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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Sub-daily virus sampling at the Bermuda Atlantic Time Series reveals diel and depth-structured population dynamics without community-level shifts

Ocean microbes contribute to biogeochemical cycles and ecosystem function, but they do so under top-down pressure imposed by viruses. While viruses are increasingly understood spatially and beginning to be incorporated into predictive modeling, high-frequency ocean virus dynamics remain understudied due to methodological challenges. Here we sampled stratified Bermuda Atlantic Time Series (BATS) waters for 112 hours at sub-daily 4- (surface) or 12- (deep chlorophyll maximum) hour intervals, purified viral particles from these samples, sequenced their metagenomes, and used the resulting data to characterize high-frequency virus community dynamics. Aggregated community diversity metrics changed with depth, but were not statistically significant temporally at a fixed location. However, finer-scale population-level analyses revealed both depth and temporal change, including physicochemical depth-driven differences and, in surface waters, thousands of viral populations that exhibited statistically significant diel rhythms. Statistical analyses revealed three main archetypes of temporal dynamics that themselves differed in abundance patterns, host predictions, viral taxonomy, and gene functions. Among these, highlights include viruses resembling an archetype with a night peaking pattern in activity that include an over-representation of viruses that putatively infect Prochlorococcus, a phototrophic cyanobacteria. Together, these efforts provide baseline community- and population-scale short-time-frame observations relevant to future climate state modeling.

Carrillo, Alfonso [The Ohio State University, Colu↗
DOE OSTI2023

FrESCO: Framework for Exploring Scalable Computational Oncology

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information from electronic health records. This data is then used to create summary statistics about cancer incidence and mortality to facilitate population health monitoring. Extracting phenotypic information from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating the information extraction process from cancer pathology reports has the potential to improve data quality by extracting information in a consistent manner across registries. It can also improve patient outcomes by reducing the time from diagnosis, enabling rapid case ascertainment for clinical trials. Here we present FrESCO, a modular deep-learning natural language processing (NLP) library initially designed for extracting pathology information from clinical text documents. This repository is not solely limited to clinical medical text, but may also be used by researchers just getting started with NLP methods and those looking for a robust solution for their classification problems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

The 14O(alpha,p)17F Reaction Rate and Type I X-ray Bursts

Oral presentation detailing recent results of measurements aimed to better determine the 14O(alpha,p)17F reaction rate of interest to Type I X-ray bursts. An aggregate R-matrix analysis of new and existing data along with a new recommended reaction rate is presented.

Blackmon, J C [Louisiana State University]↗
DOE OSTI2021

Block Scale Rooftop Solar Technical Potential for the City of Orlando

The html maps are provided as supplementary information for the publication titled Parcel Scale Assessment of Rooftop Solar Technical Potential (NREL/PR-7A40-80780). The maps contain information on rooftop solar technical potential at the block scale for the city of Orlando in Florida. The rooftop solar technical potential information is based on data from two different datasets. The first dataset is LiDAR data for the city of Orlando obtained from the Orlando Utilities Commission (OUC) (Koebrich et al. 2021). The second dataset is a national parcel dataset (HIFLD 2020) which contains descriptive data and geometries for parcels in the U.S. Parcel scale data from both these datasets have been processed and aggregated to block scale to produce these html maps. The first html map (block scale developable roof area for Orlando) contains the developable roof area for solar. The second html map (block scale rooftop solar technical potential for Orlando) contains the rooftop solar technical potential in units of kilowatts as well as additional information on the most common building use type and the most common building occupancy type for the block. These html maps are provided to demonstrate the proof-of-concept analysis conducted for Orlando.

14 SOLAR ENERGY↗
DOE OSTI2017

Configurable Crossbar Switch for Deterministic, Low-latency Inter-blade Communications in a MicroTCA Platform

As MicroTCA expands into applications beyond the telecommunications industry from which it originated, it faces new challenges in the area of inter-blade communications. The ability to achieve deterministic, low-latency communications between blades is critical to realizing a scalable architecture. In the past, legacy bus architectures accomplished inter-blade communications using dedicated parallel buses across the backplane. Because of limited fabric resources on its backplane, MicroTCA uses the carrier hub (MCH) for this purpose. Unfortunately, MCH products from commercial vendors are limited to standard bus protocols such as PCI Express, Serial Rapid IO and 10/40GbE. While these protocols have exceptional throughput capability, they are neither deterministic nor necessarily low-latency. To overcome this limitation, an MCH has been developed based on the Xilinx Virtex-7 690T FPGA. This MCH provides the system architect/developer complete flexibility in both the interface protocol and routing of information between blades. In this paper, we present the application of this configurable MCH concept to the Machine Protection System under development for the Spallation Neutron Sources's proton accelerator. Specifically, we demonstrate the use of the configurable MCH as a 12x4-lane crossbar switch using the Aurora protocol to achieve a deterministic, low-latency data link. In this configuration, the crossbar has an aggregate bandwidth of 48 GB/s.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

AggModel: A soil organic matter model with measurable pools for use in incubation studies

Current soil organic matter (SOM) models are empirical in nature by employing few conceptual SOM pools that have a specific turnover time, but that are not measurable and have no direct relationship with soil structural properties. Most soil particles are held together in aggregates and the number, size and stability of these aggregates significantly affect the size and amount of organic matter contained in these aggregates, and its susceptibility to decomposition. While it has been shown that soil aggregates and their dynamics can be measured directly in the laboratory and in the field, the impact of soil aggregate dynamics on SOM decomposition has not been explicitly incorporated in ecosystem models. Here, we present AggModel, a conceptual and simulation model that integrates soil aggregate and SOM dynamics. In AggModel, we consider unaggregated and microaggregated soil that can exist within or external to macroaggregated soil. Each of the four aggregate size classes contains particulate organic matter and mineral-associated organic matter fractions. We used published data from laboratory incubations to calibrate and validate the biological and environmental effects on the rate of formation and breakdown of macroaggregates and microaggregates, and the organic matter dynamics within these different aggregate fractions. After calibration, AggModel explained more than 70% of the variation in aggregate masses and over 90% of the variation in aggregate-associated carbon. The model estimated the turnover time of macroaggregates as 32 days and 166 days for microaggregates. Sensitivity analysis of AggModel parameterization supported the notion that macroaggregate turnover rate has a strong control over microaggregate masses and, hence, carbon sequestration. In addition to AggModel being a proof-of-concept, the advantage of a model that is based on measurable SOM fractions is that its internal structure and dynamics can be directly calibrated and validated by using experimental data. In conclusion, AggModel successfully incorporates the explicit representation for the turnover of soil aggregates and their influence on SOM dynamics and can form the basis for new SOM modules within existing ecosystem models.

Macroaggregates↗
DOE OSTI2018

Combining active learning suggestions

We study the problem of combining active learning suggestions to identify informative training examples by empirically comparing methods on benchmark datasets. Many active learning heuristics for classification problems have been proposed to help us pick which instance to annotate next. But what is the optimal heuristic for a particular source of data? Motivated by the success of methods that combine predictors, we combine active learners with bandit algorithms and rank aggregation methods. We demonstrate that a combination of active learners outperforms passive learning in large benchmark datasets and removes the need to pick a particular active learner a priori. We discuss challenges to finding good rewards for bandit approaches and show that rank aggregation performs well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

An analytic mapping of oligomer potential energy surfaces to an effective Frenkel model

While the use of Frenkel-type models for semiconducting polymer assemblies and related molecular aggregates is well established, the direct parametrization of such models based on electronic structure data is attempted less frequently. In this work, we develop a systematic mapping procedure which is adapted to J-type and H-type homo-aggregate systems. The procedure is based upon the analytic solution of an inverse eigenvalue problem for an effective Frenkel Hamiltonian with nearest-neighbor couplings. Vibronic interactions are included for both site-local and site-correlated modes. For illustration, an application is presented to the excited-state ab initio potential energy surfaces (PESs) of an oligothiophene octamer. The procedure performs a pointwise mapping of the PESs of oligomers of arbitrary chain length n, provided that the electronic ground state and any two of the n lowest adiabatic states of the excitonic manifold of interest are known. These three states are reproduced exactly by the procedure while the remaining n − 2 states of the excitonic manifold can be predicted. Explicit conditions are derived permitting to verify whether a given data set is compatible with the effective Frenkel model under study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Aggregate Exposure Pathways in Support of Risk Assessment

Over time, risk assessment has shifted from establishing relationships between exposure to a single chemical and a resulting adverse health outcome, to evaluation of multiple chemicals and disease outcomes simultaneously. As a result, there is an increasing need to better understand the complex mechanisms that influence risk of chemical and non-chemical stressors, beginning at their source and ending at a biological endpoint relevant to human or ecosystem health risk assessment. Just as the Adverse Outcome Pathway (AOP) framework has emerged as a means of providing insight into mechanism-based toxicity, the exposure science community has seen the recent introduction of the Aggregate Exposure Pathway (AEP) framework. AEPs aid in making exposure data applicable to the FAIR (i.e., findable, accessible, interoperable, and reusable) principle, especially by (1) organizing continuous flow of disjointed exposure information;(2) identifying data gaps, to focus resources on acquiring the most relevant data; (3) optimizing use and repurposing of existing exposure data; and (4) facilitating interoperability among predictive models. Herein, we discuss integration of the AOP and AEP frameworks and how such integration can improve confidence in both traditional and cumulative risk assessment approaches.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2013

Small File Aggregation with PLFS [Slides]

Today’s computational science demands have resulted in ever larger parallel computers, and storage systems have grown to match these demands. Parallel file systems used in this environment are increasingly specialized to extract the highest possible performance for large I/O operations, at the expense of other potential workloads. While some applications have adapted to I/O best practices and can obtain good performance on these systems, the natural I/O patterns of many applications result in the generation of a huge number of small files, the creation of which is poorly served by current parallel file systems at very large scale. This paper describes a new technique for optimizing small file access in parallel file systems for these very large scale systems. The idea is to use a virtual parallel log-structure file system on the compute nodes in order to aggregate large numbers of small files in compute node memory and then stream their data sequentially to a much smaller number of physical files on an underlying parallel file system. The technique is implemented and evaluated using PLFS as the aggregating middleware. We evaluate our system with micro-benchmarks on a local OSX filesystem and with an MPI extension of the standard Postmark to provide results at scale on both Lustre and PanFS parallel filesystems. We observe as much as a 33x improvement in small file create rates on a single host, and 30x improvement in small file write rates, compared to a baseline Lustre configuration on a leadership computing platform using 16,384 cores and achieve an unprecedented create rate of 200 million files per second.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

WHONDRS laboratory time series moisture manipulative experiment from soil core layers across eastern contiguous US: time series aerobic respiration, geochemistry, and aggregates

This dataset supports a broader study examining the effects of wetting and drying on soil layers across the eastern contiguous United States (CONUS). The dataset provides data generated from a laboratory moisture manipulation experiment. The contents include time series aerobic respiration and moisture; dissolved oxygen; sediment geochemistry data; and field metadata. Samples were collected as part of a collaboration between WHONDRS (Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems; https://whondrs.pnnl.gov) and MONet (Molecular Observation Network; https://www.emsl.pnnl.gov/monet). The field samples (soil cores) were labeled as MEL_##_COR and subsequent subsamples begin with MEL_##. Additional subsamples were taken for the laboratory experiment and were labeled as EL_##. The labels from the MEL field samples and the EL subsamples can be mapped directly based on the digits following the prefix and underscore (i.e., EL_01 is a subsample from MEL_01). See the critical details section below for more details on sample naming and experimental design.For details on how to navigate this data package, see this infographic from the River Corridor SFA https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About.In addition to this readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions.This dataset is comprised of (1) a folder containing environmental context photos; (2) file-level metadata; (3) data dictionary; (4) field metadata; (5) readme; (6) international generic sample number (IGSN) mapping file; and (7) a subfolder with soil sample data from field samples and the incubation experiment. The sample data subfolder contains (1) effect size; (2) gravimetric moisture from field samples and incubation experiment; (3) respiration rates, raw dissolved oxygen values, and plots; (4) specific conductance, pH, and temperature from the incubation; (5) soil aggregates; (6) a summary containing median values of each data type for each treatment (wet and dry) in the incubation; (7) a summary containing averages for each data type of each soil layer; and (8) methods codes. All files are .csv, .pdf, .jpeg, or .jpg.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The CompTox Chemistry Dashboard: a community data resource for environmental chemistry

Despite an abundance of online databases providing access to chemical data, there is increasing demand for highquality, structure-curated, open data to meet the various needs of the environmental sciences and computational toxicology communities. The U.S. Environmental Protection Agency’s (EPA) web-based CompTox Chemistry Dashboard is addressing these needs by integrating diverse types of relevant domain data through a cheminformatics layer, built upon a database of curated substances linked to chemical structures. These data include physicochemical, environmental fate and transport, exposure, usage, in vivo toxicity, and in vitro bioassay data, surfaced through an integration hub with link-outs to additional EPA data and public domain online resources. Batch searching allows for direct chemical identifer (ID) mapping and downloading of multiple data streams in several diferent formats. This facilitates fast access to available structure, property, toxicity, and bioassay data for collections of chemicals (hundreds to thousands at a time). Advanced search capabilities are available to support, for example, non-targeted analysis and identifcation of chemicals using mass spectrometry. The contents of the chemistry database, presently containing ~ 760,000 substances, are available as public domain data for download. The chemistry content underpinning the Dashboard has been aggregated over the past 15 years by both manual and auto-curation techniques within EPA’s DSSTox project. DSSTox chemical content is subject to strict quality controls to enforce consistency among chemical substance-structure identifers, as well as list curation review to ensure accurate linkages of DSSTox substances to chemical lists and associated data. The Dashboard, publicly launched in April 2016, has expanded considerably in content and user trafc over the past year. It is continuously evolving with the growth of DSSTox into high-interest or data-rich domains of interest to EPA, such as chemicals on the Toxic Substances Control Act listing, while providing the user community with a fexible and dynamic web-based platform for integration, processing, visualization and delivery of data and resources. The Dashboard provides support for a broad array of research and regulatory programs across the worldwide community of toxicologists and environmental scientists.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Deep learning multiphysics network for imaging CO 2 saturation and estimating uncertainty in geological carbon storage

Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade-off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real-time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO 2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO 2 saturation. The network is trained on pairs of CO 2 label models and multiphysics data so that it can directly image CO 2 saturation. Here we use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO 2 saturation images. Using realistic CO 2 label models and multiphysics data derived from the Kimberlina CO 2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single-physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO 2 saturation images. Our results also suggest that the deep learning multiphysics network for the non-seismic data (i.e., electromagnetic and gravity) can be used as an effective low-cost monitoring tool in between regular seismic monitoring.

58 GEOSCIENCES↗
DOE OSTI2019

A multi-scale calibration approach for process-oriented aggregated building energy demand models

Long-term energy planning relies largely on projections of future energy demand and hourly load profiles. Aggregate building models are increasingly being utilized to characterize the sensitivity of current and future building stocks to changes in climate, population, and building technology on city-to-regional-to-national scales. Due to challenges in the availability of data, those analyses have been limited to projection of energy demand at a single scale, usually state or country, while long-term planning power system models and production cost models might operate at different spatial scales such as energy regions which are focused on ensuring adequate generation infrastructure. In this work, we propose and evaluate a novel method to calibrate an aggregate building energy demand model (PNNL’s BEND model) against the best available data at the spatial scale of balancing authorities. This approach extends previous work on aggregated building energy demand by facilitating analysis of building energy demand across scales, in particular policy and operational decision-making scales. We show that the bias-corrected model estimates building electric loads reasonably well compared with estimates from a statistical model, but has the additional feature of flexibility across spatial scales. While the calibration approach is presently U.S.-centric and associated with U.S. energy regions, it can be extrapolated to other worldwide regions with similar scale challenges between policy and operational implementation decision making. We discuss the significant challenges involved in formulating and calibrating a complex physical model based on simulations of roughly 100,000 individual buildings against available aggregate regional electric load data and highlight areas for potential future work and improved data collection.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services

Flexible loads have great potential to improve the electric grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. This paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environmental impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

A Modern Ethernet Data Acquisition Architecture for Fermilab Beam Instrumentation

The Fermilab Accelerator Division, Instrumentation Department is adopting an open-source framework to replace our embedded VME-based data acquisition systems. Utilizing an iterative methodology, we first moved to embedded Linux, removing the need for VxWorks. Next, we adopted Ethernet on each data acquisition module eliminating the need for the VME backplane in addition to communicating with a rack mount server. Development of DDCP (Distributed Data Communications Protocol), allowed for an abstraction between the firmware and software layers. Each data acquisition module was adapted to read out using 1 GbE and aggregated at a switch which up linked to a 10 GbE network. Current development includes scaling the system to aggregate more modules, to increase bandwidth to support multiple systems and to adopt MicroTCA as a crate technology. The architecture was utilized on various beamlines around the Fermilab complex including PIP2IT, FAST/IOTA and the Muon Delivery Ring. In summary, we were able to develop a data acquisition framework which incrementally replaces VxWorks & VME hardware as well as increases our total bandwidth to 10 Gbit/s using off the shelf Ethernet technology.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Impact of Extreme Heat on Emergency Department Admissions for Childhood and Adult Asthma: An Evaluation of Earth Observations and Heat Wave Definitions

Extreme heat has been associated with adverse health outcomes, yet its impact on asthma exacerbations remains understudied. This is, in part, due to data limitations: research that relies on weather station records and aggregated health statistics cannot resolve fine-scale differences in heat impacts. This study investigates the association between heat wave definitions and summertime asthma-related emergency department visits in Baltimore, Maryland from 2016 to 2022, including 819 adult and 695 pediatric exacerbations. Using geocoded electronic health records and air temperature measurements at several spatial resolutions, we applied a case-crossover design with conditional logistic regressions at the census block group and tract levels. We found strong associations between asthma exacerbations and nighttime heat wave definitions based on relative thresholds of minimum temperatures when census block group or tract level temperature estimates were used. These relationships were significant for both age groups and showed elevated risks in socially vulnerable areas. In contrast, heat wave definitions derived from the city's primary National Weather Service synoptic weather station show associations between asthma and daytime heat extremes, suggesting that the character of the heat hazard depends on the scale at which it is defined. The extreme heat event definition used by Baltimore City's Code Red system showed no significant association with exacerbations. These findings highlight the importance of data resolution in shaping health inferences related to extreme heat in urban environments. Further, this study demonstrates that, regardless of spatial scale, extreme heat is associated with asthma exacerbations in both age groups.

Corpuz, B. [Johns Hopkins University, Baltimore, M↗
DOE OSTI2012

Proposal of an environmental performance index to assess solid waste treatment technologies

Highlights: Black-Right-Pointing-Pointer Proposal of a new concept in waste management: Cleaner Treatment. Black-Right-Pointing-Pointer Development of an index to assess quantitatively waste treatment technologies. Black-Right-Pointing-Pointer Delphi Method was carried out so as to define environmental indicators. Black-Right-Pointing-Pointer Environmental performance evaluation of waste-to-energy plants. - Abstract: Although the concern with sustainable development and environment protection has considerably grown in the last years it is noted that the majority of decision making models and tools are still either excessively tied to economic aspects or geared to the production process. Moreover, existing models focus on the priority steps of solid waste management, beyond waste energy recovery and disposal. So, in order to help the lack of models and tools aiming at the waste treatment and final disposal, a new concept is proposed: the Cleaner Treatment, which is based on the Cleaner Production principles. This paper focuses on the development and validation of the Cleaner Treatment Index (CTI), to assess environmental performance of waste treatment technologies based on the Cleaner Treatment concept. The index is formed by aggregation (summation or product) of several indicators that consists in operational parameters. The weights of the indicator were established by Delphi Method and Brazilian Environmental Laws. In addition, sensitivity analyses were carried out comparing both aggregation methods. Finally, index validation was carried out by applying the CTI to 10 waste-to-energy plants data. From sensitivity analysis and validation results it is possible to infer that summation model is the most suitable aggregation method. For summation method, CTI results were superior to 0.5 (in a scale from 0 to 1) for most facilities evaluated. So, this study demonstrates that CTI is a simple and robust tool to assess and compare the environmental performance of different treatment plants being an excellent quantitative tool to support Cleaner Treatment implementation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗