REopt Lite Tutorial: Load Profile Inputs
This tutorial gives an overview of the load profile inputs in the REopt Lite™ web tool.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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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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REopt Lite Tutorial: Advanced Utility Rate Inputs
This tutorial gives an overview of the advanced utility rate inputs in the REopt Lite™ web tool.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
REopt Lite Tutorial: Load Profile Inputs
This tutorial gives an overview of the load profile inputs in the REopt Lite™ web tool.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
REopt Lite Tutorial: Advanced Utility Rate Inputs
This tutorial gives an overview of the advanced utility rate inputs in the REopt Lite™ web tool.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Radiation Detection System Integration Using Multiple-Input Multiple-Output Radios
This study describes the application development of multiple-input multiple-output radios to provide persistent mobile ad hoc network (MANET) for the Department of Homeland Security. By using Man Portable Unit (MPU5) fifth generation radios (manufactured by Persistent Systems) with the Android Team Awareness Kit (ATAK), an Android smartphone geospatial infrastructure and military situational awareness application, the Remote Sensing Laboratory has developed a MANET connectivity to monitor deployed nuclear/radiological search operation assets.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Measuring Economy-Wide Circularity of the United States: An Input-Output Model in Mass Units
The ideal of creating closed material cycles by transforming the way we make, use and repurpose goods has become known as the circular economy. Besides its visionary appeal, material efficiency strategies central to circular economy can allow us to meet decarbonization goals that are otherwise out of reach. Production of basic materials such as cement, iron and steel and petrochemicals is one of the largest drivers of greenhouse gas emissions. Measuring circularity, and understanding its relationship with other sustainability metrics, however, is still difficult due to lack of data in mass units covering the entire economic system. Input-output (I-O) tables were originally created as a means of tracking the monetary flows that represent exchanges of goods and services within an economy, but have found additional uses in life cycle assessment and material flow accounts. In the traditional approach of environmentally extended I-O tables, emissions and energy data augment monetary flows. This approach can lead to price effects that distort physical quantities. Also, circular economy strategies and their goals relate to mass, not monetary flows. Therefore, it is best to simulate them using physical quantities. With these issues in mind, we have developed I-O tables in mass units to measure the flow of goods in the U.S. economy. This allows us to better measure how circular the economy really is, and how policy changes to affect this circularity may also affect decarbonization goals. Improving knowledge of these linkages could allow manufacturers to make changes in their production to achieve their sustainability and decarbonization targets. Our tool also provides a standard approach and public repository for data in physical units, making such data more available, useful, and meaningful. Following an established set of material flow metrics used by the European Union, we have used our tool to calculate material footprints over time differentiated by oil and gas versus other extractive industries. This approach also shows the relative trade balances of the U.S. in materials. We have developed a case study for the iron and steel sector, showing how different decarbonization scenarios affect not only economy-wide greenhouse gas emissions, but also total material use.
circular economy↗
A low-input strategy for chromium removal from industrial stormwater using peat sorbent
Low-cost and low-input water treatment systems are important for industrial stormwater remediation. Here we examine a flow-through reactor treatment installation where water exceeds the allowable maximum concentration for drinking water in multiple metals (e.g., chromium [Cr], cadmium [Cd], zinc [Zn]) prior to treatment. Specifically, we seek to understand why Cr attenuated in the reactors is not leachable by identifying the specific chemical form of Cr and dominant mechanisms promoting sequestration in the reactors. Total solid-phase Cr concentration in the peat media ranged from 50 to 150 mg/kg after 1 yr of exposure to stormwater to 300 to 900 mg/kg after 3.5 yr. X-ray fluorescence mapping images show Cr, iron (Fe), and Zn spatially correlated over a scale of 10 μm to 5 mm. Chromium rinds form on the edges of peat particles as Cr accumulates. Chromium and Fe K-edge X-ray absorption near edge structure spectroscopy reveal chromium predominately in the 3+ oxidation state with lesser amounts of elemental Cr. We propose the primary means of chromium attenuation in the reactors is precipitation as Cr-Fe hydroxides combined with trivalent Cr adsorption onto peat surfaces.
36 MATERIALS SCIENCE↗
Soil organic carbon accrual due to more efficient microbial utilization of plant inputs at greater long-term soil moisture
High long-term soil moisture may either stimulate or inhibit soil organic carbon (SOC) losses through changes to mineral and chemical composition, and resultant organo-mineral interactions. Yet, the trade-off between mineralization and accrual of SOC under long-term variation in unsaturated soil moisture remains uncertain. We tested the underexplored relationships between long-term soil moisture and organo-mineral chemical composition and its implications for SOC persistence in an experimental field in New York, USA, with differences in long-term mean soil volumetric water content (0–0.15 m depth) ranging from 0.40 to 0.63 (v/v) during the growing season. Long-term soil moisture across 20 subplots on four fallow plots were positively correlated with SOC (R 2 = 0.23; P = 0.019, n = 20), mineral-associated organic matter (MAOM) content (g fraction/g soil) (R 2 = 0.44; P = 0.001; n = 20) and occluded particulate organic matter (oPOM) content (R 2 = 0.18; P = 0.033; n = 20). Higher long-term soil moisture was associated with a decrease in the relative content of sodium pyrophosphate extractable Fe (R 2 = 0.33; P < 0.005; n = 20), an increase in sodium dithionite extractable Fe (R 2 = 0.44; P < 0.001; n = 20), and an increase in SOC retention by non-crystalline Al pools (R 2 = 0.51; P = 0.0002 for sodium pyrophosphate extracts, R 2 = 0.41; P = 0.0014 for hydroxylamine hydrochloride extracts; n = 20 for both). Increasing long-term soil moisture was associated with a four-fold increase in microbial biomass C (per unit SOC) and lower metabolic quotient (R 2 = 0.56, P < 0.001). MAOM fractions of high-moisture soils had lower C:N (from C:N 9.5 to 9.0, R 2 = 0.27, P = 0.011, n = 20). Consistent with decreasing C:N, increasing decomposition with increasing moisture was reflected by a 15% and 10% greater proportion of oxidized carboxylic-C to aromatic-C and O-alkyl C, respectively, as measured with 13 C NMR, and a more pronounced FTIR signature of N-containing proteinaceous compounds in high-moisture MAOM fractions, indicative of microbial metabolites and transformation products. A partial least squares regression showed that SOC content increased with greater long-term moisture (P = 0.019), pyrophosphate-extractable Al (P = 0.0001), and exchangeable Ca (P = 0.013). Here our results show that higher long-term soil moisture resulted in SOC accrual by enhancing conversion of plant inputs into microbial biomass that interacts with reactive minerals.
58 GEOSCIENCES↗
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↗
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↗
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↗
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 ↗
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↗
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↗
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↗
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↗
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↗
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↗