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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multi-device study of temporal characteristics of magnetohydrodynamic modes initiating disruptions

Disruptions in tokamaks are often preceded by magnetohydrodynamic (MHD) instabilities that can rotate or become locked to the wall. Measurements from magnetic diagnostics in the presence of MHD mode precursors to disruptions can yield potentially valuable input to the plasma control system, with a view to disruption avoidance, prediction and mitigation. This report presents an exploratory analysis of the growth of MHD modes and corresponding time scales on the basis of magnetic measurements in multiple tokamaks. To this end, a database was compiled using disruptive discharges from COMPASS, ASDEX Upgrade, DIII-D and JET, manually classified according to disruption root cause, and characterized by a great diversity of operational conditions and mode dynamics. The typical time during which a mode can be detected using saddle coils and the duration of the locked mode phase in the database both extend over several orders of magnitude, but generally the time scales increase with plasma size. Several additional factors are discussed that can influence these durations, including the disruption root cause. A scaling law for the locked phase duration was estimated, yielding predictions toward ITER of the order of hundreds of milliseconds or even seconds. In addition, a scaling law for the mode amplitude at the disruption onset, proposed earlier by de Vries et al. (2016), is applied to the database, and its predictive capabilities are assessed. Despite significant uncertainty on the predictions from both scaling laws, encouraging trends are observed of the fraction of disruptions that may be detected with sufficient warning time to allow mitigation or even avoidance, based solely on observations of MHD mode dynamics. When combined with similar analysis of measurements from diagnostics that are sensitive to other disruption precursors, our analysis methods and results may contribute to the reliability, robustness and generalization of disruption warning schemes for ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Stereotactic Ablative Radiation Therapy as First Local Therapy for Lung Oligometastases From Colorectal Cancer: A Single-Institution Cohort Study

Purpose: To estimate stereotactic ablative radiation therapy (SABR) efficacy and its potential role as an alternative to surgery for the treatment of lung metastases from colorectal cancer. Methods and Materials: Forty consecutive patients who received SABR as first local therapy at the time of lung progression were included, from 2004 to 2014. The primary study endpoint was overall survival. Secondary endpoints were progression-free survival and safety. Results: A single nodule was treated in 26 patients (65%), 2 nodules in 10 patients (25%), 3 in 3 patients (7.5%), and 4 in 1 patient (2.5%), for a total of 59 lesions. The median delivered biological effective dose was 96 Gy, in 1 to 8 daily fractions. Median follow-up time was 20 months (range, 3-72 months). Overall survival rates at 1, 2, and 5 years were, respectively, 84%, 73%, and 39%, with 14 patients (35%) dead. Median overall survival was 46 months. Progression occurred in 25 patients (62.5%), at a median interval of 8 months; failure at SABR site was observed in 3 patients (7.5%). Progression-free survival rates were 49% and 27% at 1 and 2 years, respectively. Discussion: The results of this retrospective exploratory analysis suggest safety and efficacy of SABR in patients affected with colorectal cancer lung oligometastases and urge inclusion of SABR in prospective clinical trials.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Demonstrating the viability of Lagrangian in situ reduction on supercomputers

Performing exploratory analysis and visualization of large-scale time-varying computational science applications is challenging due to inaccuracies that arise from under-resolved data. In recent years, Lagrangian representations of the vector field computed using in situ processing are being increasingly researched and have emerged as a potential solution to enable exploration. However, prior works have offered limited estimates of the encumbrance on the simulation code as they consider “theoretical” in situ environments. Further, the effectiveness of this approach varies based on the nature of the vector field, benefitting from an in-depth investigation for each application area. With this study, an extended version of Sane et al. (2021), we contribute an evaluation of Lagrangian analysis viability and efficacy for simulation codes executing at scale on a supercomputer. We investigated previously unexplored cosmology and seismology applications as well as conducted a performance benchmarking study by using a hydrodynamics mini-application targeting exascale computing. Here, to inform encumbrance, we integrated in situ infrastructure with simulation codes, and evaluated Lagrangian in situ reduction in representative homogeneous and heterogeneous HPC environments. To inform post hoc accuracy, we conducted a statistical analysis across a range of spatiotemporal configurations as well as a qualitative evaluation. Additionally, our study contributes cost estimates for distributed-memory post hoc reconstruction. In all, we demonstrate viability for each application — data reduction to less than 1% of the total data via Lagrangian representations, while maintaining accurate reconstruction and requiring under 10% of total execution time in over 90% of our experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluating the economic impact of water scarcity in a changing world

Abstract Water scarcity is dynamic and complex, emerging from the combined influences of climate change, basin-level water resources, and managed systems’ adaptive capacities. Beyond geophysical stressors and responses, it is critical to also consider how multi-sector, multi-scale economic teleconnections mitigate or exacerbate water shortages. Here, we contribute a global-to-basin-scale exploratory analysis of potential water scarcity impacts by linking a global human-Earth system model, a global hydrologic model, and a metric for the loss of economic surplus due to resource shortages. We find that, dependent on scenario assumptions, major hydrologic basins can experience strongly positive or strongly negative economic impacts due to global trade dynamics and market adaptations to regional scarcity. In many cases, market adaptation profoundly magnifies economic uncertainty relative to hydrologic uncertainty. Our analysis finds that impactful scenarios are often combinations of standard scenarios, showcasing that planners cannot presume drivers of uncertainty in complex adaptive systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Elemental Zn and its Binding Protein Zinc-α2-Glycoprotein are Elevated in HPV-Positive Oropharyngeal Squamous Cell Carcinoma

Human papillomavirus (HPV)-positive head and neck squamous cell carcinoma (HNSCC) is biologically distinct from HPV-negative HNSCC. Outside of HPV-status, few tumor-intrinsic variables have been identified that correlate to improved survival. As part of exploratory analysis into the trace elemental composition of oropharyngeal squamous cell carcinoma (OPSCC), we performed elemental quanitification by X-ray fluorescence microscopy (XFM) on a small cohort (n = 32) of patients with HPV-positive and -negative OPSCC and identified in HPV-positive cases increased zinc (Zn) concentrations in tumor tissue relative to normal tissue. Subsequent immunohistochemistry of six Zn-binding proteins-zinc-alpha 2-glycoprotein (AZGP1), Lipocalin-1, Albumin, S100A7, S100A8 and S100A9-revealed that only AZGP1 expression significantly correlated to HPV-status (p < 0.001) and was also increased in tumor relative to normal tissue from HPV-positive OPSCC tumor samples. AZGP1 protein expression in our cohort significantly correlated to a prolonged recurrence-free survival (p = 0.029), similar to HNSCC cases from the TCGA (n = 499), where highest AZGP1 mRNA levels correlated to improved overall survival (p = 0.023). By showing for the first time that HPV-positive OPSCC patients have increased intratumoral Zn levels and AZGP1 expression, we identify possible positive prognostic biomarkers in HNSCC as well as possible mechanisms of increased sensitivity to chemoradiation in HPV-positive OPSCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

POGO-DB—a database of pairwise-comparisons of genomes and conserved orthologous genes

POGO-DB (http://pogo.ece.drexel.edu/) provides an easy platform for comparative microbial genomics. POGO-DB allows users to compare genomes using pre-computed metrics that were derived from extensive computationally intensive BLAST comparisons of >2000 microbes. These metrics include (i) average protein sequence identity across all orthologs shared by two genomes, (ii) genomic fluidity (a measure of gene content dissimilarity), (iii) number of ‘orthologs’ shared between two genomes, (iv) pairwise identity of the 16S ribosomal RNA genes and (v) pairwise identity of an additional 73 marker genes present in >90% prokaryotes. Users can visualize these metrics against each other in a 2D plot for exploratory analysis of genome similarity and of how different aspects of genome similarity relate to each other. The results of these comparisons are fully downloadable. In addition, users can download raw BLAST results for all or user-selected comparisons. Therefore, we provide users with full flexibility to carry out their own downstream analyses, by creating easy access to data that would normally require heavy computational resources to generate. POGO-DB should prove highly useful for researchers interested in comparative microbiology and benefit the microbiome/metagenomic communities by providing the information needed to select suitable phylogenetic marker genes within particular lineages.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Topic modeling to discern irregular order patterns in unlabeled electronic health records

This paper presents an application of topic modeling on event sequences of Electronic Health Record (EHR) orders. Metaphorically, we approach clinical order event sequences of unlabeled data as if they are documents where words are the events that occurred in the history of an order. We demonstrate the approach leveraging Consult orders data. The details of the data preprocessing and the data structures are described along with the data sources. Latent Dirichlet Allocation (LDA) is leveraged to fit against the limited datasets prepared. Another open source tool – LDAvis is used for exploratory analysis of the LDA results. The preliminary results revealed some order patterns that are qualitatively evaluated as potential irregular transitions. The goal of this analysis is to provide unsupervised learning application to domain experts in the absence of labeled data where they can investigate captured patterns and identify irregular transitions of orders. Ultimately, such efforts will guide formalization of hazard detection algorithms that monitor EHR data to identify health information technology related hazards.

Ozmen, Ozgur↗
DOE OSTI2023

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.

chemical space exploration↗
DOE OSTI2024

AEGIS (Air Emissions Grouped by Industrial Sectors) [SWR-24-49]

AEGIS is a robust framework designed to build and compile emissions inventories for various industrial sectors. It integrates multiple emissions databases provided by the USEPA – including GHGRP, NEI, and TRI – and leverages the STEWI and STEWICOMBO tools to retrieve, merge, and process data. The framework produces facility- and process-level inventories, identifies discrepancies (e.g., NAICS or FRS mismatches), and performs exploratory analysis including emission concentration calculations and visualization.

Atnoorkar, Swaroop↗
DOE OSTI2015

Query Optimization for Graph Analytics on Linked Data Using SPARQL

Triplestores that support query languages such as SPARQL are emerging as the preferred and scalable solution to represent data and meta-data as massive heterogeneous graphs using Semantic Web standards. With increasing adoption, the desire to conduct graph-theoretic mining and exploratory analysis has also increased. Addressing that desire, this paper presents a solution that is the marriage of Graph Theory and the Semantic Web. We present software that can analyze Linked Data using graph operations such as counting triangles, finding eccentricity, testing connectedness, and computing PageRank directly on triple stores via the SPARQL interface. We describe the process of optimizing performance of the SPARQL-based implementation of such popular graph algorithms by reducing the space-overhead, simplifying iterative complexity and removing redundant computations by understanding query plans. Our optimized approach shows significant performance gains on triplestores hosted on stand-alone workstations as well as hardware-optimized scalable supercomputers such as the Cray XMT.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Assessing the Future of Distributed Wind: Opportunities for Behind-the-Meter Projects

Wind power is one of the fastest growing sources of new electricity generation in the United States. Cumulative installed capacity was more than 74,000 megawatts (MW) at year-end 2015 and wind power supplied 4.7% of total 2015 U.S. electricity generation. Despite the growth of the wind power industry, the distributed wind market has remained limited. Cumulative installations of distributed wind through 2015 totaled 934 MW. This first-of-a-kind exploratory analysis characterizes the future opportunity for behind-the-meter distributed wind, serving primarily rural or suburban homes, farms, and manufacturing facilities. This work focuses only on the grid-connected, behind-the-meter subset of the broader distributed wind market. We estimate this segment to be approximately half of the 934 MW of total installed distributed wind capacity at year-end 2015. Potential from other distributed wind market segments including systems installed in front of the meter (e.g., community wind) and in remote, off-grid locations is not assessed in this analysis and therefore, would be additive to results presented here. These other distributed wind market segments are not considered in this initial effort because of their relatively unique economic and market attributes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Drivers of the Resource Adequacy Contribution of Solar and Storage for Florida Municipal Utilities

Solar’s variable generation limits its contribution to reliably meeting peak demand, or its resource adequacy contribution. Energy storage is a leading option to increase solar’s resource adequacy contribution, yet the contribution from different configurations of solar and storage is not widely understood. We develop methods for exploring the primary drivers of an estimate of the resource adequacy contribution of solar and storage, and we apply the methods to a case study in Florida, where demand peaks in winter and summer. We find that the portion of solar nameplate capacity that contributes to resource adequacy—its capacity credit—is less than 50% and that it declines with increasing solar penetration. The capacity credit of storage, even though it is fully controllable by the system operator, strongly depends on the duration of storage. The capacity credit of 1 hour of storage can be less than the capacity credit of solar. Achieving a 90% capacity credit requires at least 4–5 hours of storage when storage capacity is small relative to the system peak. As storage deployment increases to 20% of the peak demand, 9 hours—and sometimes more than 10 hours—of storage are needed to achieve a 90% capacity credit. Increased solar deployment at the system level can increase storage’s capacity credit. Directly pairing solar and storage can also impact the capacity credit. Storage with a power rating similar to the solar inverter rating loses capacity credit when coupled with solar if its duration is more than 1–2 hours, because storage competes with solar for use of the inverter. On the other hand, there is no reduction in capacity credit when the storage is small relative to the solar inverter. The approach and tools developed here for exploratory analysis can be useful for many other utilities and regions grappling with similar preliminary questions, prior to evaluation of specific cases using more detailed and resource-intensive modeling.

14 SOLAR ENERGY↗
DOE OSTI2020

Physics-Informed Machine Learning for Epidemiological Models

One challenge of using compartmental SEIR models for public health planning is the difficulty in manually tuning parameters to capture behavior reflected in the real-world data. This team conducted initial, exploratory analysis of a novel technique to use physics-informed machine learning tools to rapidly develop data-driven models for physical systems. This machine learning approach may be used to perform data assimilation of compartment models which account for unknown interactions between geospatial domains (i.e. diffusion processes coupling across neighborhoods/counties/states/etc.). Results presented here are early, proof-of-concept ideas that demonstrate initial success in using a physically informed neural network (PINN) model to assimilate data in a compartmental epidemiology model. The results demonstrate initial success and warrant further research and development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Technology Innovation Pathways for Distributed Wind Balance-of-System Cost Reduction

This exploratory analysis characterizes the balance-of-system (BOS) cost reduction opportunity for small, commercial, medium, and large distributed wind systems. To do this, we used the National Renewable Energy Laboratory’s (NREL’s) Land-based Balance of System Systems Engineering (LandBOSSE) model (Eberle et al. 2019). This model calculates the capital expenditures (CapEx) associated with installation and the system components (e.g., foundation and electrical infrastructure) other than the rotor nacelle assembly and tower. Then, building on the cost reduction potential assessment, we provide a more qualitative evaluation of prospective technology innovation concepts that may enable realization of those cost reductions for distributed wind systems.

17 WIND ENERGY↗
DOE OSTI2025

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

The Transcriptional Response of Soil Bacteria to Long-Term Warming and Short-Term Seasonal Fluctuations in a Terrestrial Forest

Terrestrial ecosystems are an important carbon store, and this carbon is vulnerable to microbial degradation with climate warming. After 30 years of experimental warming, carbon stocks in a temperate mixed deciduous forest were observed to be reduced by 30% in the heated plots relative to the controls. In addition, soil respiration was seasonal, as was the warming treatment effect. We therefore hypothesized that long-term warming will have higher expressions of genes related to carbohydrate and lipid metabolism due to increased utilization of recalcitrant carbon pools compared to controls. Because of the seasonal effect of soil respiration and the warming treatment, we further hypothesized that these patterns will be seasonal. We used RNA sequencing to show how the microbial community responds to long-term warming (~30 years) in Harvard Forest, MA. Total RNA was extracted from mineral and organic soil types from two treatment plots (+5°C heated and ambient control), at two time points (June and October) and sequenced using Illumina NextSeq technology. Treatment had a larger effect size on KEGG annotated transcripts than on CAZymes, while soil types more strongly affected CAZymes than KEGG annotated transcripts, though effect sizes overall were small. Although, warming showed a small effect on overall CAZymes expression, several carbohydrate-associated enzymes showed increased expression in heated soils (~68% of all differentially expressed transcripts). Further, exploratory analysis using an unconstrained method showed increased abundances of enzymes related to polysaccharide and lipid metabolism and decomposition in heated soils. Compared to long-term warming, we detected a relatively small effect of seasonal variation on community gene expression. Together, these results indicate that the higher carbohydrate degrading potential of bacteria in heated plots can possibly accelerate a self-reinforcing carbon cycle-temperature feedback in a warming climate.

54 ENVIRONMENTAL SCIENCES↗