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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Life-cycle analysis of integrated biorefineries with co-production of biofuels and bio-based chemicals: co-product handling methods and implications: Co-product handling methods and implications of life-cycle analysis of biofuel and bio-chemical co-producing biorefineries

New integrated biorefinery (IBR) concepts are being investigated to co-produce hydrocarbon fuels and high-value bio-based chemicals to improve the economic viability of IBRs, to enhance biomass resource utilization efficiencies, and to maximize potential greenhouse gas (GHG) emission reductions. Unlike fuel-only biorefineries, IBRs may co-produce a significant amount of bio-based chemicals, whose emission implications for specific biorefinery products and the biorefinery as a whole need to be evaluated. We discuss this in principle and apply three sets of co-product handling methods to conduct life-cycle analysis (LCA) of modeled IBRs with co-production of two bioproduct examples - succinic acid and adipic acid - alongside a renewable diesel blendstock fuel product. The LCA results for the specific co-product handling methods that were examined shed light on potential artifacts of product-specific LCA with selected co-product methods. We discuss the advantages and limitations of each method and conclude that (i) a system-level or 'black-box' LCA allocation method is too simplistic to reflect appropriately the GHG burdens of distinctly different processing trains for fuels and chemicals in the IBR context, and (ii) the displacement method is the only co-product handling method that accounts fully for the emission effects of both the fuel product and the non-fuel bio-based co-products in the IBRs within the context of the existing fuel-focused GHG regulatory framework. Alternatively, biorefinery system-level LCA combines benefits of individual products to offer a complete picture. This system-level LCA approach offers a holistic LCA without somewhat arbitrary decisions either on an allocation basis or by the selection of an evaluation metric based on specific products.

09 BIOMASS FUELS↗
DOE OSTI2018

Computational simulation of vibrationally resolved spectra for spin‐forbidden transitions

Abstract In this computational study, we illustrate a method for computing phosphorescence and circularly polarized phosphorescence spectra of molecular systems, which takes into account vibronic effects including both Franck‐Condon and Herzberg‐Teller contributions. The singlet and triplet states involved in the phosphorescent emission are described within the harmonic approximation, and the method fully takes mode‐mixing effects into account when evaluating Franck‐Condon integrals. Spin‐orbit couplings, which are responsible for these otherwise forbidden phenomena, are accounted for by means of a relativistic two‐component time‐dependent density functional theory method. The model is applied to two types of chiral systems: camphorquinone, a rigid organic system that allows for an extensive benchmark, and some members of a class of iridium complexes. The merits and shortcomings of the methods are discussed, and some perspectives for future developments are offered.

Egidi, Franco↗
DOE OSTI2022

Delayed fire mortality has long‐term ecological effects across the Southern Appalachian landscape

Abstract Fire is a critical ecological process to the forests of the Southern Appalachians. Where fire was excluded from forest types that historically burned frequently, unanticipated changes can occur when fire is reintroduced. For example, the development of new fuel characteristics can change the patterns of fire mortality and associated ecological responses. To test the fire effects of delayed fire mortality (mortality initiated by fire that occurs subsequent to the fire year) in the Southern Appalachians, USA, we developed a fire‐effects model using both field studies and remote sensing. We then simulated these effects at a landscape scale to estimate broader ecological effects. Fire‐effects models that accounted for delayed mortality increased landscape biomass removed annually (~23%) and increased the number of sites with high light conditions (leaf area index < 4) when compared to simulations that only account for immediate mortality. While delayed mortality occurred across species and age classes, it was especially prevalent among older trees (>100 years old) and fire‐resistant species ( Quercus spp.). Overall, regeneration (trees <20 years old) changed very little, even with the inclusion of delayed mortality. This evidence suggests that, even when accounting for delayed mortality, individual fires are unlikely to shift the landscape composition toward the conditions of forests prior to fire exclusion and may even increase mesophication long term due to the loss of overstory dominant xeric trees.

Robbins, Zachary J.↗
DOE OSTI2016

Uncertainty quantification in LES of channel flow

Here, in this paper, we present a Bayesian framework for estimating joint densities for large eddy simulation (LES) sub-grid scale model parameters based on canonical forced isotropic turbulence direct numerical simulation (DNS) data. The framework accounts for noise in the independent variables, and we present alternative formulations for accounting for discrepancies between model and data. To generate probability densities for flow characteristics, posterior densities for sub-grid scale model parameters are propagated forward through LES of channel flow and compared with DNS data. Synthesis of the calibration and prediction results demonstrates that model parameters have an explicit filter width dependence and are highly correlated. Discrepancies between DNS and calibrated LES results point to additional model form inadequacies that need to be accounted for.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Predation by coyotes on white-tailed deer neonates in South Carolina

Abstract: Coyotes (Canis latrans) are novel predators throughout the southeastern United States and their depredation of white-tailed deer (Odocoileus virginianus) neonates may explain observed declines in some deer populations in the region, but direct evidence for such a relationship is lacking. Our objective was to quantify neonate survival rates and causes of mortality at the United States Department of Energy's Savannah River Site (SRS), South Carolina to directly evaluate degree of predation in this deer population. From 2006 to 2009, we radio-monitored 91 neonates captured with the aid of vaginal implant transmitters in pregnant adult females and opportunistic searches. Overall Kaplan Meier survival rate to 16 weeks of age was 0.230 (95% CI = 0.155-0.328), and it varied little among years. Our best-fitting model estimated survival at 0.220 (95% CI = 0.144-0.320). This model included a quadratic time trend variable (lowest survival rate during the first week of life and increasing to near 1.000 around week 10), and Julian date of birth (survival probability declining as date of birth increased). Predation by coyotes was the most frequent cause of death among the 70 monitored neonates that died, definitively accounting for 37% of all mortalities and potentially accounting for as much as 80% when also including probable coyote predation. Predation by bobcats (Felis rufus) accounted for 7% (definitive) to 9% (including probable bobcat predation) of mortalities. The level of coyote-induced mortality we observed is consistent with the low recruitment rates exhibited in the SRS deer population since establishment of coyotes at the site. If representative of recruitment rates across South Carolina, current harvest levels appear unsustainable. This understanding is consistent with the recent declining trend in the statewide deer population. The effects of coyote predation on recruitment should be considered when setting harvest goals, regardless of whether local deer population size is currently above or below desired levels, because coyotes can substantially reduce fawn recruitment. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

South Carolina↗
DOE OSTI2026

Seasonal variation in methane ebullition in a temperate hydropower reservoir impacts annualized estimates

Estimates of ebullitive methane (CH4) emissions from reservoirs are highly uncertain, often based on spatial and temporal upscaling from a limited number of sampling sites and periods. While some of these estimates may adjust for seasonal variation in temperature, they rarely account for drawdown and the resulting changes in site depth and the areal extent of the reservoir. In Douglas Reservoir (Tennessee, USA), we conducted monthly sampling of 20 sites over 1 year to capture temporal and spatial variation in CH4 ebullition. We then compared various approaches for obtaining annualized, whole-reservoir ebullition estimates. CH4 ebullition fluxes were zero-inflated and highly skewed, with fluxes rising appreciably above zero in July and peaking in autumn. Estimates that did not account for drawdown overestimated annualized rates by 20%, though with high uncertainty. Seasonal variability and hotspots strongly influence annualized ebullition estimates and are important to account for in inventories and global upscaling.

Cardall, Anna [Brigham Young University]↗
DOE OSTI2019

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.

Kılınç, Deniz↗
DOE OSTI2019

Fate of cellulose in primary and secondary treatment at municipal water resource recovery facilities

Abstract Cellulose from toilet paper is a significant fraction of particulate organics, which is recoverable. For the first time, comprehensive mapping and tracking the fate of cellulose across various unit processes at full‐scale in two water resource recovery facilities located in North America and Europe was undertaken. The influent cellulose content accounted for approximately one‐third of the total suspended solids (TSS). Although about 80% of the raw wastewater cellulose was removed in primary treatment, the type of primary treatment process (rotating belt filter [RBF] vs. primary clarification [PC]) had a significant impact on cellulose capture and diversion. The high cellulose content of the RBF sludge accounting for 35% of the TSS facilitates cellulose recovery. For the North American plant, with a conventional activated sludge process (SRT of 6–7 days, preceded by PC), cellulose biodegradation efficiencies of 70%–90% of the PC effluent were observed in summer and winter. For the European plant, with a modified University of Cape Town process (SRT of 14 days, without primary treatment in train 2, or preceded by RBF in train 1), comparable cellulose biodegradation efficiencies were also observed. Results from laboratory SBRs indicated that cellulose biodegradation efficiency at room temperature was 86% of the influent cellulose. Practitioner points Cellulose fate was tracked across two different WWTPs in two different geographies. Cellulose in the influent wastewater accounted for 1/3 of the total suspended solids. Primary treatments were able to capture more than 80% of the influent cellulose. Cellulose was biodegraded in secondary treatment, resulting an effluent of 2–3 mg/L.

Ahmed, Ahmed Shawki↗
DOE OSTI2013

Coupled hydromechanical and electromagnetic disturbances in unsaturated porous materials

[1] A theory of cross-coupled flow equations in unsaturated soils is necessary to predict (1) electroosmotic flow with application to electroremediation and agriculture, (2) the electroseismic and the seismoelectric effects to develop new geophysical methods to characterize the vadose zone, and (3) the streaming current, which can be used to investigate remotely ground water flow in unsaturated conditions in the capillary water regime. To develop such a theory, the cross-coupled generalized Darcy and Ohm constitutive equations of transport are extended to unsaturated conditions. This model accounts for inertial effects and for the polarization of porous materials. Rather than using the zeta potential, like in conventional theories for the saturated case, the key parameter used here is the quasi-static volumetric charge density of the pore space, which can be directly computed from the quasi-static permeability. The apparent permeability entering Darcy’s law is also frequency dependent with a critical relaxation time that is, in turn, dependent on saturation. A decrease of saturation increases the associated relaxation frequency. The final form of the equations couples the Maxwell equations and a simplified form of two-fluid phases Biot theory accounting for water saturation. A generalized expression of the Richard equation is derived, accounting for the effect of the vibration of the skeleton during the passage of seismic waves and the electrical field. A new expression is obtained for the effective stress tensor. The model is tested against experimental data regarding the saturation and frequency dependence of the streaming potential coupling coefficient. The model is also adapted for two-phase flow conditions and a numerical application is shown for water flooding of a nonaqueous phase liquid (NAPL, oil) contaminated aquifer. Seismoelectric conversions are mostly taking place at the NAPL (oil)/water encroachment front and can be therefore used to remotely track the position of this front. This is not the case for other geophysical methods.

58 GEOSCIENCES↗
DOE OSTI2017

A Hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling

As urban areas continue to grow and evolve in a world of increasingenvironmental awareness, the need for detailed information regarding residentialenergy consumption patterns has become increasingly important. Though currentmodeling efforts mark significant progress in the effort to better understand thespatial distribution of energy consumption, the majority of techniques are highlydependent on region-specific data sources and often require building- ordwelling-level details that are not publicly available for many regions in the UnitedStates. Furthermore, many existing methods do not account for errors in input datasources and may not accurately reflect inherent uncertainties in model outputs. Wepropose an alternative and more general hybrid approach to high-resolution residentialelectricity consumption modeling by merging a dasymetric model with acomplementary machine learning algorithm. The method’s flexible data requirementand statistical framework ensure that the model both is applicable to a widerange of regions and considers errors in input data sources.

cities↗
DOE OSTI2025

Hydraulic constraints to stomatal conductance in flooded trees

Stomatal closure is a pervasive response among trees exposed to flooded soil. We tested whether this response is caused by reduced hydraulic conductance in the soil-to-leaf hydraulic continuum (k total ), and particularly by reduced root hydraulic conductance (k root ), which has been widely hypothesized. We tracked stomatal conductance at the leaf level (g s ) and canopy scale (G s ) along with physiological conditions in two temperate tree species, Magnolia grandiflora and Quercus virginiana, that were subjected to flood and control conditions in a greenhouse experiment. Flooding reduced g s , G s , k root and k total . Path analysis showed strong support for direct effects of k total on g s and for flood duration on k total , but not k root on k total . A process-based model that accounted for the k total reduction predicted the timeseries of G s in flood and control treatment trees reasonably well (predicted versus observed G s R 2 = 0.80 and 0.51 for M. grandiflora and Q. virginiana, respectively). However, accounting only for k root reduction in flooded trees was insufficient for predicting observed G s reduction. Together, these results suggest that hydraulic constraints were not limited to roots and highlight the need to account for flooding effects on k total when projecting forest ecosystem function using process-based models.

Plant stress↗
DOE OSTI2018

Optimally configuring a measurement system to detect diversions from a nuclear fuel cycle

The civilian nuclear fuel cycle is an industrial process that produces electrical power from the nuclear fission of uranium. Using a measurement system to accurately account for nuclear material, such as uranium, in a fuel cycle is important because of the possible loss or diversion of this potentially dangerous material. A measurement system is defined by a set of measurement methods, or “devices,” used to account for material flows and inventory values at specific locations in the fuel cycle. Here, we develop a simulation-optimization algorithm and an integer-programming model to find the best, or near-best, resource-limited measurement system with a high degree of confidence. The simulation-optimization algorithm minimizes a weighted sum of false positive and false negative diversion-detection probabilities while accounting for material quantities and measurement errors across a finite, discrete time horizon in hypothetical non-diversion and diversion contexts. In each time period, the estimated cumulative material unaccounted for is compared to a fixed or an optimized threshold value to assess if a “significant amount of material” is lost from a measurement system. The integer-programming model minimizes the population variance of the estimated material loss, i.e., material unaccounted for, in a measurement system. We analyze three potential problems in nuclear fuel cycle measurement systems: (i) given location-dependent device precisions, find the configuration of n devices at n locations ( n = 3) that gives the lowest corresponding objective values using the simulation-optimization algorithm and integer-programming model, (ii) find the location at which improving device precision reduces objective values the most using the simulation-optimization algorithm, and determine the effect of measurement frequency on measurement system configurations and objective values using the simulation-optimization algorithm. We obtain comparable results for each problem at least an order of magnitude faster than existing methods do. Using an optimized, rather than fixed, detection threshold in the simulation-optimization algorithm reduces the weighted sum of false positive and false negative probabilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Performance of Al-Mn Transition-Edge Sensor Bolometers in SPT-3G

SPT-3G is a polarization-sensitive receiver, installed on the South Pole Telescope, that measures the anisotropy of the cosmic microwave background (CMB) from degree to arcminute scales. The receiver consists of ten 150-mm-diameter detector wafers, containing a total of similar to 16,000transition-edge sensor (TES) bolometers observing at 95, 150, and 220 GHz. During the 2018-2019 austral summer, one of these detector wafers was replaced by a new wafer fabricated with Al-Mn TESs instead of the Ti/Au design originally deployed for SPT-3G. We present the results of in-laboratory characterization and on-sky performance of this Al-Mn wafer, including electrical and thermal properties, optical efficiency measurements, and noise-equivalent temperature. In addition, we discuss and account for several calibration-related systematic errors that affect measurements made using frequency-domain multiplexing readout electronics.

Al-Mn↗
DOE OSTI2012

Acid-base properties of the Fe(CN){sub 6}{sup 3-}/Fe(CN){sub 6}{sup 4-} redox couple in the presence of various background mineral acids and salts

The acid-base behavior of Fe(CN){sub 6}{sup 4-} was investigated by measuring the formal potentials of the Fe(CN){sub 6}{sup 3-}/Fe(CN){sub 6}{sup 4-} couple over a wide range of acidic and neutral solution compositions. The experimental data were fitted to a model taking into account the protonated forms of Fe(CN){sub 6}{sup 4-} and using values of the activities of species in solution, calculated with a simple solution model and a series of binary data available in the literature. The fitting needed to take account of the protonated species HFe(CN){sub 6}{sup 3-} and H{sub 2}Fe(CN){sub 6}{sup 2-}, already described in the literature, but also the species H{sub 3}Fe(CN){sub 6}{sup -} (associated with the acid-base equilibrium H{sub 3}Fe(CN){sub 6}{sup -} ↔ H{sub 2}Fe(CN){sub 6}{sup 2-} + H{sup +}). The acidic dissociation constants of HFe(CN){sub 6}{sup 3-}, H{sub 2}Fe(CN){sub 6}{sup 2-} and H{sub 3}Fe(CN){sub 6}{sup -} were found to be pK(1)(II) = 3.9 ± 0.1, pK(2)(II) = 2.0 ± 0.1, and pK(3)(II) = 0.0 ± 0.1, respectively. These constants were determined by taking into account that the activities of the species are independent of the ionic strength. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Geant4 Model Validation of Compton Suppressed System for Process monitoring of Spent Fuel

Nuclear material accountancy is of continuous concern for the regulatory, safeguards, and verification communities. In particular, spent nuclear fuel reprocessing facilities pose one of the most difficult accountancy challenges: monitoring highly radioactive, fluid sample streams in near real-time. The Multi-Isotope Process monitor will allow for near-real-time indication of process alterations using passive gamma-ray detection coupled with multivariate analysis techniques to guard against potential material diversion or to enhance domestic process monitoring. The Compton continuum from the dominant 661.7 keV 137Cs fission product peak obscures lower energy lines which could be used for spectral and multivariate analysis. Compton suppression may be able to mitigate the challenges posed by the high continuum caused by scattering. A Monte Carlo simulation using the Geant4 toolkit is being developed to predict the expected suppressed spectrum from spent fuel samples to estimate the reduction in the Compton continuum. Despite the lack of timing information between decay events in the particle management of Geant4, encouraging results were recorded utilizing only the information within individual decays without accounting for accidental coincidences. The model has been validated with single and cascade decay emitters in two steps: as an unsuppressed system and with suppression activated. Results of the Geant4 model validation will be presented.

Bender, Sarah↗
DOE OSTI2018

A Feature-Based Stochastic Permeability of Shale: Part 1—Validation and Two-Phase Permeability in a Utica Shale Sample

Estimate of permeability plays a crucial role in flow-based studies of fractured tight-rocks. It is well known that most of the flow through tight-rocks (e.g., shales) is controlled by permeable features (e.g., fractures, laminations, etc.), and there is negligible flow through the matrix. However, current approaches in the literature to model permeability of tight-rocks do not account for such features present within the rock ranging from micro-scale to field-scale. Current permeability modeling approach assumes a single continuum without considering the presence of permeable features within the matrix (e.g., micro-fractures) or outside the matrix (e.g., natural fractures). Although the laboratory-measured permeability implicitly captures discrete features present in that sample (e.g., fractures, laminations, micro-fractures), most of the permeability models proposed for shale do not account for these features. Fracture permeability in the literature is typically modeled using an ideal slit assumption; however, this highly overestimates its permeability because fractures in real medium are non-ideal in terms of their porosity and tortuosity, which affect their permeability. Additionally, the transition zone between fracture and matrix also affects the permeability of fracture. Here in this study, part of a two-part series, a new method to predict permeability of fractured shale by discretizing the medium into matrix (inorganic and organic) and fractures is presented. New analytical expressions of permeability are derived to account for non-ideal nature of porous medium and two-phase flow in fractures. Rock feature in each cell of the grid is identified as one of the three elements (organic matter, inorganic matter, or fracture), and permeability of that cell is estimated using a suitable analytical expression. This method allows estimating permeability at any scale of interest and more robustly than by a pure analytical approach. The proposed method is validated against local and global-scale measurements on three fractured samples from laboratory. Finally, the method is used to predict two-phase flow permeability of supercritical CO 2 displacing water within a fracture in a Utica shale sample. The proposed two-phase flow permeability equations can be used as a quick analytical tool to predict relative permeability estimates of two-phase flow in fractured shale samples. In Part 2, the proposed method is used to estimate field-scale permeability through an optimization process that uses field-scale production and other readily available information.

58 GEOSCIENCES↗
DOE OSTI2021

Propagation of Input Uncertainties in Numerical Simulations of Laser Powder Bed Fusion

Laser powder bed fusion has the potential of redefining state-of-the-art processing and production methods, but defect formation and inconsistent build quality have limited the implementation of this process. Numerical models are widely used to study this process and predict the formation of these defects. Presently, the uncertainties of model input parameters and thermophysical properties used by these numerical simulations have not been investigated. In the present study, the uncertainty in these input parameters and material properties are quantified for laser powder bed fusion, with and without a simulated powder bed, to determine their influence on the predictive accuracy of an experimentally validated numerical model. Accounting for all possible sources of uncertainty quickly becomes computationally expensive on account of the curse of dimensionality. Uncertainty in laser absorption, solid, and liquid specific heat of the metal were found to have the largest effect on model prediction reliability with or without the use of a powder bed. Results also illustrate that accounting for these three uncertain parameters still captures the majority of model prediction uncertainty. Furthermore, the methodology of this study may be used to understand the uncertainty in as-built microstructure through propagation to microstructure prediction models, or applied under processing conditions where high Péclet numbers are observed and the thermal convection and fluid flow within the molten pool are substantial.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗