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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Scalable shear-exfoliation of high-quality phosphorene nanoflakes with reliable electrochemical cycleability in nano batteries

Atomically thin black phosphorus (called phosphorene) holds great promise as an alternative to graphene and other two-dimensional transition-metal dichalcogenides as an anode material for lithium-ion batteries (LIBs). But, bulk black phosphorus (BP) suffers from rapid capacity fading and poor rechargeable performance. This work reports for the first time the use of in situ transmission electron microscopy (TEM) to construct nanoscale phosphorene LIBs. This enables direct visualization of the mechanisms underlying capacity fading in thick multilayer phosphorene through real-time capture of delithiation-induced structural decomposition, which serves to reduce electrical conductivity thus causing irreversibility of the lithiated phases. Furthermore, we demonstrate that few-layer-thick phosphorene successfully circumvents the structural decomposition and holds superior structural restorability, even when subject to multi-cycle lithiation/delithiation processes and concomitant huge volume expansion. This finding provides breakthrough insights into thickness-dependent lithium diffusion kinetics in phosphorene. More importantly, a scalable liquid-phase shear exfoliation route has been developed to produce high-quality ultrathin phosphorene using simple means such as a high-speed shear mixer or even a household kitchen blender with the shear rate threshold of ~1.25 × 10 4 s -1 . Our results reported here will pave the way for industrial-scale applications of rechargeable phosphorene LIBs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

MultiTaskDeltaNet: change detection-based image segmentation for operando ETEM with application to carbon gasification kinetics

Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.

08 HYDROGEN↗
DOE OSTI2010

OVIS 3.2 user's guide.

This document describes how to obtain, install, use, and enjoy a better life with OVIS version 3.2. The OVIS project targets scalable, real-time analysis of very large data sets. We characterize the behaviors of elements and aggregations of elements (e.g., across space and time) in data sets in order to detect meaningful conditions and anomalous behaviors. We are particularly interested in determining anomalous behaviors that can be used as advance indicators of significant events of which notification can be made or upon which action can be taken or invoked. The OVIS open source tool (BSD license) is available for download at ovis.ca.sandia.gov. While we intend for it to support a variety of application domains, the OVIS tool was initially developed for, and continues to be primarily tuned for, the investigation of High Performance Compute (HPC) cluster system health. In this application it is intended to be both a system administrator tool for monitoring and a system engineer tool for exploring the system state in depth. OVIS 3.2 provides a variety of statistical tools for examining the behavior of elements in a cluster (e.g., nodes, racks) and associated resources (e.g., storage appliances and network switches). It provides an interactive 3-D physical view in which the cluster elements can be colored by raw or derived element values (e.g., temperatures, memory errors). The visual display allows the user to easily determine abnormal or outlier behaviors. Additionally, it provides search capabilities for certain scheduler logs. The OVIS capabilities were designed to be highly interactive - for example, the job search may drive an analysis which in turn may drive the user generation of a derived value which would then be examined on the physical display. The OVIS project envisions the capabilities of its tools applied to compute cluster monitoring. In the future, integration with the scheduler or resource manager will be included in a release to enable intelligent resource utilization. For example, nodes that are deemed less healthy (i.e., nodes that exhibit outlier behavior with respect to some set of variables shown to be correlated with future failure) can be discovered and assigned to shorter duration or less important jobs. Further, HPC applications with fault-tolerant capabilities would respond to changes in resource health and other OVIS notifications as needed, rather than undertaking preventative measures (e.g. checkpointing) at regular intervals unnecessarily.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Measurement and analysis of GPU-accelerated applications with HPCToolkit

To address the challenge of performance analysis on the US DOE’s forthcoming exascale supercomputers, Rice University has been extending its HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help developers understand the performance of accelerated applications as a whole, HPCToolkit’s measurement and analysis tools attribute metrics to calling contexts that span both CPUs and GPUs. To measure GPU-accelerated applications efficiently, HPCToolkit employs a novel wait-free data structure to coordinate monitoring and attribution of GPU performance. To help developers understand the performance of complex GPU code generated from high-level programming models, HPCToolkit constructs sophisticated approximations of call path profiles for GPU computations. To support fine-grained analysis and tuning, HPCToolkit uses PC sampling and instrumentation to measure and attribute GPU performance metrics to source lines, loops, and inlined code. To supplement fine-grained measurements, HPCToolkit can measure GPU kernel executions using hardware performance counters. To provide a view of how an execution evolves over time, HPCToolkit can collect, analyze, and visualize call path traces within and across nodes. Finally, on NVIDIA GPUs, HPCToolkit can derive and attribute a collection of useful performance metrics based on measurements using GPU PC samples. Here, we illustrate HPCToolkit’s new capabilities for analyzing GPU-accelerated applications with several codes developed as part of the Exascale Computing Project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Multimodal Nanoscale Tomographic Imaging for Battery Electrodes

Accurate representations of the 3D structure within a lithium-ion battery are key to understanding performance limitations. However, obtaining exact reconstructions of electrodes, where the active particles, the carbon black and polymeric binder domain, and the pore space are visualized is challenging. In this work, it is shown that multimodal imaging can be used to overcome this challenge. High-resolution ptychographic X-ray computed tomography are combined with lower resolution but higher contrast transmission X-ray tomographic microscopy to obtain 3D reconstructions of pristine and cycled graphite-silicon composite electrodes. This cross-correlation enables quantitative analysis of the surface of active particles, including the heterogeneity of carbon-black and binder domain and solid-electrolyte interphase coverage. Capturing the active particles as well as the carbon black-binder domain allows using these segmented structures for electrochemical simulations to highlight the influence of the particle embedding on local state of charge heterogeneities.

25 ENERGY STORAGE↗
DOE OSTI2017

On the transcritical mixing of fuels at diesel engine conditions

Whilst the physics of both classical evaporation and supercritical fluid mixing are reasonably well characterized and understood in isolation, little is known about the transition from one to the other in the context of liquid fuel systems. The lack of experimental data for microscopic droplets at realistic operating conditions impedes the development of phenomenological and numerical models. To address this issue we performed systematic measurements using high-speed long-distance microscopy, for three single-component fuels (n-heptane, n-dodecane, n-hexadecane), into gas at elevated temperatures (700–1200 K) and pressures (2–11 MPa). We describe these high-speed visualizations and the time evolution of the transition from liquid droplet to fuel vapour at the microscopic level. The measurements show that the classical atomization and vaporisation processes do shift to one where surface tension forces diminish with increasing pressure and temperature, but the transition to diffusive mixing does not occur instantaneously when the fuel enters the chamber. Rather, subcritical liquid structures exhibit surface tension in the near-nozzle region and then, after time surrounded by the hot ambient gas and fuel vapour, undergo a transition to a dense miscible fluid. Although there was clear evidence of surface tension and primary atomization for n-dodecane and n-hexadecane for a period of time at all the above conditions, n-heptane appeared to produce a supercritical fluid from the nozzle outlet when injected at the most elevated conditions (1200 K, 10 MPa). This demonstrates that the time taken by a droplet to transition to diffusive mixing depends on the pressure and temperature of the gas surrounding the droplet as well as the fuel properties. We summarise our observations into a phenomenological model which describes the morphological evolution and transition of microscopic droplets from classical evaporation through a transitional mixing regime and towards diffusive mixing, as a function of operating conditions. We provide criteria for these regime transitions as reduced pressure–temperature correlations, revealing the conditions where transcritical mixing is important to diesel fuel spray mixing.

02 PETROLEUM↗
DOE OSTI2016

SU-G-IeP4-09: Method of Human Eye Aberration Measurement Using Plenoptic Camera Over Large Field of View

Purpose: The measurement based on Shack-Hartmann wave-front sensor(WFS), obtaining both the high and low order wave-front aberrations simultaneously and accurately, has been applied in the detection of human eyes aberration in recent years. However, Its application is limited by the small field of view (FOV), slight eye movement leads the optical bacon image exceeds the lenslet array which result in uncertain detection error. To overcome difficulties of precise eye location, the capacity of detecting eye wave-front aberration over FOV much larger than simply a single conjugate Hartmann WFS accurately and simultaneously is demanded. Methods: Plenoptic camera’s lenslet array subdivides the aperture light-field in spatial frequency domain, capture the 4-D light-field information. Data recorded by plenoptic cameras can be used to extract the wave-front phases associated to the eyes aberration. The corresponding theoretical model and simulation system is built up in this article to discuss wave-front measurement performance when utilizing plenoptic camera as wave-front sensor. Results: The simulation results indicate that the plenoptic wave-front method can obtain both the high and low order eyes wave-front aberration with the same accuracy as conventional system in single visual angle detectionand over FOV much larger than simply a single conjugate Hartmann systems. Meanwhile, simulation results show that detection of eye aberrations wave-front in different visual angle can be achieved effectively and simultaneously by plenoptic method, by both point and extended optical beacon from the eye. Conclusion: Plenoptic wave-front method possesses the feasibility in eye aberrations wave-front detection. With larger FOV, the method can effectively reduce the detection error brought by imprecise eye location and simplify the eye aberrations wave-front detection system comparing with which based on Shack-Hartmann WFS. Unique advantage of the plenoptic method lies in obtaining wave-front in different visual angle simultaneously, which provides an approach in building up 3-D model of eye refractor tomographically. Funded by the key Laboratory of High Power Laser and Physics, CAS Research Project of National University of Defense Technology No. JC13-07-01; National Natural Science Foundation of China No. 61205144.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

SIR Glass Test Vehicle Designed to Characterize Process Materials

The exposure to harsh environments can result in reliability issues on highly dense electronic packages and circuit assemblies. One area of concern is electrochemical failure mechanisms caused by electrolytic corrosion, electrochemical migration (dendrites), and leakage currents under humid conditions. These no-fault and no-trouble found failure points are challenging to detect and to perform process control during the assembly process. The focus of this research centers on metallizing glass surface insulation resistance test vehicles to characterize materials on electronic packages and components that exhibit the highest risk of failure due to material interactions and process residues. Glass test vehicles provide a visual representation of materials when they are exposed to moisture content in the air. Harsh environments can cause material interactions resulting in morphology changes, opening, and release of active components. A transparent test vehicle is highly functional for environmental stress testing under temperature, humidity, and bias. The designed experiment will evaluate the effects of material and process residues that can undergo changes in behavior during application, which leads to local failures.

Capen, Bill↗
DOE OSTI2015

Diverging polygon-based modeling (DPBM) of concentrated solar flux distributions

This paper presents an efficient and robust methodology for modeling concentrated solar flux distributions. Compared to ray tracing methods, which provide high accuracy but can be computationally intensive, this approach makes a number of simplifying assumptions in order to reduce complexity by modeling incident and reflected flux as a series of simple geometric diverging polygons, then applying shading and blocking effects. A reduction in processing time (as compared to ray tracing) allows for evaluating and visualizing numerous combinations of engineering and operational variables (easily exceeding 106 unique iterations) to ascertain instantaneous, transient, and annual system performance. The method is demonstrated on a linear Fresnel reflector array and a number of variable iteration examples presented. While some precision is sacrificed for computational speed, flux distributions were compared to ray tracing (SolTrace) and average concentration ratio generally found to agree within ~3%. Furthermore, this method presents a quick and very flexible coarse adjust method for concentrated solar power (CSP) field design, and can be used to both rapidly gain an understanding of system performance as well as to narrow variable constraint windows for follow-on high accuracy system optimization.

14 SOLAR ENERGY↗
DOE OSTI2022

The discovery of a radio galaxy of at least 5 Mpc

Context. Giant radio galaxies (GRGs, or colloquially ‘giants’) are the Universe’s largest structures generated by individual galaxies. They comprise synchrotron-radiating active galactic nucleus ejecta and attain cosmological (megaparsec-scale) lengths. However, the main mechanisms that drive their exceptional growth remain poorly understood. Aims. To deduce the main mechanisms that drive a phenomenon, it is usually instructive to study extreme examples. If there exist host galaxy characteristics that are an important cause for GRG growth, then the hosts of the largest GRGs are likely to possess them. Similarly, if there exist particular large-scale environments that are highly conducive to GRG growth, then the largest GRGs are likely to reside in them. For these reasons, we aim to perform a case study of the largest GRG available. Methods. We reprocessed the LOFAR Two-Metre Sky Survey DR2 by subtracting compact sources and performing multi-scale CLEAN de-convolutions at 60" and 90" resolution. The resulting images constitute the most sensitive survey yet for radio galaxy lobes, whose diffuse nature and steep synchrotron spectra have allowed them to evade previous detection attempts at higher resolution and shorter wavelengths. We visually searched these images for GRGs. Results. We have discovered Alcyoneus, a low-excitation radio galaxy with a projected proper length l p = 4.99 ± 0.04 Mpc. Both its jets and lobes are detected at very high significance, and the SDSS-based identification of the host, at spectroscopic redshift z spec = 0.24674 ± 6 × 10 -5 , is unambiguous. The total luminosity density at ν = 144 MHz is L ν = 8 ± 1 × 10 25 W Hz -1 , which is below average, though near median (percentile 45 ± 3%) for GRGs. The host is an elliptical galaxy with a stellar mass M* = 2.4 ± 0.4 × 10 11 M ⊙ and a super-massive black hole mass M• = 4 ± 2 × 10 8 M ⊙ , both of which tend towards the lower end of their respective GRG distributions (percentiles 25 ± 9% and 23 ± 11%). The host resides in a filament of the Cosmic Web. Through a new Bayesian model for radio galaxy lobes in three dimensions, we estimate the pressures in the megaparsec-cubed-scale northern and southern lobes to be P min,1 = 4.8 ± 0.3 × 10 -16 Pa and P min,2 = 4.9 ± 0.6 × 10 -16 Pa, respectively. The corresponding magnetic field strengths are B min,1 = 46 ± 1 pT and B min,2 = 46 ± 3 pT. Conclusions. We have discovered what is in projection the largest known structure made by a single galaxy – a GRG with a projected proper length l p = 4.99 ± 0.04 Mpc. The true proper length is at least l min = 5.04 ± 0.05 Mpc. Beyond geometry, Alcyoneus and its host are suspiciously ordinary: the total low-frequency luminosity density, stellar mass, and super-massive black hole mass are all lower than, though similar to, those of the medial GRG. Thus, very massive galaxies or central black holes are not necessary to grow large giants, and, if the observed state is representative of the source over its lifetime, neither is high radio power. A low-density environment remains a possible explanation. The source resides in a filament of the Cosmic Web, with which it might have significant thermodynamic interaction. The pressures in the lobes are the lowest hitherto found, and Alcyoneus therefore represents the most promising radio galaxy yet to probe the warm–hot inter-galactic medium.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High-speed characterization of two-phase flow and bubble dynamics in titanium felt porous media for hydrogen production

Bubble dynamics and two-phase flow phenomena are closely related to the performance of proton exchange membrane electrolyzer cells (PEMECs). This paper reports an in-situ study of the oxygen bubble behavior and associated multiphase evolutions in the anode side of PEMECs with titanium (Ti) felt liquid gas diffusion layers (LGDLs) by a high-speed visualization system. The micro oxygen bubble dynamics was captured and analyzed at different locations and virous operating conditions. The results show that the bubble detachment frequency and detachment diameter greatly increase with the operating current density. Additionally, they are significantly impacted by the local pore structure and morphology of Ti felt LGDLs. In the flow channels, there exist only several discrete micro bubbles at a low current density (0.04 A/cm 2 ) and a large flow velocity (133 mm/s). At a current density (0.2 A/cm 2 ) and a flow velocity (67 mm/s), a number of gas slugs are formed in the follow channels, in addition to discrete micro bubbles. At a high current density (1 A/cm 2 ) and a flow velocity (67 mm/s), more bubbles appear in the channel, and the flow field is dominated by slug or annular flows. These investigations can help to better understand the two-phase flow and bubble detachment mechanism, and provide a foundation for electrochemical reaction, multiphase flow studies and optimize the design of gas diffusion layers and flow fields for PEMECs in the future.

08 HYDROGEN↗
DOE OSTI2015

Structural study of growth, orientation and defects characteristics in the functional microelectromechanical system material aluminium nitride

The real structure and morphology of piezoelectric aluminum nitride (AlN) thin films as essential components of magnetoelectric sensors are investigated via advanced transmission electron microscopy methods. State of the art electron diffraction techniques, including precession electron diffraction and automated crystal orientation mapping (ACOM), indicate a columnar growth of the AlN grains optimized for piezoelectric application with a (0 0 0 1) texture. Comparing ACOM with piezoresponse force microscopy measurements, a visual correlation of the structure and the piezoelectric properties is enabled. With a quantitative analysis of the ACOM measurements, a statistical evaluation of grain rotations is performed, indicating the presence of coincidence site lattices with Σ7, Σ13a, Σ13b, Σ25. Using a geometric phase analysis on high resolution micrographs, the occurrence of strain is detected almost exclusively at the grain boundaries. Moreover, high resolution imaging was applied for solving the atomic structure at stacking mismatch boundaries with a displacement vector of 1/2 〈1 0 -1 1〉. All real structural features can be interpreted via simulations based on crystallographic computing in terms of a supercell approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

OLED Lighting in the Offices of DeJoy, Knauf & Blood, LLP (Final Report)

The new offices of DeJoy, Knauf, & Blood, LLP in Rochester NY feature all solid state lighting, with eight different types of organic light-emitting diode luminaires. This was a chance to see and document the performance of an architectural space using OLEDs, in terms of efficacy, color, flicker, light levels, visual comfort, energy use, dimming, and appearance. Although the efficacy ranges of the luminaires are still well below the performance of LEDs, the installation demonstrates a solid state light source that is planar rather than a point source of light. Reported user acceptance was very high. Four luminaires were photometered by an independent laboratory so that future performance could be tracked.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Diagnosis of PV Cell Antireflective Coating Degradation Resulting from Hot-Humid, High Voltage Potential Aging

Corrosion of the antireflective coating on a photovoltaic cell ("ARc corrosion") has previously been observed in studies using hot-humid test conditions with external high-voltage (HV) bias. This study primarily focuses on known vulnerable, legacy aluminum back surface field (Al-BSF) cells in mini-modules (MiMos) put through comparative stepped stress tests. Each cell type had MiMos at +1500 V, -1500 V, or unbiased ("Voc") potential, which were sequentially subjected to test conditions of 60deg C/60% relative humidity (RH) for 96 h, as in International Electrotechnical Commission (IEC) Technical Specification 62804-1; 70 deg C/70% RH for 200 h; and 85 deg C/85% RH for 200 h. Characterizations at each step included visual camera and electroluminescence (EL) imaging, colorimetry, and current-voltage (I-V) curve tracing. Final characterizations included: Suns-Voc; spatial mapping of external quantum efficiency (EQE); high-resolution photoluminescence (PL), EL, and dark lock-in thermography (DLIT) imaging. Forensics were performed on extracted cores, including scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and scanning Auger microscopy (SAM). Forensics were also conducted on MiMos from previous studies that underwent stepped HV aging and separate outdoor aged full-sized modules. ARc corrosion was specifically seen for the glass/encapsulant/cell side of the +1500V (HV+) stressed MiMos and modules. Appearance, color, and reflectance were the most distinguishing characteristics relative to glass corrosion, gridline-corrosion and -delamination as well as other concurrent degradation modes. SEM/EDS and SAM identified conversion of silicon nitride to hydrated silica, hydrous silica, or hydrated amorphous silica, which preferentially occurred at the edges and tips of the pyramidal textured cell surface.

14 SOLAR ENERGY↗
DOE OSTI2019

Fighting Flicker

Thanks in part to the rapid emergence of LED lighting, the phenomenon of flicker has been getting increased attention. All conventional light sources modulate luminous flux and intensity to some degree, usually as a consequence of drawing power from AC mains sources. But whereas conventional light sources have somewhat delayed responses to changes in electrical current – which serves to “cushion” them to some degree against flickering – the LED and its driver respond instantly to such changes, which can result in stark fluctuations in light output. And while these fluctuations may not be noticeable to everyone, they can still result in health and task performance problems. Understanding and countering flicker is essential to the success of high-efficiency lighting technology. That’s why the U.S. Department of Energy (DOE) Solid-State Lighting Program conducts flicker research and is supporting a number of industry committees (CIE 1-83: Visual Aspects of Time-Modulated Lighting Systems; CIE 2-89: Measurement of Temporal Light Modulation of Light Sources and Lighting Systems; and the IES Testing Procedures Committee) that are developing flicker standards for measurement, acceptability, and metrics. A wide variety of technical resources on flicker can be found at https://www.energy.gov/eere/ssl/flicker.

lighting, flicker, flicker meters, Flicker metrics↗
DOE OSTI2019

A Knowledge Graph Approach for the Secondary Use of Cancer Registry Data

Population-based central cancer registries collect valuable structured and unstructured data primarily for cancer surveillance and research, enhancing insights into clinical features associated with cancer occurrence, cancer treatment, and cancer outcomes to guide interventions which reduce the cancer burden. Cancer registries primarily collect data on (1) cancer type (case or tumor); (2) patient demographics such as age, gender, and residential address at time of diagnosis; (3) planned first course of treatment; and (4) date of last contact, vital status, and cause of death. Cancer registry data is dynamic, structured data, which is extracted from many unstructured sources such as electronic healthcare records, and consolidated for reporting and other purposes. While available advanced analytic tools such as SEER*Stat have the ability to build SAS queries, we, however, explore an innovative knowledge graph approach to organizing cancer registry data for advanced analytics and visualization, which has unique advantages over approaches of existing tools. This innovative knowledge graph approach semantically enriches the data and easily enables linkage with third-party data, which can better explain variation in outcomes. We have developed a prototype knowledge graph based on data from the Louisiana Tumor Registry and other publicly available datasets including Behavioral Risk Factor Surveillance System, Clinical Trials, DBpedia, GeoNames, Rural-Urban Continuum Codes, and Semantic MEDLINE. The resource description framework (RDF) data model was selected to represent our knowledge graph, which contains more than 25 billion triples and is ~4TB in storage size. To exhibit the benefits of the knowledge graph approach, we used scenario specific queries, which find the relationships between cancer treatment sequences and outcomes. To illustrate its ease of use in iterative analysis, the knowledge graph was linked to external datasets for performing complex queries across multiple datasets. In addition, we used knowledge graphs to identify data discrepancies and to handle schema changes. Finally, we visualized the knowledge graph to discover data patterns. Our results demonstrate this graph-based solution enables cancer researchers to execute complex queries and more easily perform iterative analyses to improve understanding of cancer registry data. In the future, we would like to use high-performance computing (HPC) resources for faster-generating hypotheses with clinical potential from our knowledge graph.

Hasan, S M Shamimul↗
DOE OSTI2021

Transforming Energy through Computational Science: Computing for Clean Energy

This fact sheet discusses the opportunity space for NREL's computational science capabilities to address national clean energy objectives. Achieving a carbon-free power sector by 2035 as a step towards a decarbonized U.S. energy economy in 2050 will require major advances in power generation, autonomous energy systems, transportation, and buildings/communities. Development of integrated modeling approaches for complex energy systems will be essential for deployment. Success requires developments in optimization and control theory, complemented by machine learning (ML) and artificial intelligence (AI), all of which in turn need targeted investments in breadth and scale of computing. This document defines an opportunity space where: embracing computing can link established research and development (R&D) to a decarbonization agenda; pursuing emerging approaches can accelerate the pace of technology advancement across the portfolio; and leading by example could reduce the carbon footprint of computing worldwide.

advanced computing↗