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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Leveraging Interaction History for Intelligent Configuration of Multiple Coordinated Views in Visualization Tools

Visualization tools can take advantage of multiple coordinated views to support analysis of large, multidimensional data sets. Effective design of such views and layouts can be challenging, but understanding users analysis strategies can inform design improvements. We outline an approach for intelligent design configuration of visualization tools with multiple coordinated views, and we discuss a proposed software framework to support the approach. The proposed software framework could capture and learn from user interaction data to automate new compositions of views and widgets. Such a framework could reduce the time needed for meta analysis of the visualization use and lead to more effective visualization design.

infovis↗
DOE OSTI2017

Fast-neutron Multiplicity Counter for Passive and Active Measurements of Special Nuclear Material

We developed a fast-neutron multiplicity counter (FNMC) based on organic scintillators (liquid and stilbene). The system is able to detect correlated photon and neutron multiplets emitted by fission reactions. We tested the system both in passive and active operation mode, and present the results of the experiments, both carried out at the Zero Power Physics Reactor of Idaho National Laboratory. The system was operated in coincidence mode (with a gate time of 50 nanoseconds) and two calibration-based approaches were used to estimate the 240Pu effective (240Pueff) and 235U mass of the samples. In passive mode, we assayed two types of plutonium metal plates, i.e. PAHN and PANN, with a 240Pueff mass of 4.7 g and 25.05 g per plate, respectively. The 239Pu mass percentage was 74% for the PAHN and 95% for the PANN series. We measured neutrons emitted in coincidence by several plate assemblies, having an overall 240Pueff mass in the 4.7 g to 500 g range. In active mode, we characterized several uranium samples. We used two AmLi sources (~50000 neutrons emitted per second) to induce fission in the samples. The first six measured samples were certified reference materials (CRM, set 149), having a constant enrichment, i.e. 93.2 wt%, and mass in the range between 0.5 and 4 kg. Additionally, we assayed three CRM-146 samples, with constant total mass of 500 g and an enrichment of 20.1 wt%, 52.8 wt% and 93.2 wt%. In both configurations, the system succeeded in estimating the fissile mass of an unknown sample with a statistical uncertainty lower than 1% (4-minute assay) and 7% (10-minute assay), in passive and active mode, respectively. In passive mode, the statistical uncertainty is lower than the one achieved by standard 3He counters for plutonium metal samples of similar mass. The difference between the estimated and the declared 240Pueff mass, i.e., the bias error, measured by the fast neutron multiplicity counter was consistently lower compared to the bias error of a standard thermal system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Multiple-objective Reinforcement Learning for Inverse Design and Identification

The aim of inverse chemical design is to develop new molecules with given optimized molecular properties or objectives. Recently, generative deep learning (DL) networks are considered as the state-of-the-art in inverse chemical design and have achieved early success in generating molecular structures with desired properties in the pharmaceutical and material chemistry fields. However, satisfying a large number (> 10 objectives) of molecular objectives is a limitation of current generative models. To improve the model’s ability to handle a large number of molecule design objectives, we developed a Reinforcement Learning (RL) based generative framework to optimize chemical molecule generation. Our use of Curriculum Learning (CL) to fine-tune the pre-trained generative network allowed the model to satisfy up to 21 objectives and increase the generative network’s robustness. The experiments show that the proposed multiple-objective RL-based generative model can correctly identify unknown molecules with an 83% to 100% success rate, compared to the baseline approach of 0%. Additionally, this proposed generative model is not limited to just chemistry research challenges; we anticipate that problems that utilize RL with multiple objectives will benefit from this framework.

reinforcement learning, generative model, inverse ↗
DOE OSTI2023

PRINTED, PLANAR MICROWAVE CONNECTOR WITH MULTIPLE SIGNAL LINES

Microwave connectors with a planar geometry have been demonstrated using additive manufacturing techniques. This planar form factor is expected to enable connectors to be integrated directly with other components onto a printed circuit board. The connectors were developed with multiple signal lines that will increase the density of signal connections to PCBs, eliminating the physical constraints of single-signal COTS microwave connectors. This work developed electromagnetic models (using Ansys HFSS) of these planar connectors to predict performance and optimize connector design. Materials and printing processes were developed to fabricate the connectors based on high-temperature PEEK thermoplastics. The multiple signal connectors were designed to operate over the 1-6GHz band and are built upon the single signal printed planar geometry connector introduced in a previous paper [1].

Additive Manufacturing↗
DOE OSTI2017

Using the MCNP6.2 Correlated Fission Multiplicity Models, CGMF and FREYA

The culmination of a multi-year, collaborative effort by researchers at Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL) and the University of Michigan, led to the delivery of two new correlated fission multiplicity models available within the latest release of MCNP{sup R} version 6.2. The needs within the nuclear nonproliferation community, particularly in simulating and predicting the neutron and gamma-ray signatures of special nuclear materials (SNM), provided the motivation for this major collaborative effort. From LANL, the CGMF code has been implemented as an upgrade to the original Cascading Gamma-ray Multiplicity (CGM) code. Additionally, the LLNL Fission Library has been upgraded from the original implementation released in prior versions of MCNP6/X to include the LBNL/LLNL Fission Reaction Event Yield Algorithm (FREYA) code. Both new codes are capable of simulating fission events with correlated neutron and gamma-ray emissions. Due to the release of these new models within MCNP6.2, the purpose of this paper is to describe the differences between these new models and existing capabilities, demonstrate their use in the context of simulation inputs and outputs, and to discuss some of the ongoing work directed toward the next generation of these features.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Flattening of fuel importance for heterogeneous system to estimate maximal multiplication factor - 14595

In order to quantify risks of re-criticality incidents associated with loss of coolant accidents (LOCA), the maximum reactivity insertion by relocation of damaged fuel should be quantified. As the first step for the quantification, an automated search scheme is studied for an optimal geometry of damaged fuel in the coolant channel where the neutron multiplication factor k{sub eff} takes a maximal value. The scheme is based on the flattening of the fuel importance. The importance can be recognized as the substitution reactivity from fuel to water in heterogeneous systems. The flattening is achieved by substitution of water and the fuel so as to raise the k{sub eff} referring the local substitution reactivities. To calculate the reactivities efficiently, the first order perturbation scheme is adopted. In the scheme, the adjoint flux is calculated with the PARTISN code. The multi group constants used for the calculation and the perturbed reaction rates are estimated with the continuous energy Monte Carlo code MCNP-5. The proposed methods are tested for a quarter pin cell model that is constructed using Cartesian mesh. From a random distribution of a given amount of fragmented fuel, an optimal geometry is found where the fragmented fuels are aggregated. The multiplication factor in the geometry is greater than that in the original random distribution by 1.4%Δk/k. That indicates the significance of this study. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION

Here, we implement a variance-based distance metric (D n ) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased and can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness.

58 GEOSCIENCES↗
DOE OSTI2017

Optimizing multiple reliable forward contracts for reservoir allocation using multitime scale streamflow forecasts

Streamflow forecasts at multiple time scales provide a new opportunity for reservoir management to address competing objectives. Market instruments such as forward contracts with specified reliability are considered as a tool that may help address the perceived risk associated with the use of such forecasts in lieu of traditional operation and allocation strategies. A water allocation process that enables multiple contracts for water supply and hydropower production with different durations, while maintaining a prescribed level of flood risk reduction, is presented. The allocation process is supported by an optimization model that considers multitime scale ensemble forecasts of monthly streamflow and flood volume over the upcoming season and year, the desired reliability and pricing of proposed contracts for hydropower and water supply. It solves for the size of contracts at each reliability level that can be allocated for each future period, while meeting target end of period reservoir storage with a prescribed reliability. The contracts may be insurable, given that their reliability is verified through retrospective modeling. The process can allow reservoir operators to overcome their concerns as to the appropriate skill of probabilistic forecasts, while providing water users with short–term and long–term guarantees as to how much water or energy they may be allocated. An application of the optimization model to the Bhakra Dam, India, provides an illustration of the process. In conclusion, the issues of forecast skill and contract performance are examined. A field engagement of the idea is useful to develop a real–world perspective and needs a suitable institutional environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The Climate Response to Stratospheric Aerosol Geoengineering Can Be Tailored Using Multiple Injection Locations

By injecting different amounts of SO 2 at multiple different latitudes, the spatial pattern of aerosol optical depth (AOD) can be partially controlled. This leads to the ability to influence the climate response to geoengineering with stratospheric aerosols, providing the potential for design. In this work, we use simulations from the fully coupled whole-atmosphere chemistry climate model CESM1(WACCM) to demonstrate that by appropriately combining injection at just four different locations, 30°S, 15°S, 15°N, and 30°N, then three spatial degrees of freedom of AOD can be achieved: an approximately spatially uniform AOD distribution, the relative difference in AOD between Northern and Southern Hemispheres, and the relative AOD in high versus low latitudes. For forcing levels that yield 1–2°C cooling, the AOD and surface temperature response are sufficiently linear in this model so that the response to different combinations of injection at different latitudes can be estimated from single-latitude injection simulations; nonlinearities associated with both aerosol growth and changes to stratospheric circulation will be increasingly important at higher forcing levels. Optimized injection at multiple locations is predicted to improve compensation of CO 2 -forced climate change relative to a case using only equatorial aerosol injection (which overcools the tropics relative to high latitudes). The additional degrees of freedom can be used, for example, to balance the interhemispheric temperature gradient and the equator to pole temperature gradient in addition to the global mean temperature. Further research is needed to better quantify the impacts of these strategies on changes to long-term temperature, precipitation, and other climate parameters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

First Simulations of Designing Stratospheric Sulfate Aerosol Geoengineering to Meet Multiple Simultaneous Climate Objectives

In this work, we describe the first simulations of stratospheric sulfate aerosol geoengineering using multiple injection locations to meet multiple simultaneous surface temperature objectives. Simulations were performed using CESM1(WACCM), a coupled atmosphere-ocean general circulation model with fully interactive stratospheric chemistry, dynamics (including an internally generated quasi-biennial oscillation), and a sophisticated treatment of sulfate aerosol formation, microphysical growth, and deposition. The objectives are defined as maintaining three temperature features at their 2020 levels against a background of the RCP8.5 scenario over the period 2020-2099. These objectives are met using a feedback mechanism in which the rate of sulfur dioxide injection at each of the four locations is adjusted independently every year of simulation. Even in the presence of uncertainties, nonlinearities, and variability, the objectives are met, predominantly by SO2 injection at 30°N and 30°S. By the last year of simulation, the feedback algorithm calls for a total injection rate of 51 Tg SO2 per year. The injections are not in the tropics, which results in a greater degree of linearity of the surface climate response with injection amount than has been found in many previous studies using injection at the equator. Because the objectives are defined in terms of annual mean temperature, the required geeongineering results in "overcooling" during summer and "undercooling" during winter. The hydrological cycle is also suppressed as compared to the reference values corresponding to the year 2020. The demonstration we describe in this study is an important step toward understanding what geoengineering can do and what it cannot do.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Modulation-Doped Multiple Quantum Wells of Aligned Single-Wall Carbon Nanotubes

Heterojunctions, quantum wells, and superlattices with precise doping profiles are behind today's electronic and photonic devices based on III–V compound semiconductors such as GaAs. Currently, there is considerable interest in constructing similar artificial 3D architectures with tailored electrical and optical properties by using van der Waals junctions of low-dimensional materials. In this study, the authors have fabricated a novel structure consisting of multiple thin (≈20 nm) layers of aligned single-wall carbon nanotubes with dopants inserted between the layers. This “modulation-doped” multiple-quantum-well structure acts as a terahertz polarizer with an ultra-broadband working frequency range (from ≈0.2 to ≈200 THz), a high extinction ratio (20 dB from ≈0.2 to 1 THz), and a low insertion loss (<2.5 dB from ≈0.2 to 200 THz). The individual carbon nanotube films—highly aligned, densely packed, and large (2 in. in diameter)—were produced using vacuum filtration and then stacked together in the presence of dopants. This simple, robust, and cost-effective approach is applicable to the fabrication of a variety of devices relying on macroscopically 1D properties of aligned carbon nanotube assemblies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Single Bimodular Sensor for Differentiated Detection of Multiple Oxidative Gases

Semiconductive metal-oxide sensors suffer from cross-sensitivities under mixed chemical condition, specifically upon mixture of multiple oxidative or reductive gases. Herein, a single bimodular sensor is demonstrated for smart differentiation of multiple oxidative analytes by relating the resistance-metric mode to impedance-metric mode. The sensor construct based on ZnO nanorods readily outputs three response datasets upon exposure of oxidative-gas mixture including O 2 , SO 2 , and NO 2 , the resistance, real part impedance, and imaginary part impedance. The differentiative and correlated nature between these response signals allows such a single sensor platform to differentiate these oxidative gases accurately and robustly. Linear and non-linear decision boundaries are established over a large gas-concentration range from 2 ppm to 3% through a combination of principal component analysis and artificial neural network training. A facile user interface is demonstrated for recognition and measurement of unknown gas analytes, with the error of the predicted analyte-concentration as low as 2%.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Multiple Photoluminescence from Pyrene‐Fused Hexaarylbenzenes with Aggregation‐Enhanced Emission Features

Abstract Multiple photoluminescence, which included monomer emission, excimer emission, and charge‐transfer emission processes, has been observed from new pyrene‐fused hexaarylbenzene (HAB) compounds, which were synthesized in high yields through a Diels–Alder reaction between bis(2‐ tert ‐butylpyren‐6‐yl)acetylene and tetraphenylcyclopentadienone. Although the differentiation between the two molecules only arose from the geometrical position of one of the pyrenes, the NMR spectra, crystal packing, and physicochemical properties of these pyrene‐based HAB hybrids were distinctly different in both the solution and aggregate states. X‐ray diffraction analysis clearly indicated that the pyrene moieties adopted different crystal packing arrangements in the crystalline state that could induce a multiple‐photoluminescence phenomenon.

Wang, Chuan‐Zeng↗
DOE OSTI2016

Shallow snowpack inhibits soil respiration in sagebrush steppe through multiple biotic and abiotic mechanisms

Abstract In sagebrush steppe, snowpack may govern soil respiration through its effect on multiple abiotic and biotic factors. Across the Intermountain West of the United States, snowpack has been declining for decades and is projected to decline further over the next century, making the response of soil respiration to snowpack a potentially important factor in the ecosystem carbon cycle. In this study, we evaluated the direct and indirect roles of the snowpack in driving soil respiration in sagebrush steppe ecosystems by taking advantage of highway snowfences in Wyoming to manipulate snowpack. An important contribution of this study is the use of Bayesian modeling to quantify the effects of soil moisture and temperature on soil respiration across a wide range of conditions from frozen to hot and dry, while simultaneously accounting for biotic factors (e.g., vegetation cover, root density, and microbial biomass and substrate‐use diversity) affected by snowpack. Elevated snow depth increased soil temperature (in the winter) and moisture (winter and spring), and was associated with reduced vegetation cover and microbial biomass carbon. Soil respiration showed an exponential increase with temperature, with a temperature sensitivity that decreased with increasing seasonal temperature ( Q 10 = 4.3 [winter], 2.3 [spring], and 1.7 [summer]); frozen soils were associated with unrealistic Q 10 ≈ 7989 due to the liquid‐to‐ice transition of soil water. Soil respiration was sensitive to soil water content; predicted respiration under very dry conditions was less than 10% of respiration under moist conditions. While higher vegetation cover increased soil respiration, this was not due to increased root density, and may reflect differences in litter inputs. Microbial substrate‐use diversity was negatively related to reference respiration (i.e., respiration rate at a reference temperature and optimal soil moisture), although the mechanism remains unclear. This study indicates that soil respiration is inhibited by shallow snowpack through multiple mechanisms; thus, future decreases in snowpack across the sagebrush steppe have the potential to reduce losses of soil C, potentially affecting regional carbon balance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Climate and multiple dimensions of plant diversity regulate ecosystem carbon exchange along an elevational gradient

The impacts of warming on communities and ecosystems are predicted to be significant in mountain ecosystems because physiological processes, including rates of carbon (C) cycling, are often more temperature-sensitive in colder environments. Plant biodiversity can also influence C exchange, yet few studies integrate how biotic and abiotic factors may directly or interactively impact ecosystem Cflux. Here, we examine the link between simultaneous changes in multiple dimensions of plant diversity and peak growing season ecosystem C uptake across a climatic gradient in the Rocky Mountains, Colorado, USA. We found that taxonomic diversity (species richness), functional diversity (functional evenness), and phylogenetic diversity (mean pairwise distance) were significantly and positively related to peak growing sea-son ecosystem C uptake (i.e., net ecosystem exchange) when considered independently. However, when abiotic and biotic factors were integrated in a structural equation model, only plant phylogenetic diversity was significantly related to C uptake. In addition, we found that actual evapotranspiration (AET - a measure that integrates precipitation and temperature) affected ecosystem C exchange indirectly via its impact on the three dimensions of plant diversity that we examined. These findings highlight complex relation-ships among key measures of biodiversity and ecosystem C uptake in a rapidly warming ecosystem, and the possible mechanisms that underlie relationships between biodiversity and ecosystem functioning. They also point to the need for integrating multiple dimensions of biodiversity into studies of community and ecosystem ecology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Extending the eigCG algorithm to nonsymmetric Lanczos for linear systems with multiple right-hand sides

The technique that was used to build the EigCG algorithm for sparse symmetric linear systems is extended to the nonsymmetric case using the BiCG algorithm. We show that, similarly to the symmetric case, we can build an algorithm that is capable of computing a few smallest magnitude eigenvalues and their corresponding left and right eigenvectors of a nonsymmetric matrix using only a small window of the BiCG residuals while simultaneously solving a linear system with that matrix. For a system with multiple right-hand sides, we give an algorithm that computes incrementally more eigenvalues while solving the first few systems and then uses the computed eigenvectors to deflate BiCGStab for the remaining systems. Our experiments on various test problems, including Lattice QCD, show the remarkable ability of EigBiCG to compute spectral approximations with accuracy comparable to that of the unrestarted, nonsymmetric Lanczos. Furthermore, our incremental EigBiCG followed by appropriately restarted and deflated BiCGStab provides a competitive method for systems with multiple right-hand sides.

Abdel-Rehim, A M↗
DOE OSTI2021

The Bactrian effect: multiple resonances and light Dirac dark matter

The possibility of light dark matter (DM) annihilating through a dark photon (DP) which kinetically mixes (KM) with the Standard Model (SM) hypercharge field is a very attractive scenario. For DM in the interesting mass range below ~1 GeV, it is well known that bounds from the CMB provide a very strong model building constraint forcing the DM annihilation cross section to be roughly 3 orders of magnitude below that needed to reproduce the observed relic density. Under most circumstances this removes the possibility of an s-wave annihilation process for DM in this mass range as would be the case, e.g., if the DM were a Dirac fermion. In an extra-dimensional setup explored previously, it was found that the s-channel exchange of multiple gauge bosons could simultaneously encompass a suppressed annihilation cross section during the CMB era while also producing a sufficiently large annihilation rate during freeze-out to recover the DM relic density. In this paper, we analyze more globally the necessary requirements for this mechanism to work successfully and then realize them within the context of a simple model with two ‘dark’ gauge bosons having masses of a similar magnitude and whose contributions to the annihilation amplitude destructively interfere. We show that if the DM mass threshold lies appropriately in the saddle region of this destructive interference between the two resonance humps it then becomes possible to satisfy these requirements simultaneously provided several ancillary conditions are met. The multiple constraints on the parameter space of this setup are then explored in detail to identify the phenomenologically successful regions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Light-flavor particle production in high-multiplicity pp collisions at $\sqrt{\textrm{s}}$ = 13 TeV as a function of transverse spherocity

Results on the transverse spherocity dependence of light-flavor particle production (π, K, p, ϕ, K *0 , K$^{0}_{S}$, Λ, Ξ) at midrapidity in high-multiplicity pp collisions at $\sqrt{\textrm{s}}$ = 13 TeV were obtained with the ALICE apparatus. The transverse spherocity estimator ($S$$^{p_T=1}_{O}$) categorizes events by their azimuthal topology. Utilizing narrow selections on $S$$^{p_T=1}_{O}$, it is possible to contrast particle production in collisions dominated by many soft initial interactions with that observed in collisions dominated by one or more hard scatterings. Results are reported for two multiplicity estimators covering different pseudorapidity regions. The $S$$^{p_T=1}_{O}$ estimator is found to effectively constrain the hardness of the events when the midrapidity (|η| < 0.8) estimator is used.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗