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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Probabilistic Voltage Sensitivity based Preemptive Voltage Monitoring in Unbalanced Distribution Networks

With increasing penetration of renewable energy and active consumers, control and management of power distribution networks has become challenging. Renewable energy sources can cause random voltage fluctuations as their output power depends on weather conditions. Conventional voltage control schemes such as tap changers and capacitor banks lack the foresight required to quickly alleviate voltage violations. Thus, there is an urgent need for effective approaches for predicting and mitigating voltage violations as a result of random fluctuations in power injections. This work proposes a novel voltage monitoring approach based on low-complexity, data-driven probabilistic voltage sensitivity analysis. The usefulness of this work is not only in predicting voltage violations in unbalanced distribution grids, but also in opening up the door for optimal voltage control. Using system data and forecasts, the proposed approach predicts the distribution of system node voltages which is then used to to identify nodes that may violate the nominal operational limits with high probability. The method is tested on the IEEE 37 node distribution system considering integrated distributed solar energy sources. The method is validated against the classic load flow based method and offers over 95% accuracy in predicting voltage violations.

Abujubbeh, Mohammad↗
DOE OSTI2021

Unbundling Smart Meter Services Through Spatio-Temporal Decomposition Agents in DER-rich Environment

Smart meters and the advanced metering infrastructure (AMI) facilitate distribution system operators (DSOs) to gather information on energy consumption at the customer level. With the increasing penetration of building-level intermittent distributed energy resources (DERs) behind the meter, DER information is not available to DSOs. At the same time, smart meter enables users to participate in grid, with real-time information. Information for behind the meter is needed by user to coordinate building level assets for maximum benefits. The concept of unbundled smart meter (USM) needs agents to decompose smart meter measurements to provide service to DSO as well as customers. In this paper, we propose a Spatio-Temporal Decomposition Agent (STDA) for USM based on Artificial Intelligence (AI). STDA can help users optimize their energy usage, help DSO to utilize building assets for the grid operation. The energy usage strategy developed by STDA is suitable for different users, and can be customized by deep learning (DL) models according to the different energy consumption habits of each user. The power prediction performance results of various DL models and evaluation using a set of data from a Hawaii utility is presented. Furthermore, STDA integration with Home Energy management Systems (HEMS) to manage resources is presented and validated. STDA pre-processes the measurements before model training, and provides the spatio-temporal decomposed forecasting.

42 ENGINEERING↗
DOE OSTI2020

Epidemiological Investigation of a Mortality Event in a Translocated Gopher Tortoise (Gopherus polyphemus) Population in Northwest Florida

Nokuse Plantation, a 22,055 ha private conservation preserve in northwest Florida, is a recipient site for gopher tortoises translocated from development sites in Florida. Since 2006, Nokuse has received over 5,000 tortoises from multiple development sites. During 2013–2015, 52 tortoises were found sick (n = 14) or dead (n = 38) in multiple soft-release enclosures in which tortoises consistently exhibited clinical signs, with additional sick (n = 5) and dead (n = 5) tortoises presenting similarly during 2016–2017. When found alive, tortoises behaved abnormally (e.g., frequently out of burrows during cold weather, pacing along enclosure fencing), appeared emaciated, were lethargic, and had developed redness under plastron scutes. Similar numbers of male (n = 28) and female (n = 32) tortoises were recovered along with two of unidentified sex, including mainly adults (n = 59) and three subadults. Physical examination, blood analysis, and other diagnostics were indicative of starvation and dehydration. Most sick tortoises provided with supportive care recovered. Necropsy findings generally confirmed starvation, with no evidence of infectious pathogens or contaminants. There were no apparent differences in quality of habitat, plant community, or soil or water among affected and unaffected enclosures. Botanical surveys indicated adequate forage quality and quantity, with no poisonous exotic or native plants detected. No land management practices changed prior to this event. Analysis of epidemiological data and demographic factors from before and during this mortality event identified initial density of tortoises in the enclosures as exerting the strongest influence on detection of tortoise morbidity and mortality. We believe that the stress associated with mixing tortoises from different populations and at higher densities during translocation impacted an individual tortoise’s ability to obtain or absorb adequate nutrients from foraging, ultimately leading to a wasting condition consistent with starvation. Based on our findings, we recommend a maximum of 3 gopher tortoises per ha in soft-release enclosures for translocation, but further research is warranted to investigate the complexity of stress and social pressures associated with translocation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Update of the Nuclear Criticality Slide Rule: Review of the Estimation of the Number of Fissions

IRSN (France), LLNL (USA) and ORNL (USA) began a long-term collaboration effort in 2015 to update the nuclear criticality Slide Rule (https://ncsp.llnl.gov/analytical-methods/criticality-sliderule) for the emergency response to a nuclear criticality accident. The Slide Rule permits the estimation of neutron and gamma dose rates and integrated doses based upon estimated fission yields, as a function of distance from the fission source, and time after criticality accidents for different critical systems. This paper presents the review of the section “estimation of the number of fissions”, named “fission yield” in the Slide Rule document. This estimation is important because the other information provided by the Slide Rule is directly proportional to the number of fissions. The previous models, based on Hansen and Barbry formulae, will be presented with their associated assumptions and limitations. Then, new proposals will be presented with a comparison to past criticality accidents and experimental data. The new formulae, based on the heat energy equation, cover many types of criticality accidents (solution, dry or low-moderated powder, rods/assemblies in water and dry metal) and require limited information in an emergency situation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Used Nuclear Fuel Management Using the Next Generation System Analysis Model

The U.S. Department of Energy (DOE) is leading the National effort to manage the back end of the nuclear fuel cycle, encompassing the safe transportation, storage/staging, and/or eventual disposal of used nuclear fuel (UNF) and high-level radioactive waste. The Next Generation System Analysis Model (NGSAM) is DOE’s discrete-event, agent-based simulation tool designed to model the full life cycle of UNF from reactor discharge to final disposal. NGSAM supports the DOE Office of Spent Fuel and High-Level Waste Disposition by enabling a detailed, scenario-based analysis of logistics, infrastructure, and shipping strategies. NGSAM replaces legacy models with a modern, flexible platform built on Repast Simphony and enhanced by the Process Analysis Tool. NGSAM simulates the movement and interaction of individual fuel assemblies with system components such as canisters, casks, railcars, and facilities. The model integrates with the Java Transportation Operations Model to plan and execute transportation scenarios, supporting both constrained and unconstrained resource allocation. Key features include customizable allocation and acceptance algorithms, detailed facility-level operations, and a Quick Edit tool for rapid scenario adjustments. NGSAM supports multimodal transportation modeling (e.g. rail, road, barge) and provides comprehensive cost, schedule, and infrastructure data. NGSAM utilizes data from sources such as DOE’s STANDARDS UNF database and DOE’s Stakeholder Tool for Assessing Radioactive Transportation, while also allowing user-defined inputs for scenario customization. NGSAM enables stakeholders to evaluate complex UNF management strategies, assess system performance under varying assumptions, and inform decision making for future infrastructure investments. Its modular architecture and integration with other Integrated Waste Management System tools make it a critical asset for planning the safe and efficient disposition of the Nation’s growing UNF inventory.

Craig, Brian [Argonne National Laboratory (ANL)]↗
DOE OSTI2024

MatRIS: Addressing the Challenges for Portability and Heterogeneity Using Tasking for Matrix Decomposition (Cholesky)

The ubiquitous in-node heterogeneity of HPC and cloud computing platforms makes software portability and performance optimization extremely challenging. Described here, the MatRIS multilevel math library abstraction framework employs tasking to alleviate these difficulties. MatRIS includes the IRIS task-based runtime on the bottom level and exposes different layers of abstraction to render algorithms architecturally agnostic. MatRIS ensures the decomposition and creation of tasks that represent the necessary encapsulation of the optimized kernels from both vendor and open-source math libraries. Once built, MatRIS can select different combinations of accelerators at runtime, making it portable even on diverse heterogeneous architectures. By leveraging the IRIS runtime’s features for managing heterogeneity, MatRIS deploys algorithms that remove the need to specify orchestration and data transfer. This study describes how the serial task abstraction of a tiled Cholesky factorization is made portable and scalable in the case of multi-device and multi-vendor heterogeneity on a node with NVIDIA and AMD GPUs by using MatRIS. First, we demonstrate that Cholesky in MatRIS provides multi-GPU scalability that offers competitive performance versus cuSolverMG. Then, we present the challenges and opportunities for heterogeneous execution.

Monil, M. A. H.↗
DOE OSTI2021

Coupling Surface Flow with High-performance Subsurface Reactive Flow and Transport Code PFLOTRAN

Water exchange between the surface and subsurface is important for both water resource management and environmental protection. In this paper, we develop coupled surface and subsurface flow simulation capability in a parallel subsurface flow and reactive transport code PFLOTRAN. We sequentially couple the diffusion wave-based surface flow with the subsurface flow governedby the Richards equation in PFLOTRAN. These two flow domains are linked with a boundary condition switching method that ensures continuity of pressure and flux at the surface-subsurface interface. We verify the coupled code against other existing hydrologic models and observation data using a number of numerical experiments. The coupled hydrological model exhibits good performance in strong parallel scaling tests. The new coupled surface and subsurface simulator significantly advance community simulation capability towards improving integrated hydrologic and biogeochemical understanding of complex systems such as watersheds and river corridors. Keywords: Surface flow, Integrated hydrological modeling, Boundary condition switching, Parallel computing

Wu, Runjian↗
DOE OSTI2023

Increased salinity decreases annual gross primary productivity at a Northern California brackish tidal marsh

Tidal marshes sequester 11.4–87.0 Tg C yr –1 globally, but climate change impacts can threaten the carbon capture potential of these ecosystems. Tidal marshes occur across a wide range of salinity, with brackish marshes (0.5–18 ppt (parts per thousand)) dominating global tidal marsh extents. A diverse mix of freshwater- and saltwater-tolerant plant and microbial communities has led researchers to predict that carbon cycling in brackish wetlands may be less sensitive to changes in salinity than fresh- or saltwater wetlands. Rush Ranch, a well-monitored brackish tidal wetland of the San Francisco Bay National Estuarine Research Reserve, experiences highly variable annual salinity regimes. Within a five-year period (2014–2018), Rush Ranch experienced particularly extreme drought-induced salinization during the 2014 and 2015 growing seasons. During drought years, tidal channel salinity rose from a 15 year baseline of 4.7 ppt to growing season peaks of 10.3 ppt and 12.5 ppt. Continuous eddy covariance data from 2014 to 2018 demonstrate that during drought summers, gross primary productivity (GPP) decreased by 24%, whereas ecosystem respiration remained similar among all five years. Stepwise linear regression revealed that salinity, not air temperature or tidal height, was the dominant driver of annual GPP. A random forest model trained to predict GPP based on environmental data from low salinity years (i.e. naive to salinization) significantly over predicted GPP in drought years. When growing season salinities were doubled, annual estimates of net ecosystem exchange of CO 2 decreased by up to 30%. These results provide ecosystem-scale evidence that increased salinity influences CO 2 fluxes dominantly through reductions in GPP. This relationship provides a starting point for incorporating the effect of changes in salinity in wetland carbon models, which could improve wetland carbon forecasting and management for climate resilience.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Standard experimental paradigm designs and data exclusion practices in cognitive psychology can inadvertently introduce systematic “shadow” biases in participant samples

Abstract Standard cognitive psychology research practices can introduce inadvertent sampling biases that reduce the reliability and generalizability of the findings. Researchers commonly acknowledge and understand that any given study sample is not perfectly generalizable, especially when implementing typical experimental constraints (e.g., limiting recruitment to specific age ranges or to individuals with normal color vision). However, less obvious systematic sampling constraints, referred to here as “shadow” biases, can be unintentionally introduced and can easily go unnoticed. For example, many standard cognitive psychology study designs involve lengthy and tedious experiments with simple, repetitive stimuli. Such testing environments may 1) be aversive to some would-be participants (e.g., those high in certain neurodivergent symptoms) who may self-select not to enroll in such studies, or 2) contribute to participant attrition, both of which reduce the sample’s representativeness. Likewise, standard performance-based data exclusion efforts (e.g., minimum accuracy or response time) or attention checks can systematically remove data from participants from subsets of the population (e.g., those low in conscientiousness). This commentary focuses on the theoretical and practical issues behind these non-obvious and often unacknowledged “shadow” biases, offers a simple illustration with real data as a proof of concept of how applying attention checks can systematically skew latent/hidden variables in the included population, and then discusses the broader implications with suggestions for how to manage and reduce, or at a minimum acknowledge, the problem.

Siritzky, Emma M.↗
DOE OSTI2021

Cyberbiosecurity and Public Health in the Age of COVID-19

Cyberbiosecurity, the aspect of biosecurity involving the digital representation of biological data, had already been emerging as a matter of public concern even prior to the onset of the COVID-19 pandemic. Key issues of concern include, among others, the privacy of patient data, the security of public health databases, the integrity of diagnostic test data, the integrity of public biological databases, the security implications of automated laboratory systems and the security of proprietary biological engineering advances. With the onset of the COVID-19 pandemic, and the importance of digital resources in combatting it, concern about the potential for cyber attacks by state-based or non-state actors has been elevated. To illuminate the challenges, we focus on the cyber vulnerabilities that need to be addressed in public health activities such as disease surveillance and outbreak management. In particular, we examine cyber issues raised by the accelerated pace of development for COVID mitigations, treatments, and vaccines.

cybersecurity, biosecurity↗
DOE OSTI2021

Quality of service for input/output memory management unit

A data processing system includes a memory, a group of input/output (I/O) devices, an input/output memory management unit (IOMMU). The IOMMU is connected to the memory and adapted to allocate a hardware resource from among a group of hardware resources to receive an address translation request for a memory access from an I/O device. The IOMMU detects address translation requests from the plurality of I/O devices. The IOMMU reorders the address translation requests such that an order of dispatching an address translation request is based on a policy associated with the I/O device that is requesting the memory access. The IOMMU selectively allocates a hardware resource to the input/output device, based on the policy that is associated with the I/O device in response to the reordering.

Basu, Arkaprava↗
DOE OSTI2021

Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model

Accurate early season predictions of crop yield at the within-field scale can be used to address a range of crop production, management, and precision agricultural challenges. While the remote sensing of within-field insights has been a research goal for many years, it is only recently that observations with the required spatio-temporal resolutions, together with efficient assimilation methods to integrate these into modeling frameworks, have become available to advance yield prediction efforts. Here we explore a yield prediction approach that combines daily high-resolution CubeSat imagery with the APSIM crop model. The approach employs APSIM to train a linear regression that relates simulated yield to simulated leaf area index (LAI). That relationship is then used to identify the optimal regression date at which the LAI provides the best prediction of yield: in this case, approximately 14 weeks prior to harvest. Instead of applying the regression on satellite imagery that is coincident, or closest to, the regression date, our method implements a particle filter that integrates CubeSat-based LAI into APSIM to provide end-of-season high-resolution (3 m) yield maps weeks before the optimal regression date. The approach is demonstrated on a rainfed maize field located in Nebraska, USA, where suitable collections of both imagery and in-situ data were available for assessment. The procedure does not require in-field data to calibrate the regression model, with results showing that even with a single assimilation step, it is possible to provide yield estimates with good accuracy up to 21 days before the optimal regression date. Yield spatial variability was reproduced reasonably well, with a strong correlation to independently collected measurements (R 2 = 0.73 and rRMSE = 12%). When the field averaged yield was compared, our approach reduced yield prediction error from 1 Mg/ha (control case based on a calibrated APSIM model), to 0.5 Mg/ha (using satellite imagery alone), and then to 0.2 Mg/ha (results with assimilation up to three weeks prior to the optimal regression date). Such a capacity to provide spatially explicit yield predictions early in the season has considerable potential to enhance digital agricultural goals and improve end-of-season yield predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson’s Disease

Effective treatment of Parkinson’s disease (PD) is a continual challenge for healthcare providers, and providers can benefit from leveraging emerging technologies to supplement traditional clinic care. We develop a data-driven reinforcement learning (RL) framework to optimize PD medication regimens through wearable sensors. We leverage a data set of n = 26 PD patients who wore wrist-mounted movement trackers for two separate six-day periods. Using these data, we first build and validate a simulation model of how individual patients’ movement symptoms respond to medication administration. We then pair this simulation model with an on-policy RL algorithm that recommends optimal medication types, timing, and dosages during the day while incorporating human-in-the-loop considerations on medication administration. The results show that the RL-prescribed medication regimens outperform physicians’ medication regimens, despite physicians having access to the same data as the RL agent. To validate our results, we assess our wearable-based RL medication regimens using n = 399 PD patients from the Parkinson’s Progression Markers Initiative data set. We show that the wearable-based RL medication regimens would lead to significant symptom improvement for these patients, even more so than training RL policies directly from this data set. In doing so, we show that RL models from even small data sets of wearable data can offer novel, generalizable clinical insights and medication strategies, which may outperform those derived from larger data sets without wearable data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Uranyl oxalate species in high ionic strength environments: stability constants for aqueous and solid uranyl oxalate complexes

Uranyl ion, UO 2 2+ , and its aqueous complexes with organic and inorganic ligands can be the dominant species for uranium transport on the Earth surface or in a nuclear waste disposal system if an oxidizing condition is present. As an important biodegradation product, oxalate, C 2 O 4 2– , is ubiquitous in natural environments and is known for its ability to complex with the uranyl ion. Oxalate can also form solid phases with uranyl ion in certain environments thus limiting uranium migration. Therefore, the determination of stability constants for aqueous and solid uranyl oxalate complexes is important not only to the understanding of uranium mobility in natural environments, but also to the performance assessment of nuclear waste disposal. In this work, we developed a thermodynamic model for the UO 2 2+ –Na + –H + –Cl – –ClO 4 – –C 2 O 4 2– –NO 3 – –H 2 O system to ionic strength up to ~11 mol•kg –1 . We constrained the stability constants for UO 2 C 2 O 4 (aq) and UO 2 (C 2 O 4 ) 2 2– at infinite dilution based on our evaluation of the literature data over a wide range of ionic strengths up to ~11 mol•kg –1 . We also obtained the solubility constants at infinite dilution for solid uranyl oxalates, UO 2 C 2 O 4 •3H 2 O, based on the solubility data over a wide range of ionic strengths. The developed model will enable for the accurate stability assessment of oxalate complexes affecting uranium mobility under a wide range of conditions including those in deep geological repositories.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A Bayesian modeling framework for crash severity effects of active traffic management systems

Transportation agencies utilize Active traffic management (ATM) systems to dynamically manage recurrent and non-recurrent congestion based on real-time conditions. While these systems have been shown to have some safety benefits, their impact on injury severity outcomes is currently uncertain. In this paper, we used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities. The estimation results revealed lower severities with ATM deployment. Marginal effects for ATM deployments that featured hard shoulder running (HSR) revealed lower likelihoods for severe and moderate injury crashes of 15.9 % and for minor injury crashes of 10.1 %. The likelihood of severe and moderate injury crashes and minor injury crashes reduced by 12.4 % and 8.33 % with ATM without HSR. The models were observed to be temporally transferable and had forecast error of 0.301 and 0.304 for the two models, revealing better performance with validation data. These results have implications for improving freeway crash risk at critical locations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The legacy of weapons grade plutonium production: Health status of Hanford complex workers who manage the waste

The extent and etiology of health effects in workers who maintain underground storage tanks at the Hanford Nuclear Reservation (Hanford) have been subjects of controversy and concern for several decades. Hanford is a decommissioned nuclear production complex managed by the US Department of Energy in southeast Washington State. This integration-of-evidence review evaluates the relationship between exposure to vapors from mixed chemical and radioactive waste stored in underground storage tanks at Hanford and worker health. Hanford workers’ health information was gathered from technical reports, media reports, and published literature, including the systematic search of seven databases. This review describes the health status and health concerns of Hanford tank farm workers based on the integration of the available health effects data from disparate sources. In interviews with external groups, Hanford workers reported both irritant-type symptoms and diseases that they believe are attributable to tank farm vapors. However, the results of this integration-of-evidence review indicated that no pervasive pattern of occupational disease was identified that can be associated with exposure to tank farm vapors. Inhalation exposure to asbestos and beryllium is associated with lung disease from various types of nuclear industry work but not from work on tank farms. This review concluded that while irritant-type symptoms and isolated cases of occupational disease are plausible under certain conditions, the currently available data do not support a pervasive pattern of occupational disease associated with vapor exposure.

Cherry, Debra↗
DOE OSTI2021

2020 Energy Storage Pricing Survey

The annual Energy Storage Pricing Survey (ESPS) series is designed to provide a standardized reference system price for various energy storage technologies across a range of different power and energy ratings. This is an essential first step in comparing systems of the different technologies’ usage costs and total cost of ownership. The final system prices are developed based on data from an extensive set of interviews with representatives across the manufacturing and project development value chain, plus available published data. This information is incorporated into a consistent methodology structure that will allow pricing information to be incorporated at whatever level it was obtained, ranging from component to fully installed system. The ESPS system pricing methodology breaks down the cost of an energy storage system into the following component categories: the storage module; the balance of system; the power conversion system; the energy management system; and the engineering, procurement, and construction costs. By evaluating each of the different component costs separately, a more accurate system cost can be developed that provides internal pricing consistency between different project sizes using the same technology, as well as between different technologies that utilize similar components.

25 ENERGY STORAGE↗
DOE OSTI2023

Assessment of Cloud-Based Applications Enabling a Scalable Risk-Informed Predictive Maintenance Strategy Across the Nuclear Fleet

The current light water reactor fleet uses time-based or failure-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging technologies in terms of artificial intelligence (AI) and cloud computing to enable a cost-effective, predictive maintenance strategy. This report examines the feasibility of cloud computing for the nuclear industry’s needs in terms of the cloud’s computing capabilities, feasibility, and regulatory concerns. The technical viability of cloud computing was analyzed using one year worth of data from a boiling water reactor’s safety relief valve. Models were hosted on a local desktop, Idaho National Laboratory’s high-performance computer, and Microsoft Azure. Data was loaded, processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was used to determined that cloud computing has adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, there is a benefit as the plant no longer needs manage their own servers, software, cybersecurity, and IT support staff. Many of these features can be offloaded on to the cloud provider. A comprehensive analysis was completed that showed the current annual cost of operating is more expensive than using cloud computing resources. Lastly, the regulatory framework does not explicitly address AI or autonomous control. Currently, the NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than new regulations to address the use of AI and ML. But since many of the AI applications are focused on non-safety related applications, such as balance-of-plant components, they will likely have little or no regulatory restrictions or necessary approvals. Demonstrating how AI can improve maintenance and operation of these non-safety related systems seems like the likely path forward for implementing AI and cloud computing resources inside nuclear power plants (NPPs).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗