Opportunities, challenges and pitfalls in characterizing plant water-use strategies
Not provided.
Environmental Sciences & Ecology↗
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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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A Bayesian approach to calibrating hydrogen flame kinetics using many experiments and parameters
First-principles Markov Chain Monte Carlo sampling is used to investigate uncertainty quantification and uncertainty propagation in parameters describing hydrogen kinetics. Specifically, we sample the posterior distribution for thirty-one parameters focusing on the H 2 O 2 and HO 2 reactions resulting from conditioning on ninety-one experiments. Established literature values are used for the remaining parameters in the mechanism as well as other thermodynamic and transport data needed to specify fluid properties. The samples are computed using an affine invariant sampler starting with broad, noninformative priors. Autocorrelation analysis shows that O(1M) samples are sufficient to obtain a reasonable sampling of the posterior. The resulting distribution identifies strong positive and negative correlations and several non-Gaussian characteristics. Using samples drawn from the posterior, we investigate the impact of parameter uncertainty on the prediction of two more complex flames: a 2D premixed flame kernel and the ignition of a hydrogen jet issuing into a heated chamber. The former represents a combustion regime similar to the target experiments used to calibrate the mechanism and the latter represents a different combustion regime. For the premixed flame, the net amount of product after a given time interval has a standard deviation of less than 2% whereas the standard deviation of the ignition time for the jet is more than 10%. The samples used for these studies are posted online. These results indicate the degree to which parameters consistent with the target experiments constrain predicted behavior in different combustion regimes. Finally, this process provides a framework for both identifying reactions for further study from candidate mechanisms as well as combining uncertainty quantification and propagation to, ultimately, tie uncertainty in laboratory flame experiments to uncertainty in end-use numerical predictions of more complicated scenarios.
97 MATHEMATICS AND COMPUTING↗
Electrical Fault Detection, Power Quality, Distributed Energy Resource Use Cases, and Cyber Event Applications with the Cyber Grid Guard System Using Distributed Ledger Technology
Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation and are associated with distributed energy resources (DERs). The integrity and confidentiality of data from IEDs is crucial, and distributed ledger technology (DLT) could improve the resilience of microgrids by helping to make these data more secure. The most popular applications using blockchain technology for electrical utilities is in the field is based on energy trading. However, the dynamism of the penetration of customer owned DERs and the deployment of sensors with IEDs have led to the identification of new applications using DLT that are focused on other areas, such as monitoring, operation and management of the grid and its assets. In addition, the majority of studies on electrical grid applications with blockchain were validated with software simulations. Although general monitoring of power systems for using DLT could be evaluated in operational electric grids, other DLT research applications such as defense against cyber-attacks and/or electrical fault detection are not likely to be performed in a real infrastructure because of possible risks to the network/equipment security. This report summarizes the application of power system applications using distributed ledger technology (DLT), providing a secure DLT framework for collecting data from IEDs like power meters and protective relays inside and outside of an electrical substation and/or between two different electrical utilities. In this study, the use case scenarios were created and assessed for different power system application by using DLT. The electrical fault detection for faulted phases (1), power quality monitoring of phase voltage magnitudes, frequency levels and load power factor (2), DERs use case monitoring (3), and cyber-event applications (4) were performed in a test bed with a Cyber-Grid Guard (CGG) system using DLT. It had a real-time simulator with power meters and protective relays in-the-loop. The first section of this report presents a literature review of power system applications using blockchain at research level. The second section shows the theory and equations used on this report. The third section shows the description of the test bed, equipment, architecture, and electrical grid diagrams. The fourth section shows the experimental models and use case scenarios that were performed for the electrical fault detection, power quality, DERs use case, and cyber event applications with the CGG system using DLT. The fifth section shows the results collected from the tests based on comparing the time stamped events of the analog signals from the IEDs, DLT computer and real time simulator. The sixth section performed the discussion of the results for the use case scenarios. Finally, section seven presents the conclusions for this report were presented.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Determining aromatic and aliphatic carboxylic acids in biomass-derived oil samples using 2,4-dinitrophenylhydrazine and liquid chromatography-electrospray injection-mass spectrometry/mass spectrometry
Converting biomass to a useful fuel commonly incorporates the pyrolysis of the biomass feed stock. The base liquid fraction usually contains high concentrations of ketones, aldehydes and carboxylic acids, of which each can cause detrimental issues related to the storage and upgrading process. Knowing the carbonyl species and the concentration of each will provide value information to the pyrolysis researchers, specifically as that community branches into more targeted end-products such as jet fuel or biogenic-derived oxygenate-containing fuel products. The analysis of aldehydes, ketones and small alkyl carboxylic acids using 2,4-dinitrophenylhydrazine (DNPH) derivation method has been well documented and the method is commonly used the analytical community. By using liquid chromatograph coupled to tandem mass spectrometry, biomass sample analysis can be complete with identification of most carbonyl species. The issue of identifying isobaric ketone and aldehyde compounds can be resolved by utilizing differences in retention time or characteristic fragment ions of ketones and aldehydes. One issue which could not resolved using published methods was identifying aromatic or large non-aromatic carboxylic acids from their corresponding hydroxyl aldehyde or ketone analogs. By modifying the current method for determining carbonyls in biomass samples, carboxylic and hydroxyl-carbonyl can be determined. A careful adjustment of the pH during the extraction procedure and extended heating time of the DNPH solution allowed for the successful derivation of aromatic carboxylic acids. Like other dinitrophenylhydrazones, carboxylic acid derivatives also produce a unique secondary ion pattern, which was useful to distinguish these species from the non-acid analogs.
bio-oil↗
Calculating Average Hot Water Mixes of Residential Plumbing Fittings Using the ANSI 301-2019 Hot Water Draw Model and National Residential Data to Estimate Hot Water Use in Showerheads and Lavatory Faucets
Available studies of hot water use percentages may not necessarily be generalized to a national level given regional differences, varying methodological approaches, and limited sample sizes. While some publicly available usage data, reports, and surveys estimate actual household usage, a lack of water use data specific to end uses make more precise savings calculations difficult. Additionally, most hot water draw models tend to focus on overall household use and may not readily estimate lavatory fixture use. However, reviewing more recent studies and standards enables the U.S. Environmental Protection Agency’s WaterSense program to update its estimates for hot water use and consumers’ corresponding energy and monetary savings to better reflect realworld conditions. This report specifically focuses on improving hot water use and savings estimates of lavatory faucets and showerheads. To estimate the hot water use of lavatory faucets and showerheads, we employed the 2015 Residential Energy Consumption Surveys (RECS) microdata alongside the ANSI 301-2019 Hot Water Draw Model. These estimates account for regional differences in hot water use, including regional cold water inlet temperatures. As a result, the refinements presented in this report are more robust, more recent, and better describe the geographic variation than previous inputs used by the WaterSense program. In addition, the approach described in this paper can be updated over time or tailored to regionally specific needs given available inputs. We conclude that hot water percentages for showers and faucets calculated using publicly available national data are close to the percentages found by regional studies and are consistent with household-level models of water use.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Comparison of Wind Farm Layout Optimization Results Using a Simple Wake Model and Gradient-Based Optimization to Large-Eddy Simulations: Preprint
Comparison of wind farm layout optimization results with simulations or wind tunnel tests is important because the models used during optimization use simplifying assumptions that can alter the design space. Some of the characteristics of the simple models may negatively influence the resulting layouts. In this paper we perform wind farm layout optimization and compare the resulting improvements to large eddy simulation (LES) results. We begin by describing the models used, including changes specific for use with gradient-based optimization. We then compare our version of the models with the original versions and large eddy simulation LES) data from previous publications. Using the models described, we performed gradient-based wind farm layout optimization, using exact gradients, and compare the results to LES simulations. The models predicted an improvement in annual energy production (AEP) of 7.4%, while the LES reported an AEP improvement of 9:9%. We conclude that the improvements found by optimizing with the simple models are not just an artifact of the models, but are real improvements.
17 WIND ENERGY↗
Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2
Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation inland-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine)rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover
58 GEOSCIENCES↗
Modeling Building Use and Population Distribution Opportunity Using Open Geosocial Data in Urban Areas
Knowing where people are and knowing how the land in urban areas is being used is critical for assessing human mobility patterns, monitoring population health, planning for transportation services, and responding to natural disasters and emergency situations. While geophysical phenomena like land cover are more easily observed through remote sensing, social phenomena like how a city block or building is being used by the population is generally unobservable through standard remote sensing practices. Current methods and land use data by their nature are also difficult to scale to national or global levels. While land use data are often available in large cities, there are abundant data gaps globally. Furthermore, land use data can often appear relatively coarse in its spatial and semantic resolution (i.e. 1km grid cells rather than building level, and "commercial" rather than "coffee shop"). In particular, there is a need to harvest scalable data and develop methods to create global land use maps. To this end, in this work we combine building footprints and Points of Interest (POI) to estimate land use at the building level, including its mixed-use intensity. Next, we propose a method for defining semantic temporal zones referring to Morning, Day, Evening, and Night times unique to a region, based on the cumulative hourly change in the POIs' hours of operation. We then use these POIs' hours of operation to estimate non-residential population distribution opportunity and land use change over a 24-hour period. We validate our results through manual checks of randomly sampled individual buildings using multiple sources. Finally, we discuss the impact of our results on identifying population distribution opportunity and the reliability of estimating building use.
Sparks, Kevin↗
Residential Electricity End-use Disaggregation Using Whole Home Disaggregation Technologies
To understand whether a novel, cost effective approach can support a national program that reports on residential energy consumption, a two-phase research study was undertaken by a federal statistical agency and a national laboratory to evaluate the latest load disaggregation technologies. This paper will present the results of the first phase of the study, which explored whether load disaggregation technologies have the potential to inform end-use estimates for a national survey on residential energy consumption Historically, residential submetering projects have been implemented using a combination of plug-load and circuit-level monitoring. This approach is expensive, requires extensive onsite installation, and faces limitations in measuring smaller and portable end uses. In recent years, innovative computing technologies have entered the market that use algorithms to analyze electric signals and disaggregate household end uses. If proven sufficiently accurate and reliable, these technologies would simplify the identification and measurement of distinct end uses and improve end-use consumption estimation. The first phase of the study was planned as an initial assessment of the three load disaggregation products with the objective of determining the strongest candidates for further evaluation in the second phase. The load disaggregation products were evaluated based on their ability to measure total electricity for the home, detect end uses, measure electricity consumption of end uses, and perform reliably without burdening participants. If demonstrated as effective, obtaining end-use energy consumption data through load disaggregation technology would provide an invaluable check for the national survey at a minimum, and could potentially offer insight for enhancements.
Disaggregation accuracy↗
Evaluating Probability of Containment Effectiveness at a GCS Sites using integrated assessment modeling approach with Bayesian decision Networks
Improved scientific and engineering understanding of the behavior of geologic CO2 storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large-scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long-term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and post-injection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring-informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post-injection phases. The National Risk Assessment Partnership’s open-source Integrated Assessment Model (NRAP-Open-IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO2 leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP-Open-IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long-term storage complex effectiveness informed by monitoring based on the NRAP-Open-IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information.
Bayesian network, Risk assessment, Monitoring, car↗
Measurements of b -jet tagging efficiency with the ATLAS detector using $t\overline{t}$ events at $\sqrt{s}=13$ TeV
The efficiency to identify jets containing b-hadrons (b-jets) is measured using a high purity sample of dileptonic top quark-antiquark pairs ($t\bar{t}$) selected from the 36.1 fb -1 of data collected by the ATLAS detector in 2015 and 2016 from proton-proton collisions produced by the Large Hadron Collider at a centre-of-mass energy $\sqrt{s}=13$ TeV. Two methods are used to extract the efficiency from $t\bar{t}$ events, a combinatorial likelihood approach and a tag-and-probe method. A boosted decision tree, not using b-tagging information, is used to select events in which two b-jets are present, which reduces the dominant uncertainty in the modelling of the flavour of the jets. The efficiency is extracted for jets in a transverse momentum range from 20 to 300 GeV, with data-to-simulation scale factors calculated by comparing the efficiency measured using collision data to that predicted by the simulation. In conclusion, the two methods give compatible results, and achieve a similar level of precision, measuring data-to-simulation scale factors close to unity with uncertainties ranging from 2% to 12% depending on the jet transverse momentum.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Investigations of Polymethacrylate Tribochemical Films Using X-Ray Spectroscopy and Optical Profilometry
This study investigates the elemental composition and surface morphology of solid tribochemical films formed on steel surfaces. The reversible addition-fragmentation chain transfer (RAFT) method was used to synthesize nine different metal-free polymers, which were blended into commercial base oils. The polymers were either homopolymers of dodecyl methacrylate and ethylhexyl methacrylate or were co-polymers of these monomers with six polar monomers. After tribological testing at 100 degrees C using the ball-on-flat geometry, the resulting tribochemical films were imaged using scanning electron microscopy (SEM) and optical microscopy. The resulting tribochemical films have thicknesses around 50-100 nm. Two of the films corresponding to small (P1-imidazole-containing copolymer) and large (P3-less polar homopolymer) wear were cross-sectioned using focused ion beam (FIB) and analyzed for elemental composition using energy-dispersive X-Ray (EDX) mapping. Oxygen and nitrogen enrichment was observed, consistent with the relative chemical composition of the precursor polymers. Transmission electron microscopy (TEM) evidence suggests that at the worn surface, some organic elements penetrate or are mixed into the steel substrate giving an interlocking appearance. The two samples examined with TEM showed that P1 tribofilm is diffused or mixed with the steel substrate more so than P3, suggesting a stronger affinity and contact during tribofilm formation.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Global agricultural green and blue water consumption under future climate and land use changes
Not Available
Huang, Zhongwei (ORCID:0000000194383572)↗
U.S. Manufacturing Water Use Data and Estimates: Current State, Limitations, and Future Needs for Supporting Manufacturing Research and Development
Water is essential to manufacturing operations; without it, many facilities could not operate or meet production demands. Physical, reputational, and regulatory risks to water supplies compounded by climate change-induced impacts on hydrological conditions threaten the adequacy of water supplies for manufacturing. Manufacturing water use has not been a major focus of either water or manufacturing-related research. Research and development (R&D) aimed at helping manufacturers use water more sustainably and adapt to changing water conditions is needed to ensure a thriving sector and economy. However, the ability to identify R&D needs is severely limited due to a lack of current, statistically representative data on manufacturing water use and its environmental implications. In this Perspective, we outline four key questions to inform R&D on manufacturing use and highlight how the current state of water data in the United States does not support the adequate investigation of these questions. We make recommendations for the water data characteristics needed to explore the research questions and knowledgeably inform R&D on manufacturing water use.
McCall, James↗
Soil characteristics and land-use drive bacterial community assembly patterns
ABSTRACT Land-use and soil characteristics drive variation in soil community composition, but the influences of these factors on dispersal and community assembly at regional scale remain poorly characterized. Land-use remains a consistent driver of soil community composition even when exhibiting patchy spatial distribution at regional scale. In addition, disturbed and early successional soils often exhibit stochastic community assembly patterns. These observations suggest local community composition is influenced by dispersal and assembly from regional species pools. We examined bacterial community assembly within agricultural cropland, old-field, and forested sites across 10 landscapes in the region around Ithaca, New York (USA). We found that the Sloan neutral model explained assembly well at regional scale (R2 = 0.763), but that both soil pH and land-use imposed selection that shaped community composition. We show that homogeneous selection was a dominant assembly process with respect to both soil pH and land-use regime, but that these two factors interacted in their effects on bacterial community assembly. We conclude that bacterial community assembly at a regional scale is driven by dispersal from regional species pools and local selection on the basis of soil pH and other soil characteristics that vary with land-use.
Microbiology↗
Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).
79 ASTRONOMY AND ASTROPHYSICS↗
Symmetry-mode analysis for local structure investigations using pair distribution function data
Symmetry-adapted distortion modes provide a natural way of describing distorted structures derived from higher-symmetry parent phases. Structural refinements using symmetry-mode amplitudes as fit variables have been used for at least ten years in Rietveld refinements of the average crystal structure from diffraction data; more recently, this approach has also been used for investigations of the local structure using real-space pair distribution function (PDF) data. Here, the value of performing symmetry-mode fits to PDF data is further demonstrated through the successful application of this method to two topical materials: TiSe2, where a subtle but long-range structural distortion driven by the formation of a charge-density wave is detected, and MnTe, where a large but highly localized structural distortion is characterized in terms of symmetry-lowering displacements of the Te atoms. Here, the analysis is performed using fully open-source code within the DiffPy framework via two packages developed for this work: isopydistort, which provides a scriptable interface to the ISODISTORT web application for group theoretical calculations, and isopytools, which converts the ISODISTORT output into a DiffPy-compatible format for subsequent fitting and analysis. These developments expand the potential impact of symmetry-adapted PDF analysis by enabling high-throughput analysis and removing the need for any commercial software.
36 MATERIALS SCIENCE↗
Convolutional Neural Networks for Hydrometeor Classification using Dual Polarization Doppler Radars
Traditional fuzzy logic hydrometeor classification algorithm is a common way to classify precipitation type from dual polarization doppler radar. We propose a deep learning-based method to estimate hydrometeors efficiently using observed radar variables such as horizontal reflectivity (Z H ), differential reflectivity (Z DR ), correlation coefficient (ρ HV ) and specific differential phase (K DP ) from National Weather Service NEXRAD collected at Vance AFB facility at the first elevation angle from January 1st, 2015 to July 31th, 2019. We stack matrices of values from these four polarimetric variables as one 3D array. Samples are preprocessed and divided into training, validation and test set with four target hydrometeor categories (Ice Crystals (IC), Dry Snow (DS), Light and/or Moderate Rain (RA) and Big Drops (rain) (BD)). We developed and optimized five Convolutional Neural Networks (CNNs) architectures and achieved an accuracy of 87.23% and 93.736% respectively using modified ResNet with two different input data selection strategies for hydrometeor classification. Training data selection strategies were important to ensure use of available samples in training for robust performance evaluated by applying the models to novel time period beyond what was use to train the model. Seasonal variation in atmospheric conditions lead to seasonal patterns of liquid vs solid forms of precipitation, that poses challenge for classifier and offer insights into domain specific approaches required for problem of hydrometeor identification. Computationally efficient and scalable approach for classification of hydrometeors offer opportunties to effectively use the large volumes of rich time series of radar observations that are becoming increasingly available.
Lu, Yuping↗