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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Where are the Data? Automating a Workflow for Carbon Storage Data Gap Analyses

This presentation demonstrates a spatial analysis workflow to assess data availability for the many components of geologic carbon storage technical viability. The workflow relies upon a knowledge-data framework that links the different components of GCS technical viability to the data types needed for evaluation. Using this contextual information, a combination of data science methods (e.g., natural language processing) and spatial analyses are applied to identify areas where sufficient data exists for a given component. The results are aggregated into maps illustrating data density and spatial gaps across all technical viability factors and data categories, as well as the individual component and category level for a more nuanced understanding. Presented at the Geological Society of America Connects 2024 Annual Meeting in Anaheim, California, 22-25 September 2024.

Creason, Christopher↗
DOE OSTI2018

Spectroscopic and Theoretical Studies of Fluorescence Effects in 2-Methylamino-5-(2,4-dihydroxyphenyl)-1,3,4-thiadiazole Induced by Molecular Aggregation

The article presents the results of fluorescence analyses of 2-methylamino-5-(2,4-dihydroxyphenyl)-1,3,4-thiadiazole (MDFT) in an aqueous environment. MDFT dissolved in aqueous solutions with a pH value in the range from 1 to 4.5 yielded an interesting effect of two clearly separated fluorescence emissions. In turn, a single fluorescence was observed in MDFT dissolved in water solutions with a pH value from 4.5 to 12. As it was suggested in the previous investigations of other 1,3,4-thiadiazole compounds, these effects may be associated with conformational changes in the structure of the analysed molecule accompanied by aggregation effects. Crystallographic data showed that the effect of the two separated fluorescence emissions occurred in a conformation with the –OH group in the resorcyl ring bound on the side of the sulphur atom from the 1,3,4-thiadiazole ring. The hypothesis of aggregation as the mechanism involved in the change in the spectral properties at low pH is supported by the results of (Time-Dependent) Density Functional Theory calculations. The possibility of rapid analysis of conformational changes with the fluorescence spectroscopy technique may be rather important outcome obtained from the spectroscopic studies presented in this article. Additionally, the presented results seem to be highly important as they can be easily observed in solutions and biologically important samples.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Non-equilibrium phenomena and kinetic pathways in self-assembled polyelectrolyte complexes

Polyelectrolyte complexation has been conventionally focused on the thermodynamic states, where assemblies have equilibrated in solutions. Far less attention has been given to complex systems that are kinetically trapped at non-equilibrium states. A combination of time-resolved dynamic light scattering, small angle X-ray scattering (SAXS), and cryogenic transmission electron microscopy (Cryo-TEM) was employed here to investigate the internal structures and morphological evolution of non-equilibrium aggregates forming from a pair of two strong block polyelectrolytes over wide time and length scales. The role of formation pathways of electrostatically driven aggregates was assessed using two processing protocols: direct dissolution and salt annealing. The former led to thermodynamically stable products, while the latter resulted in kinetically trapped transient structures. After adding salt, the metastable structures gradually transformed into stable products. Cryo-TEM images showed the interconnected irregular morphologies of the aggregates, and SAXS data revealed the presence of fuzzy globular complexes with R-g similar to 10 nm within them. A two-step process in the time-dependent structural transformation was found and characterized by a fast breakdown of interconnected transient aggregates followed by a slow redistribution of the incipient individual electrostatic assemblies. Furthermore, the prolonged aggregate disintegration process fitting to a stretched exponential function unveiled the broad relaxation distribution and significant structural heterogeneity in these polyelectrolyte complex nanoaggregates. This work brings new insight into the comprehension of non-equilibrium phenomena in self-assembled electrostatic assemblies and represents a first step toward constructing far-from-equilibrium polyelectrolyte complexes de novo for future applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Distributed wireless sensing for fugitive methane leak detection

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It is demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2017

Distributed wireless sensing for methane leak detection technology

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It is demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

A new event builder for CMS Run II

The data acquisition system (DAQ) of the CMS experiment at the CERN Large Hadron Collider (LHC) assembles events at a rate of 100 kHz, transporting event data at an aggregate throughput of 100 GB/s to the high-level trigger (HLT) farm. The DAQ system has been redesigned during the LHC shutdown in 2013/14. The new DAQ architecture is based on state-of-the-art network technologies for the event building. For the data concentration, 10/40 Gbps Ethernet technologies are used together with a reduced TCP/IP protocol implemented in FPGA for a reliable transport between custom electronics and commercial computing hardware. A 56 Gbps Inniband FDR CLOS network has been chosen for the event builder. This paper discusses the software design, protocols, and optimizations for exploiting the hardware capabilities. In conclusion, ee present performance measurements from small-scale prototypes and from the full-scale production system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shown to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Hybrid electric buses fuel consumption prediction based on real-world driving data

Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. Here, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

File-Based Data Flow in the CMS Filter Farm

During the LHC Long Shutdown 1, the CMS Data Acquisition system underwent a partial redesign to replace obsolete network equipment, use more homogeneous switching technologies, and prepare the ground for future upgrades of the detector front-ends. The software and hardware infrastructure to provide input, execute the High Level Trigger (HLT) algorithms and deal with output data transport and storage has also been redesigned to be completely file- based. This approach provides additional decoupling between the HLT algorithms and the input and output data flow. All the metadata needed for bookkeeping of the data flow and the HLT process lifetimes are also generated in the form of small “documents” using the JSON encoding, by either services in the flow of the HLT execution (for rates etc.) or watchdog processes. These “files” can remain memory-resident or be written to disk if they are to be used in another part of the system (e.g. for aggregation of output data). We discuss how this redesign improves the robustness and flexibility of the CMS DAQ and the performance of the system currently being commissioned for the LHC Run 2.

Andre, J. M.↗
DOE OSTI2023

Single-crystal elasticity of (Al,Fe)-bearing bridgmanite up to 82 GPa

Abstract Thermoelastic properties of mantle candidate minerals are essential to our understanding of geophysical phenomena, geochemistry, and geodynamic evolutions of the silicate Earth. However, the lower-mantle mineralogy remains much debated due to the lack of single-crystal elastic moduli (Cij) and aggregate sound velocities of (Al,Fe)-bearing bridgmanite, the most abundant mineral of the planet, at the lower mantle pressure-temperature (P-T) conditions. Here we report single-crystal Cij of (Al,Fe)-bearing bridgmanite, Mg0.88Fe0.1Al0.14Si0.90O3 (Fe10-Al14-Bgm) with Fe3+/ΣFe = ~0.65, up to ~82 GPa using X-ray diffraction (XRD), Brillouin light scattering (BLS), and impulsive stimulated light scattering (ISLS) measurements in diamond-anvil cells (DACs). Two crystal platelets with orientations of (–0.50, 0.05, –0.86) and (0.65, –0.59, 0.48), that are sensitive to deriving all nine Cij, are used for compressional and shear wave velocity (νP and νS) measurements as a function of azimuthal angles over 200° at each experimental pressure. Our results show that all Cij of singe-crystal Fe10-Al14-Bgm increase monotonically with pressure with small uncertainties of 1–2% (±1σ), except C55 and C23, which have uncertainties of 3–4%. Using the third-order Eulerian finite-strain equations to model the elasticity data yields the aggregate adiabatic bulk and shear moduli and respective pressure derivatives at the reference pressure of 25 GPa: KS = 326 ± 4 GPa, µ = 211 ± 2 GPa, KS′ = 3.32 ± 0.04, and µ′ = 1.66 ± 0.02 GPa. The high-pressure aggregate νS and νP of Fe10-Al14-Bgm are 2.6–3.5% and 3.1–4.7% lower than those of MgSiO3 bridgmanite end-member, respectively. These data are used with literature reports on bridgmanite with different Fe and Al contents to quantitatively evaluate pressure and compositional effects on their elastic properties. Comparing with one-dimensional seismic profiles, our modeled velocity profiles of major lower-mantle mineral assemblages at relevant P-T suggest that the lower mantle could likely consist of about 89 vol% (Al,Fe)-bearing bridgmanite. After considering uncertainties, our best-fit model is still indistinguishable from pyrolitic or chondritic models.

Geochemistry & Geophysics↗
DOE OSTI2019

Wind direction estimation using SCADA data with consensus-based optimization

Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

17 WIND ENERGY↗
DOE OSTI2014

Intermediate conformation between native β-sheet and non-native α-helix is a precursor of trifluoroethanol-induced aggregation of Human Carbonic Anhydrase-II

Highlights: • HCAII forms amyloid-like aggregates at moderate concentration of trifluoroethanol. • Protein adopts a state between β-sheet and α-helix at moderate % of TFE. • Hydrophobic surface(s) of partially structured conformation forms amyloid. • High % of TFE induces stable α-helical state preventing aggregation. - Abstract: In the present work, we examined the correlation between 2,2,2-trifluoroethanol (TFE)-induced conformational transitions of human carbonic anhydrase II (HCAII) and its aggregation propensity. Circular dichroism data indicates that protein undergoes a transition from β-sheet to α-helix on addition of TFE. The protein was found to aggregate maximally at moderate concentration of TFE at which it exists somewhere between β-sheet and α-helix, probably in extended non-native β-sheet conformation. Thioflavin-T (ThT) and Congo-Red (CR) assays along with fluorescence microscopy and transmission electron microscopy (TEM) data suggest that the protein aggregates induced by TFE possess amyloid-like features. Anilino-8-naphthalene sulfonate (ANS) binding studies reveal that the exposure of hydrophobic surface(s) was maximum in intermediate conformation. Our study suggests that the exposed hydrophobic surface and/or the disruption of the structural features protecting a β-sheet protein might be the major reason(s) for the high aggregation propensity of non-native intermediate conformation of HCAII.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Applications of aggregation theory to sustainability assessment

In order to aid in transition towards operations that promote sustainability goals, researchers and stakeholders use sustainability assessments. Although assessments take various forms, many utilize diverse sets of indicators that can number anywhere from two to over 2000. Indices, composite indicators, or aggregate values are used to simplify high dimensional and complex data sets and to clarify assessment results. Although the choice of aggregation function is a key component in the development of the assessment, there are few examples to be found in literature to guide appropriate aggregation function selection. This paper develops a connection between the mathematical study of aggregation functions and sustainability assessment in order to aid in providing criteria for aggregation function selection. Relevant mathematical properties of aggregation functions are presented and interpreted. Lastly, we provide cases of these properties and their relation to previous sustainability assessment research. Examples show that mathematical aggregation properties can be used to address the topics of compensatory behavior and weak versus strong sustainability, aggregation of data under varying units of measurements, multiple site multiple indicator aggregation, and the determination of error bounds in aggregate output for normalized and non-normalized indicator measures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

2018 Industrial Energy Data Book

The Industrial Energy Data Book (IEDB) aggregates and synthesizes information on the trends in industrial energy use, energy prices, economic activity, and water use. The IEDB also estimates county-level industrial energy use and combustion energy use of large energy-using facilities (i.e., facilities required to report greenhouse gas emissions under the EPA's Greenhouse Gas Reporting Program). These estimates are derived from publicly available sources from EPA, Energy Information Administration, Census Bureau, USDA, and USGS. The estimation methodology is meant to be improved over time with input from the energy analysis and developer communities. Please refer to https://github.com/NREL/Industry-energy-data-book.

agriculture↗
DOE OSTI2021

An adaptive adversarial domain adaptation approach for corn yield prediction

Recently, statistical machine learning and deep learning methods have been widely explored for corn yield prediction. Though successful, machine learning models generated within a specific spatial domain often lose their validity when directly applied to new regions. To address this issue, we designed an unsupervised adaptive domain adversarial neural network (ADANN). Specifically, through domain adversarial training, the ADANN model reduced the impact of domain shift by projecting data from different domains into the same subspace. Also, the ADANN model was designed to be trained in an adaptive way, which guaranteed the model can learn the domain-invariant features and perform accurate yield prediction simultaneously. Informative variables including time-series vegetation indices and sequential weather observations were first collected from multiple data sources and aggregated to the county level. Then, we trained the ADANN model with the extracted features and corresponding reported county-level corn yield from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated in four testing years 2016–2019. The U.S. corn belt was used as the study area and counties under study were grouped into two diverse ecological regions. Overall, the experimental results showed that the developed ADANN model had better performance than three other state-of-the-art machine learning models in both local experiments (train and test in the same region) and transfer experiments (train and test in different regions). As the first study using adversarial learning for crop yield prediction, this research demonstrates a novel solution for improving model transferability on crop yield prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Hybrid phosphazene anodes for energy storage applications

The use of hybrid cyclic phosphazene polymer/graphite anodes, where the phosphazene serves as distributed loci for Li deposition, has been investigated. Capacity within the hybrid system was found to occur reversibly in distinct regions. At the most positive voltages, above 0.06 V vs Li/Li+, the capacity was associated mostly with Li+ intercalation into graphite. In the most negative region, deposition of Li within the polymer was the predominate mechanism. A transitional region is inferred by the data whereby bulk aggregation or clustering of Li atoms occurs in proximity to the phosphazene sites that then serve as a template for more widespread population of Li within the anode at higher voltages, akin to a nucleation process. In full cells with a mixed oxide cathode, controlling the extent of Li deposition by limiting the charging voltage to 4.45 V enabled repeated cycling with no loss in capacity. Capacities as high as 183 mAh g-1 have been achieved for systems containing as little as 10% graphite while retaining coulombic efficiencies of 98% over 50 cycles. This level of cycling equates to the deposition of 7.4 Li per cyclic phosphazene.

25 ENERGY STORAGE↗
DOE OSTI2022

Confronting the water potential information gap

Water potential directly controls the function of leaves, roots and microbes, and gradients in water potential drive water flows throughout the soil–plant–atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land-surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. Here, we end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely sensed proxies. Together, these considerations offer a road map for clearer links between ecohydrological processes and the water potential gradients that have the ‘potential’ to substantially reduce conceptual and modelling uncertainties.

58 GEOSCIENCES↗