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DOE OSTI2023

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2023

As the United States' largest source of renewable energy, wind is already playing a vital role in the nation's shift to electricity generated from sustainable resources. Scientists, engineers, and analysts at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) are propelling our country closer to this clean energy future with new efficient, reliable, and cost-competitive wind technologies and deployment strategies. In Fiscal Year 2023 (FY 2023), NREL received national acclaim for recent wind technology breakthroughs, applauded sustained decades of progress in wind energy research, and heralded new initiatives to support the next generation of wind development. This report and NREL's achievements of the last year set the stage for innovative research to come. Two NREL software innovations used to assess wind technology options were recognized with R&D 100 Awards. The Renewable Energy Potential (reV) model helps planners, developers, and researchers calculate wind energy capacity, generation, and cost for specific facilities, fleets and scenarios. NREL's Simulation and Emulation for Advanced Systems validates transmission and distribution solutions to minimize implementation risks. Both of these tools can be applied to a range of renewable solutions to cut costs and improve the reliability of systems in even the most remote locations. The laboratory commemorated landmark anniversaries for wind energy research facilities and programs managed by NREL. The National Wind Technology Center (NWTC) celebrated 30 years as a world-class hub for renewable energy research. An important component of the NWTC on NREL's Flatirons Campus, the one-of-a-kind 7-megawatt (MW) Controllable Grid Interface wind energy research apparatus, turned 10 this year. The Collegiate Wind Competition completed its tenth annual competition in May 2023. Six years after the grand challenges in wind energy science were first compiled, NREL brought together more than 100 wind energy experts from 15 countries to revisit the field's most pressing research needs. Other key new research initiatives examined ways to simultaneously address the needs for skilled wind energy workers and equitable job opportunities. NREL released the first national-level report evaluating offshore wind industry workforce gaps and ways to build a large, diverse pool of job candidates. The lab also co-hosted the International Partnering Forum's Offshore Wind Workforce Summit as part of its ongoing efforts to address the special challenges of that industry sector. NREL research and development (R&D) for the DOE Wind Energy Technologies Office (WETO) continues to build momentum in its efforts to combat climate change, create clean energy jobs, and promote energy justice. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2023 (between Oct. 1, 2022, and Sept. 30, 2023).

accomplishments↗
DOE OSTI2013

Natural Capital Management: An Evolutionary Paradigm for Sustainable Restoration Investment - 13455

Unlike other forms of capital assets (built infrastructure, labor, financial capital), the supply of usable or accessible air, land, and water elements (termed Natural Capital Assets or NCA) available to enterprise processes is structurally shrinking due to increased demand and regulatory restriction. This supply/demand imbalance is affecting all forms of public and private enterprise (including Federal Facilities) in the form of encroachment, production limits, cost increases, and reduced competitiveness. Department of Energy (DOE) sites are comprised of significant stocks of NCA that function as both conserved capital (providing ecosystem services and other reserve capacity), and as natural infrastructure (supporting major Federal enterprise programs). The current rubric of 'Environmental Stewardship' provides an unduly constrained management paradigm that is focused largely on compliance process metrics, and lacks a value platform for quantifying, documenting, and sustainably re-deploying re-capitalized natural asset capacity and capability. By adopting value-based system concepts similar to built infrastructure accounting and information management, 'stewarded' natural assets relegated to liability- or compliance-focused outcomes become 're-capitalized' operational assets able to support new or expanded mission. This growing need for new accounting and management paradigms to capture natural capital value is achieving global recognition, most recently by the United Nations, world leaders, and international corporations at the Rio+20 Summit in June of 2012. Natural Capital Asset Management (NCAM){sup TM} is such an accounting framework tool. Using a quantification-based design, NCAM{sup TM} provides inventory, capacity and value data to owners or managers of natural assets such as the DOE that parallel comparable information systems currently used for facility assets. Applied to Environmental Management (EM) and other DOE program activities, the natural asset capacity and value generated by EM projects and other investment and operational programming can be recorded and then allocated to mission and/or ecosystem needs as part of overall site, complex, and Federal decision-making. NCAM{sup TM} can also document post-restoration asset capability and value for use in weighing loss mitigation and ecosystem damage claims arising from past operational activities. A prototype NCAM{sup TM} evaluation developed at the Savannah River Site (SRS) demonstrates use of this framework as an advanced paradigm for NCA accounting and decision-making for the larger DOE complex and other enterprise using natural capital in operations. Applying a quantified value paradigm, the framework catalogues the results of activities that sustain, restore, and modernize natural assets for enterprise-wide value beyond that of compliance milestones. Capturing and assigning recapitalization value using NCAM{sup TM} concepts and tools improves effective reuse of taxpayer-sustained assets, records ecosystem service value, enables mission and enterprise optimization, and assures the sustainability of shared natural capital assets in regional pools vital to both complex sites and local and regional economies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Indonesian Highly Enriched Uranium-Bearing Waste Elimination - 17393

The Department of Energy's National Nuclear Security Administration (NNSA) and the Y-12 National Security Complex (Y-12) have been working with PT Industri Nuklir Indonesia (PT Inuki), an Indonesian medical isotope producer, since 2014 to disposition all highly enriched uranium (HEU) stored at the government-owned Research Center for Science and Technology (PUSPIPTEK) near Serpong, Indonesia. To eliminate the 1.5 kilograms of liquid HEU at the facility, Y-12 proposed a method to down-blend the leftover process solutions with a depleted uranium solution made on-site to render low-enriched uranium (LEU). This method is essentially irreversible since the uranium enrichment is degraded using a molecular level of intimate mixing in aqueous solution. This cooperation was tied to a presidential commitment made at the 2016 Nuclear Security Summit to down-blend all remaining HEU in Indonesia by September 2016. While all unirradiated HEU was down-blended to LEU in March 2016, the campaign to down-blend the 1.4 kilograms of irradiated HEU began in late July 2016 and was completed in August, making Indonesia and all of Southeast Asia free of HEU. As part of this cooperation, Y-12 and NNSA also proposed a subsequent disposition option for the LEU solution whereby the irradiated and unirradiated solutions will be chemically manipulated to prepare a dry LEU oxide suitable for interim storage and yield a uranium-free liquid stream that can be discarded as radioactive waste. In the fall of 2016, Y-12 and NNSA plan to begin developing the LEU solution disposition methodology with the Indonesian facility using small-scale batch-wise methods within existing hot cells, involving straightforward precipitation, ion exchange, decantation, and calcination methods. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.

Santos Souza, Renan↗
DOE OSTI2020

Lessons Learned from Demolition of Hanford's Plutonium Finishing Plant - 20507

U.S. Department of Energy (DOE) contractor CH2M Hill Plateau Remediation Company (CHPRC), a Jacobs owned company, is making significant progress demolishing the Plutonium Finishing Plant (PFP), which has long been known as one of the most hazardous buildings in the DOE Environmental Management Complex (DOE Complex). The facility is located on the Hanford Site and produced plutonium metal during the Cold War. Production was stopped in 1989, the facility was formally shut down in 1996, and material processing was completed in 2004. After approximately 25 years of demolition preparations, open-air demolition of the main portions of the Hazard Category II nuclear facility began in November 2016. To prepare the PFP for demolition, CHPRC employees performed some of the most hazardous work across the DOE Complex. In addition to extensive demolition preparations, including a wide-reaching communication strategy across the Hanford Site, CHPRC implemented robust controls and monitoring during demolition, and when necessary, adjusted demolition practices and sequence to maintain employee safety and project efficiency. Demolition of the main PFP building began in November 2016. The technical complexity, high hazards and radiological concerns have all led to a difficult demolition environment. Following a spread of contamination at the project site beyond the radiological boundaries in December 2017, work was halted and a recovery plan was generated to allow the demolition work to continue. After the implementation of additional controls and concurrence from regulatory agencies and DOE, the demolition was ready to resume work and reinitiated in August 2018. There are a number of lessons learned from the demolition activities and recovery plan process. This paper allows CHPRC and DoE's Richland Operations Office (RL) to share lessons learned and progress to date with other challenging and hazardous projects across the DOE Complex. Understanding the risk acceptance level is critical to execution of any open-air demolition project and is unique to each facility. Four key lessons learned from this project include: 1) plan execution issues, 2) stakeholder communication issues, 3) watching for summit fever and 4) minimizing exposed contaminated surfaces during demolition. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Getting to 100%: Six Strategies for the Challenging Last 10%

This presentation summarizes the challenge of decarbonizing the last 10%" of the power system and discusses six strategies for addressing that challenge. This presentation was given at the IRA, BIL, and the Future of Energy: A Summit to Support State Implementation" workshop in Washington, D.C., in April 2024.

carbon capture↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (Distributed Parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

Sattar, Naw Safrin↗
DOE OSTI2024

Scaling and performance portability of the particle-in-cell scheme for plasma physics applications through mini-apps targeting exascale architectures

We perform a scaling and performance portability study of the particle-in-cell scheme for plasma physics applications through a set of mini-apps we name "Alpine", which can make use of exascale computing capabilities. The mini-apps are based on Independent Parallel Particle Layer, a framework that is designed around performance portable and dimension independent particles and fields. We benchmark the simulations with varying parameters such as grid resolutions (5123 to 20483) and number of simulation particles (109 to 1011) with the following mini-apps: weak and strong Landau damping, bump-on-tail and two-stream instabilities, and the dynamics of an electron bunch in a charge-neutral Penning trap. We show strong and weak scaling and analyze the performance of different components on several pre-exascale architectures such as Piz-Daint, Cori, Summit and Perlmutter. While the scaling and portability study helps identify the performance critical components of the particle-in-cell scheme in the current state-of-the-art computing architectures, the mini-apps by themselves can be used to develop new algorithms and optimize their high performance implementations targeting exascale architectures.

Muralikrishnan, Sriramkrishnan↗
DOE OSTI2024

Intrinsic and environmental drivers of pairwise cohesion in wild Canis social groups

Animals within social groups respond to costs and benefits of sociality by adjusting the proportion of time they spend in close proximity to other individuals in the group (cohesion). Variation in cohesion between individuals, in turn, shapes important group-level processes such as subgroup formation and fission–fusion dynamics. Although critical to animal sociality, a comprehensive understanding of the factors influencing cohesion remains a gap in our knowledge of cooperative behavior in animals. We tracked 574 individuals from six species within the genus Canis in 15 countries on four continents with GPS telemetry to estimate the time that pairs of individuals within social groups spent in close proximity and test hypotheses regarding drivers of cohesion. Pairs of social canids (Canis spp.) varied widely in the proportion of time they spent together (5%–100%) during seasonal monitoring periods relative to both intrinsic characteristics and environmental conditions. The majority of our data came from three species of wolves (gray wolves, eastern wolves, and red wolves) and coyotes. For these species, cohesion within social groups was greatest between breeding pairs and varied seasonally as the nature of cooperative activities changed relative to annual life history patterns. Across species, wolves were more cohesive than coyotes. For wolves, pairs were less cohesive in larger groups, and when suitable, small prey was present reflecting the constraints of food resources and intragroup competition on social associations. Pair cohesion in wolves declined with increased anthropogenic modification of the landscape and greater climatic variability, underscoring challenges for conserving social top predators in a changing world. We show that pairwise cohesion in social groups varies strongly both within and across Canis species, as individuals respond to changing ecological context defined by resources, competition, and anthropogenic disturbance. Our work highlights that cohesion is a highly plastic component of animal sociality that holds significant promise for elucidating ecological and evolutionary mechanisms underlying cooperative behavior.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Modeling and Optimization of a Rotating Packed Bed Contactor with a Tetraamine-Appended Metal−Organic Framework for CO 2 Capture

A potential contactor technology for sorbent-based CO 2 capture is the rotating packed bed that contains separate sections for continuous adsorption and desorption. A heat exchanger can be embedded to remove heat in the adsorption section and add heat in the desorption section. In this work, we develop a two-dimensional (2D) model of a rotating packed bed for use in CO 2 capture applications. Mass and energy balances for the model are developed based on a Ljungström-type air preheater, which accounts for the counter-current axial flow of gas phases in separate sections of the bed and the rotation of a solid sorbent, which cycles between adsorption and desorption sections. The sorbent used for this analysis is the tetraamine-appended metal−organic framework Mg 2 (dobpdc)(3−4− 3), chosen for its stability and affinity for CO 2 at low partial pressures, such as those from a natural gas power plant source. An optimization problem is solved that considers the trade-off between maximizing the productivity of the bed and minimizing energy consumption. Maximum productivity and minimum energy are found to be 8.53 kg/h/m 3 and 3.84 MJ/kg, respectively, when these objectives are optimized independently. It is observed that the flue gas pressure and bed rotational speed are the desired operating variables to vary for model-based design of experiments to reduce uncertainty in parameter estimation, as these two variables yielded the most information content based on the Fisher information matrix.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Optimal Design and Techno-Economic Analysis of 3D-Printed, Intensified Packings for Absorbers and Strippers in Solvent-Based CO 2 Capture

A potential technology for the CO 2 absorption process is utilizing intensified structured packing with embedded cooling/heating channels for continuous heat exchange, which can overcome limitations of discrete methods, such as discrete intercooling and centralized reboilers, to aid in reducing energy consumption and decreasing costs. This work investigates the modeling of intensified packing (IP) for the stripper tower, extending on previous work for the absorber, which distributes heat internally within the column, improving the thermodynamics for the solvent regeneration process. The model includes submodels for steam turbine extraction to produce steam at various qualities as well as a surrogate model for calculating steam enthalpy. A cost model for a plant-scale absorption capture process was developed, allowing for the design of the plant to be optimized, subject to minimizing capture cost using two different power plant flue gas sources. In this optimization, the placement of IP in both towers is optimized to balance the trade-off between enhanced heat transfer and reduced mass transfer volume. For natural gas combined cycle flue gas, the standard process configuration had a minimum cost of $\$$65.40/tonne CO 2 , and considering IP, the minimum capture cost is reduced to $\$$62.73/tonne, with utilization in the stripper column, which reduces yearly costs by up to $\$$2.67 MM/yr. Cooling the absorber through IP, or intercoolers, was only found to be beneficial at higher capture rates, with IP in both towers having a cost of capture of $\$$68.08/tonne at 99.9% capture, a reduction of $\$$12.64/tonne when using only intercoolers at the same capture rate. When capturing from pulverized-coal power plants, the minimum cost of capture when using IP in both towers is $\$$44.18/tonne (at 97% capture), while the standard configuration with and without intercoolers was $\$$45.69 and $\$$47.22 per tonne, respectively. This results in a reduction in yearly costs of $\$$16.98 MM/yr from the base-case configuration. At this higher CO 2 concentration, cooling in the absorber from the IP becomes extremely beneficial, reducing energy consumption by up to 6%.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Optimal Design and Operation of Intensified Absorbers with 3D-Printed Packing for Solvent-Based CO 2 Capture

Many potential solvent-based carbon capture processes suffer from a high heat of absorption of CO 2 that adversely affects the thermodynamic driving force. While interstage coolers are often used for removing a portion of the generated heat by removing the solvent or a portion of the solvent from a stage and cooling and returning it back to the absorber, they can be placed only at discrete locations in the tower. This work investigates intensified absorbers with 3D-printed packing that includes an internal cooler and therefore can be potentially used for maximizing the operational efficiency of the absorbers for CO 2 capture. The intensified absorber is modeled by using a generic, first-principles, equation-oriented absorber column model. Since the placement of these intensified packings would cause a loss of area/volume used for mass transfer, optimization of the proposed intensified absorber is performed by optimally selecting the locations at which to place these devices and designing them such that the trade-off due to the addition of the heat removal area and the resulting loss in the mass transfer area is accounted for. Results show that optimally placed and designed intensified packings can lead to a significant increase in the capture efficiency of the process in comparison to a similar column with no internal cooling. It is also observed that by optimally placing and designing intensified packings, the lean solvent flow rate to the absorber can be decreased, and the CO 2 lean loading can be increased while still maintaining the same capture efficiency. These process changes can lead to a substantial reduction in the cost of capture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Net Present Value Optimization of a Natural Gas Combined Cycle Plant with CO 2 Capture using a Water-Lean Solvent Considering Transient Electricity Price for Multiple Regions

Global CO 2 emissions are increasing at about a 1.5% rate per year. Fossil fuel-based plants are one of the main contributors to this rise. In the power generation industry, fossil fuel plants are dominant, and many plants are under development. In this study, a natural gas combined cycle (NGCC) power plant with postcombustion capture using a leading water-lean solvent is considered. For optimal design and operating schedule, large-scale dynamic optimization is undertaken for net present value (NPV) optimization. The first principle dynamic model of NGCC is developed, including a model of the highly efficient H-class gas turbines. For computational tractability of the dynamic optimization problem, a reduced-order model is developed by using the Hankel singular value decomposition. A waterlean solvent, N-(2-ethoxyethyl)-3-morpholinopropan-1-amine, is used for carbon capture. A model of the capture system is developed in Aspen Plus, which is used to develop a reduced-order model by using ALAMO, a machine learning software. In addition, a reduced model of the CO 2 compression system with a dehydration unit is also considered. The integrated system is used for NPV optimization by using the Python-based PYOMO platform. The PCC process is analyzed for three configurations-conventional packed bed, rotating packed bed (RPB), and a combination of RPB and direct contact cooler. The NPV optimization is performed for 14 regional markets by considering year-long clustered and continuous locational marginal price data with a 1 h interval. Optimization results show that the PCC can achieve 90% CO 2 capture with a positive NPV for six regions. Sensitivity studies conducted by using the PCC configurations indicate that the process is economically feasible for 9 regions out of 14 regional electricity markets with NPV values in the range of 33−540 $MM.

cabon capture↗
DOE OSTI2025

Scalable Hybrid Learning Techniques for Scientific Data Compression

Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms that limit errors to primary data (PD), scientists require compression techniques that accurately preserve derived quantities of interest (QoIs). Here, this article presents a physics-informed compression technique implemented as an end-to-end, scalable, GPU-based pipeline for data compression that addresses this requirement. Our hybrid compression technique combines machine learning techniques and standard compression methods. Specifically, we combine an autoencoder, an error-bounded lossy compressor to provide guarantees on raw data error, and a constraint satisfaction post-processing step to preserve the QoIs within a minimal error (generally less than floating point error). The effectiveness of the data compression pipeline is demonstrated by compressing nuclear fusion simulation data generated by a large-scale fusion code, XGC, which produces hundreds of terabytes of data in a single day. Our approach works within the ADIOS framework and results in compression by a factor of more than 150 while requiring only a few percent of the computational resources necessary for generating the data, making the overall approach highly effective for practical scenarios.

ITER↗
DOE OSTI2022

Alpine disk failure dataset

This dataset consists of 4114 disk failure events collected from Alpine from 07/01/2019 to 04/18/2022. Individual events are identified by their timestamp in ISO 8601 format, rack and enclosure where the failed disk is located.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Geothermal Case Studies

The US Geological Survey (USGS) resource assessment (Williams et al., 2009) outlined a mean 30GWe of undiscovered hydrothermal resource in the western US. One goal of the Geothermal Technologies Office (GTO) is to accelerate the development of this undiscovered resource. The Geothermal Technologies Program (GTP) Blue Ribbon Panel (GTO, 2011) recommended that DOE focus efforts on helping industry identify hidden geothermal resources to increase geothermal capacity in the near term. Increased exploration activity will produce more prospects, more discoveries, and more readily developable resources. Detailed exploration case studies akin to those found in oil and gas (e.g. Beaumont, et al, 1990) will give operators a single point of information to gather clean, unbiased information on which to build geothermal drilling prospects. To support this effort, the National Renewable Energy laboratory (NREL) has been working with the Department of Energy (DOE) to develop a template for geothermal case studies on the Geothermal Gateway on OpenEI. In fiscal year 2013, the template was developed and tested with two case studies: Raft River Geothermal Area (http://en.openei.org/wiki/Raft_River_Geothermal_Area) and Coso Geothermal Area (http://en.openei.org/wiki/Coso_Geothermal_Area). In fiscal year 2014, ten additional case studies were completed, and additional features were added to the template to allow for more data and the direct citations of data. The template allows for: Data - a variety of data can be collected for each area, including power production information, well field information, geologic information, reservoir information, and geochemistry information. Narratives ? general (e.g. area overview, history and infrastructure), technical (e.g. exploration history, well field description, R&D activities) and geologic narratives (e.g. area geology, hydrothermal system, heat source, geochemistry.) Exploration Activity Catalog - catalog of exploration activities conducted in the area (with dates and references.) NEPA Analysis ? a query of NEPA analyses conducted in the area (that have been catalogued in the OpenEI NEPA database.) In fiscal year 2015, NREL is working with universities to populate additional case studies on OpenEI. The goal is to provide a large enough dataset to start conducting analyses of exploration programs to identify correlations between successful exploration plans for areas with similar geologic occurrence models.

15 GEOTHERMAL ENERGY↗