SEARCH · Engineering Papers

Results for “deployment strategy”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 829 records · Page 46

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Mining waste-driven carbon capture via ocean alkalinity enhancement

Ocean alkalinity enhancement (OAE) has emerged as a promising strategy to mitigate ocean acidification and reduce global warming. Traditional metal (e.g., critical materials) mining industries release alkaline waste via mining tailings with high concentrations (99.2%) of calcium and magnesium oxide (CaO, MgO). Incorporating mining waste into OAE processes is less energy intensive than processes relying on calcination of limestone for CaO production. The solubility limit of simulated mining waste in American Society for Testing and Materials (ASTM) seawater is 75 mg⋅L -1 , which can sequester 118 mg of carbon dioxide (CO 2 ). The solubility in seawater retrieved from Sunset Beach, FL was 25 mg⋅L -1 . Changes in pH, total alkalinity, and total inorganic carbon were analyzed to confirm the successful addition of simulated alkaline mining waste without the formation of secondary precipitation. This study proposes a new OAE strategy where a facility is developed nearby ocean waters that mixes alkaline waste with seawater. Subsequently, the seawater is met with previously captured, pure CO 2 to bring the pH back to 8.2 and eliminate the risks of pH shock and secondary precipitation. Technoeconomic analysis estimated an energy requirement of 1.4 GJ per ton of CO 2 stored that resulted in a processing cost of $\$$266 per ton of CO 2 sequestered (4.2 GJ per ton, $\$$807 per ton of CO 2 for real seawater). Results from this study underscore the potential for utilizing mining waste in OAE processes and provide a pathway for practical deployment.

Absorption↗

DOE OSTI2026

Informing Transmission Supply Chain Needs from National Transmission Studies

Recent national studies indicate significant transmission expansion can provide the lowest-cost option to maintain grid reliability while meeting growing demand. However, constraints in domestic supply chains may limit grid expansion across the U.S., with higher costs and longer delays for required transmission equipment. Despite growing evidence of supply chain constraints for transmission components, transmission planning studies often assume transmission equipment is readily available for deployment or analyze future demand using historical trade and manufacturing data that may not capture evolving grid needs. This report aims to address this gap by demonstrating methods to quantify future demand for critical transmission components and input materials from national-scale planning models. These components include power transformers, generator step-up transformers, converter transformers, conductors, circuit breakers, and transmission towers and the materials include aluminum, steel, grain-oriented electrical steel (GOES), and copper. The analytical approach is applied to two nodal transmission expansion scenarios from the National Transmission Planning Study (NTP) to illustrate the methods. These scenarios represent different transmission expansion strategies for the contiguous U.S. to the year 2035: the Alternating Current (AC) scenario includes AC transmission expansion within each interconnection and the Multiterminal (MT) scenario includes interregional transmission expansion across the country using both AC and multiterminal HVDC options between neighboring zones. We also explore potential heuristics to derive transmission component demand from zonal capacity expansion models (CEMs) with coarse representation of the transmission grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Informing Transmission Supply Chain Needs from National Transmission Studies

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Results from the DUNE ND-LAr 2x2 Demonstrator Run 2

The Deep Underground Neutrino Experiment (DUNE) is a cutting-edge, long-baseline experiment under construction in the United States, based on large liquid-argon time projection chambers (LArTPCs). The DUNE Near Detector LArTPC (ND-LAr) will employ a novel modular architecture using a pixelated LArPix charge readout. To validate this design and characterize detector response, the 2×2 demonstrator—an array of eight optically isolated LArTPC modules—was deployed at Fermilab in 2024. During winter 2025, the 2x2 demonstrator was operated for a second data-taking campaign (Run 2) with the aim of studying the low-energy response of the detector. Run 2 focused on calibration and response studies using a suite of deployed radioactive sources. Gamma sources (²²Na, ⁶⁰Co, and ⁸⁸Y) were used to probe module-to-module performance variations, energy resolution, and calibrations. Neutron sources (AmBe and a pulsed neutron generator) enabled studies of neutron interactions in liquid argon, including inelastic scatters and neutron capture signals, relevant for low-energy backgrounds and detector modeling. In addition, the detector was doped with ²²⁰Rn, providing Bi–Po coincidence signals that allow precise calibration of the charge and scintillation response. This poster will present results from these calibration campaigns. These studies provide critical validation of the ND-LAr modular LArTPC concept and inform calibration and reconstruction strategies for the full DUNE near detector.

Mora-Lepin, Luis [Florida State U.] (ORCID:0000000↗

DOE OSTI2024

Promoting the regulatory acceptance of combined ion and neutron irradiation for material degradation in nuclear reactors

The Advanced Materials and Manufacturing Technologies (AMMT) program within the Department of Energy (DOE) Office of Nuclear Energy has developed its current recommendation for promoting the use of combined ion irradiation and neutron irradiation for the accelerated qualification of materials to be deployed in nuclear reactors. This plan is intended to provide a collaborative path forward that can be adopted by academia, national laboratories, and industry, and has been developed with input from the regulatory research arm of the U.S. Nuclear Regulatory Commission (NRC). To deploy new materials or materials manufactured with new technologies, such as additive manufacturing, materials must be evaluated for reactor-induced degradation from the combination of harsh temperatures, corrosive environments, and radiation fields. However, rapid deployment of materials necessitates accelerated testing methods rather than relying on years of neutron irradiation in a material test reactor. Ion irradiation has demonstrated success in reproducing material microstructure and select property evolution resulting from neutron irradiation with three to four orders of magnitude reduction in time and cost, making it an ideal candidate for accelerated irradiation testing. This presentation provides context governing both the scientific and regulatory aspects of the proposed goal. The discussion is aimed at a broad audience including researchers from industry, national laboratories, and academia. The recommended path forward is presented as a conceptual framework of specific steps. In brief, the strategy entails developing an integrated ion and neutron irradiation test plan for the material property of interest based on the fundamental tenet of the linkage of microstructure and properties in materials. Physics-based modeling interprets ion irradiation data and predicts neutron irradiation microstructure and properties with uncertainty bounds. The first round of testing is sufficient for an initial licensing application using a risk-informed approach, while a minimum required neutron irradiation test plan reduces cost and time requirements. A surveillance program with witness specimens in-reactor provides additional data over time to improve model predictions to higher damage levels and further reduce uncertainty bounds, which can be used for license extensions or longer lifetimes in new license applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

SUNDIALS time integrators for exascale applications with many independent systems of ordinary differential equations

Many complex systems can be accurately modeled as a set of coupled time-dependent partial differential equations (PDEs). However, solving such equations can be prohibitively expensive, easily taxing the world’s largest supercomputers. One pragmatic strategy for attacking such problems is to split the PDEs into components that can more easily be solved in isolation. This operator splitting approach is used ubiquitously across scientific domains, and in many cases leads to a set of ordinary differential equations (ODEs) that need to be solved as part of a larger “outer-loop” time-stepping approach. The SUNDIALS library provides a plethora of robust time integration algorithms for solving ODEs, and the U.S. Department of Energy Exascale Computing Project (ECP) has supported its extension to applications on exascale-capable computing hardware. In this paper, we highlight some SUNDIALS capabilities and its deployment in combustion and cosmology application codes (Pele and Nyx, respectively) where operator splitting gives rise to numerous, small ODE systems that must be solved concurrently.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Scalable DAQ system operating the CHIPS-5 neutrino detector

The CHIPS R&D project focuses on development of low-cost water Cherenkov neutrino detectors through novel design strategies and resourceful engineering. This work presents an end-to-end DAQ solution intended for a recent 5 kt CHIPS prototype, which is largely based on affordable mass-produced components. Much like the detector itself, the presented instrumentation is composed of modular arrays that can be scaled up and easily serviced. A single such array can carry up to 30 photomultiplier tubes (PMTs) accompanied by electronics that generate high voltage in-situ and deliver time resolution of up to 0.69 ns. In addition, the technology is compatible with the White Rabbit timing system, which can synchronize its elements to within 100 ps. While deployment issues did not permit the presented DAQ system to operate beyond initial evaluation, the presented hardware and software successfully passed numerous commissioning tests that demonstrated their viability for use in a large-scale neutrino detector, instrumented with thousands of PMTs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Potential environmental effects of deepwater floating offshore wind energy facilities

Over the last few decades, the offshore wind energy industry has expanded its scope from turbines mounted on fixed platforms driven into the seafloor and standing in less than 50 meters of water, to floating turbines moored in 120 meters of water, to prospecting the development of floating turbines moored in ~1000 meters of water. Since there are few prototype turbines and mooring systems of these deepwater, floating offshore wind energy facilities (OWFs) currently deployed, their effects on the marine environment are largely unknown. Using the available scientific literature concerning appropriate analogs, this study provides the first synthesis of the potential environmental effects of deepwater, floating OWFs during operation, as well as potential mitigation measures to some of the risks. Potential effects we identify and evaluate include changes to atmospheric and oceanic dynamics, electromagnetic fields, habitat alterations, noise, structural impediments, and changes to water quality that could affect a variety of marine species across trophic levels. Our synthesis suggests that many of these potential effects could be mitigated to pose a low risk to the marine environment if developers adopt appropriate mitigation strategies and best-practice protocols. This review takes the necessary first steps in summarizing the available information on the potential environmental effects of deepwater, floating OWFs and can serve as a valuable reference document for marine scientists and engineers, the energy industry, permitting agencies and regulators of the energy industry, project developers, and concerned stakeholders such as coastal residents, conservationists, and fisheries.

Farr, Hayley K.↗

DOE OSTI2022

Battery Energy Storage Scenario Analyses Using the Lithium-Ion Battery Resource Assessment (LIBRA) Model

Meeting aggressive carbon emission goals will entail widespread deployment of renewable sources of electricity. Because these sources are variable, there is a need to develop scalable energy storage technologies. The U.S. Department of Energy is supporting efforts to increase U.S. manufacturing and recycling capabilities for LIBs and to decrease costs of stationary storage batteries. Many factors influence the domestic manufacturing and cost of stationary storage batteries, including availability of critical raw materials (lithium, cobalt, and nickel), competition from various demand sectors (consumer electronics, vehicles, and battery energy storage), resource recovery (recycling), government policies, and learning in the industry, among other factors. Understanding how these factors interact and identifying synergies and bottlenecks is important for developing effective strategies for the LIB stationary energy storage system. We developed the Lithium-Ion Battery Resource Analysis (LIBRA) model as a tool to help stakeholders better understand the following types of questions: What are the roles of R&D, industrial learning, and scaling of demand in lowering the cost of battery energy storage system production? How do the intersections between the EV and stationary storage sectors affect the battery supply chain? For various stationary storage and EV penetration scenarios, what volumes of critical materials might be required and what role can resource recovery play? What does expected demand for both EVs and stationary storage portend for mineral resources and overall mineral scarcity? The LIBRA model is developed using a System dynamics (SD) modeling approach to represent interactions across the segments of the battery materials supply chain. System dynamics models can capture the complex interactions and feedback between the various system components that influence supply and demand. The LIBRA model is comprised of several interacting modules that represent specific portions of the LIB supply chain. The model tracks the buildout of the domestic LIB industry over time (2020 - 2050) and in the context of competing demands for raw materials, recycling, and markets for LIBs. The LIBRA model represents major systemic feedback loops and delays across the supply chain. This report provides a complete documentation for the LIBRA model, including model assumptions, data, scenario analysis results, and sensitivity analysis of the model's input space.

25 ENERGY STORAGE↗

DOE OSTI2024

Risk-based System Upgrade Planning for AOT-IC [Capstone Project]

The Accelerator Operations and Technology Instrumentation and Controls Group (AOT IC) at the Los Alamos Neutron Science Center (LANSCE) has established a comprehensive risk-based system upgrade planning strategy which has been utilized as a tool to prioritize system upgrade projects for the past several years. A challenge with the current system is that the group often lacks the data to quantify the probability and impact of system failures, so the group often relies instead on qualitative risk assessments as well as evaluations of a potential upgrade project’s alignment with group strategy and vision to prioritize projects. A proposed framework to enable quantitative risk probability and impact assessments has been developed and tested on three pilot systems chosen to broadly represent the types and conditions of equipment used by the group. The proposed framework incorporates availability data gathered from the LANSCE control room logbooks and the LANSCE work control system, as well as a system health evaluation which is conducted from a variety of resources to assess the probability of equipment failure. The impact of system failure is viewed from the perspective of impact on mission and schedule, where spares status, system documentation status, and the functional distribution of deployed systems are used to quantify these impacts.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Avancando na Transicao Energetica: Desafios e Estrategias para a Implantacao Sustentavel de FV

As the world embarks on an ambitious journey towards global decarbonization, the spotlight turns to photovoltaic (PV) technology as a cornerstone for sustainable energy solutions. This talk delves into the current status and projections of PV for the World, US and Brasil, and the necessary considerations to do this increase in manufacturing and deployment sustainably. With the projected scale of deployment, the industry faces significant challenges related to material demand and the management of PV modules at their end of life. The principles of the Circular Economy (CE) and its associated R-Actions - Reduce, Reuse, Recycle, among others - present a promising framework to address these challenges by mitigating end-of-life management and material sourcing concerns. However, traditional CE metrics, often focused solely on mass, fall short by excluding vital energy flow considerations. In this talk, we will highlight how various metrics are needed to understand sustainable PV solutions, how continuing the search for increased efficiency can significantly reduce material demands and enhance energy metrics, while strategies around material circularity and module lifetime and reliability improvements will offer substantial reductions in both material and energy demands.

Brasil↗

DOE OSTI2021

Multi Time-scale Imputation aided State Estimation in Distribution System

With the transition to a smart grid, we are witnessing a significant growth in sensor deployments and smart metering infrastructure in the distribution system. However, information from these sensors and meters are typically unevenly sampled at different time-scales and are incomplete. It is critical to effectively aggregate these information sources for situational awareness. In order to reconcile the heterogeneous multi-scale time-series data, we present a multi-task Gaussian process framework. This framework exploits the spatio-temporal correlation across the time-series data to impute data at any desired timescale while providing confidence bounds on the imputations. The value of the imputed data for distribution system operation is illustrated via a matrix completion based state estimation strategy. Results on the IEEE 37 bus distribution system reveals the superior performance of the proposed approach relative to linear interpolation approaches.

Dahale, Shweta↗

DOE OSTI2022

Politics of problem definition: Comparing public support of climate change mitigation policies using machine learning

Public support is a key contributor to successful policy adoption and implementation. Given the urgency of climate change mitigation, scholars have explored various determinants that affect public support for climate change mitigation policy. However, the relative decisiveness of these factors in shaping public support is insufficiently examined. Therefore, we deploy interpretable machine learning to understand which factors, among many previously investigated, are most decisive for structuring public support for various climate change mitigation policies. In this paper, we particularly look at the decisiveness of problem definition for shaping public support among various factors. Using U.S national survey data, we find that how individuals define the issue of climate change is more decisive for structuring public support for promoting renewable energy and regulating pollutants to mitigate the risks associated with climate change. However, the results also indicate that the most decisive factors associated with public support vary depending on the types of mitigation policy. Here, we conclude that different strategies should be utilized to increase public support for various climate change mitigation policy options. Our findings contribute to a scholarly understanding of the specific politics of problem definition in the context of environmental and climate change policy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

High-performance data management for whole slide image analysis in digital pathology

When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we’ve developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario (“CPU scenario”), and (2) a GPU-based deep learning framework scenario (“GPU scenario”). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios.

Wang, Xiao↗

DOE OSTI2024

Safety in Artificial Intelligence: Challenges and Opportunities for the U.S. National Labs and Beyond

This report discusses the importance of the critical and underexplored topic of artificial intelligence (AI) safety, as highlighted during the “Strategy Alignment on AI Safety” workshop convened at Lawrence Livermore National Laboratory (LLNL) in April 2024. Through a summary of keynote talks, panel discussions, and breakout sessions, world-leading AI safety experts from academic, industry, national labs, and government agencies clearly agree on the need for and importance of large-scale investments for research and capabilities in AI safety. With the field innovating at unprecedented rates, there is increasing urgency to develop novel evaluation methodologies that allow full considerations of risks/threats of AI technologies in different domains. Quantitative metrics and effective methodologies that can evaluate and audit the “safeness” of how a given AI technology is trained, deployed, or regulated are, at best, nascent for certain scenarios or, more commonly, nonexistent. This maturation gap presents the possibility of serious threats to national security, and further inaction may have serious consequences. Additionally, the gap between the public’s and research community’s perceptions of AI risks/rewards is significant. While numerous voices from the AI community have expressed concern that the risks could be so high that future AI systems could inflict extinction-level damage to humanity if deployed incorrectly, the public largely is aware only of risk in low-impact scenarios. This discrepancy highlights the crucial need for researchers to articulate to governmental bodies what, why, and when various AI risks matter as part of motivating funding requests. Thus, the call to action for this community is to pursue AI safety as a “Big Science” project on a scale comparable to the Manhattan Project. High risks and high payoffs are on the table, but safe AI is a fast-moving target, and large-scale investments are needed to guide development of this technology in a responsible way. We highlight the need for a multilayered solution combining the development of new methods and algorithmic approaches to mitigate threats with an active participation of the government(s) in setting high industry standards and regulations based on state-of-the-art technology. The U.S. Department of Energy (DOE) national laboratories have served as leading institutions for scientific innovation in the U.S. for more than 70 years. Drawing on their expertise in the AI community and their history of safeguarding critical and sensitive information, and as we look to the future, national labs are the best choice for evaluating and safeguarding AI technologies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Proof-of-Concept for Sensor Modeling in MOOSE for the Design of Autonomous Nuclear Reactor Control

Autonomous operation is essential for the deployment of microreactors and fission batteries, both in terrestrial and space applications. For this reason, recent studies have investigated autonomous control by using adaptive model predictive control and multi-objective optimization for heat pipe–cooled microreactors under normal and heat pipe failure conditions. However, prototypes of microreactors and fission batteries do not exist yet, and even the design space has not been narrowed down conclusively, making the instrumentation and control system design difficult. For this reason, there is a need for flexible computational capabilities to create a numerical stand-in of potential microreactor and fission battery designs. The latter can be used to design and test control strategies to support autonomous operations. In this poster, we describe the initial implementation of a pluggable sensor system for the easy implementation of realistic sensor models in the multiphysics object-oriented simulation environment (MOOSE) framework. This new capability will enable MOOSE users to create a numerical stand-in of microreactors and fission batteries, ultimately allowing them to easily test new control algorithms, and instrumentation strategies for advanced systems in the design phase.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Advances in Carbon Electrode Integration for Stable and Efficient Perovskite Solar Cells and Modules

Carbon-electrode-based perovskite solar cells (C-PSCs) have emerged as a cost-effective and scalable alternative to noble-metal-based PSCs, addressing critical challenges related to device stability, fabrication complexity, and commercialization potential. This review explores the recent progress in C-PSC development, focusing on the benefits of carbon electrodes (CEs), including their hydrophobicity, chemical inertness to halide corrosion, and compatibility with low-temperature cost-effective solution-based deposition methods. Despite relatively lower power conversion efficiencies (PCEs) than their metal (e.g., Au/Ag) counterparts, recent advances in carbon paste formulation, interfacial contact engineering, and work function modification have elevated C-PSC efficiencies above 22%. This review further examines strategies to enhance electrode conductivity, interfacial properties, and charge selectivity to further increase C-PSC performance. Progress in carbon-electrode-based perovskite solar modules (C-PSMs), particularly within mesoporous and planar architectures, is also analyzed, revealing significant developments in active area scaling and long-term stability. Notably, the limited research in inverted (PIN) C-PSCs is also highlighted as a compelling opportunity for innovation, given the architecture's inherent advantages in flexibility, tandem integration, and low-temperature processing. Collectively, these insights affirm the potential of C-PSCs and C-PSMs to deliver affordable, stable, and high-performance photovoltaics suitable for scalable deployment.

Carbon Electrodes↗

DOE OSTI2026

Evaluation of 1/100-Scale Mooring Systems for Wave Energy Converters

This study evaluates the feasibility, accuracy, and limitations of using 1/100th scale physical mooring systems to represent full-scale mooring behavior for wave energy converters (WECs) during small-scale tank testing. The work focuses on an RM3-style point absorber deployed in conditions representative of the PacWave South test site and examines whether small-scale physical testing can reliably inform numerical modeling, design decisions, and future prototype development. Overall, this study concludes that small-scale (1/100th) physical mooring models can provide valuable qualitative insights, including relative comparisons between mooring types and trends in device behavior, but cannot reliably replicate full-scale mooring loads or dynamic response without significant scaling distortion. Physical tests at this scale are most appropriate for motion characterization and model validation within known limitations, not for deriving absolute mooring loads or final engineering design values. Key lessons learned emphasize the importance of improved instrumentation strategies, iterative wave tank tuning, more precisely manufactured scaled mooring components, and enhanced anchoring systems to reduce experimental uncertainty.

16 TIDAL AND WAVE POWER↗