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At least 145 records · Page 8

Radionuclide Imaging Technologies for Biological Systems

The main objective of this project is to develop technologies and experimental techniques for studying the dynamics of physiological responses of plants to changes in their interface with the local environment and to educate a new generation of scientists in an interdisciplinary environment of biology, physics and engineering. Also an important goal is to perform measurements to demonstrate the new data that can be produced and made available to the plant-biology community using the imaging technologies and experimental techniques developed in this project. The study of the plant-environment interface includes a wide range of topics in plant physiology, e.g., the root-soil interface, resource availability, impact of herbivores, influence of microbes on root surface, and responses to toxins in the air and soil. The initial scientific motivation for our work is to improve understanding of the mechanisms for physiological responses to abrupt changes in the local environment, in particular, the responses that result in short-term adjustments in resource (e.g., sugars, nutrients and water) allocations. Data of time-dependent responses of plants to environmental changes are essential in developing mechanistic models for substance intake and resource allocation. Our approach is to use radioisotope tracing techniques to study whole-plant and plant organ (e.g., leaves, stems, roots) dynamical responses to abrupt changes in environmental conditions such as concentration of CO 2 in the atmosphere, nutrient availability and lighting. To this aim we are collaborating with the Radiation Detector and Imaging Group at the Thomas Jefferson National Laboratory Facility (JLab) to develop gamma-ray and beta particle imaging systems optimized for plant studies. The radioisotope tracing measurements are conducted at the Phytotron facility at Duke University. The Phytotron is a controlled environment plant research facility with a variety of plant growth chambers. One chamber is dedicated to radioisotope labeling measurements. All measurements for this project were carried out in that specially equipped chamber. The radioisotopes used in this project were produced in the tandem accelerator laboratory operated by the Triangle Universities Nuclear Laboratory (TUNL). The target room where the isotopes are produced is located about 100 m from the Phytotron facility. The radioactive substances produced in the tandem laboratory are transported to the Phytotron through underground conduits that run between the two buildings.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Low-Temperature Geothermal Geospatial Datasets: An Example from Alaska

This project is a component of a broader effort focused on geothermal heating and cooling (GHC) with the aim of illustrating the numerous benefits of incorporating GHC and geothermal heat exchange (GHX) into community energy planning and national decarbonization strategies. To better assist private sector investment, it is currently necessary to define and assess the potential of low-temperature geothermal resources. For shallow GHC/GHX fields, there is no formal compilation of subsurface characteristics shared among industry practitioners that can improve system design and operations. Alaska is specifically noted in this work, because heretofore, it has not received a similar focus in geothermal potential evaluations as the contiguous United States. The methodology consists of leveraging relevant data to generate a baseline geospatial dataset of low-temperature resources (less than 150 degrees C) to compare and analyze information accessible to anyone trying to understand the potential of GHC/GHX and small-scale low-temperature geothermal power in Alaska (e.g., energy modelers, communities, planners, and policymakers). Importantly, this project identifies data related to (1) the evaluation of GHC/GHX in the shallow subsurface, and (2) the evaluation of low-temperature geothermal resource availability. Additionally, data is being compiled to assess repurposing of oil and gas wells to contribute co-produced fluids toward the geothermal direct use and heating and cooling resource potential. In this work we identified new data from three different datasets of isolated geothermal systems in Alaska and bottom-hole temperature data from oil and gas wells that can be leveraged for evaluation of low-temperature geothermal resource potential. The goal of this project is to facilitate future deployment of GHC/GHX analysis and community-led programs and update the low-temperature geothermal resources assessment of Alaska. A better understanding of shallow potential for GHX will improve design and operations of highly efficient GHC systems. The deployment and impact that can be achieved for low-temperature geothermal resources will contribute to decarbonization goals and facilitate widespread electrification by shaving and shifting grid loads.

15 GEOTHERMAL ENERGY↗

IDAES-PSE 2.1.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.1.0 Release Highlights New IDAES Examples Repository Starting with this release, the IDAES examples are developed in the new IDAES/examples repository. Along with many content and usability improvements, the most significant changes are: To install the examples, after installing IDAES, run pip install idaes-examples The idaes get-examples command, previously used for this, has been removed The HTML version is now available at https://idaes-examples.readthedocs.io The previous URL, https://idaes.github.io/examples-pse, will not be updated and may be removed at some point in the future For more details, refer to the resources available at IDAES/examples. Removal of Non-Functional Apps A review of the code in the idaes/apps and idaes/models_extra folders was undertaken, and a number of tools were identified as being outdated or non-functional and no longer supported by their development teams. Due to this, the following tools have been removed: idaes/apps/alamopy_depr (note that the new ALAMOpy interface remains avaialble in idaes/core/surrogates) idaes/apps/helmet idaes/apps/ripe idaes/apps/roundingRegression idaes/models_extra/carbon_capture Pyomo 6.6 This version of IDAES is the first requiring Pyomo 6.6. This version of Pyomo contains multiple internal improvements and refactorings. While for the majority of cases this should have positive or no impact on solvability of IDAES models, we are aware of a small number of models that have been affected as a result of these changes. For more information, refer to the Pyomo 6.6.1 release notes. Other highlights Model Initialization A prototype API for a new approach to initializing IDAES models is now available which makes available some new techniques for initializing models. This is documented in the Initializing Models Reference Guide Modular Properties Framework Support for some transport properties Helmholtz Equation of State properties Better error checking for case where unit models are set to include phase equilibrium but the property package is set to support only a single phase Multi-Stream Contactor model: a new base model for systems involving contacting of two or more streams with mass transfer. This model is intended to be used as the foundation for models such as membrane separators, solvent extraction and other similar processes. This is documented in the Multi-Stream Contactor Reference Guide idaes/models_extra/power_generation report() methods for unit models using Helmholtz equation of state General Code Maintenance Streamlining of dependencies and creation of new optional dependency groupings to support non-core tools General linting of codebase to ensure compliance with most pylint checks Spell checking of all code and doc strings Removal of backward compatibility code for Python 2

IDAES↗

Computational Science - Scientific Visualization: Closeout Certifications; CSSV Newsletter Volume I; CSSV Newsletter Volume II; CSSV Activity Journal; CSSV Final Financial Report (Final Technical Progress Report)

This Project's third year of implementation in 2007-2008, the final year, as designated by Elizabeth City State University (ECSU), in cooperation with the National Association of Mathematicians (NAM) Inc., in an effort to promote research and research training programs in computational science & scientific visualization (CSSV). A major goal of the Project was to attract the energetic and productive faculty, graduate and upper division undergraduate students of diverse ethnicities to a program that investigates science and computational science issues of long-term interest to the Department of Energy (DoE) and the nation. The breadth and depth of computational science & scientific visualization and the magnitude of resources available are enormous for permitting a variety of research activities. ECSU's Computational Science-Science Visualization Center will serve as a conduit for directing users to these enormous resources.

97 MATHEMATICS AND COMPUTING↗

Low-Temperature Geothermal Geospatial Datasets: An Example from Alaska: Preprint

This project is part of a broader effort focused on geothermal heating and cooling with the purpose of demonstrating the multi-faceted value of integrating GHC/GHX into national decarbonization plans and community energy plans. Currently, there is a need to better define and evaluate low-temperature geothermal resource potential to provide the basis for supporting private sector investment. For shallow GHC/GHX fields, there is no formal compilation of subsurface characteristics shared among industry practitioners that can improve system design and operations. Alaska is specifically noted in this work, because heretofore, it has not received a similar focus in geothermal potential evaluations as the contiguous United States. The methodology consists of leveraging relevant data generate a baseline low-temperature resources (< 150 degrees C) geospatial dataset to compare and analyze information accessible to anyone trying to understand the potential of GHC/GHX and small-scale low-temperature geothermal power in Alaska (e.g., energy modelers, communities, planners, and policymakers). Importantly, this project is identifying data related to (1) the evaluation of GHC/GHX in the shallow subsurface, and (2) the evaluation of low-temperature geothermal resource availability. Additionally, data is being compiled to assess repurposing of oil and gas wells to contribute co-produced fluids toward the geothermal direct use and heating and cooling resource potential. The goal of this project is facilitating future deployment of GHC/GHX analysis and community-led programs and update the low-temperature geothermal resources assessment of Alaska. A better understanding of shallow potential for geothermal heat exchange (GHX) will improve design and operations of highly efficient GHC systems. The deployment and impact that can be achieved for low temperature geothermal resources will contribute to decarbonization goals and facilitate widespread electrification by shaving and shifting grid loads.

bottom-hole temperature↗

Energy Community Atlas

The Energy Community Atlas provides efficient access to authoritative, curated, and relevant data that is vital to supporting energy planning, development, and economic growth across the U.S. In this effort, researchers at the National Energy Technology Laboratory (NETL) are utilizing advanced data visualization and transformation capabilities to develop an integrated, data atlas and resource focused on supporting energy community transitions to new manufacturing opportunities. Specifically, this project is working to find, acquire, integrate, and virtually host in a user-friendly, public and private solution from available resources, relevant to understanding and characterizing fossil energy communities themselves and inform energy planning, development, and economic growth opportunities, including opportunities for co-development to support manufacturing, critical materials, and more. This Atlas when complete is to offer a one-stop-shop for stakeholders to derive new insights to accelerate energy investments and strategic decision support needs. These are following datasets that are available as part of this ongoing project • Energy Community Atlas Map Package - This is ArcPro Map package and it contains all of the symbolized layers along with ArcPro map and geodatabase • Energy Community Atlas ArcGIS REST service - https://www.arcgis.com/apps/mapviewer/index.html?panel=gallery&suggestField=true&layers=537ced69bd88440380a62c2ec8aca30c • README Energy Community Atlas - Read me word document that has details about feature classes in Map package, ArcPro map and ArcGIS Rest Service

Bipartisan Infrastructure Law↗

Climate, soil and plant functional types as drivers of global fine-root trait variation

Ecosystem functioning relies heavily on below-ground processes, which are largely regulated by plant fine-roots and their functional traits. However, our knowledge of fine-root trait distribution relies to date on local- and regional-scale studies with limited numbers of species, growth forms and environmental variation. We compiled a world-wide fine-root trait dataset, featuring 1115 species from contrasting climatic areas, phylogeny and growth forms to test a series of hypotheses pertaining to the influence of plant functional types, soil and climate variables, and the degree of manipulation of plant growing conditions on species fine-root trait variation. Most particularly, we tested the competing hypotheses that fine-root traits typical of faster return on investment would be most strongly associated with conditions of limiting versus favourable soil resource availability. We accounted for both data source and species phylogenetic relatedness. We demonstrate that: (i) Climate conditions promoting soil fertility relate negatively to fine-root traits favouring fast soil resource acquisition, with a particularly strong positive effect of temperature on fine-root diameter and negative effect on specific root length (SRL), and a negative effect of rainfall on root nitrogen concentration; (ii) Soil bulk density strongly influences species fine-root morphology, by favouring thicker, denser fine-roots; (iii) Fine-roots from herbaceous species are on average finer and have higher SRL than those of woody species, and N 2 -fixing capacity positively relates to root nitrogen; and (iv) Plants growing in pots have higher SRL than those grown in the field. Synthesis. This study reveals both the large variation in fine-root traits encountered globally and the relevance of several key plant functional types and soil and climate variables for explaining a substantial part of this variation. Climate, particularly temperature, and plant functional types were the two strongest predictors of fine-root trait variation. High trait variation occurred at local scales, suggesting that wide-ranging below-ground resource economics strategies are viable within most climatic areas and soil conditions.

54 ENVIRONMENTAL SCIENCES↗

Chemical Characterization and Conversion Assessment of BurCell® System Treated MSW Materials

Cornerstone Resources has developed a proprietary advanced preprocessing technology, the BurCell® System, for production of a high quality organic biomass feedstock from numerous waste resources. While suitable for a variety of feedstock, the focus of development for the BurCell® System has been thus far Municipal Solid Waste (MSW). In MSW, the system technology is able to capture non-recyclable waste paper, cardboard, and organic materials, homogenize these materials and allow for separation of the non-organic fraction. The resulting homogenized organic material typically represents in excess of 50% of the raw MSW feed. The recently released Billion Ton Study now includes MSW as a feedstock resource showing it to be one of the lowest cost and most widely distributed biomass resources available; however, acknowledging the concerns with the potential physical and chemical variability of an MSW based feedstock stream. The focus on MSW has also been driven by MSW being a resource with a clear, well understood and mature supply-chain. Treating MSW in the BurCell® System will make that source of biomass substantially more accessible. To further promote the use of this MSW derived biomass and beneficially exploit the use of the BurCell® System for processing of biomass for Bioenergy production facilities, more detailed lab data is now required

09 BIOMASS FUELS↗

Cornerstone Resources Feedstock Characterization and Conversion Assessment of Treated MSW Materials (CRADA Closeout Final Report)

Cornerstone Resources has developed a proprietary advanced preprocessing technology, the BurCell® System, for production of a high quality organic biomass feedstock from numerous waste resources. While suitable for a variety of feedstock, the focus of development for the BurCell® System has been thus far municipal solid waste (MSW). In MSW, the system technology is able to capture non-recyclable waste paper, cardboard, and organic materials, homogenize these materials and allow for separation of the non-organic fraction. The resulting homogenized organic material typically represents in excess of 50% of the raw MSW feed. The recently released Billion Ton Study now includes MSW as a feedstock resource showing it to be one of the lowest cost and most widely distributed biomass resources available; however, acknowledging the concerns with the potential physical and chemical variability of an MSW based feedstock stream. The focus on MSW has also been driven by MSW being a resource with a clear, well understood and mature supply-chain. Treating MSW in the BurCell® System will make that source of biomass substantially more accessible. To further promote the use of this MSW derived biomass and beneficially exploit the use of the BurCell® System for processing of biomass for Bioenergy production facilities, more detailed lab data is now required.

09 BIOMASS FUELS↗

Exploring Bioeconomy Growth through the Public Release of the Biomass Scenario Model

The Biomass Scenario Model (BSM) is an important tool for exploring vibrant future bioeconomy scenarios that leverage domestic resources. Developed by NREL and BETO, this model of the domestic biofuels supply chain has been used to explore success strategies for BETO's activities towards bioeconomy growth. The BSM offers a robust test bed for detailed exploration of effects of BETO activities within the complex context of resource availability; physical, technological, and economic constraints; behavior; and policy. The public release of the model in 2017 will allow broad engagement with the theme of the conference as model users can analyze bioeconomy growth, domestic biomass resource use, and associated effects. The BSM is a carefully validated, state-of-the-art, dynamic model of the biomass to biofuels supply chain. Using a system dynamics simulation modeling approach, the model tracks long-term deployment of biofuels given technology development and investment, considering land availability, the competing oil market, consumer demand, and government policies over time. Sample outputs include biofuels production, feedstock use, capital investment, incentives, and costs of feedstocks and fuels. BSM scenarios reveal technological, economic, and policy challenges, as well as opportunities for dynamic growth of the bioeconomy with strategic public and private investment at key points in the system. The model logic and results have been reviewed extensively, through collaborative analysis, expert reviews and external publications (https://www.zotero.org/groups/bsm_publications/).

09 BIOMASS FUELS↗

The Design of a Multiphase CO2 Turbine Test Facility

As the energy market continues to evolve with increased renewables, such as solar and wind energy, the need to balance energy demand with its availability has become increasingly more challenging. The country’s aggressive decarbonization goals in the upcoming decades will increase renewable energy market penetration, thus exacerbating the disparity between energy demand and renewable resource availability. Long-duration energy storage technologies, such as pumped thermal energy storage (PTES), are potential solutions to this challenge with high potential round-trip efficiency (RTE), no geological or geographical constraints, and low safety risks. The charge mode of a PTES system operates a heat pump cycle, converting input power to thermal energy in hot and cold reservoirs. Fundamentally, heat pumps are commonly used in daily life, but the operating conditions required by high RTE PTES systems are outside the design experience of these systems. The low-pressure side of the cycle for a supercritical CO2 (sCO2) PTES operates just above the vapor-dome and potentially drives the turbine to expand into the dome, resulting in multiphase operation in the rear stages of the turbine. This paper presents the design of the test facility for an advanced multiphase-tolerant sCO2 turbine for the heat pump of a PTES system. Heat rejection and pressure loss estimations for the multiphase sections of the test loop presented unforeseen challenges.

25 ENERGY STORAGE↗

Fast Multiresolution Reads of Massive Simulation Datasets

Today’s massively parallel simulation codes can produce output ranging up to many terabytes of data. Utilizing this data to support scientific inquiry requires analysis and visualization, yet the sheer size of the data makes it cumbersome or impossible to read without computational resources similar to the original simulation. We identify two broad classes of problems for reading data and present effective solutions for both. The first class of data reads depends on user requirements and available resources. Tasks such as visualization and user-guided analysis may be accomplished using only a subset of variables with a restricted spatial extent at a reduced resolution. The other class of reads requires full resolution multivariate data to be loaded, for example to restart a simulation. We show that utilizing the hierarchical multiresolution IDX data format enables scalable and efficient serial and parallel read access on a variety of hardware from supercomputers down to portable devices. Here, we demonstrate interactive view-dependent visualization and analysis of massive scientific datasets using low-power commodity hardware, and we compare read performance with other parallel file formats for both full and partial resolution data.

97 MATHEMATICS AND COMPUTING↗

ParaOpt: Automated Application Parameterization and Optimization for the Cloud

The variety of instance types available on cloud platforms offers enormous flexibility to match the requirements of applications with available resources. However, selecting the most suitable instance type and configuring an application to optimally execute on that instance type can be complicated and time-consuming. For example, application parallelism flags must match available cores and problem sizes must be tuned to match available memory. As the search space of application configurations can be enormous, we propose an automated approach, called ParaOpt, to automatically explore and tune application configurations on arbitrary cloud instances. ParaOpt supports arbitrary applications, enables use of custom optimization methods, and can be configured with different optimization targets such as runtime and cost. We evaluate ParaOpt by optimizing genomics, molecular dynamics, and machine learning applications with four types of optimizers. We show with as few as 15 parameterized executions of an application, representing between 1.2%-26.7% of the search space, that ParaOpt is able to identify the optimal configuration in 32.7% of experiments and a near-optimal configuration in 83.2% of cases. As a result of using near-optimal configurations, ParaOpt reduces overall execution time by up to 85.8% when compared with using the default configuration.

Wu, Chaofeng↗

Safely Exploring Solar: A Guide for Austin Energy Customers

It's easy to get overwhelmed as you start to explore your solar options. There is a wealth of information available out there, particularly online, and it can be hard to know who to trust. This informational guide has been adapted from Austin Energy's Solar Education Course and other national best practice resources, as of May 1, 2023. It will help you understand some of the key concepts and resources available to help you on your solar journey if you have a home in Austin Energy's service territory and are considering installing solar. This document includes: (1) Austin Energy Programs and Services Related to Solar; (2) Tips for Spotting a Solar Scam; (3) Things to Know When Designing Your System; (4) Key Considerations When Shopping for a Solar Contractor; and (5) Final Takeaways.

14 SOLAR ENERGY↗

Impact of automated battery sorting for mineral recovery from lithium-ion battery recycling in the United States

The United States has identified several lithium-ion battery materials as critical for reaching the national emission reduction targets that have been set in accordance with the 2015 Paris Agreement. However, with few natural resources available domestically, there is a rapidly growing focus on the development of a domestic recycling industry to recover these materials from end-of-life batteries. Here, we use the Lithium-Ion Battery Resources Assessment (LIBRA) system dynamics model to evaluate the impact of automated battery sorting technology in terms of the shares of cobalt and nickel that are recovered through recycling. Findings show that automated sorting has clear benefits over manual sorting methods by helping recyclers selectively process high-cobalt batteries. By maximizing cobalt recovery, recycling becomes more profitable and drives greater investment in recycling capacity, resulting in a higher share of nickel and cobalt recovered from EOL batteries over time.

25 ENERGY STORAGE↗

EERE Resources for Undergraduate Students

Looking to expand your experience outside of the classroom? The Office of Energy Efficiency and Renewable Energy (EERE) at the U.S. Department of Energy (DOE) has a number of resources available for undergraduate students, including competitions, internships, and career planning information to help you navigate the education to employment pathway in energy.

Education, Resources, Student, Teach, School↗

EERE Resources for Undergraduate Students

Looking to expand your experience outside of the classroom? The Office of Energy Efficiency and Renewable Energy (EERE) at the U.S. Department of Energy (DOE) has a number of resources available for undergraduate students, including competitions, internships, and career planning information to help you navigate the education to employment pathway in energy.

Education, Resources, Student, Teach, School↗

Fast inference of deep neural networks in FPGAs for particle physics

Here, recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗