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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

GenomeDepot: data management system for microbial comparative genomics

Summary GenomeDepot is an open-source web-based platform for annotation, management, and comparative analysis of microbial genomic sequences and associated data including ortholog families, protein domains, operons, regulatory interactions, strain taxonomy, and sample metadata. GenomeDepot supports rapid creation of websites for user-defined genome collections that include bioinformatic tools for interactive genome browsing, Basic Local Alignment Search Tool (BLAST) search, annotation search, comparative genomic neighborhood visualization, and sequence download. Gene function annotations are generated by a customizable annotation pipeline. The pipeline runs annotation tools in Conda environments and can be easily extended with additional user-specified tools. Availability and implementation GenomeDepot is open source and distributed under the GNU General Public License via GitHub (https://github.com/aekazakov/genome-depot). GenomeDepot is implemented in Python and was tested in Ubuntu Linux. Full installation instructions and documentation are available at https://aekazakov.github.io/genome-depot/. GenomeDepot demo server is freely accessible at https://iseq.lbl.gov/demogd/.

Kazakov, Alexey [Lawrence Berkeley National Labora↗
DOE OSTI2023

Preliminary Results of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory is building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying the solvent extraction process and the use of centrifugal contactors. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, color, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data supports the development of safeguards by design, provides operator process awareness, and aids in the discovery of process anomalies. This paper highlights some of the preliminary results from initial data collection campaigns and shares some of the lessons learned.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Performance analysis of single-phase immersion cooling system of data center using FC-40 dielectric fluid

Immersion cooling technologies have become an emerging cooling method for data center cooling. The studies available in open literature dealt with immersion cooling performance of data servers using mineral oil, EC-100, and Opticool 872552, but the studies focusing on the performance of FC-40 dielectric fluid is not available. Therefore, this study aimed to develop a single-phase liquid immersion cooling system using FC-40 dielectric fluid for a 1U (44.45 mm height) data server. The present study experimentally investigated the influence of different design and operating parameters, including the inlet/outlet port configuration (T and Z configurations), the bypass effect, the flowrate (1–3 LPM), the heating load (200–600 W), the inlet fluid temperature (15–35 °C), the presence of suction fan with various arrangements, and different heat sink bases (solid base, vapor chamber and heat pipe base). The results showed that the T-configuration could provide a 12.6% and 0.5–2.8 °C reduction in thermal resistance and case temperature compared to the Z-configuration, respectively. Additionally, the heat sink with heat pipe and vapor chamber base offered a minimum case temperature of 56.7 °C and 55.2 °C against the 60 °C experienced in the heat sink with flat plate base, respectively.

42 ENGINEERING↗
DOE OSTI2022

Development of a Multi-Sensor Data Science System Used for Signature Development on Solvent Extraction Processes in support of safeguards- an overview

A new nuclear fuel cycle test bed is being built at Idaho National Laboratory to support the purification of special nuclear material recovered from used fuel. The test bed provides an opportunity to research process flow and the application of computational tools in solvent extraction processes. A deeper understanding of process and equipment behavior coupled with real time data collection can indicate whether a process failure is accidental or purposeful. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data can support the development of safeguards by design and security by design measures for such a facility. Additionally, it can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. This overview encompasses the current research and testing of sensors to develop a spectrum of process signatures. To be followed by planned experiments aimed to characterize said signatures and study potential feature extraction techniques to identify a fault in the system (i.e. flow diversion).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Core Model Proposal #368: Adding fugitive CO2 emissions from fossil resources

This Core Model Proposal adds fugitive CO2 emissions from coal, crude oil, and natural gas resources to the data system. It also includes an improved method to replace outlier emissions factors for fossil resources. Lastly, it makes some minor changes to the data system and Model Interface headers to fix some inconsistencies in how resource technologies are named in the XMLs.

core model proposal↗
DOE OSTI2026

Artificial Intelligence in Nuclear Safeguards; Evaluating Safeguards and Security Risks and Benefits for Advanced and Small Modular Reactor Deployments

Rapidly growing interest in advanced and small modular reactor (A/SMR) technologies presents challenges as well as opportunities for implementing international safeguards and security. A/SMR deployments are expected to be more numerous, more geographically dispersed, and more varied in their designs, placing new demands on the data systems and analytical tools used to support oversight (Alberti et al., 2023; Canadian Nuclear Safety Commission et al., 2024). Because of this variability, the importance and reliance on data systems for A/SMR deployments is expected to be higher than for previous reactor generations. Artificial Intelligence and Machine Learning (AI/ML) offer potential capabilities to address the high variability inherent in A/SMR technology. The beneficiaries of AI-assisted tools include facility operators, government regulators, IAEA inspectors, and A/SMR vendors. This report analyzes how AI/ML-assisted technologies can strengthen the implementation of IAEA safeguards and security measures. It also identifies AI-assisted tools to strengthen operator, facility, and regulator knowledge management practices and examines the potential risks AI/ML-based tools may introduce to IAEA safeguards and security efforts. It concludes with a set of hypothetical, standards-style requirements for AI/ML systems used in safeguards contexts, grounded in an inspector-centric view of system verification. Despite the potential benefits of AI/ML systems, understanding potential intentional and unintentional failure modes is critical for ensuring adequate protection of nuclear materials and facilities. Unique features of A/SMRs including sealed cores, remote and novel paradigms of operation, off-site reactor fabrication, novel fuel forms, and varied refueling requirements, introduce challenges for traditional safeguards technological approaches (Pensado et al., 2024; Federation of American Scientists, 2025). AI/ML systems deployed to address these challenges may introduce new risks requiring systematic evaluation rooted in both AI-specific risk frameworks, such as the NIST AI Risk Management Framework (NIST AI RMF), and established cyber risk management standards such as NIST SP 800-30 (National Institute of Standards and Technology [NIST], 2023; NIST, 2012).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Data compression system using base values and methods thereof

In some embodiments, a memory controller in a processor includes a base value cache, a compressor, and a metadata cache. The compressor is coupled to the base value cache and the metadata cache. The compressor compresses a data block using at least a base value and delta values. The compressor determines whether the size of the data block exceeds a data block threshold value. Based on the determination of whether the size of the compressed data block generated by the compressor exceeds the data block threshold value, the memory controller transfers only a set of the compressed delta values to memory for storage. A decompressor located in the lower level cache of the processor decompresses the compressed data block using the base value stored in the base value cache, metadata stored in the metadata cache and the delta values stored in memory.

Seyedzadehdelcheh, SeyedMohammad↗
DOE OSTI2023

Data compression system using base values and methods thereof

In some embodiments, a memory controller in a processor includes a base value cache, a compressor, and a metadata cache. The compressor is coupled to the base value cache and the metadata cache. The compressor compresses a data block using at least a base value and delta values. The compressor determines whether the size of the data block exceeds a data block threshold value. Based on the determination of whether the size of the compressed data block generated by the compressor exceeds the data block threshold value, the memory controller transfers only a set of the compressed delta values to memory for storage. A decompressor located in the lower level cache of the processor decompresses the compressed data block using the base value stored in the base value cache, metadata stored in the metadata cache and the delta values stored in memory.

Seyedzadehdelcheh, Seyed Mohammad↗
DOE OSTI2020

Resiliency-Driven Proactive Distribution System Reconfiguration With Synchrophasor Data

Measuring and enabling resiliency of electric distribution systems with increasing weather and cyber events are important. Some of the extreme events (e.g. Earthquakes, Hurricanes) and associated paths are predicted and monitored closely in advance and allow to take pre-event proactive control actions. The Distribution Phasor Measurement Units (D-PMUs) provide new opportunities and supporting such proactive actions. Here, a synchrophasor based resiliency driven pre-event reconfiguration can ensure minimizing impact of the expected event on the power distribution system and associated performance. However, the D-PMUs will also face challenges in terms of data quality similar to the transmission PMUs. The focus of this paper is to provide data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data. Results are validated for real industrial feeders and test cases with satisfactory response

controlled islanding↗
DOE OSTI2022

An Overview in the Development of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

To enhance nuclear nonproliferation stewardship, Idaho National Laboratory is building the Beartooth nuclear fuel cycle processing test bed. The Beartooth test bed will allow researchers the opportunity to study nuclear fuel processing operations including the use of centrifugal contactors in solvent extraction processes. The test bed is designed to support data collection and machine learning to monitor process operations. As part of this project, researchers will study data collected from a host of sensors that have not been typically used to monitor solvent extraction processes such as vibration, acoustic, colormetric, and thermal measurement data. The goal of this research is to employ machine learning and data analytics to study the confluence of signals collected from both traditionally and non-traditionally used sensors to provide operator process awareness and discover process anomalies. An overview of planned sensors and experiments that will focus on signal discovery will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

An Overview in the Development of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

To enhance nuclear nonproliferation stewardship, Idaho National Laboratory is building the Beartooth nuclear fuel cycle processing test bed. The Beartooth test bed will allow researchers the opportunity to study nuclear fuel processing operations including the use of centrifugal contactors in solvent extraction processes. The test bed is designed to support data collection and machine learning to monitor process operations. As part of this project, researchers will study data collected from a host of sensors that have not been typically used to monitor solvent extraction processes such as vibration, acoustic, colormetric, and thermal measurement data. The goal of this research is to employ machine learning and data analytics to study the confluence of signals collected from both traditionally and non-traditionally used sensors to provide operator process awareness and discover process anomalies. An overview of planned sensors and experiments that will focus on signal discovery will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Learning dynamical systems from data: An introduction to physics-guided deep learning

Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Environmental Life Cycle Assessment of Electricity from PV Systems: 2021 Data Update

PV Life Cycle Assessment (LCA) is a structured, comprehensive method of quantifying and assessing material and energy flows and their associated emissions from manufacturing, transport, installation, use and end of life. This is the second version of this Fact Sheet, published in 2022 based on the 2021 update of the LCA database.

carbon emissions↗
DOE OSTI2020

Andes Data Analysis System at the Oak Ridge Leadership Computing Facility

Andes is a (704)-node commodity-type Linux® cluster. Each of Andes’s 704 nodes contain two 16-core 3.0 GHz AMD EPYC 7302 processors with AMD’s Simultaneous Multithreading (SMT) Technology and 256GB of main memory. Andes also has nine large memory GPU nodes. These nodes each have 1TB of main memory and two NVIDIA K80 GPUs with two 14-core 2.30 GHz Intel Xeon processors with HT Technology.

AMD EPYC↗
DOE OSTI2020

Evaluating T 2 Conversion to Tritiated Water under Ambient Conditions - Case Study

In 2015, an incident released approximately 40 Ci of T 2 gas directly into the Tritium Exhaust System. Data from a bubbler system that monitored the stack effluent during the time period encompassing the accident, from 9 days prior through approximately 26 hours following the release, indicated that approximately 0.25% of the total accumulated tritium gas was in the form of tritiated water; however this value does not account for sources of tritium exhaust from other building operations and processes during the 9 days prior to this incident. Further analysis of the bubbler data around this time period considered the 9-day background contributions and shows that the actual fraction of the tritium that was released as tritiated water vapor (during and within 26 hours after the release) was likely lower than 0.1%.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

asset health management↗
DOE OSTI2021

Prevalence of typical operational problems and energy savings opportunities in U.S. commercial buildings

In the United States, as much as 30% of the 19 EJ that commercial buildings consume is considered excess. Much of the excess energy is due to the inability to manage building operations efficiently. Because almost 20% of the total primary energy consumption is associated with commercial buildings, significant energy reductions in this sector are needed to mitigate climate change. Therefore, many cities and states are mandating periodic “tune-ups” of these buildings to eliminate excess energy consumption. Although the benefits of tune-ups and retro-commissioning are clear, focusing these mandates to look for specific opportunities has been a challenge because of the lack of studies that document the prevalence of opportunities. Therefore, we analyzed building automation system data from 151 buildings across the United States to document common operational problems and opportunities to improve building operations. This analysis showed that opportunities to improve building operations exist in almost every building. These opportunities were not strongly correlated with building vintage or size, but were reflective of how the buildings are operated. The prevalence of the top 20 opportunities ranged between 74% and 23%, with 40% of these associated with air-handling units. The rest of the opportunities are associated with schedules, chilled and hot-water distribution, and zone controls. Of the 151 buildings, 69 of them implemented corrective actions of some or all opportunities that were identified. Implementation varied across the Re-tuning categories, with 60% for schedule opportunities, 50% for zone opportunities, over 40% for the air-handling unit and hot-water opportunities, and 35% of the chilled-water opportunities. There was wide variation in whole building energy savings, ranging from 0 to 50% and 0 to 18 $/m2 with median percent annual whole building savings of 12% and median normalized annual cost savings of $1.75/m2. In addition to documenting these key findings, the paper provides a list of opportunities that can be automatically and continuously identified and corrected and offers a list of those opportunities that should be the focus of the mandates.

Katipamula, Srinivas↗