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At least 775 records · Page 43

PV Reliability Operations and Maintenance (PVROM) Database Initiative: 2014 Project Report

To fill a major knowledge gap, Sandia National Laboratories (SNL) and the Electric Power Research Institute (EPRI) are jointly engaged in a multi-year research effort, supported by the Department of Energy’s SunShot Program, to examine real-world photovoltaic (PV) plant reliability and performance. Findings and analyses, derived from field data documented in the PV Reliability Operations Maintenance (PVROM) database tool as well as from convened workshops and working group discussions, are intended to inform industry best practices around the optimal operations and maintenance (O&M) of solar PV assets. To improve upon and evolve existing solar PV O&M approaches, this report: 1. Provides perspective on the concept of PV “system” reliability and how it can inform plant design, operations, and maintenance decisions that produce better long-term outcomes; 2. Describes the PVROM data collection tool, its technical capabilities, and results generated from database content in 2014; 3. Presents ongoing research efforts that are meant to drive the solar industry toward PV O&M best practice protocols and standards; and 4. Reflects on future areas of inquiry that can help better forecast plant health (e.g., system component availability, component wear out, etc.) and associated lifecycle costs. Ultimately, this report adds to the knowledge base of improving PV system O&M activities by discussing data collection and analysis techniques that can be used to better understand and enhance the reliability, availability, and performance of a photovoltaic system.

14 SOLAR ENERGY↗

Calculating a checksum with inactive networking components in a computing system

Calculating a checksum utilizing inactive networking components in a computing system, including: identifying, by a checksum distribution manager, an inactive networking component, wherein the inactive networking component includes a checksum calculation engine for computing a checksum; sending, to the inactive networking component by the checksum distribution manager, metadata describing a block of data to be transmitted by an active networking component; calculating, by the inactive networking component, a checksum for the block of data; transmitting, to the checksum distribution manager from the inactive networking component, the checksum for the block of data; and sending, by the active networking component, a data communications message that includes the block of data and the checksum for the block of data.

97 MATHEMATICS AND COMPUTING↗

Calculating a checksum with inactive networking components in a computing system

Calculating a checksum utilizing inactive networking components in a computing system, including: identifying, by a checksum distribution manager, an inactive networking component, wherein the inactive networking component includes a checksum calculation engine for computing a checksum; sending, to the inactive networking component by the checksum distribution manager, metadata describing a block of data to be transmitted by an active networking component; calculating, by the inactive networking component, a checksum for the block of data; transmitting, to the checksum distribution manager from the inactive networking component, the checksum for the block of data; and sending, by the active networking component, a data communications message that includes the block of data and the checksum for the block of data.

97 MATHEMATICS AND COMPUTING↗

Survey of emerging nuclear data needs for nonproliferation applications with advanced reactors

Nuclear science plays a key role in non-proliferation activities supporting advanced reactor technologies. Nuclear data underpin predictions and interpretations of nuclear material behavior and signatures in reactor fuel production, use, transport, and storage. Advanced reactors provide new challenges compared to the current fleet of thermal fission reactors. This report consolidates reported nuclear data needs from representative workshops, conferences, and publications, identifying six themes for recommended future investments supporting non-proliferation and safeguards applications. While also identified as data needs, major fission product evaluations and (α,n) reactions were omitted as there are ongoing activities producing new data under NA22/Objective O. Each theme is summarized below with example data and association with the nonproliferation mission for advanced fuels and reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Eriodictyol-7-O-glucoside activates Nrf2 and protects against cerebral ischemic injury

Stroke is a complex disease that may involve oxidative stress-related pathways in its pathogenesis. The nuclear factor erythroid-2-related factor 2/antioxidant response element (Nrf2/ARE) pathway plays an important role in inducing phase II detoxifying enzymes and antioxidant proteins and thus has been considered a potential target for neuroprotection in stroke. The aim of the present study was to determine whether eriodictyol-7-O-glucoside (E7G), a novel Nrf2 activator, can protect against cerebral ischemic injury and to understand the role of the Nrf2/ARE pathway in neuroprotection. In primary cultured astrocytes, E7G increased the nuclear localization of Nrf2 and induced the expression of the Nrf2/ARE-dependent genes. Exposure of astrocytes to E7G provided protection against oxygen and glucose deprivation (OGD)-induced oxidative insult. The protective effect of E7G was abolished by RNA interference-mediated knockdown of Nrf2 expression. In vivo administration of E7G in a rat model of focal cerebral ischemia significantly reduced the amount of brain damage and ameliorated neurological deficits. These data demonstrate that activation of Nrf2/ARE signaling by E7G is directly associated with its neuroprotection against oxidative stress-induced ischemic injury and suggest that targeting the Nrf2/ARE pathway may be a promising approach for therapeutic intervention in stroke. - Highlights: • E7G activates Nrf2 in astrocytes. • E7G stimulates expression of Nrf2-mediated cytoprotective proteins in astrocytes. • E7G protects astrocytes against OGD-induced cell death and apoptosis. • The neuroprotective effect of E7G involves the Nrf2/ARE pathway. • E7G protects rats against cerebral ischemic injury.

60 APPLIED LIFE SCIENCES↗

Demonstration of Active Power Controls by Utility-Scale PV Power Plant in an Island Grid: Preprint

The National Renewable Energy Laboratory (NREL), AES, and the Puerto Rico Electric Power Authority conducted a demonstration project on a utility-scale photovoltaic (PV) plant to test the viability of providing important ancillary services from this facility. As solar generation increases globally, there is a need for innovation and increased operational flexibility. A typical PV power plant consists of multiple power electronic inverters and can contribute to grid stability and reliability through sophisticated 'grid-friendly' controls. In this way, it may mitigate the impact of its variability on the grid and contribute to important system requirements more like traditional generators. In 2015, testing was completed on a 20-MW AES plant in Puerto Rico, and a large amount of test data was produced and analyzed that demonstrates the ability of PV power plants to provide various types of new grid-friendly controls. This data showed how active power controls can leverage PV's value from being simply an intermittent energy resource to providing additional ancillary services for an isolated island grid. Specifically, the tests conducted included PV plant participation in automatic generation control, provision of droop response, and fast frequency response.

14 SOLAR ENERGY↗

Endpoint-based parallel data processing with non-blocking collective instructions in a parallel active messaging interface of a parallel computer

Methods, apparatuses, and computer program products for endpoint-based parallel data processing with non-blocking collective instructions in a parallel active messaging interface (`PAMI`) of a parallel computer are provided. Embodiments include establishing by a parallel application a data communications geometry, the geometry specifying a set of endpoints that are used in collective operations of the PAMI, including associating with the geometry a list of collective algorithms valid for use with the endpoints of the geometry. Embodiments also include registering in each endpoint in the geometry a dispatch callback function for a collective operation and executing without blocking, through a single one of the endpoints in the geometry, an instruction for the collective operation.

97 MATHEMATICS AND COMPUTING↗

New developments in cost modeling for the LHC computing

The increase in the scale of LHC computing during Run 3 and Run 4 (HL-LHC) will certainly require radical changes to the computing models and the data processing of the LHC experiments. The working group established by WLCG and the HEP Software Foundation to investigate all aspects of the cost of computing and how to optimise them has continued producing results and improving our understanding of this process. In particular, experiments have developed more sophisticated ways to calculate their resource needs, we have a much more detailed process to calculate infrastructure costs. This includes stud-ies on the impact of HPC and GPU based resources on meeting the computing demands. We have also developed and perfected tools to quantitatively study the performance of experiments workloads and we are actively collaborating with other activities related to data access, benchmarking and technology cost evolution. In this contribution we expose our recent developments and results and outline the directions of future work.

97 MATHEMATICS AND COMPUTING↗

Marine Corps Base Camp Lejeune Shorebird foraging data

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of development pressures in surrounding areas, impairments due to other anthropogenic disturbances, and increasing requirements for compliance with environmental regulations. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and to optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a minimum 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the first six years of the program (DCERP1) are presented here.The overarching objectives of DCERP are to: (1) understand the effects of military training activities, infrastructure development, and other coastal military installations; (2) develop models, tools, and indicators to evaluate ecosystem health; and (3) recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of development pressures in surrounding areas, impairments due to other anthropogenic disturbances, and increasing requirements for compliance with environmental regulations. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and to optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a minimum 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the first six years of the program (DCERP1) are presented here.The overarching objectives of DCERP are to: (1) understand the effects of military training activities, infrastructure development, and other coastal military installations; (2) develop models, tools, and indicators to evaluate ecosystem health; and (3) recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package contains a raw dataset for paired logistic regression examining habitat and prey variables affecting foraging site selection vs a non-used site.

54 ENVIRONMENTAL SCIENCES↗

Basal Plane Hydrogen Evolution Activity from Mixed Metal Nitride MXenes Measured by Scanning Electrochemical Microscopy

2D early transition metal carbide and nitride MXenes have intriguing properties for electrochemical energy storage and electrocatalysis. These properties can be manipulated by modifying the basal plane chemistry. Here, mixed transition metal nitride MXenes, M-Ti 4 N 3 T x (M = V, Cr, Mo, or Mn; T x = O and/or OH), are developed by modifying pristine exfoliated Ti 4 N 3 T x MXene with V, Cr, Mo, and Mn salts using a simple solution-based method. The resulting mixed transition metal nitride MXenes contain 6–51% metal loading (cf. Ti) that exhibit rich electrochemistry including highly tunable hydrogen evolution reaction (HER) electrocatalytic activity in a 0.5 m H 2 SO 4 electrolyte as follows: V-Ti 4 N 3 T x > Cr-Ti 4 N 3 T x > Mo-Ti 4 N 3 T x > Mn-Ti 4 N 3 T x > pristine Ti 4 N 3 T x with overpotentials as low as 330 mV at -10 mA cm -2 with a charge-transfer resistance of 70 O. Scanning electrochemical microscopy (SECM) reveals the electrochemical activity of individual MXene flakes. The SECM data corroborate the bulk HER activity trend for M-Ti 4 N 3 T x as well as provide the first experimental evidence that HER results from catalysis on the MXene basal plane. These electrocatalytic results demonstrate a new pathway to tune the electrochemical properties of MXenes for water splitting and related electrochemical applications.

2D materials↗

Enhancing Clean Energy Innovation Ecosystem Discovery Tool

During March 2016, the DOE’s Office of Energy Policy and Systems Analysis (EPSA) sought a methodology to identify and quantify the strength of the existing clean energy innovation ecosystems (IE) in the U.S. and their characteristics. Oak Ridge National Lab (ORNL) did a pilot study (Phase 1) and demonstrated the feasibility of an application comprised of natural language processing, link analysis, and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography. The data-collection, ingest and analytics pipelines were combined with an advanced user interface, together known as the Ecosystem Discovery Tool, to enhance DOE’s understanding of existing geographic innovation clusters. During Phase 2 of this project, this tool has been further enhanced by integrating new data sets and through backend software architecture changes, validation and bug fixing, making it a much more robust and powerful application. The tool’s user interface is a lot more intuitive, which enables a user to visualize the IE rankings for various geographic regions(CBSA, state level) for different clean energy technologies and seek automated insights. ORNL also created a DOE-specific dashboard that allows the user to visualize and analyze federal funding from various offices including Energy Efficiency and Renewable Energy(EERE), Office of Electricity(OE), Fossil Energy(FE), and Nuclear Energy(NE). During Phase 1 of this project, EPSA defined a clean energy innovation ecosystem as the overlap of five Ecosystem Components: 1) nascent clean energy Indicators, 2) investors, 3) enabling environment, 4) networking assets and 5) large companies. EPSA and ORNL worked together to collect data for each component: 1) small and medium companies, ARPA-E awardees, SBIR awardees, patents, publications, ERCs for Nascent Clean Energy Indicators; 2) qualified investors; 3) the Clean Edge Policy Index/DSIRE for the Enabling Environment; 4) universities, national laboratories, ERCs and incubators for Networking Assets; and 5) large companies and a subset of the Russell 1000 list for Large Companies. The ORNL team created a visual tool based on Tableau that integrated ecosystem component data to score, rank and map IEs. The tool was created with the flexibility to allow the user to choose the weights of each of the five ecosystem components and the subcomponents. This flexibility allows the user to visualize different subsets and to use the underlying data for different types of analysis. During Phase 2 of this project, the backend database and computation process have undergone major changes to provide a much more refined user-interface and added new features into the scoring algorithm. This phase also involved addition of newer databases such as a list of Energy Research Centers (ERC) ,DSIRE, and funding data from EERE, FE, NE, and OE to provide more granular information and more accurate/better quantification.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

54 ENVIRONMENTAL SCIENCES↗

The CMS RPC system readiness for LHC Run-3 data taking

During Run-3, the LHC is preparing to deliver instantaneous luminosity in the range from 5 × 10 34 cm -2 s -1 to 7.5 × 10 34 cm -2 s -1 . To ensure stable data taking, providing redundant information for robust muon triggering, reconstruction and identification, the CMS RPC collaboration has used the opportunity given by the LHC long shutdown 2 (LS2), to perform a series of maintenance and preparation activities for the new data taking period. The overall performance of the RPC system after the LS2 commissioning period and the activities in preparation for future data taking will be presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Pooling the resources of the CMS Tier-1 sites

The CMS experiment at the LHC relies on 7 Tier-1 centres of the WLCG to perform the majority of its bulk processing activity, and to archive its data. During the first run of the LHC, these two functions were tightly coupled as each Tier-1 was constrained to process only the data archived on its hierarchical storage. This lack of flexibility in the assignment of processing workflows occasionally resulted in uneven resource utilisation and in an increased latency in the delivery of the results to the physics community.The long shutdown of the LHC in 2013-2014 was an opportunity to revisit this mode of operations, disentangling the processing and archive functionalities of the Tier-1 centres. The storage services at the Tier-1s were redeployed breaking the traditional hierarchical model: each site now provides a large disk storage to host input and output data for processing, and an independent tape storage used exclusively for archiving. Movement of data between the tape and disk endpoints is not automated, but triggered externally through the WLCG transfer management systems.With this new setup, CMS operations actively controls at any time which data is available on disk for processing and which data should be sent to archive. Thanks to the high-bandwidth connectivity guaranteed by the LHCOPN, input data can be freely transferred between disk endpoints as needed to take advantage of free CPU, turning the Tier-1s into a large pool of shared resources. The output data can be validated before archiving them permanently, and temporary data formats can be produced without wasting valuable tape resources. Lastly, the data hosted on disk at Tier-1s can now be made available also for user analysis since there is no risk any longer of triggering chaotic staging from tape.In this contribution, we describe the technical solutions adopted for the new disk and tape endpoints at the sites, and we report on the commissioning and scale testing of the service. We detail the procedures implemented by CMS computing operations to actively manage data on disk at Tier-1 sites, and we give examples of the benefits brought to CMS workflows by the additional flexibility of the new system.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

INL Fleet Vehicle Characterization Study for the U.S. Department of Navy

Battelle Energy Alliance, LLC, managing and operating contractor for the U.S. Department of Energy’s Idaho National Laboratory, is the lead laboratory for U.S. Department of Energy Advanced Vehicle Testing. Battelle Energy Alliance, LLC collected and evaluated data on federal fleet operations as part of the Advanced Vehicle Testing Activity’s Federal Fleet Vehicle Data Logging and Characterization Study. The Advanced Vehicle Testing Activity’s study seeks to collect and evaluate data to validate use of advanced plug-in electric vehicle (PEV) transportation. This report focuses on US Department of Navy's fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of PEVs into the agency’s fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle or plug-in hybrid electric vehicle (collectively referred to as PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗

Expanding on Successful Concepts, Models, and Organization

In her letter to the editor1 regarding our recent Feature Article “Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework” 2, Dr. von Göetz expressed several concerns about terminology, and the perception that we propose the replacement of successful approaches and models for exposure assessment with a concept. We are glad to have the opportunity to address these issues here. If the goal of the AEP framework was to replace existing exposure models or databases for organizing exposure data with a concept, we would share Dr. von Göetz concerns. Instead, the outcome we promote is broader use of an organizational framework for exposure science. The framework would support improved generation, organization, and interpretation of data as well as modeling and prediction, not replacement of models. The field of toxicology has seen the benefits of wide use of one or more organizational frameworks (e.g., mode and mechanism of action, adverse outcome pathway). These frameworks influence how experiments are designed, data are collected, curated, stored and interpreted and ultimately how data are used in risk assessment. Exposure science is poised to similarly benefit from broader use of a parallel organizational framework, which Dr. von Göetz correctly points out, is currently used in the exposure modeling community. In our view, the concepts used so effectively in the exposure modeling community, expanded upon in the AEP framework, could see wider adoption by the field as a whole. The value of such a framework was recognized by the National Academy of Sciences.3 Replacement of models, databases, or any application with the AEP framework was not proposed in our article. The positive role broader more consistent use of such a framework might have in enabling and advancing “general activities such as data acquisition, organization…,” and exposure modeling was discussed in some detail. Like Dr. von Göetz, we recognized the challenges associated with acceptance of the terminology, definitions, and structure proposed in the paper. To address these challenges, an expert workshop was held in May, 2016 to consider and revise the “basic elements” outlined in the paper. The attendees produced revisions to the terminology (e.g., key events) that align with terminology currently in use in the field. We were also careful in our paper to acknowledge a point raised by Dr. von Göetz, that the term AEP implies aggregation, providing these clarifications: “The simplest form of an AEP represents a single source and a single pathway and may more commonly be referred to as an exposure pathway,”; and “An aggregate exposure pathway may represent multiple sources and transfer through single pathways to the TSE, single sources and transfer through multiple pathways to the target site exposure (TSE), or any combination of these.” These clarifications address the concern that the AEP term is not accurate or logical, and further expands upon the word “aggregate” in a broader context. Our use of AEP is consistent with the definition for “aggregate exposure”, which refers to the combined exposures to a single chemical across multiple routes and pathways.3 The AEP framework embraces existing methods for collection, prediction, organization, and interpretation of human and ecological exposure data cited by Dr. von Göetz. We remain hopeful that wider recognition and use of an organizing concept for exposure information across the exposure science, toxicology and epidemiology communities advances the development of the kind of infrastructure and models Dr. von Göetz discusses. This outcome would be a step forward, rather than a step backward.

exposure science↗

Conformational Changes and Substrate Recognition in Pseudomonas aeruginosa d-Arginine Dehydrogenase

DADH catalyzes the flavin-dependent oxidative deamination of D-amino acids to the corresponding {alpha}-keto acids and ammonia. Here we report the first X-ray crystal structures of DADH at 1.06 {angstrom} resolution and its complexes with iminoarginine (DADH{sub red}/iminoarginine) and iminohistidine (DADH{sub red}/iminohistidine) at 1.30 {angstrom} resolution. The DADH crystal structure comprises an unliganded conformation and a product-bound conformation, which is almost identical to the DADH{sub red}/iminoarginine crystal structure. The active site of DADH was partially occupied with iminoarginine product (30% occupancy) that interacts with Tyr53 in the minor conformation of a surface loop. This flexible loop forms an 'active site lid', similar to those seen in other enzymes, and may play an essential role in substrate recognition. The guanidinium side chain of iminoarginine forms a hydrogen bond interaction with the hydroxyl of Thr50 and an ionic interaction with Glu87. In the structure of DADH in complex with iminohistidine, two alternate conformations were observed for iminohistidine where the imidazole groups formed hydrogen bond interactions with the side chains of His48 and Thr50 and either Glu87 or Gln336. The different interactions and very distinct binding modes observed for iminoarginine and iminohistidine are consistent with the 1000-fold difference in k{sub cat}/K{sub m} values for D-arginine and D-histidine. Comparison of the kinetic data for the activity of DADH on different D-amino acids and the crystal structures in complex with iminoarginine and iminohistidine establishes that this enzyme is characterized by relatively broad substrate specificity, being able to oxidize positively charged and large hydrophobic D-amino acids bound within a flask-like cavity.

08 HYDROGEN↗