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DOE OSTI2021

Embodied carbon in the U.S. manufacturing and trade

On average, one quarter of the global carbon footprint is embodied in traded goods. These emissions are a growing issue for global efforts to decarbonize the world economy. Embodied emissions in trade are not accounted for by existing greenhouse gas accounting systems. For example, countries only report their domestic carbon emissions (also known as production-based or territorial accounting) to the Intergovernmental Panel on Climate Change (IPCC). If the embodied carbon in trade were accounted for and reported, the promising climate trends depicted by many countries would be negated or reversed. For example, many achievements of reducing emissions by developed countries under the Kyoto Protocol would actually appear as emissions outsourced to developing countries. Like many other developed countries, the United States is a net importer of embodied greenhouse gas (GHG) emissions in trade. The goal of this study was to calculate the carbon footprint of various sectors of the U.S. economy using the latest available data and based on that, estimate the embodied carbon in certain manufacturing sector products imported and exported by the U.S. First, we conducted an input-output analysis to calculate the carbon footprint of 401 aggregate level sectors of the U.S. economy. After that, for a selected number of manufacturing subsectors /products(hereafter referred to as products),we collected their trade(import/export) data and combined that with their computed carbon footprint to estimate the embodied carbon in import and export of these products for the U.S. While we conducted the carbon footprint analysis for sectors covering the entire U.S. economy (401 sectors), we only conducted the embodied carbon in trade analysis for a few products within specific aggregate level manufacturing sectors,such as computer and electronic product manufacturing (NAICS 334), transport equipment manufacturing (NAICS 336), machinery manufacturing (NAICS 333), and chemical manufacturing (NAICS 325). It is possible to do the detailed embodied carbon analysis for all other products within the various aggregate level manufacturing sectors of the U.S. economy, but given the scope of this study and space constraint of this report, we only present the results for a few products. The results of our analysis for the selected products show the embodied carbon in trade for each product by country and also show the rankings of countries in terms of top importers and exporters of embodied carbon in trade with the U.S. Our results show that in majority of cases, the U.S. is a net importer of embodied carbon in trade. Unless consumption-based accounting is used, the U.S. and other net embodied carbon importing countries may continue to outsource their emissions to meet their climate change mitigation targets under the Paris Agreement, as observed previously with the Kyoto Protocol. Some countries reported reductions that exceeded their Kyoto targets, however, the changes in emissions embodied in imports were comparable to or larger than changes in domestic emissions. Traded emissions undermined emissions reductions in the Kyoto Protocol, and threaten to continue to do so for the Paris Agreement. New climate policies such as California’s Buy Clean act, EU’s proposed border carbon tax adjustment, as well as efforts in the private sector can help address the embodied carbon in traded products.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Data Albums: An Event Driven Search, Aggregation and Curation Tool for Earth Science

One of the largest continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available. Approaches used in Earth science research such as case study analysis and climatology studies involve gathering discovering and gathering diverse data sets and information to support the research goals. Research based on case studies involves a detailed description of specific weather events using data from different sources, to characterize physical processes in play for a specific event. Climatology-based research tends to focus on the representativeness of a given event, by studying the characteristics and distribution of a large number of events. This allows researchers to generalize characteristics such as spatio-temporal distribution, intensity, annual cycle, duration, etc. To gather relevant data and information for case studies and climatology analysis is both tedious and time consuming. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the datasets of interest can obtain the specific files they need using these systems. However, in cases where researchers are interested in studying a significant event, they have to manually assemble a variety of datasets relevant to it by searching the different distributed data systems. In these cases, a search process needs to be organized around the event rather than observing instruments. In addition, the existing data systems assume users have sufficient knowledge regarding the domain vocabulary to be able to effectively utilize their catalogs. These systems do not support new or interdisciplinary researchers who may be unfamiliar with the domain terminology. This paper presents a specialized search, aggregation and curation tool for Earth science to address these existing challenges. The search tool automatically creates curated "Data Albums", aggregated collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; and information about the event contained in news reports, images or videos to supplement research analysis. Curation in the tool is driven via an ontology based relevancy ranking algorithm to filter out non-relevant information and data.

Ramachandran, Rahul↗

DOE OSTI2024

Ligand-Based Compound Activity Prediction via Few-Shot Learning

Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a “few-shot learning” challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2011

Preoperative Versus Postoperative Radiotherapy in Soft-Tissue Sarcoma: Multi-Institutional Analysis of 821 Patients

Purpose: To assess the impact of radiotherapy (RT) sequencing with surgery on overall survival (OS), cause-specific survival (CSS), local failure, and distant failure in soft-tissue sarcoma (STS). Methods and Materials: A retrospective analysis was conducted using the National Oncology Database, a proprietary database of aggregated tumor registries owned by IMPAC Medical Systems (Sunnyvale, CA). Patients with STS of all major anatomic sites who received definitive surgery and either preoperative (preop) or postoperative (postop) RT were included. Patients were also required to have known stage and grade. Prognostic factors for survival were identified using multivariate techniques. Survival was calculated using the Kaplan-Meier method, and compared for statistical significance (p < 0.05) using the log-rank test. Results: A total of 821 patients met inclusion criteria. The median follow-up time was 63 months. Age, stage, histology, gender, tumor size, and RT sequence were independent predictors for OS (p < 0.05). Preop RT was associated with significantly improved OS and CSS compared with postop RT (hazard ratio [HR] = 0.72, 95% confidence interval [CI] 0.56-0.91, p < 0.01, and HR = 0.64, 95% CI 0.46-0.88, p < 0.01, respectively). The 5-year CSS was 79% and 74%, in favor of preop RT (log-rank, p < 0.05). Preop RT was also significantly associated with a reduced risk for local and distant relapse compared with postop RT. Conclusion: Preoperative RT is associated with a reduced cancer-specific mortality compared with postoperative RT in STS. The results of this study may serve as motivation to conduct future prospective studies with larger patient numbers.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

Determining an Imaging Literacy Curriculum for Radiation Oncologists: An International Delphi Study

Purpose: Rapid evolution of imaging technologies and their integration into radiation therapy practice demands that radiation oncology (RO) training curricula be updated. The purpose of this study was to develop an entry-to-practice image literacy competency profile. Methods and Materials: A list of 263 potential imaging competency items were assembled from international objectives of training. Expert panel eliminated redundant or irrelevant items to create a list of 97 unique potential competency items. An international 2-round Delphi process was conducted with experts in RO. In round 1, all experts scored, on a 9-point Likert scale, the degree to which they agreed an item should be included in the competency profile. Items with a mean score ≥7 were included, those 4 to 6 were reviewed in round 2, and items scored <4 were excluded. In round 2, items were discussed and subsequently ranked for inclusion or exclusion in the competency profile. Items with >75% voting for inclusion were included in the final competency profile. Results: Forty-nine radiation oncologists were invited to participate in round 1, and 32 (65%) did so. Participants represented 24 centers in 6 countries. Of the 97 items ranked in round 1, 80 had a mean score ≥7, 1 item had a score <4, and 16 items with a mean score of 4 to 6 were reviewed and rescored in round 2. In round 2, 4 items had >75% of participants voting for inclusion and were included; the remaining 12 were excluded. The final list of 84 items formed the final competency profile. The 84 enabling competency items were aggregated into the following 4 thematic groups of key competencies: (1) imaging fundamentals (42 items); (2) clinical application (27 items); (3) clinical management (5 items); and (4) professional practice (10 items). Conclusions: We present an imaging literacy competency profile which could constitute the minimum training standards in radiation oncology residency programs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2022

Reducing Memory Consumption in Calico with Shared Memory

This document details the work to reduce memory consumption in Calico. Calico is SimTools’ Constructive Solid Geometry (CSG) and geometry painting library. It is primarily used to paint material volume fractions in the Eulerian meshes of the physics codes. Calico provides point-in-body checks for the geometry supplied by an Oso model, which are then aggregated by the host codes. In addition, Calico can be used to build Oso models and is used by Ingen for that purpose. Oso models, and thus Calico, provide support for various CSG primitives such as spheres, cylinders, surfaces generated by rotating tabular curve data, and STL files as well as binary combinations of those primitives. Prior to refactoring Calico will run out of memory on CTS-1 machines when 36 MPI ranks are used per node when reading STL models on the order of 1.5 GB. This limitation is a bottleneck in designer workflow. This problem has been alleviated through the use of data structures to both reduce memory consumption and to leverage MPI-3 shared memory. This report details the data structures targeted for refactoring in Calico, the methods and implementation details for reducing memory consumption and leveraging shared memory, and results for one test problem. Results show a memory reduction when loading a 1 GB STL file by a factor of 27.5, from 93.4 to 3.4 GB.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Anomaly Detection in Multiple Scale for Insider Threat Analysis

We propose a method to quantify malicious insider activity with statistical and graph-based analysis aided with semantic scoring rules. Different types of personal activities or interactions are monitored to form a set of directed weighted graphs. The semantic scoring rules assign higher scores for the events more significant and suspicious. Then we build personal activity profiles in the form of score tables. Profiles are created in multiple scales where the low level profiles are aggregated toward more stable higherlevel profiles within the subject or object hierarchy. Further, the profiles are created in different time scales such as day, week, or month. During operation, the insider s current activity profile is compared to the historical profiles to produce an anomaly score. For each subject with a high anomaly score, a subgraph of connected subjects is extracted to look for any related score movement. Finally the subjects are ranked by their anomaly scores to help the analysts focus on high-scored subjects. The threat-ranking component supports the interaction between the User Dashboard and the Insider Threat Knowledge Base portal. The portal includes a repository for historical results, i.e., adjudicated cases containing all of the information first presented to the user and including any additional insights to help the analysts. In this paper we show the framework of the proposed system and the operational algorithms.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2012

Building Agent™ Software (BA) [SWRs 12-05 and 17-21]

Energy interfaces provide a new level of motivational information. Many studies have reported energy savings after dashboards for operators are installed, but these provide little actionable information to the occupant and the occupant cannot report individual comfort. The Building Agent™ (BA), in development at the National Renewable Energy Laboratory (NREL), provides an interface for the occupant to converse with the building control system and aims to actively engage and affect occupant behavior to achieve positive energy outcomes. Energy savings achieved via occupant behavioral changes could be as high as 25%, and could rival savings achieved by technological measures. For example, energy audits often show that more than 50% of the energy used in a building is consumed at night. Occupants leaving equipment and lighting on after they leave work is a large contributing factor. Similar savings opportunities have been identified when occupants were away from their desks 50% of the time, while most of their computers, lights, and other equipment remained powered on. Building Agent aims to create cohesion between building automation system (BAS) data, local measurements, and occupant feedback to provide visualizations, empowering occupants and building engineers to take diagnostic, proactive, energy-saving, and comfort-improving actions. The multilayer BA architecture consists of central server, measurement, and distributed application layers. The server layer aggregates global and localized measurements with occupant feedback to correlate perceived comfort with actual data throughout the building. The measurement layer complements BAS data with localized, environmental condition measurements at workstations. The application layer provides a bidirectional communications mechanism between the building and the occupants that accepts comfort feedback and provides energy performance information. The interface layer also uses three displays to influence building energy use: empathetic and personalized gauges, graphical data, and competitive rankings. Building Agent was implemented at NREL in the Research Support Facility (RSF), a LEED Platinum and Net Zero office building, which has received many awards. The constant feedback from occupants has enabled BA to better enhance the BAS data controls. Continued work is being done on BA to increase visualization of data and enhanced the user experience.

Long, Nicholas↗

DOE OSTI2017

Polyamide-crosslinked graphene oxide membrane for forward osmosis

We report a novel polyamide-graphene oxide (PA-GO) membrane was synthesized on a polyethersulfone support by first intra-crosslinking GO aggregates via m-xylylenediamine (MXDA) and then inter-crosslinking GO aggregates via trimethyle chloride (TMC). This method allows for the use of hydrophilic, more porous supports in forward osmosis (FO) membranes, thereby lending much flexibility to membrane synthesis and also potentially reducing internal concentration polarization in FO. The elemental composition, morphology, and hydrophilicity of the synthesized PA-GO membrane were characterized to confirm intra- and inter-crosslinking reactions and understand membrane properties. It was found that the mixing temperature of MXDA and GO should be controlled below 20 °C to avoid the formation of large GO aggregates and hence reduce membrane heterogeneity and defects. The performance of the PA-GO membrane was compared with that of a commercial FO membrane using trisodium citrate (TSC), Na 2 SO 4 , and MgCl 2 , respectively, as draw solutes. Under the same osmotic pressure, the water flux of the PA-GO membrane was the highest with TSC as draw solute, moderate with Na 2 SO 4 , and almost zero with MgCl 2 , while the ranking of solute fluxes followed a reverse order, revealing that an effective solute barrier plays an important role in making FO membranes with high water flux and low solute flux.

42 ENGINEERING↗

DOE OSTI2022

A multi-criteria CCUS screening evaluation of the Gulf of Mexico, USA

Continued research into reservoir characterization along with offshore carbon dioxide (CO 2 ) transportation and infrastructure assets is needed to facilitate development of safe and successful carbon capture, utilization, and storage (CCUS) projects. This paper outlines a multi-criteria evaluation methodology that incorporates disparate sets of quantitative, spatially variable data into a decision-making framework for screening the Gulf of Mexico (GOM) outer continental shelf (OCS) for potentially viable CO 2 storage and enhanced oil recovery (EOR) sites. Criteria categories include favorable geologic characteristics, logistics, and potential risks. Data compiled for 14 criteria from several publicly available geographic information system (GIS) layers was aggregated over 2559 spatially balanced points across the study area using the National Energy Technology Laboratory (NETL)-developed Cumulative Spatial Impact Layers™ (CSIL) GIS tool. Criteria are weighted by qualitative expert opinion relative to their perceived importance to given scenarios— the output of combined criteria values and weights enables regional CO 2 storage suitability differentiation. The methodology considers both technical and non-technical factors impacting CCUS decision-making. The flexible methodology enables a systematic approach to regional ranking at high spatial resolution over a large study domain. Additionally, the framework enables high-grading of priority sites that warrant further characterization and follow-on analysis. Areas along the Louisiana coast and Mississippi River Delta consistently rank high for all scenarios largely a result of the favorable geology with the potential for stacked storage, as well as the density of existing pipelines and platforms, and proximity to several onshore CO 2 sources. High-graded regions for the CO 2 EOR-related scenarios are typically located further offshore towards the middle and edge of the OCS compared to higher priority regions for the geologic storage scenarios which fall closer to the Louisiana coastline.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Aggregation Tool to Create Curated Data albums to Support Disaster Recovery and Response

Despite advances in science and technology of prediction and simulation of natural hazards, losses incurred due to natural disasters keep growing every year. Natural disasters cause more economic losses as compared to anthropogenic disasters. Economic losses due to natural hazards are estimated to be around $6-$10 billion dollars annually for the U.S. and this number keeps increasing every year. This increase has been attributed to population growth and migration to more hazard prone locations such as coasts. As this trend continues, in concert with shifts in weather patterns caused by climate change, it is anticipated that losses associated with natural disasters will keep growing substantially. One of challenges disaster response and recovery analysts face is to quickly find, access and utilize a vast variety of relevant geospatial data collected by different federal agencies such as DoD, NASA, NOAA, EPA, USGS etc. Some examples of these data sets include high spatio-temporal resolution multi/hyperspectral satellite imagery, model prediction outputs from weather models, latest radar scans, measurements from an array of sensor networks such as Integrated Ocean Observing System etc. More often analysts may be familiar with limited, but specific datasets and are often unaware of or unfamiliar with a large quantity of other useful resources. Finding airborne or satellite data useful to a natural disaster event often requires a time consuming search through web pages and data archives. Additional information related to damages, deaths, and injuries requires extensive online searches for news reports and official report summaries. An analyst must also sift through vast amounts of potentially useful digital information captured by the general public such as geo-tagged photos, videos and real time damage updates within twitter feeds. Collecting and aggregating these information fragments can provide useful information in assessing damage in real time and help direct recovery efforts. The search process for the analyst could be made much more efficient and productive if a tool could go beyond a typical search engine and provide not just links to web sites but actual links to specific data relevant to the natural disaster, parse unstructured reports for useful information nuggets, as well as gather other related reports, summaries, news stories, and images. This presentation will describe a semantic aggregation tool developed to address similar problem for Earth Science researchers. This tool provides automated curation, and creates "Data Albums" to support case studies. The generated "Data Albums" are compiled collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; information about the event contained in news reports, and images or videos to supplement research analysis. An ontology-based relevancy-ranking algorithm drives the curation of relevant data sets for a given event. This tool is now being used to generate a catalog of Hurricane Case Studies at Global Hydrology Resource Center (GHRC), one of NASA's Distribute Active Archive Centers. Another instance of the Data Albums tool is currently being created in collaboration with NASA/MSFC's SPoRT Center, which conducts research on unique NASA products and capabilities that can be transitioned to the operational community to solve forecast problems. This new instance focuses on severe weather to support SPoRT researchers in their model evaluation studies

Ramachandran, Rahul↗

NASA NTRS1993

Screening of solar cells

Because solar cells in a production batch are not identical, screening is performed to obtain similar cells for aggregation into arrays. A common technique for screening is based on a single operating point of the I-V characteristic of the cell, usually the maximum power point. As a result, inferior cell matching may occur at the actual operating points. Screening solar cells based on the entire I-V characteristic will inherently result in more similar cells in the array. An array consisting of more similar cells is likely to have better overall characteristics and more predictable performance. Solar cell screening methods and cell ranking are discussed. The concept of a mean cell is defined as a cell 'best' representing all the cells in the production batch. The screening and ranking of all cells are performed with respect to the mean cell. The comparative results of different screening methods are illustrated on a batch of 50 silicon cells of the Space Station Freedom.

Appelbaum, J.↗

DOE OSTI2018

Submicron-scale mineralogy of lithotypes and the implications for trace element associations: Blue Gem coal, Knox County, Kentucky

Transmission electron microscopy accompanied by energy-dispersive spectroscopy and selected area electron diffraction of density-gradient separates from two lithotypes of the low-ash, low-sulfur Blue Gem coal, eastern Kentucky, revealed an array of previously unrecognized (in this coal, and arguable in most others) sub-micron minerals, some <10 nm in size. The first sample representing the 1.22–1.24 specific gravity fraction of the middle bench contains a mineral identified as a La-, Ce-, Nd-bearing monazite; other minerals with CrFe, CuFeS, FeZn-S, and Pb; and areas, probably comprising agglomerates of several grains, if not several minerals, with concentrations of Mg, Ca, Ti, Fe, Zn, Zr, and Mo. The second sample representing the 1.30–1.31 specific gravity fraction of the basal lithotype has aggregates of particles enriched in Mg, Ca, Ti, and Fe. Individual grains not specifically quantified include CrNiMnCuFeS, AgS, and CuS. Detailed investigation of one area (most of the variation within a <4 μm 2 region) demonstrates the presence of greenockite (CdS); minute phases containing NiCoGe and AgCdBi, the latter with a more evident S association than the former; metallic Bi; nisnite (Ni 3 Sn); silver cadmium; manganosite (MnO); and siderite. Some minerals, such as the monazite, are most likely of detrital or tuffaceous origin. Many of the other assemblages could be of hydrothermal origin, a hypothesis supported by known regional geochemical and coal rank trends, but not previously demonstrated in mineral assemblages at the 10's of nm scale in this region.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS2014

Use of Semantic Technology to Create Curated Data Albums

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discover tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out nonrelevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science.

Ramachandran, Rahul↗

NASA NTRS2014

Use of Semantic Technology to Create Curated Data Albums

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discovery tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out non-relevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science.

Ramachandran, Rahul↗

DOE OSTI2018

Deploy threading in Nalu solver stack

The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary code in the ExaWind project is Nalu, which is an unstructured-grid solver for the acousticallyincompressible Navier-Stokes equations, and mass continuity is maintained through pressure projection. The model consists of the mass-continuity Poisson-type equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as re-initialization of matrices and re-computation of preconditioners is required at every time step In this Milestone, we examine the effect of threading on the solver stack performance against flat-MPI results obtained from previous milestones using Haswell performance data full-turbine simulations. Whereas the momentum equations are solved only with the Trilinos solvers, we investigate two algebraic-multigrid preconditioners for the continuity equations: Trilinos/Muelu and HYPRE/BoomerAMG. These two packages embody smoothed-aggregation and classical Ruge-Stiiben AMG methods, respectively. In our FY18 Q2 report, we described our efforts to improve setup and solve of the continuity equations under flat-MPI parallelism. While significant improvement was demonstrated in the solve phase, setup times remained larger than expected. Starting with the optimized settings described in the Q2 report, we explore here simulation performance where OpenMP threading is employed in the solver stack. For Trilinos, threading is acheived through the Kokkos abstraction where, whereas HYPRE/BoomerAMG employs straight OpenMP. We examined results for our mid-resolution baseline turbine simulation configuration (229M DOF). Simulations on 2048 Haswell cores explored the effect of decreasing the number of MPI ranks while increasing the number of threads. Both HYPRE and Trilinos exhibited similar overal solution times, and both showed dramatic increases in simulation time in the shift from MPI ranks to OpenMP threads. This increase is attributed to the large amount of work per MPI rank starting at the single-thread configuration. Decreasing MPI ranks, while increasing threads, may be increasing simulation time due to thread synchronization and start-up overhead contributing to the latency and serial time in the model. These result showed that an MPI+OpenMP parallel decomposition will be more effective as the amount per MPI rank computation per MPI rank decreases and the communication latency increases. This idea was demonstrated in a strong scaling study of our low-resolution baseline model (29M DOF) with the Trilinos-HYPRE configuration. While MPI-only results showed scaling improvement out to about 1536 cores, engaging threading carried scaling improvements out to 4128 cores — roughly 7000 DOF per core. This is an important result as improved strong scaling is needed for simulations to be executed over sufficiently long simulated durations (i.e., for many timesteps). In addition to threading work described above, the team examined solver-performance improvements by exploring communication-overhead in the HYPRE-GMRES implementation through a communicationoptimal- GMRE algorithm (CO-GMRES), and offloading compute-intensive solver actions to GPUs. To those ends, a HYPRE mini-app was allow us to easily test different solver approaches and HYPRE parameter settings without running the entire Nalu code. With GPU acceleration on the Summitdev supercomputer, a 20x speedup was achieved for the overall preconditioner and solver execution time for the mini-app. A study on Haswell processors showed that CO-GMRES provides benefits as one increases MPI ranks.

17 WIND ENERGY↗

DOE OSTI2012

In Vitro Phototoxicity and Hazard Identification of Nano-scale Titanium Dioxide

Titanium dioxide nanoparticles (nano-TiO{sub 2}) catalyze reactions under UV radiation and are hypothesized to cause phototoxicity. A human-derived line of retinal pigment epithelial cells (ARPE-19) was treated with six samples of nano-TiO{sub 2} and exposed to UVA radiation. The TiO{sub 2} nanoparticles were independently characterized to have mean primary particle sizes and crystal structures of 22 nm anatase/rutile, 25 nm anatase, 31 nm anatase/rutile, 59 nm anatase/rutile, 142 nm anatase, and 214 nm rutile. Particles were suspended in cell culture media, sonicated, and assessed for stability and aggregation by dynamic light scattering. Cells were treated with 0, 0.3, 1, 3, 10, 30, or 100 μg/ml nano-TiO{sub 2} in media for 24 hrs and then exposed to UVA (2 hrs, 7.53 J/cm{sup 2}) or kept in the dark. Viability was assessed 24 hrs after the end of UVA exposure by microscopy with a live/dead assay (calcein-AM/propidium iodide). Exposure to higher concentrations of nano-TiO{sub 2} with UVA lowered cell viability. The 25 nm anatase and 31 nm anatase/rutile were the most phototoxic (LC{sub 50} with UVA < 5 μg/ml), while the 142 nm anatase and 214 nm rutile were the least phototoxic. An acellular assay ranked TiO{sub 2} nanoparticles for their UVA photocatalytic reactivities. The particles were found to be capable of generating thiobarbituric acid reactive substances (TBARS) under UVA. Flow cytometry showed that nano-TiO{sub 2} combined with UVA decreased cell viability and increased the generation of reactive oxygen species (ROS, measured by Mitosox). LC{sub 50} values under UVA were correlated with TBARS reactivity, particle size, and surface area. -- Highlights: ► Nano-TiO{sub 2} enters cells within 24 hours ► Nano-TiO{sub 2} causes dose-dependent cytotoxicity greatly enhanced by UVA radiation ► Treatment with nano-TiO{sub 2} and UVA produces reactive oxygen species ► Phototoxicity is correlated with particle size, surface area, and TBARS reactivity.

60 APPLIED LIFE SCIENCES↗