us to find the coefficients by solving a nonconvex optimization problem, over the input space of the generative model and the space of sparse vectors. We obtain theoretical recovery results for a Lipschitz continuous generative model and for a more specific generative model, based on coefficient decay rate bounds. We examine three high-dimensional problems and show that, for all three examples, the generative model approach outperforms sparsity promoting methods at small sample sizes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs

Fully distributed, integrated surface–subsurface hydrological models (ISSHMs) have seen renewed interest due to availability of better software, high performance computing facilities, and high-resolution, spatially extensive data products. ISSHMs are valuable as tools for advancing system understanding as they can resolve multiple processes defined on the plot scale including three-dimensional interaction of surface water and groundwater. Here, we evaluated the performance of an ISSHM, the Advanced Terrestrial Simulator (ATS), on seven diverse catchments across the continental US using widely available data products to define model inputs without calibration. We compare the ATS-simulated streamflow and evapotranspiration with gauge observations and MODIS-derived evapotranspiration, respectively. Using the Kling-Gupta Efficiency (KGE) as metric, ATS with default data products performed reasonably well at 6 of 7 catchments for streamflow. However, in one of those 6 catchments ATS had poor performance on baseflow and ATS’s overall performance was thus judged to be inadequate despite the acceptable KGE. ATS performance for evapotranspiration was good in all 7 catchments using default data products. In the two catchments where ATS streamflow performance using default data products was not acceptable, the performance was significantly improved by using local information on subsurface properties below the soil. We also compare the model-simulated streamflow and evapotranspiration with the Sacramento soil moisture accounting (SAC-SMA) model, a semi-distributed model that was calibrated on a catchment-by-catchment basis. Uncalibrated ATS performance is comparable to the calibrated SAC-SMA model in terms of streamflow while ATS performance is similar to or better (much better in certain catchments) in reproducing MODIS-derived evapotranspiration. Reasonably good performance of ATS without catchment-specific calibration provides new confidence in the ISSHM class of models and community data products as tools for advancing understanding of watershed function in a changing environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Omega-Limit Sets and Input-To-State Stability in Power Grids with Switching Equilibria

This paper studies a power transmission system with both conventional generators (CGs) and distributed energy assets (DEAs) providing frequency control. We consider an operating condition with demand aggregating two dynamic components: one that switches between different values on a finite set, and one that varies smoothly over time. Such dynamic operating conditions may result from protection scheme activations, external cyber-attacks, or due to the integration of dynamic loads, such as data centers. Mathematically, the dynamics of the resulting system are captured by a system that switches between a finite number of vector fields - or modes -, with each mode having a distinct equilibrium point induced by the demand aggregation. To analyze the stability properties of the resulting switching system, we leverage tools from hybrid dynamic inclusions and the concept of ..omega.. -limit sets from sets. Specifically, we characterize a compact set that is semi-globally practically asymptotically stable under the assumption that the switching frequency and load variation rate are sufficiently slow. For arbitrarily fast variations of the load, we use a level-set argument with multiple Lyapunov functions to establish input-to-state stability of a larger set and with respect to the rate of change of the loads. The theoretical results are illustrated via numerical simulations on the IEEE 39-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Time-interval distributions in a digital gamma multi-channel analyzer at extreme input rates

Here, this study explores the relationship between experimentally observed inter-event time-interval distributions (TIDs) and measured dead times for pulses generated by a HPGe gamma detector and processed by a digital spectrometer. The system utilizes a fast and slow channel, pile-up rejector, trapezoid filtering, and flash analog-to-digital converter. The experimentally derived TIDs were compared with theory for validation. The results demonstrate that the theoretical model reliably describes measured TIDs up to 40% dead time. However, significant distortion effects become increasingly pronounced at higher input rates. It appears that the deviation between the measured and calculated time-interval distributions take the shape of higher-order convolutions of the TID, which represent the time difference between counts for more than two successive events. In this work, theoretical functions for the TID are expanded to reproduce measurements up to 90%. This refinement in the interpretation and treatment of the measured TIDs provides improved accuracy and precision in the prediction of the true event rate and measured dead time in the counter. Even though it reflects occasional count loss that is specific to the set-up, it is expected that a similar adjustment may be applicable to other detection systems as well.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Atomistic Simulations of Polydisperse Lignin Melts Using Simple Polydisperse Residue Input Generator

Understanding the physics of lignin will help rationalize its function in plant cell walls as well as aiding practical applications such as deriving biofuels and bioproducts. Here, in this work, we present SPRIG (Simple Polydisperse Residue Input Generator), a program for generating atomic-detail models of random polydisperse lignin copolymer melts i.e., the state most commonly found in nature. Using these models, we use all-atom molecular dynamics (MD) simulations to investigate the conformational and dynamic properties of polydisperse melts representative of switchgrass (Panicum virgatum L.) lignin. Polydispersity, branching and monolignol sequence are found to not affect the calculated glass transition temperature, T g . The Flory–Huggins scaling parameter for the segmental radius of gyration is 0.42 ± 0.02, indicating that the chains exhibit statistics that lie between a globular chain and an ideal Gaussian chain. Below T g the atomic mean squared displacements are independent of molecular weight. In contrast, above T g , they decrease with increasing molecular weight. Therefore, a monodisperse lignin melt is a good approximation to this polydisperse lignin when only static properties are probed, whereas the molecular weight distribution needs to be considered while analyzing lignin dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Life-Cycle Emissions and Human Health Implications of Multi-Input, Multi-Output Biorefineries

To meaningfully broaden the supply of fuels for the transportation sector, biofuel production must be scaled up and this requires a wider array of biomass feedstocks, including agricultural residues and organic waste. Rather than pursuing conversion of lignocellulosic biomass to fuels and anaerobic digestion of wastes as separate pathways, there are economic and environmental advantages associated with integrating these processes in a single facility. However, existing research rarely goes beyond carbon footprints in quantifying the effects of such a shift in bioenergy production. In addition to CO2, CH4, and N2O, this study explores the life-cycle air pollution (NH3, volatile organic compounds, NOx, SO2, and PM2.5), marine eutrophication, acidification, and local external cost implications of biorefineries capable of taking in crop residues, food waste, and manure to produce liquid fuel, electricity, and/or other options such as renewable natural gas (RNG), hydrogen, bioplastics, and protein-rich livestock feed. Relative to a single-input, single-output baseline, biorefineries integrated with organic waste codigestion to coproduce electricity or RNG can reduce life-cycle CO2-equivalent emissions by 84-149%, and the monetized external impacts across all scenarios range from $1.07/gallon to -$0.75/gallon ethanol.

Air pollution↗
DOE OSTI2018

Perturbed Input Ensemble Modeling With the Space Weather Modeling Framework

Abstract To assess the effect of uncertainties in solar wind driving on the predictions from the operational configuration of the Space Weather Modeling Framework, we have developed a nonparametric method for generating multiple possible realizations of the solar wind just upstream of the bow shock, based on observations near the first Lagrangian point. We have applied this method to the solar wind inputs at the upstream boundary of Space Weather Modeling Framework and have simulated the geomagnetic storm of 5 April 2010. We ran a 40‐member ensemble for this event and have used this ensemble to quantify the uncertainty in the predicted Sym‐H index and ground magnetic disturbances due to the uncertainty in the upstream boundary conditions. Both the ensemble mean and the unperturbed simulation tend to underpredict the magnitude of Sym‐H in the quiet interval before the storm and overpredict in the storm itself, consistent with previous work. The ensemble mean is a more accurate predictor of Sym‐H, improving the mean absolute error by nearly 2 nT for this interval and displaying a smaller bias. We also examine the uncertainty in predicted maxima in ground magnetic disturbances. The confidence intervals are typically narrow during periods where the predicted d B H /d t is low. The confidence intervals are often much wider where the median prediction is for enhanced d B H /d t . The ensemble also allows us to identify intervals of activity that cannot be explained by uncertainty in the solar wind driver, driving further model improvements. This work demonstrates the feasibility and importance of ensemble modeling for space weather applications.

58 GEOSCIENCES↗
DOE OSTI2022

The Unexpected Oceanic Peak in Energy Input to the Atmosphere and Its Consequences for Monsoon Rainfall

Abstract Monsoons have historically been understood to be caused by the low thermal inertia of land, allowing more energy from summer insolation to be transferred to the overlying atmosphere than over adjacent ocean. Here, we show that during boreal summer, the global maximum net energy input (NEI) to the atmosphere unexpectedly lies over the Indian Ocean, not over land. Observed radiative fluxes suggest that cloud‐radiative effects (CRE) almost double the NEI over ocean, shifting the NEI peak from land to ocean. Global climate model experiments with both land and interactive sea surface temperatures confirm that CRE create the oceanic NEI maximum. Interactions between CRE, NEI, circulation, and land‐sea contrast in surface heat capacity shift precipitation from Southeast to South Asia. CRE thus alter the global partitioning of precipitation between land and ocean and the spatial structure of Earth's strongest monsoon, in ways that can be understood through the NEI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

USEEIO v2.0, The US Environmentally-Extended Input-Output Model v2.0

USEEIO v2.0 is an environmental-economic model of US goods and services that can be used for life cycle assessment, footprinting, national prioritization, and related applications. This paper describes the development of the model and accompanies the release of a full model dataset as well as various supporting datasets of national environmental totals by US industry. Novel methodological elements since USEEIO v1 models include waste sector disaggregation, final demand vectors for US consumption and production, a domestic form of the model that can be used to separate domestic and foreign impacts, and price adjustment matrices for converting outputs to purchaser price and in various US dollar years. Improvements in modeling national totals of industry and environmental flows are described. The model is validated through reproduction of national totals from input data sources and through analysis of changes from the most recent complete USEEIO model that can be explained based on data updates or method changes. The model datasets can all be reproduced with open source software packages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sensory stimulation for upper limb amputations modulates adaptability of cortical large-scale systems and combination of somatosensory and visual inputs

Abstract Touch-like phantom limb sensations can be elicited through targeted transcutaneous electrical nerve stimulation (tTENS) in individuals with upper limb amputation. The corresponding impact of sensory stimulation on cortical activity remains an open question. Brain network research shows that sensorimotor cortical activity is supported by dynamic changes in functional connections between relevant brain regions. These groups of interconnected regions are functional modules whose architecture enables specialized function and related neural processing supporting individual task needs. Using electroencephalographic (EEG) signals to analyze modular functional connectivity, we investigated changes in the modular architecture of cortical large-scale systems when participants with upper limb amputations performed phantom hand movements before, during, and after they received tTENS. We discovered that tTENS substantially decreased the flexibility of the default mode network (DMN). Furthermore, we found increased interconnectivity (measured by a graph theoretic integration metric) between the DMN, the somatomotor network (SMN) and the visual network (VN) in the individual with extensive tTENS experience. While for individuals with less tTENS experience, we found increased integration between DMN and the attention network. Our results provide insights into how sensory stimulation promotes cortical processing of combined somatosensory and visual inputs and help develop future tools to evaluate sensory combination for individuals with amputations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Hg isotopes reveal in-stream processing and legacy inputs in East Fork Poplar Creek, Oak Ridge, Tennessee, USA

In this paper, natural abundance stable Hg isotope measurements were used to place new constraints on sources, transport, and transformations of Hg along the flow path of East Fork Poplar Creek (EFPC), a point-source contaminated headwater stream in Oak Ridge, Tennessee. Particulate-bound Hg in the water column of EFPC within the Y-12 National Security Complex, was isotopically similar to average metallic Hg(0) used in industry, having a mean δ 202 Hg value of -0.42 ± 0.09‰ (1SD) and near-zero Δ 199 Hg. On average, particulate fraction δ 202 Hg values increased downstream by 0.53‰, while Δ 199 Hg decreased by -0.10‰, converging with the Hg isotopic composition of the fine fraction of streambed sediment along the 26 km flow path. The dissolved fraction behaved differently. Although initial Δ 199 Hg values of the dissolved fraction were also near-zero, these values increased transiently along the flow path. Initial δ 202 Hg values of the dissolved fraction were more variable than in the particulate fraction, ranging from -0.44 to 0.18‰ among three seasonal sampling campaigns, but converged to an average δ 202 Hg value of 0.01 ± 0.10‰ (1SD) downstream. Dissolved Hg in the hyporheic and riparian pore water had higher and lower δ 202 Hg values, respectively, compared to dissolved Hg in stream water. Finally, variations in Hg isotopic composition of the dissolved and suspended fractions along the flow path suggest that: (1) physical processes such as dilution and sedimentation do not fully explain decreases in total mercury concentrations along the flow path; (2) in-stream processes include photochemical reduction, but microbial reduction is likely more dominant; and (3) additional sources of dissolved mercury inputs to EFPC at baseflow during this study predominantly arise from the hyporheic zone.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

The study of capability natural uranium as fuel cycle input for long life gas cooled fast reactors with helium as coolant

The objective of the present research is to assess the feasibility design of small long-life Gas Cooled Fast Reactor with helium as coolant. GCFR included in the Generation-IV reactor systems are being developed to provide sustainable energy resources that meet future energy demand in a reliable, safe, and proliferation-resistant manner. This reactor can be operated without enrichment and reprocessing forever, once it starts. To obtain the capability of consuming natural uranium as fuel cycle input modified CANDLE burn-up scheme was adopted in this system with different core design. This study has compared the core with three designs of core reactors with the same thermal power 600 MWth. The fuel composition each design was arranged by divided core into several parts of equal volume axially i.e. 6, 8 and 10 parts related to material burn-up history. The fresh natural uranium is initially put in region 1, after one cycle of 10 years of burn-up it is shifted to region 2 and the region 1 is filled by fresh natural uranium fuel. This concept is basically applied to all regions, i.e. shifted the core of the region (i) into region (i+1) region after the end of 10 years burn-up cycle. The calculation results shows that for the burn-up strategy on “Region-8” and “Region-10” core designs, after the reactors start-up the operation furthermore they only needs natural uranium supply to the next life operation until one period of refueling (10 years).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Robust identification of multiple-input single-output system response for efficient pickup noise removal from tokamak diagnostics

We report electromagnetic pickup noise in the tokamak environment imposes an imminent challenge for measuring weak diagnostic photocurrents in the nA range. The diagnostic signal can be contaminated by an unknown mixture of crosstalk signals from coils powered by currents in the kA range. To address this issue, an algorithm for robust identification of linear multi-input single-output (MISO) systems has been developed. The MISO model describes the dynamic relationship between measured signals from power sources and observed signals in the diagnostic and allows for a precise subtraction of the noise component. The proposed method was tested on experimental diagnostic data from the DIII-D tokamak, and it has reduced noise by up to 20 dB in the 1–20 kHz range.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Amplified spontaneous emission spectrum at the output of a diode amplifier saturated by an input monochromatic wave

Expressions for the amplitudes of amplified spontaneous emission waves in a diode amplifier near the frequency ω{sub 0} of a 'strong' input monochromatic wave have been derived in terms of a random function of a stationary Gaussian process. We have found expressions for the spectral density of the amplitudes and shown that, on the red side of the spectrum with respect to frequency ω{sub 0}, spontaneous emission waves obtain additional nonlinear gain, induced by the strong wave, whereas on the blue side of the spectrum an additional loss is induced. Such behaviour of the amplitudes of amplified waves agrees with previous results. (lasers)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Influence of the input radiation pulse characteristics on the parameters of a XeF(C – A) amplifier in a THL-100 laser system

We report the results of experimental and theoretical investigations on the influence of spatial and energy parameters of input radiation with a pulse duration of 50 ps on output characteristics of a XeF(C – A) amplifier in a visible-range, multi-terawatt THL-100 laser system. Dynamics of the energy density radial distribution for laser radiation passing through the amplifier is studied. Results of numerical simulation are presented for amplification of laser beams with Gaussian and super-Gaussian radial energy density distributions. It is shown that the laser energy of 3.2 J obtained experimentally is not the limiting value. According to calculations, the output energy of the amplifier with such mirror configuration may reach 4.1 J, which in the case of a pulse compressed down to 50 fs corresponds to the radiation power of 82 TW. (paper)

42 ENGINEERING↗
DOE OSTI2016

Dopaminergic inputs in the dentate gyrus direct the choice of memory encoding

Rewarding experiences are often well remembered, and such memory formation is known to be dependent on dopamine modulation of the neural substrates engaged in learning and memory; however, it is unknown how and where in the brain dopamine signals bias episodic memory toward preceding rather than subsequent events. Here we found that photostimulation of channelrhodopsin-2–expressing dopaminergic fibers in the dentate gyrus induced a long-term depression of cortical inputs, diminished theta oscillations, and impaired subsequent contextual learning. Computational modeling based on this dopamine modulation indicated an asymmetric association of events occurring before and after reward in memory tasks. In subsequent behavioral experiments, preexposure to a natural reward suppressed hippocampus-dependent memory formation, with an effective time window consistent with the duration of dopamine-induced changes of dentate activity. Altogether, our results suggest a mechanism by which dopamine enables the hippocampus to encode memory with reduced interference from subsequent experience.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Data-Consistent Inversion for Stochastic Input-to-Output Maps

Data-consistent inversion is a recently developed measure-theoretic framework for solving a stochastic inverse problem involving models of physical systems. The goal is to construct a probability measure on model inputs (i.e., parameters of interest) whose associated push-forward measure matches (i.e., is consistent with) a probability measure on the observable outputs of the model (i.e., quantities of interest). Previous implementations required the map from parameters of interest to quantities of interest to be deterministic. This work generalizes this framework for maps that are stochastic, i.e., contain uncertainties and variation not explainable by variations in uncertain parameters of interest. Generalizations of previous theorems of existence, uniqueness, and stability of the data-consistent solution are provided while new theoretical results address the stability of marginals on parameters of interest. A notable aspect of the algorithmic generalization is the ability to query the solution to generate independent identically distributed samples of the parameters of interest without requiring knowledge of the so-called stochastic parameters. This work therefore extends the applicability of the data-consistent inversion framework to a much wider class of problems. This includes those based on purely experimental and field data where only a subset of conditions are either controllable or can be documented between experiments while the underlying physics, measurement errors, and any additional covariates are either uncertain or not accounted for by the researcher. Finally, numerical examples demonstrate application of this approach to systems with stochastic sources of uncertainties embedded within the modeling of a system and a numerical diagnostic is summarized that is useful for determining if a key assumption is verified among competing choices of stochastic maps.

97 MATHEMATICS AND COMPUTING↗