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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Leading Edge Microspoilers for Load Dumping in Wind Turbine Rotors

As rotor designs have grown in size and flexibility, new design challenges have arisen that could benefit from novel control strategies. Leading edge microspoilers (LEMS) have been shown to effectively reduce forces on airfoils by inducing stall at lower angles of attack. This paper investigates how LEMS can be used to reduce blade loading on large, flexible, downwind rotors during design-limiting design load cases. During shutdown in turbulent flows near cutout speed, LEMS could reduce the tip deflection spikes toward the tower by over 50% and keep flapwise root bending moments below normal operating conditions, thus eliminating these as design-driving load cases. In power producing load cases, deploying LEMS for a short period of time during a load spike can be effective to reduce toward-tower tip deflections without significantly impacting power produced, assuming these load spikes can be identified by a controller. Leveraging this technology could allow for lower specific power machines to be designed.

17 WIND ENERGY↗
DOE OSTI2024

Development Results on Replacement Materials for Current Scarce or High Supply Chain Risk Materials

In September 2020, the U.S. government issued an executive order to address the threat to the domestic supply chain from its reliance on critical minerals (CMs) from foreign competitors and to support the domestic mining and processing industry. The Advanced Materials and Manufacturing Technology (AMMT) program is addressing this executive order by evaluating advanced manufacturing (AM) and its impact on the demands of CMs for energy production in general and how the deployment of AM in nuclear energy will support the projected goals of the Paris Accord and further a net-zero carbon economy (NZE) by 2050. Three strategic reports were previously prepared by the AMMT program to date and identified two areas for more detailed exploration: (1) the replacement of high-risk CMs such as cobalt and niobium by more abundant minerals and (2) the minimization and utilization of CM waste streams. The design of nuclear materials without critical elements as alloying elements, is a part of the nuclear materials strategy to overcome the critical minerals scarcity. In this report, two approaches are evaluated namely (1) replacement of critical elements as alloying elements in nuclear materials, and (2) the design of a new alloys that does not contain critical minerals as an alloying element.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Electric Vehicle Charging Management in Smart Energy Communities to Increase Renewable Energy Hosting Capacity

Abnormal climates due to global warming have emerged as a big concern in the global community. To mitigate climate change and achieve sustainability, distributed energy resources (DERs), including solar and wind, have been recently deployed in power systems. As the penetration level of DERs has increased, however, it caused a multitude of issues in the power systems, such as voltage fluctuation in the distribution network limiting renewable hosting capacity. On the other hand, the electric vehicle (EV) industry is rapidly growing to facilitate the transition to a carbon-neutral community, illuminating the potential of EVs as a flexible grid asset to mitigate some of the issues and improve grid operation, if properly exploited. To explore the potential of EVs, this paper proposes an EV scheduling strategy. By using an optimal EV charging scheduling proposed, distribution system operators (DSOs) can minimize their operating costs and stably operate the system with a high level of DERs. To validate the method, a modified IEEE 33-bus system with DERs is developed. The case study shows the proposed scheduling strategizes EV charging to reduce the cost of PV curtailment. In the study, the method outperforms the renewable-only case with curtailment by 4.97% in DSO cost. It also demonstrates its potential to increase the renewable hosting capacity by harmonizing EV charging with renewables.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
DOE OSTI2022

Review of Radio Frequency Interference and Potential Impacts on the CMB-S4 Cosmic Microwave Background Survey

CMB-S4 will map the cosmic microwave background to unprecedented precision, while simultaneously surveying the millimeter-wave time-domain sky, in order to advance our understanding of cosmology and the universe. CMB-S4 will observe from two sites, the South Pole and the Atacama Desert of Chile. A combination of small- and large-aperture telescopes with hundreds of thousands of polarization-sensitive detectors will observe in several frequency bands from 20-300 GHz, surveying more than 50% of the sky to arcminute resolution with unprecedented sensitivity. CMB-54 seeks to make a dramatic leap in sensitivity while observing across a broad range of largely unprotected spectrum which is increasingly being utilized for terrestrial and satellite transmissions. Fundamental aspects of CMB instrument technology leave them vulnerable to radio frequency interference (RFI) across a wide range of frequencies, including frequencies outside of their observing bands. Ground-based CMB instruments achieve their extraordinary sensitivities by deploying large focal planes of superconducting bolometers to extremely dry, high-altitude sites, with large fractional bandwidths, wide fields of view, and years of integration time. Suitable observing sites have historically offered significant protection from RFI, both naturally through their extremely remote locations as well as through restrictions on local emissions. Since the coupling mechanisms are complex, "safe" levels or frequencies of emission that would not interfere with CMB measurements cannot always be determined through straightforward calculations. We discuss models of interference for various types of RFI relevant to CMB-54, mitigation strategies, and the potential impacts on survey sensitivity.

Cosmic Microwave Background↗
DOE OSTI2023

Design Considerations and Analysis of Multi-Level Erasure Coding in Large-Scale Data Centers

Multi-level erasure coding (MLEC) has seen large deployments in the field, but there is no in-depth study of design considerations for MLEC at scale. In this paper, we provide comprehensive design considerations and analysis of MLEC at scale. We introduce the design space of MLEC in multiple dimensions, including various code parameter selections, chunk placement schemes, and various repair methods. We quantify their performance and durability, and show which MLEC schemes and repair methods can provide the best tolerance against independent/correlated failures and reduce repair network traffic by orders of magnitude. To achieve this, we use various evaluation strategies including simulation, splitting, dynamic programming, and mathematical modeling. We also compare the performance and durability of MLEC with other EC schemes such as SLEC and LRC and show that MLEC can provide high durability with higher encoding throughput and less repair network traffic over both SLEC and LRC.

Wang, Meng↗
DOE OSTI2022

SRF Cavity Fault Classification and Prediction at Jefferson Lab

Over the last few years several machine learning projects at Jefferson Lab have had a common focus to optimize operation of superconducting RF (SRF) cavities in the Continuous Electron Beam Accelerator Facility (CEBAF). In this talk we highlight work to identify and classify types of faults from C100-type cavities and then to extend those capabilities to provide real-time fault prediction. Early prediction may enable mitigation strategies to prevent some types of faults. In our approach we apply a two-step fault prediction pipeline. In the first step, a model distinguishes between faulty and normal signals. In the second step, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. In additional to model performance, we also highlight challenges in working with real-world data and challenges for deploying models.

Tennant, C.↗
DOE OSTI2021

Learning from the Pandemic: the Future of Meetings in HEP and Beyond

The COVID-19 pandemic has by-and-large prevented in-person meetings since March 2020. While the increasing deployment of effective vaccines around the world is a very positive development, the timeline and pathway to "normality" is uncertain and the "new normal" we will settle into is anyone's guess. Particle physics, like many other scientific fields, has more than a year of experience in holding virtual meetings, workshops, and conferences. A great deal of experimentation and innovation to explore how to execute these meetings effectively has occurred. Therefore, it is an appropriate time to take stock of what we as a community learned from running virtual meetings and discuss possible strategies for the future. Continuing to develop effective strategies for meetings with a virtual component is likely to be important for reducing the carbon footprint of our research activities, while also enabling greater diversity and inclusion for participation. This report summarizes a virtual two-day workshop on Virtual Meetings held May 5-6, 2021 which brought together experts from both inside and outside of high-energy physics to share their experiences and practices with organizing and executing virtual workshops, and to develop possible strategies for future meetings as we begin to emerge from the COVID-19 pandemic. This report outlines some of the practices and tools that have worked well which we hope will serve as a valuable resource for future virtual meeting organizers in all scientific fields.

Neubauer, Mark S.↗
DOE OSTI2022

Independent Review of Groundwater Remediation Strategy for Hexavalent Chromium and RDX Groundwater Plumes at Los Alamos National Laboratory (Rev. 1)

Site operations at the Los Alamos National Laboratory (LANL) resulted in the release of oxidized chromium, Cr(VI), into Sandia Canyon from cooling tower effluent from 1956 until 1972. The chromium traveled with the surface water approximately 3 miles downstream before migrating below ground surface. Chromium concentrations exceed 50 μg/L in the upper portion of the aquifer. Another LANL groundwater plume of concern is associated with RDX (Royal Demolition Explosives, 1,3,5-trinitro-1,3,5-triazine). Between 1951 and 1996, RDX was released to the mesa-top facilities' process water outfall, adjacent and underlying soils, and alluvial sediments, along with surface water in Cañon de Valle. Between 2000 and 2010, two remedial actions were deployed, removing much of the near-surface RDX, however, recharge due to precipitation has transported RDX into the perched-intermediate zone and into the regional aquifer. The report documents an independent technical review by scientists from the Department of Energy (DOE) Network of National Laboratories for Environmental Management and Stewardship (NNLEMS) to provide recommendations for potential near term actions to address and optimize remediation for both the Cr(VI) and RDX plumes. The proposed near-term remedial actions include design of pump and treat systems for Cr(VI) and monitoring and study for natural attenuation for RDX. The review assesses existing data, conceptual and numerical modeling, and it recommends a technical integration process to support identifying and implementing strategic, effective and efficient remedies. The DOE Environmental Management Los Alamos Field Office (EM-LA) and their cleanup contractor Newport News Nuclear-BWTX, LLC Los Alamos (N3B) provided the information required for the review. Interviews were also conducted with regulators to obtain the full spectrum of technical, regulatory and scientific perspectives. The independent review team was impressed by the capabilities, experiences, innovativeness, and insightfulness of the technical representatives from both the regulator, the New Mexico Environment Department (NMED) and N3B. Incorporation of vadose zone flow pathways in the conceptual site model (CSM) and configuring the numerical modeling for the site was generally state-of-the-practice (or better). This could be considered state-of-the-art by addressing uncertainties related to spatial extent of hydraulic windows. The reviews from the regulators were thorough and often provided useful concepts for consideration and future/study resolutions. The overarching consensus recommendation of independent review team is that the LANL groundwater plumes should be addressed in context of the emerging "management of complex sites" paradigm.

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

Evaluating Optical Techniques to Characterize Solid State Samples for the Pu-238 Supply Program

This report documents steps that have been taken to establish advanced characterization capabilities for solid-state samples to support the 238 Pu Supply Program. The program has already invested significantly in spectroscopy and online monitoring development for liquid processes. Many of the data, analytical methods, and equipment could also be leveraged for the analysis of Np/Pu oxides and Np pellets to help classify material properties and link them to operations. The techniques described in this work include diffuse reflectance spectroscopy (DRS), Raman spectroscopy, and laser-induced breakdown spectroscopy (LIBS). Each technique is sensitive to various chemical and/or elemental species and can provide complimentary information to more standard methods such as powder x-ray diffraction and scanning electron microscopy. These options are remotely deployable via fiber-optic cables. DRS and Raman spectroscopy are already approved for glove box and hot cell work, but LIBS requires US Department of Energy approval for incorporation. Raman spectroscopy does require laser shielding, which means that DRS is the easiest method to implement. However, each technique provides complimentary data, and the most robust sample characterization could be achieved when all three, among other methods, are harnessed together. This report documents some of the progress made testing these capabilities, some of the challenges, and next steps to optimize the technology and integration strategies.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Graph coarsening: from scientific computing to machine learning

Abstract The general method of graph coarsening or graph reduction has been a remarkably useful and ubiquitous tool in scientific computing and it is now just starting to have a similar impact in machine learning. The goal of this paper is to take a broad look into coarsening techniques that have been successfully deployed in scientific computing and see how similar principles are finding their way in more recent applications related to machine learning. In scientific computing, coarsening plays a central role in algebraic multigrid methods as well as the related class of multilevel incomplete LU factorizations. In machine learning, graph coarsening goes under various names, e.g., graph downsampling or graph reduction. Its goal in most cases is to replace some original graph by one which has fewer nodes, but whose structure and characteristics are similar to those of the original graph. As will be seen, a common strategy in these methods is to rely on spectral properties to define the coarse graph.

Chen, Jie↗
DOE OSTI2024

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. Furthermore, these outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO₂ emissions amounting to 7.9142×10 3 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

13 HYDRO ENERGY↗
DOE OSTI2021

Review of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics

Deployment of digital technologies within a modern shift in cyber defense systems is essential for protecting the energy production units. One of the important components of defense is cyberforensics: once an attack has been detected to locate its origin. In this paper, a review of well-known cyberattacks in nuclear facilities is provided, with the lessons learned leading to the development of a machine learning approach implementing identification of internal at- tacks in the facility's data networks. Our approach may be seen as one of the layers in a defense-in-depth strategy that identifies if the attack comes from inside, which may result in identifying faster the attacker's origin. The presented model exploits network packet examination to cast accurate predictions on detailing the origin of malicious network connections. The approach fuses multiple mathematical functions within an artificial neural network to provide a response in the form of 0/1, i. e., whether the attack is identified as internal or not. The utilization of a variety of test cases is developed to explore the relevance and validity of the predictive approach. The proposed implementation is examined with network data packet variance, and the results obtained exhibit a highly accurate detection rate.

Nuclear Science & Technology↗
DOE OSTI2024

Equipping Neural Network Surrogates with Uncertainty for Propagation in Physical Systems

Coarse-grained or filtered models typically rely on closure models to account for unresolved scales. For instance, large eddy simulation for modeling turbulent fluid flows explicitly resolves the largest scales, but requires modeling closure terms to account for the sub-filter scales. With the vast amount of data available from high-fidelity simulations, there are unique opportunities to leverage data-driven modeling techniques to formulate expressive and flexible closure models. Despite their flexibility, data-driven models struggle in domain shift settings, i.e. when deployed in configurations not captured in the training dataset. In particular, the efficacy of neural network surrogates is difficult to assess a priori due to the deterministic, point-estimate nature of predictions. In high-consequence applications, such models require reliable uncertainty estimates in the data-informed and out-of-distribution regimes. To quantify uncertainties in both regimes, we employ Bayesian neural networks which are able to capture both epistemic and aleatoric uncertainties. We will discuss challenges associated with the training and evaluation of these networks. Furthermore, we will discuss uncertainty embedding strategies to enable efficient sampling and propagation of uncertainty through high-fidelity simulations.

Bayesian neural networks↗
DOE OSTI2026

Zeteotech, LLC TRGR Project Final Report

Reliable detection of aerosolized pathogens is difficult due to need to distinguish between the benign bioaerosols such as dander and pollen and the thousands of pathogens capable of infecting people. Accurate identification of airborne pathogens of concern in the past has required the collection of aerosol samples in a filter, periodic collection of the samples, and processing and identification in the laboratory. This process is labor intensive and expensive. Additionally, this method necessarily has a time to detection window of hours to days depending on the collection frequency. Biological pathogens have an incubation period before the onset of symptoms and severe health effects and/or mortality, people will typically be exposed to them without realizing it. This has resulted in a detect to treat strategy for protection against bio releases. While prophylactic measures can still be effective over these time scales, reducing the time to detection will significantly improve the effectiveness of these measures and subsequently reduce the consequences of a release. Various attempts to reduce the time to detection and identification have been plagued by highly undesirable false-positives which degrade confidence in the system. Zeteotech, LLC has developed a mass spectrometer based bioaerosol sensing system which is capable of autonomously identifying airborne pathogens of concern and alert authorities within minutes instead of hours to days. They have deployed these instruments to protect high-risk facilities by alerting authorities of public health events and intentional bioterrorism events in near real time. This makes it possible to more accurately identify the time and location of the release and minimize the number of people that are exposed through prompt quarantining of affected areas.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

POWER ELECTRONICS GRID TIED SYSTEM FINAL REPORT

Across the country, electric utilities are grappling with the persistent hurdles of integrating Distributed Energy Resources (DERs). Managing these assets safely and effectively is a complex endeavor, complicated by varying ownership structures, management philosophies, and the diversity of the technologies themselves. Consequently, the industry has seen a proliferation of bespoke system designs, control strategies, and communication frameworks—forcing utilities to spend significant time and resources developing one-off integration solutions. This project addressed these integration hurdles through a scalable demonstration of intelligent devices designed to coordinate and control diverse resources in low-voltage applications. This concept minimized the need for complex integration by transforming the separate DERs into a dispatchable virtual power plant (VPP) with integrated resiliency functions (called a Node). By collaborating with a utility partner, the project focused on developing rapidly implementable use cases that bridged the gap between theoretical control and real-world deployment

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Mitigating electrochemical degradation in CsPbBr{sub 3} gamma detectors by organic and inorganic encapsulation.

CsPbBr3 perovskite semiconductors have emerged as a leading candidate for nextgeneration radiation detectors because of their exceptional charge transport properties, defect tolerance, and record-breaking sensitivity and energy resolution. Their long-term stability, however, is hindered by electrode-driven electrochemical decomposition, which is accelerated by moisture- and oxygen-assisted ion migration during operation. Here, we investigated organic and inorganic encapsulation strategies as both environmental barriers and means to suppress interfacial degradation pathways. Atomic layer deposition (ALD) of Al2O3 provided a conformal passivation layer that blocked environmental ingress, suppressed ionic diffusion, reduced leakage current, enhanced energy resolution and expanded the operational electric-field window beyond 5 kV∙cm1 . By contrast, organic encapsulants such as paraffin wax and polystyrene slowed moisture diffusion but did not suppress interfacial reactions, with wax extending stability to over 90 days. These results show that ALD-Al2O3 suppresses dominant interfacial degradation pathways, enabling stable, high-field operation and advancing the practical deployment of CsPbBr3 γ-ray detectors.

Unal, Mustafa↗