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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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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Complete Evaluation of ION Cost Reduction Opportunities for LWRS Pathways

The Light Water Reactor Sustainability (LWRS) Program, sponsored by the U.S. Department of Energy (DOE), plays a pivotal role in enhancing the sustainability, safety, and economic viability of the nation's fleet of nuclear power plants. This program collaborates extensively with industry, vendors, suppliers, regulatory agencies, and research and development organizations within the nuclear energy sector. The overarching goals of the LWRS program are twofold: to provide innovative science and technology-based solutions to the nuclear industry, surpassing current performance models, and to manage the aging of systems, structures, and components (SSCs) to extend the operational lifetimes of nuclear power plants safely, efficiently, and economically. The program focuses on five research and development pathways: Plant Modernization: This pathway seeks to enhance the economic viability of nuclear power plants in evolving energy markets through innovation, efficiency improvements, and digital transformation. The integration of digital technologies into plant operations is a central theme, aiming to create a seamless digital environment that improves plant economics and safety. Flexible Plant Operation and Generation: Research efforts in this pathway explore opportunities for light-water reactors (LWRs) to directly supply energy to industrial processes, diversifying revenue generation approaches and improving economic sustainability.

42 ENGINEERING↗

DOE OSTI2022

Data Architecture and Analytics Requirements for Artificial Intelligence and Machine Learning Applications to Achieve Condition-Based Maintenance

This report identified some of the important requirements that needs to be taken into consideration as part of the data evolution for the CBM application of a CWS in a NPP. In the data evolution process, the information is converted into insight leading into actions using advancements in AI/ML technologies. A notion of RESET AI: design, development, deployment, and operation principals are introduced to lifecycle of AI technologies. Towards the end of the report, we discussed how this CBM can be realized in a SDE. As path forward, this report lays the foundation for developing a more detailed industry guidance supporting data evolution for other plant applications like operations and plant support. These would be developed as part of ongoing research in the fiscal year 2023.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

CDL2PLC translator v0.1.0

The CDL-PLC translator aims at translating control sequences for building energy systems from the CDL CXF format to the PLCopen XML format. The CDL CXF developed at LBL within the OpenBuildingControl project, and now being standardized via ASHRAE Standard 231P, enables expressing control sequences developed in the simulation environment Modelica in a JSON format. The PLCopen XML is an existing exchange format standardized in IEC 61131-10 for Programmable Logic Controllers (PLCs) following the IEC 61131 standard as one target system of CDL among others. The translation from the CDL CXF to the PLCopen XML contributes to a seamless workflow from the model-based development of control sequences in simulation environments, which is not building practice today, and their digital implementation on building controllers, which replaces graphical and textual documents used for this purpose today. The translator is at a prototypical stage and enables, as a proof of concept, the translation of very simple control sequences composed of 4 selected function blocks out of 137 function blocks defined in CDL. The translation includes the connection of inputs and outputs of function blocks and the expression of a control function in CDL to the equivalent code in IEC 61131-3.

Walther, Karl↗

DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗

DOE OSTI2025

Teleoperation of Construction Robots Using Human Interaction: Case Study Examples: Preprint

The integration of advanced teleoperation methods in construction robotics has the potential to enhance productivity and safety in demanding environments. Among different methods, hand gestures have emerged as an intuitive approach, enabling seamless communication between human operators and robotic systems. This study demonstrates the use of hand gestures for controlling a model robotic excavator and a virtual robotic arm in construction applications. Two case study examples are presented. In the first case study, a series of hand gestures are performed to control a model robotic excavator's bucket and arm movements. The second case study focuses on using hand gestures to control a virtual robotic arm to perform a material handling task in a simulated environment. In both cases, the performed hand gestures are all successfully captured and interpreted by the gesture-based teleoperation method to conduct the corresponding tasks, which demonstrates the application of the gesture-based control method for various types of construction robots. Also, the limitations of the gesture-based teleoperation method (e.g., recognition delay, sensitivity to motion outliers) are discussed. Future work will focus on developing a digital twin system to support gesture-based teleoperation for a wider range of construction robots.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

A Radiation-Hard 8-Channel 15-Bit 40-MSPS ADC for the ATLAS Liquid Argon Calorimeter Readout

The custom design of a radiation-hardened, 8-channel, 40-MSPS, 15-bit resolution, 14.2-bit dynamic range, 11.4-ENOB ADC data acquisition ASIC fabricated in a commercial 65-nm triple-well CMOS technology is presented. The ADC is developed for and integrates seamlessly into the readout system for the ATLAS liquid argon (LAr) calorimeter in the high-luminosity large hadron collider (HLLHC) upgrade at CERN, which will require a total of 364 936 ADC channels. A three-stage MDAC+SAR pipelined ADC architecture was designed to meet the physics requirements and scientific goals of the ATLAS experiment. The ADC is a fully self-contained data acquisition system that includes foreground calibration, digital data processing, digital control, and supporting circuitry. The measured performance shows the ADC achieves a competitive dynamic range and SNDR, and it meets or exceeds the ATLAS analog requirements. Radiation tolerance and scalability design considerations were implemented at the device-, circuit-, and system-level. Radiation-hardening-by-design techniques used include redundancy for digital circuits, the use of MiM capacitors, and a hybrid RC-DAC for the ADC core. The ADC ASIC was demonstrated to be robust against the effects of the intense radiation expected in the HL-LHC experimental environment.

DAQ↗

DOE OSTI2025

rcsb-api : Python Toolkit for Streamlining Access to RCSB Protein Data Bank APIs

The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.

FAIR principles↗