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At least 1,495 records · Page 83

All sources NASA NTRS DOE OSTI

DOE OSTI2023

A Lookup Table Based Low Resource Usage Waveform Data Compression Block in FPGA

aA lookup table based lossless data compression scheme suitable for FPGA implementation, with very low silicon resource usage, is developed. Different algorithms can be implemented by simply changing the contents of the lookup tables in the functional block while keeping the logic circuits unchanged. It exhibits performance comparable to the zip compression software in Linux distributions. The compression block can fit into almost any existing FPGA used in the online data acquisition systems of high energy physics experiments. The decompression process can be performed in either software or in FPGA firmware, allowing users to process compressed data flexibly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Passive Safety System Reliability Analysis: Methodology, Application, and Results

Many advanced reactor designs rely on passive systems to provide assurance that certain safety functions, such as reactivity control and reactor heat removal, are maintained during transient scenarios. The performance of these systems is sensitive to boundary and environmental conditions and can lead to functional failure, which is the inability of the system to perform as intended even without any physical failures. Additionally, conditions can cause the passive systems to operate in an intermediate or degraded state. These operational modes of passive systems and their overall reliability can be difficult to capture in traditional probabilistic frameworks where discrete operating modes are assumed or time-dependent boundary conditions are not accounted for. Novel strategies and methods are needed to be able to capture the reliability of these passive systems and their impact on facility safety. The current abstract provides a summary of such a methodology and examples of its use in a case study, the GE Hitachi (GEH) PRISM probabilistic safety assessment (PSA), and the U.S. Department of Energy (USDOE) Versatile Test Reactor (VTR) PSA.

Bucknor, M.↗

DOE OSTI2025

Empowering Lineworkers: The Case for Active Exoskeletons in Utility Work

Exoskeletons have evolved from early medical prototypes to advanced systems capable of addressing physical demands in various industries. This report explores the potential of active exoskeleton technology within the utility sector, focusing on its application for linemen who face significant risks of work-related musculoskeletal disorders (WMSDs). By analyzing existing literature on exoskeletons across industries such as construction, manufacturing, and military, the study identifies a gap in utility-specific applications. Task-specific design features like gravity compensation, limb support, and advanced safety measures, improve exoskeletons’ potential to alleviate physical strain, reduce workplace injuries, and enhance productivity. This review emphasizes the need for targeted research and development to optimize exoskeleton designs for the utility sector to provide benefits for workers, companies, and the broader community.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Integrating Safety, Security, and Nuclear Operations for Advanced Reactors

The traditional separation between safety, security, and operations teams has created significant barriers to achieving optimal outcomes. When security considerations are introduced late in the design process, they often conflict with already-established architectural, operational, or engineering parameters. Retrofitting security measures can lead to increased costs, schedule delays, and compromises in security effectiveness. For instance, the need to retrofit physical barriers or surveillance systems often results in trade-offs that could have been avoided with earlier input from security professionals. Delayed integration can also affect regulatory processes and result in licensing delays. Security reviews conducted at later stages frequently identify gaps that necessitate significant redesign efforts, impacting not only scope, schedule, and budget, but also adding risk and lowering stakeholder confidence in the project. This paper aims to address these challenges by identifying practical opportunities for integrating security considerations seamlessly with design and operations teams throughout the entire lifecycle of nuclear facilities—from conceptual design to commissioning and beyond. The research emphasizes the value of early and continuous collaboration among stakeholders to ensure that security measures are robust, operationally effective, and cost-efficient. By examining case studies, analyzing past incidents, and leveraging best practices from other high-security industries, this study highlights actionable strategies for bridging the gap between safety, security, and operations teams.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗

DOE OSTI2025

Predictive Modeling and Uncertainty Quantification in Condition Monitoring of Active Components: A Reactor Coolant Pump Use Case

This work develops data-driven models for onset of thermal barrier leakage in reactor coolant pumps. It incorporates uncertainty quantification to enhance the reliability and robustness of pre- dictions. Using synthetic data generated by the Generic Pressurized Water Reactor simulator, realistic degradation scenarios were simulated across lifecycle stages—beginning, middle, and end of life. Key variables, including differential pressure, flow rate, vibration, and temperatures, were analyzed using machine learning framework. The fully connected neural network models demonstrated exceptional performance, achieving R2 scores exceeding 0.99 and root mean square errors as low as around 8.23 × 10-2 gallon per minute (gpm) for the three stages of the lifecy- cle. UQ analysis further validated the model’s robustness, with narrow uncertainty bounds during steady-state operations and appropriately wider bounds during transitional phases, reflecting the physical behavior of the system. This work addresses important gaps in real-time condition moni- toring and regulatory compliance by integrating advanced condition monitoring technologies with UQ into IST programs. The ability to detect thermal barrier leakage early and quantify prediction reliability supports optimizing maintenance strategies while ensuring nuclear power plants’ safe and reliable operation.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Machine Learning Digital Twin for Lithium Ion Battery State of Health Predictions

A digital twin system has been established to model the long term degradation of the state of health of lithium ion batteries. Two data streams result from the computational model of the system and from the physical experiment measurements. A machine learning pipeline has been developed at the nexus of these data streams. Leveraging the unique data sources in multiple transfer learning approaches has lead to the development of multiple cell specific machine learning digital twins. Our discussion on this first of its kind technology will cover challenges in data ingestion, scalability, architecture and orchestration, and research findings.

25 - ENERGY STORAGE↗

DOE OSTI2026

Reconstruction of Six-Dimensional Phase Space

A phase space is a mathematical representation of all possible physical states of a system. Particle beams at Fermilab exist within a six-dimensional (6D) phase space defined by three positional components, (x, y, z) and three momentum components, (px, py, pz). To reconstruct this space implies taking measurement data from detectors and mapping out particle behavior using computational methods. The beam detectors, however, are only able to detect spatial distribution among the events of the beam, therefore being limited to positional data. Also, due to the vast number of events in a particle beam, it is extremely difficult to analyze and differentiate every single one’s behavior. However, with Machine Learning (ML), which can distinguish between patterns and map out particle behavior more efficiently. We first used the particle beam software, G4beamline, to simulate a 10,000-event muon beam, adjusting parameters such as initial momentum magnitude (p¬0) and virtual detector position. Using ten virtual detectors, we analyzed p0 values such that minimum 9,990 events were analyzed by every detector. We then input the data from these beam simulations to a C++ program, that randomly selects 100 events, and creates a 2D histogram based on spatial distribution, detector position, and event intensity. This process is repeated 100 times to create 100 histograms per p0 value. These images were then input to a modified ResNet18 Convolutional Neural Network (CNN) for training, and to predict p0 from some unseen set of histograms. The model was accurate when trained on momentum increments of 5 MeV/c and provided with denser training samples around highly variable test values. These results displayed machine learning being able to accurately predict p0 from being trained on different particle behaviors.

Shirlee, Jermain [Fermilab]↗

DOE OSTI2021

Cyber-Physical Events Emulation Based Transmission and Distribution Co-Simulation for Situation Awareness and Grid Anomaly (SAGA) Detection: Preprint

Energy management of transmission and distribution networks is becoming more challenged with the accelerated increasing of distributed energy resources (DERs) such as distributed photovoltaic (PV) generation and distributed energy storage. To better analyze the impacts of DERs on both transmission and distribution systems, a comprehensive transmission and distribution co-simulation platform should be developed. Furthermore, with DERs more actively participated in system operation such as providing real time grid services, their cyber vulnerability should be better understood to maintain system reliability. This paper discussed a cyber-physical events emulation based transmission & distribution co-simulation platform to perform different cyber events emulation and analyze the impacts of cyber physical events happened in distribution system on the T&D system operation. The case studies with both a transmission network and a synthetic distribution network data validate that the proposed T&D co-simulation platform can perform comprehensive cyber physical events emulation. Therefore, with extensive simulation using the proposed model, the system operator can accumulate adequate training data for the system situation awareness and grid anomaly detection purpose.

31 CESER - Office of Cybersecurity, Energy Securit↗

DOE OSTI2020

Developing a sustainability science approach for water systems

We convened a workshop to enable scientists who study water systems from both social science and physical science perspectives to develop a shared language. This shared language is necessary to bridge a divide between these disciplines’ different conceptual frameworks. As a result of this workshop, we argue that we should view socio-hydrological systems as structurally co-constituted of social, engineered, and natural elements and study the “characteristic management challenges” that emerge from this structure and reoccur across time, space, and socioeconomic contexts. This approach is in contrast to theories that view these systems as separately conceptualized natural and social domains connected by bi-directional feedbacks, as is prevalent in much of the water systems research arising from the physical sciences. A focus on emergent characteristic management challenges encourages us to go beyond searching for evidence of feedbacks and instead ask questions such as: What types of innovations have successfully been used to address these challenges? What structural components of the system affect its resilience to hydrological events and through what mechanisms? Are there differences between successful and unsuccessful strategies to solve one of the characteristic management challenges? If so, how are these differences affected by institutional structure and ecological and economic contexts? To answer these questions, social processes must now take center stage in the study and practice of water management. We also argue that water systems are an important class of coupled systems with relevance for sustainability science because they are particularly amenable to the kinds of systematic comparisons that allow knowledge to accumulate. Indeed, the characteristic management challenges we identify are few in number and recur over most of human history and in most geographical locations. This recurrence should allow us to accumulate knowledge to answer the above questions by studying the long historical record of institutional innovations to manage water systems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Measurements of TRACER pre-convective conditions and mesoscale circulations using small unmanned aircraft systems (sUAS)

Improved comprehension of the physical processes governing convective cloud formation and lifecycle are of critical importance for understanding and predicting future climate states. The influence of these clouds on the planetary energy budget, including on precipitation, is significant. Things are particularly complex in coastal regimes, where gradients in aerosol particle properties, localized circulations such as sea breezes, and large population centers are found. To date, numerical models struggle to accurately represent these critical clouds and are therefore challenged to provide a realistic view on the planetary energy budget. Through the proposed research, we deployed two uncrewed aircraft systems (UAS) equipped with a variety of instruments alongside sensors deployed by the US Department of Energy Atmospheric Radiation Measurement (ARM) program for the TRACER (Tracking Aerosol Convection Interactions Experiment) field campaign. The two small UAS platforms consisted of a CU RAAVEN fixed-wing airplane and an OU CopterSonde system, with the copter collecting frequent vertical profiles of thermodynamic and kinematic variables such as temperature, pressure, wind and humidity. At the same time, the fixed-wing captured horizontal gradients of these quantities and aerosol size distribution. These systems were deployed south of the Houston metro area, in an area that is impacted by the Gulf of Mexico sea breeze on a daily basis. These observations offer enhanced and complementary perspectives to those provided by the DOE ARM Mobile Facility (AMF) which is was deployed in southeast Houston, and an ancillary site in a more rural location west of the urban Houston area. Quality-controlled versions of the UAS data were collected and posted on the DOE ARM data archive after the conclusion of the campaign where they are accessible by the research community and general public. The UAS perspective offers revolutionary insight into key spatial and temporal effects that have not been evaluated previously.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Holographic Quantum Simulation of Strongly Correlated Electron Systems

The project aimed to demonstrate a new holographic quantum simulation approach and co‐ designed quantum hardware to tackle three specific problems that fall within the broad umbrella of unraveling the physics of strongly correlated electron systems (SCES). These tasks were: (1) holographic preparation of ground‐ and thermal‐ states of correlated magnetic and electronic systems including quasi‐2d frustrated‐spin, Fermi‐Hubbard, and fractional quantum Hall (FQH) systems, (2) holographic‐simulation of long‐time out‐of‐equilibrium dynamics and (3) holographic analogs of embedding methods such as dynamical mean‐ field theory (DMFT) and density‐matrix embedding theory (DMET) to solve systems with complex structure or long‐range interactions. These tasks are prototypes for the kinds of material simulation problems of interest to BES, such as the simulation of multiferroic materials, perovskite photovoltaics and high‐temperature superconductors, that tax the capabilities of the most powerful classical supercomputers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

A next generation ultra short pulse reflectometry (USPR) diagnostic

Ultrashort Pulse Reflectometry (USPR) is a plasma diagnostic technique involving the propagation and reflection of ultrashort duration (~few ns) chirps. The reflected packets pass through a multichannel filter with time-of-flight measurements performed on each of the filtered packets. A next generation USPR system is under development, spanning 28–75 GHz, for use on compact, short duration, magnetically confined fusion devices. This system presents a dramatic increase in performance compared with an earlier USPR system employed on the LLNL Sustained Spheromak Physics Experiment device more than a decade ago. The new system replaces upconverting mixers with higher power active multiplier chains to generate mm-wave transmitter chirps, with custom time-of-flight electronics reducing the time per measurement by a factor of 3X. Finally, the system is equipped with a field programmable gate array for data acquisition and analysis.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Generalized optimization-based synthesis of membrane systems for multicomponent gas mixture separation

Synthesizing a membrane system to separate multicomponent gas mixture is challenging due to the combinatorial number of feasible configurations and the difficulties in describing the multicomponent permeators. Here we present a mixed-integer nonlinear programming (MINLP) model for synthesizing membrane systems for multicomponent gas mixture separation. The approach employs a richly connected superstructure to represent numerous potential system configurations, and different physics-based surrogate permeator models, such as countercurrent flow or crossflow, to be used in each stage. Moreover, to describe realistic systems, pressure drop equations can be included. We also present solution methods to accelerate the solution process. Through a case study of natural gas sweetening, we demonstrate that the proposed approach is able to obtain good solutions using an off-the-shelf global optimization solver. Finally, we expand the conventional membrane system synthesis problem by introducing feed variability in our model through a case study of an integrated reactor-separation system.

42 ENGINEERING↗

DOE OSTI2023

Thermalization at low temperatures via weakly damped multisite baths

We study the thermalization properties of one-dimensional open quantum systems coupled to baths at their boundary. The baths are driven to their thermal states via Lindblad operators, while the system undergoes Hamiltonian dynamics. We specifically consider multisite baths and investigate the extent to which the late-time steady state resembles a Gibbs state at some controllable temperature set by the baths. We study three models: a noninteracting fermion model accessible via free-fermion technology, and two interacting models, the XZ model and the chiral clock model, which are accessible via tensor network methods. We show that, by tuning towards the weak coupling and slow relaxation limits, one can engineer low temperatures in the bulk of the system provided the bath size is big enough. Here, we use this capability to study energy transport in the XZ model at lower temperatures than previously reported. Our work paves the way for future studies of interacting open quantum systems at low temperatures.

1-dimensional spin chains↗

DOE OSTI2023

Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

Kim, Junyung↗

DOE OSTI2023

Constructing Neural Network Based Models for Simulating Dynamical Systems

Dynamical systems see widespread use in natural sciences like physics, biology, and chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in applying data-driven modeling techniques to solve a wide range of problems in physics and engineering. Here this article provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

ezAlign: A Tool for Converting Coarse-Grained Molecular Dynamics Structures to Atomistic Resolution for Multiscale Modeling

Soft condensed matter is challenging to study due to the vast time and length scales that are necessary to accurately represent complex systems and capture their underlying physics. Multiscale simulations are necessary to study processes that have disparate time and/or length scales, which abound throughout biology and other complex systems. Herein we present ezAlign, an open-source software for converting coarse-grained molecular dynamics structures to atomistic representation, allowing multiscale modeling of biomolecular systems. The ezAlign v1.1 software package is publicly available for download at github.com/LLNL/ezAlign. Its underlying methodology is based on a simple alignment of an atomistic template molecule, followed by position-restraint energy minimization, which forces the atomistic molecule to adopt a conformation consistent with the coarse-grained molecule. The molecules are then combined, solvated, minimized, and equilibrated with position restraints. Validation of the process was conducted on a pure POPC membrane and compared with other popular methods to construct atomistic membranes. Additional examples, including surfactant self-assembly, membrane proteins, and more complex bacterial and human plasma membrane models, are also presented. By providing these examples, parameter files, code, and an easy-to-follow recipe to add new molecules, this work will aid future multiscale modeling efforts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning

The resilience of safety-critical systems is gaining importance due to the rise in cyber and physical threats, especially within critical infrastructure. Traditional static resilience metrics may not capture dynamic system states, leading to inaccurate assessments and ineffective responses to cyber threats. This work aims to develop a data-driven, adaptive method for resilience metric learning. We propose a data-driven approach using inverse reinforcement learning (IRL) to learn a single, adaptive resilience metric. The method infers a reward function from expert control actions. Unlike previous approaches using static weights or fuzzy logic, this work applies adversarial inverse reinforcement learning (AIRL), training a generator and discriminator in parallel to learn the reward structure and derive an optimal policy. The proposed approach is evaluated on multiple scenarios: optimal communication network rerouting, power distribution network reconfiguration, and cyber–physical restoration of critical loads using the IEEE 123-bus system. The adaptive, learned resilience metric enables faster critical load restoration in comparison to conventional RL approaches.

97 MATHEMATICS AND COMPUTING↗