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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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This project aims to evaluate and develop the concept of optical basicity to solar molten salts, design an online monitoring system for implementation in commercial plants, and train a diverse workforce for the CSP industry. This is the final report.
In this presentation, we will explore the Rucio experience with the Rubin Observatory experiment. Our discussion will cover several key areas: Scalability Tests: Insights into the performance and scalability evaluations of Rucio in the context of Rubin's data needs and what we have learned, especially with many small files. Role in Rubin's Data Curation: Rubin's Data Butler: An overview of how Rucio, along with with Rubin's Data Butler using Hermes-K, which involves message passing through Kafka, is integrated in the Rubin's data curation system. Monitoring and Support: Current status of Rucio and PostgreSQL monitoring and Rucio deployment and support within the Rubin environment. Tape RSE Implementation: Deal with the order of magnitude more files going to tape than HEP. Future Needs: An examination of Rubin's evolving requirements for Rucio services and how we plan to address them.
The Deep Underground Neutrino Experiment (DUNE) is a flagship international collaboration designed to study neutrinos tiny, nearly massless particles that may hold answers to fundamental questions about the Universe. Fermilab s Robotic Test Stand (RTS) plays a critical role in ensuring the quality of approximately 50,000 Application-Specific Integrated Circuit (ASIC) chips that will be used in DUNE s massive liquid argon detectors. These electronics will be inside the cryostat; therefore, they will need to have a high yield of working chips and low noise. To improve the automation and reliability of the RTS, this project focused on designing and implementing a Python-based finite state machine (FSM) to manage chip handling workflows. The FSM was developed as a modular software framework to coordinate robotic arm movements, manage chip tray positions, and monitor system states during testing. Key features include robust error handling routines, a pause/resume system for safe mid-cycle interruptions, and a simulation mode for iterative testing without hardware dependencies. The system was designed to prepare for seamless integration with RTS hardware components such as the robotic arm and vision system. This integration will streamline collaboration and enable efficient deployment of updates across the six total institutions performing testing. The outcomes of this internship contribute to Fermilab s mission to advance high-energy physics and support the DOE s national goals by directly improving the testing of equipment to be used in DUNE. The project also provided valuable experience in software design and contributing to the success of DUNE.
Quantifying the underwater sound produced by tidal turbines is essential both to understand their potential environmental impacts and to understand how that sound might interfere with the intended application of the turbine (e.g., powering acoustic monitoring systems). In this project, we measured the sound radiated by a small-scale crossflow tidal turbine. The turbine was deployed from October 2023 to March 2024 in the tidal channel at the entrance to Sequim Bay, WA. Acoustic measurements were made with three different sensor packages: a commercial-off-the-shelf vector sensor (operated by PNNL), a vector sensor array (operated by Integral Consulting), and drifting hydrophones (operated by UW). Acoustic recordings from the three sensors highlight changes in the turbine acoustic signature over the course of a tidal cycle and throughout the 6-month turbine deployment. Our results also highlight the utility of acoustic vector sensors for localizing sound attributable to marine energy devices in acoustically complex environments.
The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and in-person interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.
Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.
Additively fabricated metal parts using laser powder bed fusion (L-PBF) possess sophisticated morphology due to the recurrent use of laser-induced metal powder melting and solidification. The surface and 3D morphology of these parts often include defects in the form of protrusions, depressions, pores, voids, keyholes, or cracks that are known to be influenced by laser scanning paths and layer-to-layer processing. Such inconsistent part quality hampers the extensive adoption of L-PBF. Pores and cracks are detrimental to the fatigue life of the parts and components. Quantifying and controlling part defects and optimizing processing and scanning strategy parameters adaptively in real-time through in situ monitoring systems are highly desired. This study investigates the optimization of experimental process parameters (power, scan velocity, and hatch spacing) and their effects on the cracking and porosity of Al6061 alloy using machine learning techniques. Multi-objective optimization is formulated and conducted to determine the L-PBF parameters that minimize both porosity and crack densities.
The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and inperson interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.
Passive acoustic monitoring of the global ocean has increased dramatically over the last decade, providing insights into seasonal sea ice and wind/wave variability, biodiversity, geophysical hazards, and anthropogenic noise impacts. All of these phenomena are sentinels of marine ecosystem health and ocean climate change. Recognizing the utility of underwater sound, the Pacific Marine Environmental Laboratory (PMEL) formed a passive acoustic research program with the goal of quantifying deep-ocean and coastal soundscapes in support of NOAA’s mission to conserve and manage marine ecosystems. PMEL Acoustics Program researchers have built a stable of novel ocean technologies, including autonomous stationary hydrophones, mobile platforms, and near-real-time surface buoys with satellite communication capability. These passive acoustic monitoring systems have been deployed in every major ocean basin on Earth, enabling significant advancements in understanding of natural and anthropogenic sounds. This progress includes evaluation of human-made sound levels across US waters, observations of ship noise fluctuations during the COVID-19 pandemic, and evaluation of noise levels from offshore wave-energy devices. Progress in natural sound research includes assessment of seasonal variability in the presence of endangered cetacean species due to population recovery and/or changing ocean temperatures as well as early detection of the collapse of an Antarctic ice shelf.
Responders need tools to rapidly detect and identify airborne alpha radioactivity during consequence management scenarios. Traditional continuous air monitor systems used for this purpose compute the net counts in various energy windows to determine the presence of specified isotopes, such as 235U, 239Pu, and 241Am. These calculations rely on having a well-calibrated detector, which is challenging in low-background environments. Here an alternative approach of using artificial neural networks to classify alpha spectra is presented. Two network architectures, fully connected and convolutional networks, were trained to classify alpha spectra into four categories: background and background plus the three isotopes above. Sources were injected into measured background at various fractions of the derived response level (DRL) corresponding to early-phase Protective Action Guides. The convolutional network identifies all sources at 1% of the DRL with average probability of detection of 95% and false alarm probability of 1%. Further, the network identifies sources ranging between 0.25% and 1% of the DRL with higher than 80% probability of detection and lower than 7% false alarm probability. Most significantly, the network performance improves in low-count background conditions, increasing its minimum probability of detection to 93% and reducing the false alarm probabilities to lower than 0.25%. These results show that, once trained on datasets representing a range of detection scenarios, artificial neural networks can accurately identify alpha isotopes of interest without the need for detector calibration.
Overall, the first three cycles of irradiation proceeded as planned Items of note: Failure rate of thermocouples was higher than expected Downstream needle valves were throttled resulting in part of the exhaust flow being diverted prior to reaching the fission product monitors (this was corrected near the end of cycle 164B). This was more of annoyance than anything and did not prevent proper analysis of fission product data. No evidence of failures on fission product monitoring system.
A Sentry Remote Monitoring System (Sentry-RMS) is a stand-alone security system that provides detection, assessment, and communication of priority alarms as an additional means of thwarting internal and external threats to sites that maintain radiological material. The SEntry-RMS CommUnications and REsponse (Sentry-SECURE) platform is an optional feature of the Sentry-RMS that relays priority alarm information to the identified response stakeholders. Sentry-SECURE is hosted in a cloud environment that abstracts the data owner’s and data consumer’s platforms to allow for greater information sharing. This promotes situational awareness amongst authorized users and enables future innovation among modern response platforms. When securely architecting a cloud solution such as this, the use of design paradigms can be an effective tool to increase the accuracy and reliability of cyber- and information-security-related decisions made throughout the development process. This approach also supports the categorization of design considerations into three levels: industry concepts, project approaches, and data protections for digital processes. Industry concepts consist of the notional underpinnings that guide or motivate a security process, system, or design but often lack any tangible attributes. Project approaches represent decisions made during the design and development process to prioritize a solution, method, or practice above another that may provide a comparable functional output but lacks a desired security benefit. Data protections for digital processes represent the selection, integration, and implementation of specific controls for a given asset. This paper will explore specific examples of how Sentry-SECURE has been designed to account for considerations at each of these three levels, while balancing the operational intent of the platform with the security enhancements necessary to maintain data integrity, availability, and confidentiality.
A corrosion monitoring system includes one or more objects coupled to respective portions of a transformer tank. The one or more objects are configured to corrode before the respective portions of the transformer tank. At least one optical sensor is coupled to each of the objects. The at least one optical sensor has an optical output that changes in response to strain of the object. An analyzer is coupled to the at least one optical sensor. The analyzer is configured to perform one or more of detecting and predicting corrosion of the transformer tank based on the output of the at least one optical sensor.
A monitoring system includes an array of optical sensors disposed within a transformer tank. Each optical sensor is configured to have an optical output that changes in response to a temperature within the transformer tank. An analyzer is coupled to the array of optical sensors. The analyzer is configured to determine a sensed temperature distribution based on the sensed temperature. The sensed temperature distribution is compared to an expected distribution. Exterior contamination of the transformer tank is detected based on the comparison.
The GlueX Experiment at Jefferson Lab directs a linearly polarized photon beam on a liquid hydrogen target surrounded by an almost hermetic detector. The experiment aims to study the meson spectrum in the light-quark sector and search for exotic spin-parity states predicted by lattice QCD calculations. The lightest exotic candidate is the π 1 meson, which is predicted to decay dominantly to b 1 π. In this thesis, we present efforts to characterize the ωπ 0 decay channel of the b 1 meson as precursor to an analysis of the sought-after π 1 exotic state. We present an introduction to the physics involved and an overview of the detector subsystems with a focus on the Barrel Calorimeter gain monitoring system, as part of the service work expected by the GlueX Collaboration. We discuss the data analysis method used and present two frameworks, for angular moments and partial waves analyses. Details of the simulation used to calculate the detector acceptance. A proof of concept partial wave analysis is presented along with experimental angular moments as first step to a full analysis of the angular distribution Our partial wave analysis indicates that both S- and D-waves are needed, in qualitative agreement with theoretical expectations. The cross-section of the omega π 0 channel is extracted to be of the scale of 1 μb in agreement with previous measurements. The differential cross-section indicates the presence of two production processes with p 1 = -5:24 ± 0:04 for 0:25 < -t < 0:95 GeV 2 ~c 2 and p 2 = -1:24 ± 0:05 for 0:95 < -t < 2:0 GeV 2 ~c 2 , which does not agree with results from previous experiments. The s-channel helicity conservation and helicity amplitudes of the b 1 meson are presented as additional experimental observables. Our results confirm that the b1 photoproduction process does not conserve s-channel helicity though they do not align with previous measurements. The helicity amplitude of the omega are extracted to be |F 1 | 2 = 0:3037 ± 0:0003 which does not agree with the expected value of a b 1 decay or results from a previous experiment. Future steps in all these analyses will be continued by a new graduate student in the group, culminating in a publication.
The fourth and final irradiation experiment in the Advanced Reactor Technologies (ART) Advanced Gas Reactor (AGR) fuel development and qualification program is designated as AGR-5/6/7. Data collected from the fabrication, irradiation, and post-irradiation examination (PIE) of this tristructural isotropic (TRISO) fuel are intended to serve as the primary data set for the qualification of this fuel for use in high-temperature gas-cooled reactors (INL 2021, Collin 2018b). However, data collected from the three preceding irradiations (i.e., AGR-1, AGR-2, and AGR-3/4) may also be used to supplement data collected from AGR-5/6/7. All components of the AGR-5/6/7 fuel (i.e., UCO kernels, TRISO coatings, and fuel compacts) were produced on an engineering scale at BWXT (Lynchburg, Virginia USA) according to the fuel specification (Marshall 2017). This fuel was irradiated in the northeast flux trap (NEFT) at the Advanced Test Reactor (ATR) at Idaho national Laboratory (INL) from February 16, 2018 to July 22, 2020 (Pham et al. 2021). Measurements in the fission product monitoring system (FPMS) indicated unexpected and significant numbers of failures of TRISO particles in Capsule 1 near the end of the sixth irradiation cycle (ATR Cycle 166A). In the fourth cycle (ATR Cycle 164B) and beyond, the sweep gas flow became very low (presumably from degradation of the capsule gas outlet line via an unidentified mechanism), and the program deliberately isolated Capsule 1 from gas flow periodically. In later cycles, attempts to reestablish any kind of flow in Capsule 1 were unsuccessful. With little or no flow through Capsule 1, FPMS measurements and enumerations of failed particles in Capsule 1 were difficult or impossible as was the ability to control the helium/neon gas mixture used for temperature control. Gas flows and fission gas activity in the effluent gas from the other AGR-5/6/7 capsules were also impacted by the Capsule 1 gas flow issues and the large increase in fission gas released from the Capsule 1.
The investment decisions within the nation's transportation sector have traditionally been prioritized and measured against safety and travel efficiency goals. Increasingly, federal, state, and local policies are requiring consideration of energy use and emissions in the design of our transportation infrastructure. Yet the processes, analytics, and knowledge to include these new metrics into transportation decisions are lacking. Although targeted studies and before and after analysis are performed from time to time, no comprehensive real-time monitoring systems provide energy and emissions to the same level as congestion and safety. To address this gap, this paper developed and demonstrated the Transportation Energy Analytics Dashboard, or TEAD, to raise awareness of the energy and emission impacts to the same level of observability as that of safety and mobility concerns.
AGR-5/6/7 is the last of a series of Advanced Gas Reactor (AGR) experiments conducted in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) in support of development and qualification of tristructural isotropic (TRISO) low-enriched fuel for use in the high-temperature gas cooled reactor (HTGR). AGR configuration and irradiation conditions are based on prismatic HTGR technology that is distinguished primarily through the use of helium coolant, a low-power-density ceramic core capable of withstanding very high temperatures, and TRISO-coated particle fuel. The AGR tests provide valuable irradiation-performance data to support fuel process development, qualify fuel for normal operation and accident conditions, and support development and validation of fuel performance and fission-product (FP) transport models and codes. Each AGR test consists of multiple independently controlled and monitored capsules containing fuel compacts placed in a graphite cylinder shrouded by a steel shell. Release-to-birth ratios (R/B) for fission-gas isotopes released from each capsule are calculated from release rates, measured by germanium detectors in the Fission Product Monitoring System (FPMS) installed downstream from each capsule, and birth rates calculated using numerical models of FG generation. The R/Bs are a critical measure of the ability of the fuel kernel, the particle coating layers, and the compact matrix to retain fission-gas atoms, preventing their release into the sweep-gas flow, and the impact of initially defective particles and/or particle-coating failures that occur during irradiation. For fission-gas isotopes, particle failure is defined as failure of all coating layers, allowing gaseous fission atoms to escape from a particle. During the first five cycles (162B ? 165A), R/Bs were stable in the 10-8?10-6 range, and no in-pile particle failures were observed, based on the gross gamma counts. The maximum R/B value of around 2 ? 10-6 for Kr-85m resulted from the presence of as-fabricated exposed kernels (based on the high exposed kernel fraction), the dispersed uranium, and high fuel particle temperatures in Capsule 1. Comparison of capsule-measured R/Bs from these early cycles to predictions using the previously developed AGR R/B model demonstrated FG release from the AGR-5/6/7 TRISO fuel was comparable to that of previous experiments. In addition, the Kr-85m R/B per-exposed-kernel values are comparable to R/B values obtained in AGR-3/4 irradiation experiment and four irradiation experiments performed during 1980s: (1) HRB-17/18, (2) COMEDIE-BD1, (3) HFR B1, and (4) HRB-21. In contrast, all measured R/B values are lower than predictions by the commonly used Richards and German models, which are intentionally conservative. A large number of in-pile particle failures occurred in Capsule 1 by the end of Cycle 166A. During the final four cycles (166A ? 168A), apparent damage to the Capsule 1 gas line appeared to cause FG leakage from that capsule into the other four capsules, resulting in an increase in fission gas (FG) detected in the effluent for all capsules. Isolation of the Capsule 1 gas line during the last three cycles also prevented measurement of its FG release. Thus, R/Bs in all capsules after Cycle 166A are highly uncertain because of undefined amount of leakage from Capsule 1, especially for long-lived isotopes. A few hundred in-pile particle failures were estimated for Capsule 1 before the end of Cycle 166A, but the total number of failures is unknown due to the lack of FG release data in the later cycles. Based primarily on evidence from the gross gamma counts during Cycle 168A, approximately 15 particles failed in Capsule 3 and four particles failed in Capsule 2. In-pile failures in Capsule 3 were anticipated because this capsule was designed to operate beyond the HTGR normal operating temperature range. In contrast, no in-pile failures were identified in the top two capsules (4 and 5) based on the absence of the typical spikes in gross gamma counts and low failure estimates using the AGR model, developed in INL/EXT-14-32970, for R/B of the short-lived isotopes (Kr-89 and Xe-137) with minimal leakage from Capsule 1.