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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

ResStock™ v3.2.0 [SWR-19-15 and SWR-20-07]

The ResStock™ analysis tool was built on NREL's OpenStudio® platform, and is a project geared at modeling existing residential building stocks at national, regional, or local scales with a high-degree of granularity (e.g., one physics-based simulation model for every 200 dwelling units), using the EnergyPlus® simulation engine. Information about ComStock™, a sister tool for modeling the commercial building stock, can be found here: https://www.nrel.gov/buildings/comstock.html This repository contains: Housing characteristics of the U.S. residential building stock, in the form of conditional probability distributions stored as tab-separated value (.tsv) files. Comments at the bottom of each file document data sources and assumptions for each. A library of housing characteristic "options" that translate high-level characteristic parameters into arguments for OpenStudio measures, and which are referenced by the housing characteristic .tsv files and building energy upgrades defined in project definition files Project definition files: v2.3.0 and later: buildstockbatch YML files openable in any text editor v2.2.5 and prior: Project folder openable in PAT Unit-level OpenStudio Measures for automatically constructing OpenStudio Models of each representative dwelling unit model: v3.0.0 and later: OpenStudio-HPXML Measures v2.5.0 and prior: OpenStudio Measures Higher-level OpenStudio Measures for controlling simulation inputs and outputs This repository does not contain software for running ResStock simulations, which can be found as follows: Versions 2.3.0 and later only support the use of buildstockbatch for deploying simulations on high-performance or cloud computing. Version 2.3.0 also removed separate projects for single-family detached and multifamily buildings, in lieu of a combined project_national representing the U.S. residential building stock. See the changelog for more details. Versions 2.2.5 and prior support the use of the publicly available OpenStudio-PAT software as an interface for deploying simulations on cloud computing. Read the documentation for v2.2.5.

Horowitz, Scott↗
DOE OSTI2020

Using Residential and Office Building Archetypes for Energy Efficiency Building Solutions in an Urban Scale: A China Case Study

Building energy consumption accounts for 36% of the overall energy end use worldwide and is growing rapidly as developing countries continue to urbanize. Understanding the energy use at urban scale will lay the foundation for identification of energy efficiency opportunities to be deployed at speed. China has almost half of global new constructions and plays an important role in building suitability. However, an open source national building energy consumption database is not available in China. To provide data support for building energy consumptions, this paper used a simulation method to develop an urban building energy consumption database for a pilot city in Wuhan, China. First, residential, small, and large office building archetype energy models were created in EnergyPlus to represent typical building energy consumption in Wuhan. The baseline reference model simulation results were further validated using survey data from the literature. Second, stochastic simulations were conducted to consider different design parameters and occupants’ energy usage intensity scenarios, such as thermal properties of the building envelope, lighting power density, equipment power density, HVAC (heating, ventilation and air conditioning) schedule, etc. A building energy consumption database was generated for typical building archetypes. Third, data-driven regression analysis was conducted to support quick building energy consumption prediction using key high- level building information inputs. Finally, a web-based urban energy platform and an interface were developed to support further third-party application development. The research is expected to provide fast energy efficiency building design solutions for urban planning, new constructions as well as building retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Market optimization and technoeconomic analysis of hydrogen-electricity coproduction systems

Decarbonization efforts across North America, Europe, and beyond rely on variable renewable energy sources such as wind and solar, as well as alternative fuels, such as hydrogen, to support the sustainable energy transition. These advancements have prompted a need for more flexibility in the electric grid to complement non-dispatchable energy sources and increased demand from electrification. Integrated energy systems are well suited to provide this flexibility, but conventional technoeconomic modeling paradigms neglect the time-varying dynamic nature of the grid and thus undervalue resource flexibility. In this work, we develop a computational optimization framework for dynamic market-based technoeconomic comparison of integrated energy systems that coproduce low-carbon electricity and hydrogen (e.g., solid oxide fuel cells, solid oxide electrolysis) against technologies that only produce electricity (e.g., natural gas combined cycle with carbon capture) or only produce hydrogen. Our framework starts with rigorous physics-based process models, built in the open-source Institute for the Design of Advanced Energy Systems (IDAES) modeling and optimization platform, for six energy process concepts. Using these rigorous models and a workflow to optimally design each technology, the framework is shown to be capable of evaluating new and emerging technologies in varying energy markets under a plethora of future scenarios (i.e., renewables penetration, carbon tax, etc.). Ultimately, our framework finds that solid oxide fuel cell-based coproduction systems achieve positive profits for 85% of the analyzed market scenarios. From these market optimization results, we use multivariate linear regression (R 2 values up to 0.99) to determine which electricity price statistics are most significant to predict the optimized annual profit of each system. The proposed framework provides a powerful tool for directly comparing flexible, multi-product energy process concepts to help discern optimal technology and integration options.

08 HYDROGEN↗
DOE OSTI2014

Coastal Ohio Wind Project

The Coastal Ohio Wind Project intends to address problems that impede deployment of wind turbines in the coastal and offshore regions of Northern Ohio. The project evaluates different wind turbine designs and the potential impact of offshore turbines on migratory and resident birds by developing multidisciplinary research, which involves wildlife biology, electrical and mechanical engineering, and geospatial science. Firstly, the project conducts cost and performance studies of two- and three-blade wind turbines using a turbine design suited for the Great Lakes. The numerical studies comprised an analysis and evaluation of the annual energy production of two- and three-blade wind turbines to determine the levelized cost of energy. This task also involved wind tunnel studies of model wind turbines to quantify the wake flow field of upwind and downwind wind turbine-tower arrangements. The experimental work included a study of a scaled model of an offshore wind turbine platform in a water tunnel. The levelized cost of energy work consisted of the development and application of a cost model to predict the cost of energy produced by a wind turbine system placed offshore. The analysis found that a floating two-blade wind turbine presents the most cost effective alternative for the Great Lakes. The load effects studies showed that the two-blade wind turbine model experiences less torque under all IEC Standard design load cases considered. Other load effects did not show this trend and depending on the design load cases, the two-bladed wind turbine showed higher or lower load effects. The experimental studies of the wake were conducted using smoke flow visualization and hot wire anemometry. Flow visualization studies showed that in the downwind turbine configuration the wake flow was insensitive to the presence of the blade and was very similar to that of the tower alone. On the other hand, in the upwind turbine configuration, increasing the rotor blade angle of attack reduced the wake size and enhanced the vortices in the flow downstream of the turbine-tower compared with the tower alone case. Mean and rms velocity distributions from hot wire anemometer data confirmed that in a downwind configuration, the wake of the tower dominates the flow, thus the flow fields of a tower alone and tower-turbine combinations are nearly the same. For the upwind configuration, the mean velocity shows a narrowing of the wake compared with the tower alone case. The downwind configuration wake persisted longer than that of an upwind configuration; however, it was not possible to quantify this difference because of the size limitation of the wind tunnel downstream of the test section. The water tunnel studies demonstrated that the scale model studies could be used to adequately produce accurate motions to model the motions of a wind turbine platform subject to large waves. It was found that the important factors that affect the platform is whether the platform is submerged or surface piercing. In the former, the loads on the platform will be relatively reduced whereas in the latter case, the structure pierces the wave free surface and gains stiffness and stability. The other important element that affects the movement of the platform is depth of the sea in which the wind turbine will be installed. Furthermore, the wildlife biology component evaluated migratory patterns by different monitoring systems consisting of marine radar, thermal IR camera and acoustic recorders. The types of radar used in the project are weather surveillance radar and marine radar. The weather surveillance radar (1988 Doppler), also known as Next Generation Radar (NEXRAD), provides a network of weather stations in the US. Data generated from this network were used to understand general migratory patterns, migratory stopover habitats, and other patterns caused by the effects of weather conditions. At a local scale our marine radar was used to complement the datasets from NEXRAD and to collect additional monitoring parameters such as passage rates, flight paths, flight directions, and flight altitudes of nocturnal migrating species. Our work focused on the design and development of custom built marine radar that used t-bar and parabolic dish antennas. The marine radar used in the project was Furuno (XANK250) which was coupled with a XIR3000B digitizing card from Russell Technologies for collection of the radar data. The radar data was processed by open source radR processing software using different computational techniques and methods. Additional data from thermal IR imaging cameras were collected to detect heat emitted from objects and provide information on movements of birds and bats, data which we used for different animal flight behavior analysis. Lastly, the data from the acoustic recorders were used to provide the number of bird calls for assessing patterns and peak passage rates during migration. The development of the geospatial database included collection of different data sources that are used to support offshore wind turbine development. Many different data sets were collected and organized using initial version of web-based repository software tools that can accommodate distribution of rectified pertinent data sets such as the lake depth, lake bottom engineering parameters, extent of ice, navigation pathways, wind speed, important bird habitats, fish efforts and other layers that are relevant for supporting robust offshore wind turbine developments. Additional geospatial products developed during the project included few different prototypes for offshore wind farm suitability which can involve different stakeholders and participants for solving complex planning problems and building consensus. Some of the prototypes include spatial decision support system (SDSS) for collaborative decision making, a web-based Participatory Geographic Information System (PGIS) framework for evaluating importance of different decision alternatives using different evaluation criteria, and an Android application for collection of field data using mobile and tablet devices . In summary, the simulations of two- and three-blade wind turbines suggested that two-bladed machines could produce comparable annual energy as the three-blade wind turbines but have a lighter tower top weight, which leads to lower cost of energy. In addition, the two-blade rotor configuration potentially costs 20% less than a three blade configuration that produces the same power at the same site. The cost model analysis predicted a potential cost savings of approximately 15% for offshore two-blade wind turbines. The foundation design for a wind turbine in Lake Erie is likely to be driven by ice loads based on the currently available ice data and ice mechanics models. Hence, for Lake Eire, the cost savings will be somewhat smaller than the other lakes in the Great Lakes. Considering the size of cranes and vessels currently available in the Great Lakes, the cost optimal wind turbine size should be 3 MW, not larger. The surveillance data from different monitoring systems suggested that bird and bat passage rates per hour were comparable during heavy migrations in both spring and fall seasons while passage rates were significantly correlated to wind directions and wind speeds. The altitude of migration was higher during heavy migrations and higher over water relative to over land. Notable portions of migration on some spring nights occurred parallel the shoreline, often moving perpendicular to southern winds. The birds approaching the Western basin have a higher propensity to cross than birds approaching the Central basin of Lake Erie and as such offshore turbine development might be a better option further east towards Cleveland than in the Western basin. The high stopover density was more strongly associated with migration volume the following night rather than the preceding night. The processed mean scalar wind speeds with temporal resolutions as fine as 10-minute intervals near turbine height showed that August is the month with the weakest winds while December is the month, which typically has the strongest winds. The ice data suggests that shallow western basin of Lake Erie has higher ice cover duration many times exceeding 90 days during some winters.

17 WIND ENERGY↗
DOE OSTI2019

Production Application Performance Data Streaming for System Monitoring

In this article, we present an approach to streaming collection of application performance data. Practical application performance tuning and troubleshooting in production high-performance computing (HPC) environments requires an understanding of how applications interact with the platform, including (but not limited to) parallel programming libraries such as Message Passing Interface (MPI). Several profiling and tracing tools exist that collect heavy runtime data traces either in memory (released only at application exit) or on a file system (imposing an I/O load that may interfere with the performance being measured). Although these approaches are beneficial in development stages and post-run analysis, a systemwide and low-overhead method is required to monitor deployed applications continuously. This method must be able to collect information at both the application and system levels to yield a complete performance picture. In our approach, an application profiler collects application event counters. A sampler uses an efficient inter-process communication method to periodically extract the application counters and stream them into an infrastructure for performance data collection. We implement a tool-set based on our approach and integrate it with the Lightweight Distributed Metric Service (LDMS) system, a monitoring system used on large-scale computational platforms. LDMS provides the infrastructure to create and gather streams of performance data in a low overhead manner. We demonstrate our approach using applications implemented with MPI, as it is one of the most common standards for the development of large-scale scientific applications. We utilize our tool-set to study the impact of our approach on an open source HPC application, Nalu. Our tool-set enables us to efficiently identify patterns in the behavior of the application without source-level knowledge. We leverage LDMS to collect system-level performance data and explore the correlation between the system and application events. Finally, we demonstrate how our tool-set can help detect anomalies with a low latency. We run tests on two different architectures: a system enabled with Intel Xeon Phi and another system equipped with Intel Xeon processor. Our overhead study shows our method imposes at most 0.5% CPU usage overhead on the application in realistic deployment scenarios.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Phenomena-based graph representations and applications to chemical process simulation

Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture the physical phenomena taking place in the process (e.g., material and energy conservation, phase equilibrium, reactions). In this work, we show that graph-theoretic representations of the physical phenomena within unit operations can help navigate and decompose equations to systematically identify alternative approaches for fast and robust numerical solutions. Specifically, we present a graph-theoretic abstraction that captures the connectivity between the model variables/equations and use this abstraction to group variables/equations into fundamental phenomena. We show that phenomena-based decomposition of the underlying equations can help decouple nonlinearities and enforce material/energy conservation at the process level to accelerate convergence. The proposed decomposition approach differs from the more traditional sequential modular simulation approach, in which equations are grouped and decomposed by unit operations. We implemented the phenomena-based decomposition in BioSTEAM—an open-source process simulation platform in Python—and demonstrated that this approach can converge a variety of separation process models. Compared to sequential modular simulation, the phenomena-based approach can converge idealized systems faster, but it can be slower for (or even fail to converge) highly coupled and nonideal process systems.

Convergence↗
DOE OSTI2018

Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform

Abstract Many spectral responses in materials science, physics, and chemistry experiments can be characterized as resulting from the superposition of a number of more basic individual spectra. In this context, unmixing is defined as the problem of determining the individual spectra, given measurements of multiple spectra that are spatially resolved across samples, as well as the determination of the corresponding abundance maps indicating the local weighting of each individual spectrum. Matrix factorization is a popular linear unmixing technique that considers that the mixture model between the individual spectra and the spatial maps is linear. Here, we present a tutorial paper targeted at domain scientists to introduce linear unmixing techniques, to facilitate greater understanding of spectroscopic imaging data. We detail a matrix factorization framework that can incorporate different domain information through various parameters of the matrix factorization method. We demonstrate many domain-specific examples to explain the expressivity of the matrix factorization framework and show how the appropriate use of domain-specific constraints such as non-negativity and sum-to-one abundance result in physically meaningful spectral decompositions that are more readily interpretable. Our aim is not only to explain the off-the-shelf available tools, but to add additional constraints when ready-made algorithms are unavailable for the task. All examples use the scalable open source implementation from https://github.com/ramkikannan/nmflibrary that can run from small laptops to supercomputers, creating a user-wide platform for rapid dissemination and adoption across scientific disciplines.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

National Geothermal Data System: Case Studies on Exploration and Development of Potential Geothermal Sites Through Distributed Data Sharing

The NGDS released version 1 of the system on April 30, 2014 using the US Geoscience Information Network (USGIN) as its data integration platform. NGDS supports the 2013 Open Data Policy, and as such, the launch was featured at the 2014 Energy Datapalooza. Currently, the NGDS features a comprehensive user interface for searching and accessing nearly 41,000 documents and more than 9 million data points shared by scores of data providers across the U.S. The NGDS supports distributed data sharing, permitting the data owners to maintain the raw data that is made available to the consumer. Researchers and industry have been utilizing the NGDS as a mechanism for promoting geothermal development across the country, from hydrothermal to ground source heat pump applications. Case studies in geothermal research and exploration from across the country are highlighted.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Enabling Data Exchange and Data Integration with the Common Information Model: An Introduction for Power Systems Engineers and Application Developers

The Common Information Model (CIM) is an open-source information model that is used to model an electrical network and the various equipment used on the network. CIM is widely used for data exchange of bulk transmission power systems and is finding increasing use for distribution systems. Use of a non-proprietary information model (such as CIM) that has been agreed upon and adopted by numerous utilities, vendors, and researchers allows significant reduction in the effort and cost of data integration. Likewise, adoption of open data platforms built around the CIM increases available functionalities for managing and optimizing the smart grid of the future. This report is intended as an introduction to CIM for utility engineers, power systems researchers, and application developers, providing a broad view of the CIM and how particular profiles can be adapted for various use cases. Unlike most other CIM introduction documents and the International Electrotechnical Commission (IEC) standards (which are mostly targeted to an audience of data scientists, enterprise database managers, and platform developers), this report is intended for users of traditional power systems analysis software and other readers without any prior experience with canonical information models, data profiles, or UML modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast

The mesoscale characterization of biological specimens has traditionally required compromises between resolution, field-of-view, depth-of-field, and molecular specificity, with most approaches relying on external labels. Here we present the Deep-ultrAviolet ptychogRaphic pockeT-scope (DART), a handheld platform that transforms label-free molecular imaging through intrinsic deep-ultraviolet spectroscopic contrast. By leveraging biomolecules’ natural absorption fingerprints and combining them with lensless ptychographic microscopy, DART resolves down to 308-nm linewidths across centimeter-scale areas while maintaining millimeter-scale depth-of-field. The system’s virtual error-bin methodology effectively eliminates artifacts from limited temporal coherence and other optical imperfections, enabling high-fidelity molecular imaging without lenses. Through differential spectroscopic imaging at deep-ultraviolet wavelengths, DART quantitatively maps nucleic acid and protein distributions with femtogram sensitivity, providing an intrinsic basis for explainable virtual staining. We demonstrate DART’s capabilities through imaging of tissue sections, cytopathology specimens, blood cells, and neural populations, revealing detailed molecular contrast without external labels. The combination of high-resolution molecular mapping and broad mesoscale imaging in a portable platform opens new possibilities from rapid clinical diagnostics, tissue analysis, to biological characterization in space exploration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Bioconversion of methane to lactate by an obligate methanotrophic bacterium

Methane is the second most abundant greenhouse gas (GHG), with nearly 60% of emissions derived from anthropogenic sources. Microbial conversion of methane to fuels and value-added chemicals offers a means to reduce GHG emissions, while also valorizing this otherwise squandered high-volume, high-energy gas. However, to date, advances in methane biocatalysis have been constrained by the low-productivity and limited genetic tractability of natural methane-consuming microbes. Here, leveraging recent identification of a novel, tractable methanotrophic bacterium, Methylomicrobium buryatense , we demonstrate microbial biocatalysis of methane to lactate, an industrial platform chemical. Heterologous overexpression of a Lactobacillus helveticus L-lactate dehydrogenase in M. buryatense resulted in an initial titer of 0.06 g lactate/L from methane. Cultivation in a 5 L continuously stirred tank bioreactor enabled production of 0.8 g lactate/L, representing a 13-fold improvement compared to the initial titer. The yields (0.05 g lactate/g methane) and productivity (0.008 g lactate/L/h) indicate the need and opportunity for future strain improvement. Additionally, real-time analysis of methane utilization implicated gas-to-liquid transfer and/or microbial methane consumption as process limitations. This work opens the door to develop an array of methanotrophic bacterial strain-engineering strategies currently employed for biocatalytic sugar upgrading to “green” chemicals and fuels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models feature diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Validation of Power Distribution Models using Load Flow Analysis in an ADMS Environment

Electric utilities are facing the need for better monitoring, analysis, and control of their distribution systems. An accurate mathematical model is a key to both the development of cutting-edge, scalable model-based algorithms and the assessment of emerging technologies such as distributed energy resources (DER) for grid planning and operation. However, the constantly evolving nature of power distribution systems poses challenges to maintaining accurate models. In this paper, we propose a novel load flow based approach to validate power distribution models. Networked equipment models described according to the Common Information Model (CIM) standard and a measurement model are used to formulate the distribution load flow problem. First, a system admittance matrix (Ybus) is derived from device-level CIM parameters. Next, the operational parameters (dynamic Ybus and nodal injections) are extracted from the measurement model using sensor configuration and equipment state. An iterative power flow method is then used to compute nodal voltages and branch flows that are compared against the measurement data to find any inconsistencies in the networked equipment model. This approach is implemented within GridAPPS-D, an open-source standards-based platform for advanced distribution management system (ADMS) application development, and demonstrated on the IEEE 13-bus, 123-bus, and 8500-node test feeders.

Common information model, model validation, power ↗
DOE OSTI2015

GRDC. A Collaborative Framework for Radiological Background and Contextual Data Analysis

The Radiation Mobile Analysis Platform (RadMAP) is unique in its capability to collect both high quality radiological data from both gamma-ray detectors and fast neutron detectors and a broad array of contextual data that includes positioning and stance data, high-resolution 3D radiological data from weather sensors, LiDAR, and visual and hyperspectral cameras. The datasets obtained from RadMAP are both voluminous and complex and require analyses from highly diverse communities within both the national laboratory and academic communities. Maintaining a high level of transparency will enable analysis products to further enrich the RadMAP dataset. It is in this spirit of open and collaborative data that the RadMAP team proposed to collect, calibrate, and make available online data from the RadMAP system. The Berkeley Data Cloud (BDC) is a cloud-based data management framework that enables web-based data browsing visualization, and connects curated datasets to custom workflows such that analysis products can be managed and disseminated while maintaining user access rights. BDC enables cloud-based analyses of large datasets in a manner that simulates real-time data collection, such that BDC can be used to test algorithm performance on real and source-injected datasets. Using the BDC framework, a subset of the RadMAP datasets have been disseminated via the Gamma Ray Data Cloud (GRDC) that is hosted through the National Energy Research Science Computing (NERSC) Center, enabling data access to over 40 users at 10 institutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Pathosphere.org: pathogen detection and characterization through a web-based, open source informatics platform

The detection of pathogens in complex sample backgrounds has been revolutionized by wide access to next-generation sequencing (NGS) platforms. However, analytical methods to support NGS platforms are not as uniformly available. Pathosphere (found at Pathosphere.org) is a cloud - based open - sourced community tool that allows for communication, collaboration and sharing of NGS analytical tools and data amongst scientists working in academia, industry and government. The architecture allows for users to upload data and run available bioinformatics pipelines without the need for onsite processing hardware or technical support. The pathogen detection capabilities hosted on Pathosphere were tested by analyzing pathogen-containing samples sequenced by NGS with both spiked human samples as well as human and zoonotic host backgrounds. Pathosphere analytical pipelines developed by Edgewood Chemical Biological Center (ECBC) identified spiked pathogens within a common sample analyzed by 454, Ion Torrent, and Illumina sequencing platforms. ECBC pipelines also correctly identified pathogens in human samples containing arenavirus in addition to animal samples containing flavivirus and coronavirus. These analytical methods were limited in the detection of sequences with limited homology to previous annotations within NCBI databases, such as parvovirus. Utilizing the pipeline-hosting adaptability of Pathosphere, the analytical suite was supplemented by analytical pipelines designed by the United States Army Medical Research Institute of Infectious Diseases and Walter Reed Army Institute of Research (USAMRIID-WRAIR). These pipelines were implemented and detected parvovirus sequence in the sample that the ECBC iterative analysis previously failed to identify. By accurately detecting pathogens in a variety of samples, this work demonstrates the utility of Pathosphere and provides a platform for utilizing, modifying and creating pipelines for a variety of NGS technologies developed to detect pathogens in complex sample backgrounds. These results serve as an exhibition for the existing pipelines and web-based interface of Pathosphere as well as the plug-in adaptability that allows for integration of newer NGS analytical software as it becomes available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

A Microalgae-Based Platform for the Beneficial Re-use of Carbon Dioxide Emissions from Power Plants

This project sought to address the technical and economic barriers to carbon dioxide capture and utilization using microalgae. Operating data were collected in 2016 and 2017 during cultivation of Scenedesmus acutus at Duke Energy’s East Bend Station – a coal-fired power plant located in northern Kentucky – using flue gas as the CO 2 source. Algae were grown in a 1200 L “cyclic flow” photobioreactor (PBR) designed by the University of Kentucky. A key finding was that the harvested algae contained only very low concentrations of heavy metals (As, Cd, Hg, Se), consistent with heavy metals incorporation from the supplied nutrients. This indicates that algal biomass produced from coal-derived flue gas would be suitable for a variety of applications, including the production of bioplastics, use as fertilizer, etc. A lifecycle assessment showed that the UK-designed PBR employed in this work qualifies as a net CO 2 capture technology. Indeed, over a 30-year period, net CO 2 capture would equate to 43% of the targeted amount, i.e., the amount captured from the supplied flue gas. A techno-economic analysis indicated that the minimum production cost of Scenedesmus acutus biomass in the US is in the order of 875 USD/ton, excluding the cost of capital. While this figure is not too dissimilar to values reported for open raceway ponds in similar scenarios, it emphasizes that for current cultivation technology any pathway to economic viability will require applications for which algal boimass can be sold at prices in excess of 1,000 USD/ton. Currently, such applications represent relatively small markets, such as pigments (e.g., astaxanthin) and nutraceuticals (ω-3 unsaturated fatty acids), as well as nutritional supplements (whole algae) for human consumption and for use in pet food. Consequently, the commercialization of large-scale algae-based CO 2 capture and utilization will require the development of new technologies to reduce the cost of algae production and/or the development of new, high-value applications for algal biomass. One of the more promising applications for algal biomass is in the production of bioplastics. In this work, the potential for the algae grown in the East Bend PBR for the production of bioplastic with adequate mechanical properties was clearly shown. Positive features of the produced biomass included a high protein content and a composition that was generally more homogeneous than biomass grown in open ponds (in which many species may be present). The best candidate for further review, after incorporation into ethylene-vinyl acetate, was a lipid and sugar extracted material, which showed the highest extension values with comparable load values to other UK-derived samples. It also demonstrated extension benefits against the Algix Bloom product (currently offered commercially), even though it contained agglomerates which generally exert a negative effect on mechanical properties. This leads to speculation that with enhanced milling that exists on the commercial scale, the sugar and fat extracted product may be even more competitive. This, in turn, points to the need for additional work in order to assess the properties of such optimized algae-based plastics and the price point they can command.

09 BIOMASS FUELS↗
DOE OSTI2025

Preliminary Study on Fine-Grained Power and Energy Measurements on Grace Hopper GH200 with Open-Source Performance Tools

The increasing adoption of tightly integrated, heterogeneous architectures, combined with the slowdown of Moore’s law, has made application power and energy-driven optimizations critical to efficiently use high-performance computing systems. This paper introduces a newly developed open-source toolkit that seamlessly integrates the Linux real-time hardware monitoring program hwmon with the Performance Application Programming Interface and the Score-P performance measurement system, thereby enabling fine-grained power and energy measurements for high-performance computing applications. Our primary target platform is the Wombat test bed, which is a system based on the NVIDIA GH200 superchip. The toolkit can capture transient power peaks with high temporal resolution (50 ms) and, thanks to Score-P integration, can map power metrics to specific code regions, thereby providing actionable information on power-intensive operations and inefficiencies. The toolkit also provides a holistic view of both the power and the energy consumption of the entire GH200 superchip by covering all major components: the Grace CPU, the Hopper GPU, and the I/O subsystem. Experiments that use Locally Self-consistent Multiple Scattering, which is an application for first-principles calculations of materials developed at Oak Ridge National Laboratory, have demonstrated the tool’s ability to identify transient power spikes and uncover opportunities for energy-aware optimizations. Additionally, we introduce a Python-based utility for converting Open Trace Format 2 traces to Parquet format, thus enabling advanced data analysis for numerical integration methods applied to power data for accurate energy profiling.

Hernandez Mendoza, Oscar [ORNL] (ORCID:00000002538↗