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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Pinellas County, Florida, Site Environmental Restoration Project Sitewide Environmental Monitoring Semiannual Progress Report for the Young - Rainey STAR Center June Through November 2016, January 2017

The Young - Rainey STAR Center (Science, Technology, and Research Center) at the Pinellas County, Florida, Site is a former U.S. Department of Energy (DOE) facility constructed in the mid-1950s. The 96-acre STAR Center is located in Largo, Florida, and lies in the northeast quarter of Section 13, Township 30 South, Range 15 East (Figure 1). While it was owned by DOE, the purpose of the site was to develop and manufacture components for the nation’s nuclear weapons program. In 1987, the U.S. Environmental Protection Agency (EPA) performed a Resource Conservation and Recovery Act Facility Assessment (EPA 1988) at the site to gather information on potential releases of hazardous materials. In February of 1990, EPA issued a Hazardous and Solid Waste Amendments permit to DOE, requiring DOE to investigate and perform remediation activities in those areas designated as solid-waste management units (SWMUs) contaminated by hazardous materials resulting from DOE operations. A total of 17 SWMUs were identified and investigated at the STAR Center. By 1997, 13 of the 17 SWMUs had been remediated or approved for no further action. More recently, the Florida Department of Environmental Protection (FDEP) executed Conditional Site Rehabilitation Completion Orders for the Northeast Site and the Wastewater Neutralization Area on July 27, 2016, stating that no further action is required for those SWMUs. The Building 100 Area (a combination of the Old Drum Storage Site and the Building 100-Industrial Drain Leaks SWMUs) comprises the only two active SWMUs at the STAR Center (Figure 2). This document serves as the semiannual progress report for the SWMUs by providing the results of recent monitoring activities and a summary of ongoing and projected work. The STAR Center is owned by the Pinellas County Industrial Development Authority, but DOE is responsible for remediation activities at the site. Additional background information for the site is contained in the Long-Term Surveillance and Maintenance Plan for the Pinellas Site (DOE 2016a). That document and other site-related documents can be accessed at this website: http://www.lm.doe.gov/Pinellas/Sites.aspx.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

SCALE 6.3.2 User Manual

SCALE is a comprehensive modeling and simulation suite for nuclear safety analysis and design developed and maintained by Oak Ridge National Laboratory under contract with the U.S. Nuclear Regulatory Commission, U.S. Department of Energy, and the National Nuclear Security Administration to perform reactor physics, criticality safety, radiation shielding, and spent fuel characterization for nuclear facilities and transportation/storage package designs. Visit the SCALE website for additional information.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Utility Energy Service Contract Guide: A Resource for Contracting Officers Working on UESC Projects

A Utility Energy Service Contract (UESC) is a limited-source contract between a federal agency and serving utility for energy management services including energy and water efficiency improvements and demand-reduction services. In a UESC, the Contracting Officer (CO) will lead the acquisition effort, supported by technically knowledgeable staff working closely with the utility, to assess potential, investigate technical and economic feasibility, and ensure a fair and reasonable price for design and implementation of the project. The UESC Guide is a compilation of samples and templates developed as a resource to help COs implement Task Orders (TOs) for UESCs under existing U.S. General Services Administration (GSA) Areawide Contracts (AWC). All samples and templates provided in this guide were derived from actual UESC project contract documents and will be available for download on FEMP’s website at https://www.energy.gov/node/850656. Templates in Microsoft Word can be modified to address each agency’s unique acquisition process.

Federal Energy Management Program↗
DOE OSTI2014

Developing an Online Database of National and Sub-National Clean Energy Policies

The Database of State Incentives for Renewables and Efficiency (DSIRE) was established in 1995 to provide summaries of energy efficiency and renewable energy policies offered by the federal and state governments. This primer provides an overview of the major policy, research, and technical topics to be considered when creating a similar clean energy policy database and website.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

SU-E-T-253: Open-Source Automatic Software for Quantifying Biological Assays of Radiation Effects

Purpose: Clonogenic cell survival is a common assay for quantifying the effect of drugs and radiation. Manual counting of surviving colonies can take 30–90seconds per plate, a major limitation for large studies. Currently available automatic counting tools are not easily modified for radiation oncology research. Our goal is to provide an open-source toolkit for precise, accurate and fast analysis of biological assays in radiation oncology. Methods: As an example analysis, we used HeLa cells incubated with gadolinium nanoparticles prior to irradiation. After treatment, the cells are grown for 14days to allow for colony formation. To analyze the colony growth, we capture images of each dish for archiving and automatic computer-based analysis. A FujifilmX20 camera is placed at the top of a box setup, 20cm above the sample, which is backlit by a LED lamp placed at the bottom of the box. We use a Gaussian filter (width=1.3mm) and color threshold (19–255). The minimum size for a colony to be counted is 1mm. For this example, 20 dishes with a large range of colonies were analyzed. Each dish was counted 3 times manually by 3 different users and then compared to our counter. Results: Automatic counting of cell colonies takes an average of 7seconds, enabling the analysis process to be accelerated 4–12 times. The average precision of the automatic counter was 1.7%. The Student t-test demonstrated the non-significant differences between the two counting methods (p=0.64). The ICC demonstrated the reliability of each method with ICC>0.999 (automatic) and ICC=0.95 (manual). Conclusion: We developed an open-source automatic toolkit for the analysis of biological assays in radiation oncology and demonstrated the accuracy, precision and effort savings for clonogenic cell survival quantification. This toolkit is currently being used in two laboratories for routine experimental analysis and will be made freely available on our departmental website.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

SAM™ (System Advisor Model™) [SWR-16-02, SWR-10-13]

See the SAM™ website to build a desktop version of the National Laboratory of the Rockies' (NLR's) System Advisor Model™ (SAM). https://sam.nlr.gov/ The System Advisor Model™ (SAM™) is a free techno-economic software model that facilitates decision-making for people in the renewable energy industry: -Project managers and engineers -Policy analysts -Technology developers -Researchers SAM can model many types of renewable energy systems: -Photovoltaic systems, from small residential rooftop to large utility-scale systems -Battery storage with Lithium ion, lead acid, or flow batteries for front-of-meter or behind-the-meter applications -Concentrating Solar Power systems for electric power generation, including parabolic trough, power tower, and linear Fresnel -Industrial process heat from parabolic trough and linear Fresnel systems -Wind power, from individual turbines to large wind farms -Marine energy wave and tidal systems -Solar water heating -Fuel cells -Geothermal power generation -Biomass combustion for power generation -High concentration photovoltaic systems SAM's financial models are for the following types of projects: -Residential and commercial projects where the renewable energy system is on the customer side of the electric utility meter (behind the meter), and power from the system is used to reduce the customer's electricity bill. -Power purchase agreement (PPA) projects where the system is connected to the grid at an interconnection point, and the project earns revenue through power sales. The project may be owned and operated by a single owner or by a partnership involving a flip or leaseback arrangement. -Third party ownership where the system is installed on the customer's (host) property and owned by a separate entity (developer), and the host is compensated for power generated by the system through either a PPA or lease agreement. For a more detailed description of SAM, see Blair et al. (2018), System Advisor Model (SAM) General Description (Version 2017.9.5), NREL/TP-6A20-70414. https://www.nrel.gov/docs/fy18osti/70414.pdf

Ryberg, David↗
DOE OSTI2020

Documenting numerical experiments in support of the Coupled Model Intercomparison Project Phase 6 (CMIP6)

Abstract. Numerical simulation, and in particular simulation of the earth system, relies on contributions from diverse communities, from those who develop models to those involved in devising, executing, and analysing numerical experiments. Often these people work in different institutions and may be working with significant separation in time (particularly analysts, who may be working on data produced years earlier), and they typically communicate via published information (whether journal papers, technical notes, or websites). The complexity of the models, experiments, and methodologies, along with the diversity (and sometimes inexact nature) of information sources, can easily lead to misinterpretation of what was actually intended or done. In this paper we introduce a taxonomy of terms for more clearly defining numerical experiments, put it in the context of previous work on experimental ontologies, and describe how we have used it to document the experiments of the sixth phase for the Coupled Model Intercomparison Project (CMIP6). We describe how, through iteration with a range of CMIP6 stakeholders, we rationalized multiple sources of information and improved the clarity of experimental definitions. We demonstrate how this process has added value to CMIP6 itself by (a) helping those devising experiments to be clear about their goals and their implementation, (b) making it easier for those executing experiments to know what is intended, (c) exposing interrelationships between experiments, and (d) making it clearer for third parties (data users) to understand the CMIP6 experiments. We conclude with some lessons learnt and how these may be applied to future CMIP phases as well as other modelling campaigns.

Pascoe, Charlotte (ORCID:0000000223616778)↗
DOE OSTI2024

Supporting ARPA-E Power Grid Optimization (Final Report)

Pacific Northwest National Laboratory (PNNL), Arizona State University (ASU), Georgia Institute of Technology (Georgia Tech), Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), The University of Texas at Austin (UT), and the University of Wisconsin-Madison (UW-M) supported the ARPA-E Grid Optimization (GO) Competition by providing a common problem formulation, data format, datasets, evaluation mechanism, scoring, rules, and results that resulted in the awarding of $\$9.24$ million dollars to teams from academia, industry, and national labs for solving three sets of increasingly difficult non-linear, security- constrained AC Optimal Powerflow (AC-OPF) optimization problems in order to increase the efficiency of the US Electric Grid. It is estimated that a 1% increase in efficiency can save $\$1$ billion. Current industry practices typically use a linear DC model (DC-OPF) in order solve the OPF problem within the time constraints of the operation schedule. The GO Competition challenges the best power engineers, mathematicians, and computer scientists to make possible operational decisions based on accurate physical models. To accomplish this, the GO Competition created a series of Challenges and funded teams to produce the best solver. Challenge 1 was to solve the security constrained Alternating Current Optimal Power Flow (ACOPF) problem. Challenge 2 extended that to by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment (UC). Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. While Challenge 3 was being developed, the entrants were invited to find better solutions to the Challenge 2 synthetic datasets with no restrictions on time, hardware, or algorithms. The Challenge 2 solutions turned out to be very good. Challenge 3 expanded the Challenge 2 problem further by using multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. These problems included active bid-in demand and topology optimization. Together the Challenges used nearly 30 million CPU hours. Since each team was working on the same problem, using the same data, and running on the same hardware, fair comparisons could be drawn as to the best solver. The datasets were varied enough, however, that the best solver for one dataset was not necessarily the best at another, so cumulative scores were used. The process was managed by the PNNL maintained website https://GOCompetition.energy.gov, where Entrants could find information about the problem, the data, the rules, submit their solver for evaluation, and see the scores of all the competing teams on a Leaderboard. Interest was world-wide but only American teams were eligible for prizes. The Competition has produced 34 journal articles 115 papers and been cited over 500 times in the literature, including 12 dissertations (4 from foreign countries; Columbia (2), Germany, and Italy) and 3 from the DOE ExaScale project. Software developed by Pearl Street Technologies for Challenges 1 and 2 is now deployed by Southwest Power Pool (SPP) and Midcontinent Independent Service Operator (MISO). Other teams have received inquiries from venture capitalists. Google DeepMind has thanked the Competition for making the datasets developed for the Competition public. They are using it to train machine learning models. The larger datasets have billions of unknowns to be solved for, but only a small percent matter in the final solution. Knowing what unknowns are important can dramatically speedup the solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Initial CEED API (CEED-MS10)

Key components of CEED software development involve fast finite element operator storage and evaluation, architecture optimizations, performant algorithms for all orders, global kernels for finite element operators, efficient use of the memory sub-system, optimal data locality and motion, enhanced scalability and parallelism, and fast tensor contractions. As ultimate goals of the project, CEED is exploring and identifying the best algorithms for the full range of discretizations and applying algorithmic and software development to support ECP applications’ needs. In this milestone, we initiated the development and implementation of a common CEED API consisting of two main components: low-level and high-level APIs. We accomplished the goal of having the low-level component ready for the release of CEED v1.0 in the next quarter, see CEED-MS13. In this report we also provide a highlight from CEED’s integration with the MARBL app and the corresponding Laghos miniapp. A main theme in this work has been the systematic transition of advanced algorithms, developed and tested in Laghos, to the large-scale multi-physics settings of the MARBL code. The low-level CEED API provides a set of FE kernels and components for writing new low-level kernels. Examples include: vector and sparse linear algebra, element matrix assembly over a batch of elements, partial assembly and action for efficient high-order operators like mass, diffusion, advection, etc. The main goal of the low-level API is to establish the basis for the high-level API. Also, identifying such low-level kernels and providing a reference implementation for them serves as the basis for collaboration with vendors and STs. Another goal for the low-level API is to make it easy for applications with their own discretization infrastructure, who do not want to switch directly to using the high-level CEED API, to still evaluate and use the core operations provided by the low-level API. One of the challenges with high-order methods is that a global sparse matrix is no longer a good representation of a high-order linear operator, both with respect to the FLOPs needed for its evaluation, as well as the memory transfer needed for a matvec. Thus, another important question we tried to address while developing the low-level API is: is it possible to abstract out in a natural and efficient way the different implementations and data structures that arise when dealing with various computational device types: CPUs, GPUs, etc. Fully answering this question is beyond the scope of this particular milestone, since that requires longer time as we develop and expand the devices we support. The future high-level API will be the frontend provided by CEED consisting of high-level abstractions such as meshes, finite element spaces and solutions, bilinear forms, solvers, etc. At this high-level, an important aspect of the API will be to provide a uniform abstraction layer for writing code in a portable manner. To achieve that, we plan to utilize the MPI programming model combined with a device abstraction layer (DAL) for finite element computations that we will develop. The DAL will serve as the bridge between the low- and the high-level APIs, allowing a variety of programming models to be used for the low-level kernel implementations. The software artifacts delivered as part of this milestone include an initial CEED API software implementation (libCEED) provided through the CEED website, http://ceed.exascaleproject.org and the CEED GitHub organization, http://github.com/ceed. In addition to details and results from the above R&D efforts, in this document we are also reporting on other project-wide activities performed in Q1 of FY18, including: the Nek5000 v17.0 MFEM v3.3.2 and PETSc v3.8 releases, collaborations with ECP/ST and SciDAC projects, the inaugural Nek5000 hackathon and various outreach activities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Accessorizing Building Science – A Web Platform to Support Multiple Market Transformation Programs

As demand for improved energy efficiency in homes increases, builders need information on the latest findings in building science, rapidly ramping-up energy codes, and technical requirements for labeling programs. The Building America Solution Center is a Department of Energy (DOE) website containing hundreds of expert guides designed to help residential builders install efficiency measures in new and existing homes. Builders can package measures with other media for customized content. Website content provides technical support to market transformation programs such as ENERGY STAR and has been cloned and adapted to provide content for the Better Buildings Residential Program. The Solution Center uses the Drupal open source content management platform to combine a variety of media in an interactive manner to make information easily accessible. Developers designed a unique taxonomy to organize and manage content. That taxonomy was translated into web-based modules that allow users to rapidly traverse structured content with related topics, and media. We will present information on the current design of the Solution Center and the underlying technology used to manage the content. The paper will explore development of features, such as “Field Kits” that allow users to bundle and save content for quick access, along with the ability to export PDF versions of content. Finally, we will discuss development of an Android based mobile application, and a visualization tool for interacting with Building Science Publications that allows the user to dynamically search the entire Building America Library.

Madison, Michael C.↗
DOE OSTI2015

Rooftop Unit Comparison Calculator User Manual

This document serves as a user manual for the Packaged rooftop air conditioners and heat pump units comparison calculator (RTUCC) and is an aggregation of the calculator’s website documentation. Content ranges from new-user guide material like the “Quick Start” to the more technical/algorithmic descriptions of the “Methods Pages.” There is also a section listing all the context-help topics that support the features on the “Controls” page. The appendix has a discussion of the EnergyPlus runs that supported the development of the building-response models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts

The authors are founding team members of a new effort to develop an Open Energy Outlook for the United States. The effort aims to apply best practices of policy-focused energy system modeling, ensure transparency, build a networked community, and work toward a common purpose: examining possible US energy system futures to inform energy and climate policy efforts. Individual author biographies can be found on the project website: https://openenergyoutlook.org/.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Geospatial Toolkit

The Geospatial Toolkit is an NREL-developed map-based software application that integrates resource data and other geographic information systems (GIS) data for integrated resource assessment. The non-resource, country-specific data for each toolkit comes from a variety of agencies within each country as well as from global datasets. Originally developed in 2005, the Geospatial Toolkit was completely redesigned and re-released in November 2010 to provide a more modern, easier-to-use interface with considerably faster analytical querying capabilities. The revised version of the Geospatial Toolkit has been released for all original toolkit countries/regions and each software package is made available on NREL's website,

Source record↗
DOE OSTI2024

Road Lidar Dataset for the TxDOT Austin District

This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

NOVA making stuff: Season 2

Over the course of four weeks in fall 2013, 11.7 million Americans tuned in to PBS to follow host David Pogue as he led them in search of engineering and scientific breakthroughs poised to change our world. Levitating trains, quantum computers, robotic bees, and bomb-detecting plants—these were just a few of the cutting-edge innovations brought into the living rooms of families across the country in NOVA’s four-part series, Making Stuff: Faster, Wilder, Colder, and Safer. Each of the four one-hour programs gave viewers a behind-the-scenes look at novel technologies poised to change our world—showing them how basic research and scientific discovery can hold the keys to transforming how we live. Making Stuff Season 2 (MS2) combined true entertainment with educational value, creating a popular and engaging series that brought accessible science into the homes of millions. NOVA’s goal to engage the public with such technological innovation and basic research extended beyond the broadcast series, including a variety of online, educational, and promotional activities: original online science reporting, web-only short-form videos, a new online quiz-game, social media engagement and promotion, an educational outreach “toolkit” for science educators to create their own “makerspaces,” an online community of practice, a series of nationwide Innovation Cafés, educator professional development, a suite of teacher resources, an “Idealab,” participation in national conferences, and specialized station relation and marketing. A summative evaluation of the MS2 project indicates that overall, these activities helped make a significant impact on the viewers, users, and participants that NOVA reached. The final evaluation conducted by Concord Evaluation Group (CEG) confidently concluded that the broadcast, website, and outreach activities were successful at achieving the project’s intended impacts. CEG reported that the MS2 series and website content were successful in raising awareness and sparking interest in innovation, and increased public awareness that basic research leads to technological innovation; this interest was also sustained over a six month period. Efforts to create an online community of practice were also successful: the quality of collaboration increased, and community members felt supported while using Maker pedagogy. These findings provide clear evidence that large-scale science media projects like MS2 are an effective means of “moving the needle” on attitudes about and excitement for science. NOVA’s broadcast audience and ratings have always indicated that a large portion of the population is interested in and engages with educational science media on a weekly basis. Yet these evaluation results provide the empirical evidence that beyond being capable of attracting, maintaining, and growing a dedicated group of citizens interested in science, these shows—with their diverse content provided on a variety of media channels—are capable of sparking new interest in science, raising public awareness of the importance of science, and maintaining and growing that interest over time. In a country where approximately a quarter of the population doesn’t know the earth rotates around the sun,1 roughly half still don’t accept evolution,2 and about 20% don’t think climate change is happening,3 the importance of these findings cannot be overstated. The success of MS2 suggests that large-scale media projects dedicated to and linked by coverage of scientific “big ideas” are an effective means of shifting public opinion on—and improving understanding of—science. REFERENCES 1, 2 National Science Foundation, Science and Engineering Indicators (2014). Chapter 7: Science and Technology: Public Attitudes and Understanding. 3 Leiserowitz, A., Maibach, E., Roser-Renouf, C., Feinberg, G., & Rosenthal, S. (2014) Climate change in the American mind: April, 2014. Yale University and George Mason University. New Haven, CT: Yale Project on Climate Change Communication.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparison of RESRAD-ONSITE and PRG/DCC Calculators and Application to Navy Environmental Media

The purpose of this project is to develop an improved Navy-wide approach for estimating risk and calculating cleanup goals for radiologically impacted environmental media (e.g., soils). This comparative analysis focused on the new features added (or improved) to the PRG/DCC Calculators since the 2015 comparison study (Yu et al. 2015a). RESRAD-ONSITE code Version 7.2 and the PRG/DCC Calculators available on the USEPA website between January and June of 2024 were used.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Application of Physiologically Based Pharmacokinetic (PBPK) Modeling to Support Dose Selection: Report of an FDA Public Workshop on PBPK: Physiologically Based Pharmacokinetic Modeling to Support Dose Selection

A workshop entitled “Application of Physiologically-based Pharmacokinetic (PBPK) Modeling to Support Dose Selection” was hosted on March 10, 2014 by the US Food and Drug Administration (FDA) at its White Oak Campus in Silver Spring, MD.1 The workshop endeavored to (i) assess the current state of knowledge in the application of PBPK in regulatory decision-making, and (ii) share and discuss best practices in the use of PBPK modeling to inform dose selection in specific patient populations. The conference benefited from strong and diverse participation from the FDA Center for Drug Evaluation and Research (CDER) leaders, European Medicine Agency (EMA) delegates, and pharmaceutical industry and academia representatives. The workshop commenced with plenary presentations by speakers from the FDA, industry, and academia. In the two panel sessions, detailed questions on the current status and future applications of PBPK in drug development and regulatory decisions were discussed. An important focus of the dialog was the need to better define what constitutes a credible physiological system and the evidentiary standards needed for using such a system in making regulatory decisions. This report provides a content summary of the workshop, based on plenary presentations and panel session transcripts posted on the FDA’s website.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Albedo Data Sets for Bifacial PV Systems

For use by the PV and financial communities to better estimate the performance and to reduce the risk of bifacial PV systems, data sets of ground albedo and associated meteorological data were developed by using existing measurement network data and data contributed by the PV industry. The data sets include time-series data as well as summary information of tabular monthly and yearly data and plots of monthly and hourly albedo values. Complete information is presented in a user's guide and data are available for download from NREL's DuraMAT website.

albedo↗