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At least 1,351 records · Page 75

Cryogenics Python Coding Calculations for Nitrogen & Helium Loss

Helium and Nitrogen are two significant chemical elements that are used to advance cryogenic scientific breakthroughs. Helium is a colorless, odorless, and tasteless gas that becomes liquid at (−452 °F). Nitrogen is a gas at its standard conditions; It becomes solid once temperatures reach below (-346 °F), and it boils and becomes a gas once temperatures reach above (-320 °F). During this research project I used a web-based interactive computing platform called Jupyter Lab to modify various python programs that calculate, and display helium and nitrogen inventory lost from three cryo plants

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Towards a Software Development Framework for Interconnected Science Ecosystems

The innovative science of the future must be multi-domain and interconnected to usher in the next generation of “self-driving” laboratories enabling consequential discoveries and transformative inventions. Such a disparate and interconnected ecosystem of scientific instruments will need to evolve using a system-of-systems (SoS) approach. The key to enabling application integration with such an SoS will be the use of Software Development Kits (SDKs). Currently, SDKs facilitate scientific research breakthroughs via algorithmic automation, databases and storage, optimization and structure, pervasive environmental monitoring, among others. However, existing SDKs lack instrument-interoperability and reusability capabilities, do not effectively work in an open federated architectural environment, and are largely isolated within silos of the respective scientific disciplines. Inspired by the scalable SoS framework, this work proposes the development of INTERSECT-SDK to provide a coherent environment for multi-domain scientific applications to benefit from the open federated architecture in an interconnected ecosystem of instruments. This approach will decompose functionality into loosely coupled software services for interoperability among several solutions that do not scale beyond a single domain and/or application. Furthermore, the proposed environment will allow operational and managerial inter-dependence while providing opportunities for the researchers to reuse software components from other domains and build universal solution libraries. We demonstrate this research for microscopy use-case, where we show how INTERSECT-SDK is developing the tools necessary to enable advanced scanning methods and accelerate scientific discovery.

Malviya, Addi Thakur↗

Techno-Economic Analysis and Optimization for MOF-Based CO2 Capture from NGCC Applications

This poster presents the development of isotherm and contactor models for a tetraamine MOF for CO2 capture from NGCC flue gas sources. It presents results for development of an isotherm model for a tetraamine MOF, validation of a fixed bed model to experimental fixed bed breakthrough data, and optimization of a fixed bed TSA process and moving bed TSA process for CO2 capture from a NGCC flue gas source.

Matuszewski, Michael↗

Ultimate Colliders

Our understanding of the Universe critically depends on the fundamental knowledge of particles and fields, which represents a central endeavor of modern high-energy physics. Energy frontier particle colliders - arguably, among the largest, most complex and advanced scientific instruments of modern times - for many decades have been at the forefront of scientific discoveries in high-energy physics. Due to technology advances and beam physics breakthroughs, the colliding beam facilities have progressed immensely and now operate at energies and luminosities many orders of magnitude greater than the pioneering instruments of the early 1960s. While the Large Hadron Collider and the Super-KEKB factory represent the frontier hadron and lepton colliders of today, respectively, future colliders are an essential component of a strategic vision for particle physics. Conceptual studies and technical developments for several exciting near- and medium-term future collider options are underway internationally. Analysis of numerous proposals and studies for far-future colliders indicate the limits of the collider beam technology due to machine size, cost, and power consumption, and call for a paradigm shift of the particle physics research at ultra-high energy but low luminosity colliders approaching or exceeding 1 PeV center-of-mass energy scale.

43 PARTICLE ACCELERATORS↗

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↗

Revealing the Latent Atomic World Through Data-Driven Microscopy

Many emerging technologies depend on the precise design of materials structure, chemistry, and defects. As devices shrink, manufacturing tolerances tighten, and performance envelopes improve, we must increasingly measure and manipulate materials at or near the single atom level. Here we describe how transmission electron microscopy (TEM) underpins our ability to see and direct the latent atomic world. We review a selection of our recent high-resolution TEM studies of the synthesis of oxide-based nanomaterials and their evolution in extreme environments. We then discuss powerful new artificial intelligence (AI) and machine learning (ML) approaches we have developed for rich, reproducible, and scalable experimentation. Furthermore, we conclude by discussing future developments that will enable new materials for breakthrough technologies.

97 MATHEMATICS AND COMPUTING↗

Energy Systems Integration Facility Stewardship Summary: Fiscal Year 2023

A summary of NREL's good stewardship of the nationally unique Energy Systems Integration Facility (ESIF) highlighting performance metrics, infrastructure and capability upgrades, and examples of R&D impact. In fiscal year 2023, ESIF installed its third-generation high-performance computer, expanded capabailities for validating building energy controls, and demonstrated a leap in scale through virtual networking with another national laboratory. ESIF researchers made breakthroughs in cybersecurity for energy systems, efficient high-powered electric vehicle charging, leveraging reinforcement learning for grid resilience, and more.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗

Cesium Removal and Hydraulic Performance Comparisons of CST Ion Exchange Media Batches in Support of the Tank Closure Cesium Removal Project - 20522

Three batches of Crystalline Silicotitanate (CST) ion exchange media have been evaluated for cesium removal efficiency from a Savannah River Site (SRS) High Level Waste (HLW) supernate simulant in support of the Tank Closure Cesium Removal (TCCR) Project, which involves at-tank column waste treatment. Results will be utilized to select the CST media batch to be added to the next TCCR unit columns. Two of the CST media samples were recent production batches and the third was an archived batch studied extensively at SRS nearly two decades ago. The archived CST batch had a significantly smaller average particle diameter than more recent batches. Small-scale (∼23 mL), side-by-side column tests were conducted at 35 deg. C with each of the CST batches using the same simulant at flow rates near 1.2 mL/min (3.0-3.4 CST bed volumes/hr). The cesium ion exchange column performance results were lower than expected for all three CST batches, but the data indicated that 30% more simulant volume and 40% more equivalent CST bed volumes of solution can be processed with the older CST batch prior to reaching the 50% cesium breakthrough point than can be processed with the newer CST batches. Hydraulic evaluations of the CST columns were also conducted in simulant at 24 deg. C to determine the impact of the different particle size distributions and associated bed porosities on frictional pressure drop under dynamic flow conditions. Higher differential pressure drops were observed with the archived CST batch relative to recent production batches, due to the smaller particle size distribution and the packing characteristics of the archived batch. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Analysis of Hydrology and Interim Measure Performance in the Chromium Plume at Los Alamos National Laboratory - 20498

Hexavalent chromium, Cr(VI), is present in the regional aquifer beneath Los Alamos National Laboratory (LANL) at concentrations greater than the New Mexico groundwater standard (50 ppb). The source of the Cr is blowdown from a power plant where potassium dichromate was used as a corrosion inhibitor from 1956 to 1972. Blowdown comingled with effluent from another source and migrated approximately 3 miles down a canyon, primarily as surface flow, until reaching an alluvial infiltration zone. The infiltrating water moved vertically through a thick (approximately 900 ft), complex stratigraphic sequence of unsaturated tuffs, basalt, and unconsolidated alluvial deposits. Horizontal transport also took place within spatially limited perched groundwater horizons in the vadose zone (VZ). Breakthrough at the regional aquifer water table is thought to have occurred in multiple hydraulic windows or 'drip points' located approximately 3 miles from the initial release site at the power plant. The regional aquifer is highly heterogeneous and anisotropic, and displays unconfined behavior in an upper zone and confined behavior at depth. The Cr(VI) contamination has so far remained confined to the upper approximately 25 m of the aquifer. The ambient hydraulic gradient in the regional aquifer is toward the east/southeast, but the water table is extremely flat in the Cr plume area compared to surrounding areas. There is some local variability in flow direction, potentially related to effects of seasonal pumping from nearby Los Alamos county water-supply wells, which draw from the regional aquifer several hundred feet below the water table. An interim measure (IM) is currently operating at the site to achieve hydraulic control of plume migration using a series of extraction wells, ion-exchange treatment, and injection along the downgradient portion of the plume. The migration of Cr contamination in the aquifer has been previously evaluated using a calibrated probabilistic numerical model, which has also been used to design the spatial configuration of extraction and injection wells for the IM, and to identify locations for monitoring wells. The model is built using the Finite Element Heat and Mass (FEHM) transfer code, and the Model Analysis and Decision Support (MADS) software is used for analyses. To date, the portion of the IM system that has operated in a continuous manner has been very effective at hydraulic control. Modifications to the flow field associated with the influence of IM pumping and injection are evaluated here, with modeling constraints to be provided by recent observations of tracers injected into both monitoring wells and IM injection wells, as well as opportunistic observations of geochemical signatures of treated ion-exchange effluent injected into IM injection wells. The tracer and geochemical data provide insights into modified flow directions within the Cr plume area that cannot be otherwise obtained. Results of the modeling work are being used to inform adaptive management of IM operations and will be a valuable tool for development and refinement of long-term remediation strategies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Data-Driven Unit Commitment Refinement - a Scalable Approach for Complex Modern Power Grids

Integration of renewable generation, which is often intermittent and decentralized, substantially increases the stochasticity and complexity of power grid operations. Future power systems planning will require significant computational capability to evaluate balance between demand and supply under varying conditions, both temporally and spatially. The standard approach for generation unit commitment is to use mixed-integer linear programming to find the optimal generation schedule considering ramping and generator constraints. In the future grid this poses computational scalability challenges because generation and demand are not known with certainty due to stochasticity in weather and complexity of the grid. To address this challenge, we present a data-driven unit commitment approach that can efficiently include stochastic weather impacts and contingency considerations to improve unit commitment. Our approach uses graph-based data analytics techniques on solutions to the security constrained (and possibly stochastic) economic dispatch problem to identify potential improvements to a given unit commitment. Recent breakthroughs in fully-parallel stochastic economic dispatch software allow this approach to be scalably deployed. Simulations on synthetic South Carolina and Texas grids show this method can improve grid reliability with security constraints over a set of contingencies, while also meaningfully lowering total generation cost.

Holt, Timothy↗

Developing Metrics to Assess Justice and Equity Implications of Early-Stage Research

Physics is a central component of early-stage research and development of virtually all technologies. But as much as scientific breakthroughs often hold the key to high efficiencies, long lifetimes, and high stability in devices for countless applications, early-stage choices research choices, such as those around materials or processing, can also serve to lock in long-term social and equity impacts of deployed technologies. This talk will explore how scientists can start to consider these impacts, with a focus on the energy sector and achieving a just and sustainable energy transition. We discuss development of the Justice Underpinning Science and Technology Research (JUST-R) metrics framework - a suite of metrics targeted at early-stage researchers to assess justice considerations in their work. The framework is evaluated for its appeal to researchers and effectiveness at promoting integration of justice considerations into research through case studies, which reveal its ability to broaden researcher perspectives and key avenues for future improvement.

energy equity↗

Developing and Evaluating Energy Justice Metrics for Early-Stage Materials Research

Materials science is a central component of early-stage research and development of virtually all clean energy technologies. But as much as material breakthroughs often hold the key to high efficiencies, long lifetimes, and high stability in eventual devices, early-stage choices about material types, structures, and processing can also serve to lock in long-term social and equity impacts of deployed energy technologies. Thus, to achieve a just and sustainable energy transition, tools to assess the energy justice impacts of early-stage materials research are critical. Here, we discuss development of the Justice Underpinning Science and Technology Research (JUST-R) metrics framework - a suite of metrics targeted at early-stage researchers to assess energy justice considerations in their work. The framework is evaluated for its appeal to researchers and effectiveness at promoting integration of energy justice into research through case studies, which reveal its ability to broaden researcher perspectives and key avenues for future improvement.

energy justice↗

Time-lapse electrical resistivity and induced polarization monitoring of a simulated ecosystem-scale coastal flooding experiment

Accurate estimation of the spatio-temporal variation in soil saturation and salt concentration is valuable for calibrating and evaluating Earth system models as well as detecting important eco-hydrological interactions. Such representative models play a vital role in investigating the stability of ecosystems facing changing hydrologic disturbance regimes. Ecosystem-scale field experiments are a useful way to capture hydrological disturbance and gain a deeper understanding of the complex mechanisms driving environmental changes. However, previous studies have not explored the use of quantitative imaging at a large spatial scale to reproduce breakthrough curves and inform system response and recovery following hydrologic disturbance events. Therefore, in this study, non-invasive geophysical methods were used to capture the spatio-temporal variation in subsurface saturation and salt concentrations during an ecosystem-scale coastal forest flooding experiment.

coastal↗

"Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test"

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

Jang, Kathy↗

Streaming Matching and Edge Cover in Practice

Graph algorithms with polynomial space and time requirements often become infeasible for massive graphs with billions of edges or more. State-of-the-art approaches therefore employ approximate serial, parallel, and distributed algorithms to tackle these challenges. However, such approaches require storing the entire graph in memory and thus need access to costly computing resources such as clusters and supercomputers. In this paper, we present practical streaming approaches for solving massive graph problems using limited memory for two prototypical graph problems: maximum weighted matching and minimum weighted edge cover. For matching, we conduct a thorough computational study on two of the semi-streaming algorithms including a recent breakthrough result that achieves a $1/(2+\varepsilon)$-approximation of the weight while using $O( n \log W /\epsilon)$ memory (here $n$ is the number of vertices and $W$ is the maximum edge weight), designed by Paz and Schwartzman [SODA, 2017]. Empirically, we show that the semi-streaming algorithms produce matchings whose weight is close to the best $1/2$-approximate offline algorithm while requiring less time and an order-of-magnitude less memory. For minimum weighted edge cover, we develop three novel semi-streaming algorithms. Two of these algorithms require a single pass through the input graph, require $O(n \log n)$ memory, and provide a 2-approximation guarantee on the objective. We also leverage a relationship between approximate maximum weighted matching and approximate minimum weighted edge cover to develop a two-pass $3/2+\epsilon$-approximate algorithm with the memory requirement of Paz and Schwartzman's semi-streaming matching algorithm. These streaming approaches are compared against the state-of-the-art 3/2-approximate offline algorithm. The semi-streaming matching and the novel edge cover algorithms proposed in this paper can process graphs with several billions of edges in under 30 minutes using 6 GB of memory, which is at least an order of magnitude improvement from the offline (non-streaming) algorithms. For the largest graph, the best alternative offline parallel approximation algorithm (GPA+ROMA) could not finish in three hours even while employing hundreds of processors and 1 TB of memory. We also demonstrate an application of the semi-streaming algorithm by computing a matching using linearly bounded memory on item intersection graphs derived from three machine learning datasets, whereas the existing offline algorithms could not complete on one of these datasets since their memory requirements exceeded 1TB.

Ferdous, S M.↗

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗

Brazil's Action Plan for Rapid Decarbonization of Power Sectors

A collaborative report from the Clean Energy Ministerial (CEM) on Lessons Learned for Rapid Decarbonization of Power Sectors was delivered to energy ministers and presented at the CEM13 in the United States in September 2022. In light of these lessons learned and discussed at CEM13, several jurisdictions signaled intent to develop Action Plans for power sector decarbonization. The first cohort of Action Plans was released at CEM14 in India in July 2023. The second cohort of action plans will be presented at CEM15 in Brazil. The Action Plans, supported by the 21st Century Power Partnership, and other CEM workstreams via direct technical assistance and capacity building, are intended to focus on select implementation actions, given each country's existing power sector goals and activities, and are an opportunity for countries to display leadership in power sector decarbonization. The Action Plans are organized in a framework for Planning, Building and Operating, as well as Stakeholder Engagement where appropriate based on country priorities. They complement, but are differentiated from, other international power sector initiatives such as the Breakthrough Agenda (whose broad purpose is to raise collective ambition) and the Global Power System Transformation Consortium (whose goals are to convene power system operators to accelerate research innovations and foster peer learning). These Action Plans are voluntary, developed by each country individually, not comprehensive of all activities within the jurisdiction, and are living documents that are subject to change.

21st Century Power Partnership↗

Uruguay's Action Plan and Experience for Power Sector Decarbonization

A collaborative report from the Clean Energy Ministerial (CEM), Lessons Learned for Rapid Decarbonization of Power Sectors, was delivered to energy ministers and presented at the 13th CEM (CEM13) in the United States in September 2022. In light of these lessons learned and discussed at CEM13, several jurisdictions signaled intent to develop Action Plans for power sector decarbonization. The first cohort of Action Plans was released at CEM14 in India in July 2023. The Uruguay Ministry of Industry, Energy and Mining is pleased to release this Action Plan as a contribution to the second cohort of Action Plans released at CEM15 in Brazil in October 2024. The Action Plans, supported by the 21st Century Power Partnership, and other CEM workstreams via direct technical assistance and capacity building, are intended to focus on select implementation actions given each country's existing power sector goals and activities, and are an opportunity for countries to display leadership in power sector decarbonization. The Action Plans are organized in a framework for planning, building, and operating, as well as stakeholder engagement where appropriate based on country priorities. They complement, but are differentiated from, other international power sector initiatives such as the Breakthrough Agenda (whose broad purpose is to raise collective ambition) and the Global Power System Transformation Consortium (whose goals are to convene power system operators to accelerate research innovations and foster peer learning). These Action Plans are voluntary, developed by each country individually, not comprehensive of all activities within the jurisdiction, and are living documents that are subject to change.

21st Century Power Partnership↗