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

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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Large Volume Airborne Contamination Monitoring To Support Nuclear Processes' Deactivation and Decommissioning

Current D and D operations at Hanford have demonstrated a flaw in the current state of the art capability of defining airborne contamination boundaries - Airborne particulate emissions of Pu-239 from CM2H operations on the Hanford Pu Finishing Plant were detected well beyond areas controlled for airborne Pu, putting numerous workers at risk for radiological assimilations - Current air samplers and CAM systems were surveying insufficient volumes of air to accurately predict where respiratory protection was required - Hanford is located in a high radon environment, making air monitoring for alpha emitting actinides challenging in high alpha radon induced backgrounds. - Inexpensive HEPA based home and industrial air purifiers filter significantly higher volumes of air than commercially available continuous-air monitoring (CAM) systems. - Inexpensive models capable of filtered air volumes exceeding 1000 times that of a CAM. - Detector system can be built to detect x-rays generated from actinide decay. - Branching ratios of x-rays are 4 orders of magnitude more intense from Plutonium decay than its gamma emissions. - Detector system can be a low resolution systems, as x-ray region of interest is not terribly congested. - Detection systems evaluated will be designed based on slabs of NaI or pixelated NaI or CsI panels. - Simple graphical user interface software will be developed to operate the detection system. - Concept is to deploy some of these air-purifying systems in nonradiological areas around SRNL to confirm radon can be rejected with confidence. - Generate some filters contaminated with plutonium via electroplated Pu or lab generated simulated particles. - then deploy units in known airborne radiological environments to establish systems ability to accurately measure actinide based hot particles Lab analyses will follow up the system analyses to ensure hot particles were correctly identified. Analysis by Scintillation: - Application of scintillation media to the surface of the filters is being explored. - Evaluating slabs of ZnS(Ag), application of powdered ZnS(Cu) and spray on Perkin Elmer Enhance. - Scintillation events would then be digitized with a digital camera and quantified. - Comparing against sensitivity of a PMT or SiPD readout. - Currently evaluating a Thorlabs 8 Megapixel Monochrome Scientific CCD Camera, hermetically sealed cooled package with a wide angle lens. - Wide angle lens allows complete view of HEPA filter from 7 inches away. - Images taken of glow in the dark paint, as well as plutonium-induced fluorescence. - Plutonium was flamed mounted on a 1 inch diameter stainless steel planchet, covered with a layer of mylar and a section of scintillating ZnS obtained from Eljon. - Currently working on reducing signal-to-noise levels to boost sensitivity. - Dark box to hold contaminated or electroplated source covered filters under fabrication. Analysis by x-ray Spectroscopy: - Branching ratios for x-ray emissions from Plutonium isotopes are orders of magnitude more intense than gamma-ray emissions. - The nuclear databases are incomplete on the branching ratios of some of the isotopes. - Evaluating some custom built SrI2 x-ray spectrometers vs a conventional windowed NaI detector. - SrI2 detectors are carbon-composite-windowed 51 mm x 51 mm. - MCNP calculations to establish self absorbance of filter media on 17 keV x-ray. Radon Rejection: - Activated charcoal pre-filter acts as a Radon trap. - Adding a time lapse feature to camera to aid in radon rejection. SRS Plutonium Fuel Form D and D Operations: Currently deploying air sampler to D and D operation of Pu-238 facility at SRS to generate some field samples to analyze.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Development of Remote Cutting Tools for use at ATR - 20438

As a result of years of operations of the Advanced Test Reactor (ATR) at the Idaho National Lab (INL), the ATR canal has become congested due to the storage of materials including waste consisting of highly irradiated remote handled (RH) metals. In order to support its continuing nuclear mission the ATR requires canal space to support fuel storage and future core internal change out (CIC) components. INL staff determined that the canal must be cleaned out and waste removed to efficiently utilize the valuable canal floor space. It was determined that a methodology for processing, packaging, characterizing, and removing current and future irradiated hardware from the ATR canal is needed. Orano Federal Services (OFS) and its subcontractor Babcock Services Inc. (BSI) were selected to provide remote tooling in order to safely and efficiently size reduce the material currently stored in the ATR canal. INL developed detailed requirements that established the design and performance requirements for the tooling. Orano TN and BSI had recently successfully completed similar tasks in clearing highly activated materials from spent fuel pools and canals at commercial nuclear plants. That experience directly translated into a proven approach that could be used at the ATR thereby saving time and money and lowering risks. Based on BSI's demonstrated proficiency in the design and deployment of remote size reduction specialty tooling at comparable commercial facilities, Orano Federal Services was confident that a solution to INL's requirements could be delivered. The tooling had to be designed to be compatible with the operational requirements of the ATR canal as well as the functionality needed by the facility operators that will use the equipment. Some of the major design requirements and functions of the equipment included remote operations, capability to shear highly irradiated sections of aluminum and steel tubing, pipe, and components ranging in size from 0.6 cm to 16.5 cm diameter into 1.5 m -1.8 m lengths, utilizing buoyancy compensation to assist with underwater operations, and use commercially available existing technology and components as much as practical. The design team evaluated and selected commercially available equipment that could be modified to meet the design and performance specifications. In addition, a waste sizing table (WST) was designed and fabricated to further ensure the size reduction equipment would perform as expected and give the facility operators a stable and usable remote, underwater work platform. After successful development of the tooling, a mock up test and operator training was conducted to ensure the tooling performed as expected and met the requirements of INL. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Infrastructure and DFLAW Support at Hanford - 20443

Manhattan Project era infrastructure systems are degrading at an accelerated pace across the Department of Energy's (DOE) Environmental Management complex. Revitalizing, rejuvenating and right-sizing these systems to ensure reliability for ongoing cleanup missions is a major focus at the 580-square- mile Hanford Site in southeast Washington State. The push to complete construction, commissioning, startup, and operation of facilities and systems in the Direct-Feed Low-Activity Waste (DFLAW) program at Hanford by 2023 requires considerable coordination among site contractors and a significant investment in infrastructure. The passage of time also poses a challenge. It has been about 75 years since facilities were first operated at the site, and approximately 40 years of plutonium production created a legacy of unidentified active and abandoned underground obstacles and waste sites that must be avoided when building and upgrading the infrastructure. Converting infrastructure systems originally built to support plutonium production from the 1940's to the 1980's and upgrading those systems to optimize technology is an ever-changing balance of funding profiles, available resources and execution strategies. The DOE Richland Operations Office (RL) has long recognized the need for increased investment in Hanford Site infrastructure to support the future processing of approximately 56 million gallons of waste currently stored in large underground tanks. When DFLAW is fully operational, safe and reliable infrastructure systems will be needed to ensure continuity of operations around the clock, 365 days a year. These include roadways, water, power, sewer, information technology systems and facilities. To ensure the readiness of Hanford's infrastructure to support treating tank waste in the next three years, 15 projects were identified with a combined value of $133.8 million. Through calendar year 2019, 7 infrastructure projects have been substantially completed and the remaining 8 projects, with a remaining value of $103.8 million, are scheduled to be completed by December 2023. RL and its site services/infrastructure contractor, Mission Support Alliance (MSA), regularly evaluate the needs of the DFLAW program and other Hanford Site missions to ensure the highest priority systems are addressed. In addition to a fast-approaching deadline for round-the-clock treatment operations, RL and MSA face another significant infrastructure challenge. As cleanup is being completed in a 220-square-mile area called the River Corridor, most of the cleanup operations going forward will occur in a 20-square-mile area in the center of the Hanford Site, known as the Central Plateau. Infrastructure systems are becoming more congested in an already overcrowded area. This paper/presentation will outline some of the challenges Hanford faces while executing infrastructure reliability projects. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Market mechanism to enable grid-aware dispatch of Aggregators in radial distribution networks

This paper presents a market-based optimization framework wherein Aggregators can compete for nodal capacity across a distribution feeder and guarantee that allocated flexible capacity cannot cause overloads or congestion. This mechanism, thus, allows Aggregators with allocated capacity to pursue a number of services at the whole-sale market level to maximize revenue of flexible resources. Based on Aggregator bids of capacity (MW) and network access price ($/MW), the distribution system operator (DSO) formulates an optimization problem that prioritizes capacity to the different Aggregators across the network while implicitly considering AC network constraints. This grid-aware allocation is obtained by incorporating a convex inner approximation into the optimization framework that prioritizes hosting capacity to different Aggregators. We adapt concepts from transmission-level capacity market clearing, utility demand charges, and Internet-like bandwidth allocation rules to distribution system operations by incorporating nodal voltage and transformer constraints into the optimization framework. Simulation based results on IEEE distribution networks showcase the effectiveness of the approach.

Nazir, Mohammad Nawaf↗
DOE OSTI2024

Communication engine for hybrid interconnect technologies

Systems, apparatuses, and methods for dynamically selecting between wired and wireless interconnects for sending packets are disclosed. A system includes at least a hybrid communication engine and a plurality of interconnects for connecting to various end-points. The communication engine dynamically discovers and utilizes the best interconnect technology available in between given end-points. The communication engine dynamically chooses the physical interconnect that is best suited at any given time to send data from one source to one or multiple destinations. This communication can be either on-chip or across nodes. The communication engine makes a decision based on a set of predetermined parameters that can be re-adjusted by the application layer, such as latency of the transmission, message data size, physical distance from source to destination, the energy cost, and the current congestion on the alternative interconnects.

Blagodurov, Sergey↗
DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

Ridesharing allows people to share a vehicle with others traveling in the same direction, which can reduce costs and traffic congestion. Pooled rideshare (PR) services, such as UberX Share and Lyft Shared, offer an economical and environmentally friendly alternative by matching passengers traveling similar routes. However, despite these benefits, PR adoption remains low due to concerns about safety, privacy, and convenience. This research explores the factors influencing PR adoption and provides recommendations to improve user acceptance. A nationwide survey of 5,385 participants across the U.S. was conducted to understand why people choose or avoid PR. The study identified five key factors influencing PR consideration: safety, service experience, privacy, traffic/environment, and time/cost. Additional research examined ways to optimize PR experiences by identifying four critical factors: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. To measure the impact of these factors, a statistical model called the Pooled Rideshare Acceptance Model (PRAM) was developed, providing insights into how each element influences PR adoption. Further analysis using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) revealed how demographic characteristics such as age, gender, income, and past rideshare experience shape PR perceptions. Some key findings from the multigroup analyses showed that younger users valued technological features and environmental benefits, while older users prioritized reliability and service transparency. Additionally, privacy concerns were more significant for female users, while convenience was critical for higher-income groups. These results emphasize that a 'onesize-fits-all' approach to PR service design is not effective, highlighting the need for tailored strategies to address different user segments. Further, workshops were conducted with researchers and students to translate the findings into real-world solutions. These workshops and 3 all the statistical analyses led to the development of 95 actionable recommendations. The recommendations focus on key areas such as safety, service reliability, user education, and accessibility, offering tangible improvements to PR services. The insights from this study provide valuable guidance for policymakers, transportation network companies (TNCs), and researchers aiming to make PR services safer, more accessible, and widely accepted. By addressing user concerns, PR can become a more viable transportation option, supporting sustainable urban mobility and reducing reliance on private vehicles. Additionally, these findings emphasize the importance of user-centric service design in encouraging broader PR adoption. Future research should explore evolving trends in PR preferences, technological advancements, and policy changes to ensure continued improvements. By implementing these recommendations, PR services can better align with user expectations, enhance trust in shared mobility, and contribute to a more efficient transportation ecosystem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Dynamic Pricing Method to Manage the Impact of EV Charging on the Grid Using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

EV charging, dynamic pricing, grid-informed chargi↗
DOE OSTI2025

Quantifying Message Aggregation Optimisations for Energy Savings in PGAS Models

Upon breaking past the exascale barrier, HPC systems are facing their greatest challenge yet - a power wall that must be addressed through new methods in both hardware and software. While energy costs are becoming a major issue at all levels, of particular concern is that of the network, as the relative cost of moving data is increasing faster than ever. The partitioned global address space (PGAS) model is critical within certain HPC domains, but is known to suffer from the small message problem, where irregular many-to-many access patterns result in congesting the network with excessive numbers of small messages. To address this, the conveyor aggregation library was developed to defer individual messages and group them for subsequent bulk processing. In this paper, we investigate its impact on energy use related to the network, with a focus on the Slingshot 11 interconnect. We will demonstrate that this strategy is not only highly performant, but also crucial to reducing energy footprints to remain within target power envelopes.

Welch, Aaron [ORNL]↗
DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

This dissertation explores factors influencing pooled rideshare (PR) adoption to provide actionable insights for transportation network companies (TNCs) and policymakers. PR allows travelers to share rides with unknown passengers, offering benefits such as cost reduction and congestion relief. However, adoption remains limited due to safety concerns, privacy issues, and trust in rideshare platforms. A national U.S. survey with 5,385 respondents examined transportation preferences and barriers to PR adoption. Exploratory and confirmatory factor analyses identified five key factors influencing PR consideration—safety, service experience, privacy, traffic/environment, and time/cost. Second factor analyses examined ways to optimize PR experiences, revealing four factors—comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Privacy concerns, for instance, using regression analysis, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. The Pooled Rideshare Acceptance Model (PRAM), based on the Technology Acceptance Model, assessed the impact of these factors using the Structural Equation Model (SEM). Privacy, safety, trust, and convenience had a large effect (Cohen's f2 > 0.35) on PR acceptance, while multigroup analyses (PRAMMA) explored 16 demographic variables such as gender, generation, and income, emphasizing the need for tailored strategies. Based on all the statistical analysis and workshops using descriptive statistics, 95 actionable recommendations were made from the riders' perspective. Findings highlight the importance of customized services, user experience improvements, and policy interventions to enhance PR adoption. This dissertation provides a roadmap for future research and policy development, ensuring evidence-based, practical strategies to improve PR services in the U.S. and beyond.

Gangadharaiah, Rakesh↗
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A dynamic pricing method to manage the impact of EV charging on the grid using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Secure Route: Roadway Risk Mapping for Transportation Planners

The secure transport of sensitive materials across U.S. road networks pose unique challenges for local, state, and federal agencies. Threats range from random events (e.g., accidents, medical emergencies, mechanical failures) to opportunistic or organized tactical assaults. Although the probability of such attacks is very low, the consequences of material loss to foreign states or terrorists can be catastrophic, qualifying these scenarios as “grey swan” events—low-probability, high-impact occurrences that are predictable but difficult to quantify. Traditional risk assessments struggle in these contexts, necessitating a shift toward subjective risk perception to inform planning. Risk perception in transport planning is shaped by various factors, including knowledge of adversarial capabilities, vehicle defenses, manifest details, and geographic features along the route. Geographic features such as bridges, tunnels, roadside elevation, and gaps in cellular coverage introduce vulnerabilities, while mitigative features include safe havens, police stations, and medical services. Temporal variables such as congestion, accidents, and weather further complicate route planning. Despite their importance, existing routing tools like Google Maps and commercial software do not explicitly account for geographic risk features, requiring planners to rely on personal familiarity with routes—a time-intensive, non-scalable approach. This work addresses these gaps by: (1) developing datasets that catalog geographic risk features along U.S. roadways, (2) eliciting risk perceptions from experienced transportation security experts, and (3) linking these perceptions to roadway conditions and geographic data. We implement these capabilities within Secure Route a novel mapping tool for classifying route segment risks associated with roadway conditions. This system provides transportation planners with an intuitive interface to assess and contextualize risk along potential routes, improving decision-making for secure transport. We present current progress in this effort and identify next steps.

Stewart, Robert [ORNL] (ORCID:0000000281867559)↗
DOE OSTI2025

Navigating Integration: Key Challenges for Data Centers, Nuclear Stakeholders, and Utility Operators

he exponential growth of data centers—driven by artificial intelligence and cloud computing—is reshaping the U.S. energy landscape, presenting urgent challenges and transformative opportunities for data center developers, nuclear energy providers, and utility operators. As data centers are projected to consume up to 12% of U.S. electricity by 2028, stakeholders must address rapid deployment needs, grid congestion, and the demand for reliable, high-quality power. This presentation explores the multifaceted barriers to integrating data centers with nuclear and utility infrastructure, including land use constraints, public perception, regulatory complexity, and workforce alignment. It highlights the distinct priorities and operational cultures of each sector, and the friction that arises from misaligned planning horizons and risk tolerances. We examine collaborative strategies such as co-siting, hybrid power-purchase agreements, unified community engagement, and innovative financing models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Network traffic control for multi-homed end-hosts via SDN

Software-defined networking (SDN) is an emerging technology of efficiently controlling and managing computer networks, such as in data centres, wide-area networks, as well as in ubiquitous communication. In this study, the authors explore the idea of embedding the SDN components, represented by SDN controller and virtual switch, in end-hosts to improve network performance. In particular, the authors consider load balancing across multiple network interfaces on end-hosts with different link capacity scenarios. The authors have explored and implemented different SDN-based load-balancing approaches based on OpenFlow software switches, and have demonstrated the feasibility and the potential of this approach. The proposed system has been evaluated with MultiPath transmission control protocol (MPTCP). Furthermore, the proposed results demonstrated the potential of applying the SDN concepts on multi-homed devices resulting in an increase in achieved throughput of 55% compared to the legacy single network approach and 10% compared to the MPTCP.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis

The Theory of Bottleneck Structures is a recently-developed framework for studying the performance of data networks. It describes how local perturbations in one part of the network propagate and interact with others. This framework is a powerful analytical tool that allows network operators to make accurate predictions about network behavior and thereby optimize performance. Previous work implemented a software package for bottleneck structure analysis, but applied it only to toy examples. In this work, we introduce the first software package capable of scaling bottleneck structure analysis to production-size networks. Here, we benchmark our system using logs from ESnet, the Department of Energy's high-performance data network that connects research institutions in the U.S. Using the previously published tool as a baseline, we demonstrate that our system achieves vastly improved performance, constructing the bottleneck structure graphs in 0.21 s and calculating link derivatives in 0.09 s on average. We also study the asymptotic complexity of our core algorithms, demonstrating good scaling properties and strong agreement with theoretical bounds. These results indicate that our new software package can maintain its fast performance when applied to even larger networks. They also show that our software is efficient enough to analyze rapidly changing networks in real time. Overall, we demonstrate the feasibility of applying bottleneck structure analysis to solve practical problems in large, real-world data networks.

benchmark↗
DOE OSTI2021

Are coupled renewable-battery power plants more valuable than independently sited installations?

Coupled renewable-battery powerplants differ from the traditional concept of independent siting of electricity resources within transmission networks. Prior research on the value proposition and cost savings from coupling did not consider the geographic constraint of co-location. This paper fills the gap by assessing how pricing volatility differences between nodes within electricity markets impact the system value of coupled renewable-battery projects as compared to independent VRE and battery installations. We use wholesale power market prices from 2012–2019 across the seven main U.S. independent system operators (ISOs) with a linear optimization program to compare the electricity market value of coupled projects to the value of the same underlying sub-components, deployed separately. We find that additional value from adding a 4-hour battery sized to 50% of renewable-plant nameplate capacity is $\$$10/MWh across ISOs on average. The highest boost occurs in California ($\$$15/MWh), where the value of adding storage to solar rises over time in tandem with increased solar penetration in the region. If renewables and batteries are deployed independently, we estimate that $\$$12.5/MWh of additional value could be achieved because of more flexibility on battery siting and operation. The $\$$12.5/MWh coupling penalty is reduced to $\$$1.6/MWh when considering alternative approaches to integrating battery storage. This result implies that renewable-battery power plants will play an increasing role in electricity systems if they can be built for $\$$2–$\$$13/MWh less than independent projects of comparable size. However, the wide regional variation in coupling penalties, along with the importance of conditions captured in our sensitivity cases, suggests the tradeoff between coupling penalties and savings will vary by situation. Therefore, roles exist for independent and coupled projects from a system optimization perspective.

14 SOLAR ENERGY↗