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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multi-physics Modeling of Radiative Heat Transfer and Fluid Flow for the Reactor Cavity Cooling System

High-temperature gas-cooled reactors (HTGRs) are notable for their high thermal efficiency and potential for combined heat and power applications. These reactors are particularly appealing due to their advanced passive safety features. HTGRs utilize passive safety systems that function without requiring active components like pumps or compressors during emergencies. These reactor designs depend on a Reactor Cavity Cooling System (RCCS) to manage decay heat removal from the reactor pressure vessel (RPV) during accident conditions. The RCCS consists of vertical rectangular channels known as "risers" or riser ducts positioned around the RPV. These risers receive heat from the RPV through both convective and radiative heat transfer mechanisms. Understanding the interplay of multiple physical phenomena, such as fluid dynamics, heat transfer, and neutron interactions, is essential for the effective design and operation of nuclear reactors, particularly for systems like the RCCS. Multi-physics simulations provide a comprehensive approach to studying these interactions, offering detailed insights and enhancing accuracy. They are especially important in RCCS designs, where the interaction between radiative and convective heat transfer can significantly impact system performance. By leveraging multi-physics simulations, complex reactor behaviors can be modeled without compromising the fidelity of the underlying physical processes. This work aims to establish a robust methodology for coupling multiple physical processes in an air-cooled RCCS. By focusing on validating this multi-physics approach, the study involves designing test cases that simulate various conditions to verify the numerical models employed. The outcomes of this research will provide critical insights for accurately modeling and optimizing complex nuclear systems like the RCCS.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Automated Experiment in 4D-STEM: Exploring Emergent Physics and Structural Behaviors

Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and “intelligent” probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. Therefore, we verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS 3 . This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

WORKSHOP ON URBAN SCALE PROCESSES AND THEIR REPRESENTATION IN HIGH SPATIAL RESOLUTION EARTH SYSTEM MODELS

It is essential that numerical models used to study physics, chemistry, and biology affecting the Earth system at regional and global scales represent the effects of urban areas on climate and the effects of changing the climate on urban areas. At the same time, it is essential to develop state of the art, simple and accurate urban models to better understand the relevant processes and also toaddress issues related to urban security against the spectra of chemical, biological and radiological (CBR) hazards. Towards bringing these communities together, a 2.5-day international workshop was held at Argonne National Laboratory (ANL) in the Chicago area on May 22-24, 2019. This workshop brought together national and international experts to develop a roadmap for a better understanding of the issues associated with urban areas and at enhancing the capabilities of regional and global Earth System models (ESMs) in representing the atmospheric dynamics and chemistry, unique aspects of the biosphere and land use, and human dimensions of the urban environment. This workshop was especially important to those developing very high-resolution versions of regional and global models. The workshop also discussed existing datasets, including in situ and satellite observations, and the emerging smart city sensing technologies and their possible use in model development (e.g., the NSF-funded Array of Things network currently operating in Chicago and expanding to other cities).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Tunable fragile topology in Floquet systems

We extend the notion of fragile topology to periodically driven systems. Here we demonstrate driving-induced fragile topology in two different models, namely, the Floquet honeycomb model and the Floquet π-flux square-lattice model. In both cases, we discover a rich phase diagram that includes Floquet fragile topological phases protected by crystalline rotation or mirror symmetries, Floquet Chern insulators, and trivial atomic phases, with distinct boundary features. Remarkably, the transitions between different phases can be feasibly achieved by simply tuning the driving amplitudes, which is a unique feature of driving-enabled topological phenomena. Moreover, corner-localized fractional charges are identified as a “smoking-gun” signal of fragile topology in our systems. Our work paves the way for studying and realizing fragile topology in Floquet systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Acceleration of Power System Dynamic Simulations Using a Deep Equilibrium Layer and Neural ODE Surrogate

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. Here, in this paper, we propose a data-driven surrogate model based on implicit machine learningspecifically deep equilibrium layers and neural ordinary differential equationsto learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.

Neural ordinary differential equations↗
DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗
DOE OSTI2025

COnfirmation using Gamma-ray Non-Imaging Zero-knowledge ANti-mask Time-encoding (COGNIZANT) Final Summary Report

In potential future arms reduction treaties in which the numbers of nuclear warheads may approach small numbers, using delivery systems as a proxy for the warheads themselves may be insufficient. Therefore, a technical means of verifying the presence of a nuclear warhead may become necessary. Verifying that a declared item actually is a warhead is technically challenging within a verification regime: providing assurance to the monitoring party that a presented item is a warhead while protecting sensitive information about that warhead may be required. It is generally believed that strong assurance will require the confirmation of key attributes that may reveal closely-guarded critical design information. This provides high confidence to the monitoring party, but presents a risk of information loss to the host. A verification system must overcome this hurdle. Over the last several decades, systems have been developed that balance host and monitoring partner needs by using sensitive information to confirm treaty accountable items (TAI) as warheads while sequestering that information behind an information barrier (1). These are designed to meet the needs of the host but places the onus on the monitor to authenticate the hardware, firmware, and software. Authentication requires that the monitor confirm that all components of the system have not been modified and work as intended. In 2014, Glaser et al. proposed applying the concept of “zero knowledge protocols” (ZKP) from the field of cryptography to the problem of warhead verification (2). In mathematical cryptography, ZKP is accomplished by challenging one party to solve a problem that is only possible if that party possesses the information being authenticated. After repeated challenges, the party provides confidence that it possesses this information without revealing any details about the information itself. Systems have been in development based on this idea at both Princeton and MIT (2) (3) (4). The final measurement results produced by these systems can be viewed by both the host and the monitoring party without the worry of revealing sensitive information. However, in both of these physical implementations, there remains an information barrier within the system. The need for a digital information barrier to protect a measurement result is eliminated, but it has been replaced with the need to sequester physical components of the system, potentially obfuscating the measurement process itself. Both implementations physically insert information into the system that requires protection to prevent undesired disclosure of sensitive information: in the Princeton method, one must physically load the complement of the expected image of a true warhead into the system, and in the MIT technique, one loads a collection of spectator foils whose thicknesses physically encrypt a measured spectrum. This complicates authentication of the hardware and measurement process. The CONFIDANTE/COGNIZANT concept developed in this project do not load sensitive information into the system at any time, and could therefore open the possibility of allowing the inspector to not only view the final data but also the measurement as it is being performed and all associated equipment.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Supporting Cyber Security of Power Distribution Systems by Detecting Differences Between Real-time Micro-Synchrophasor Measurements and Cyber-Reported SCADA (Final Report)

As modern power grids tend towards greater levels of automation and communication, the challenges of identifying and mitigating vulnerabilities to cyber-attacks are ones that are increasingly demanding attention. Today’s power system has evolved to form the foundational bedrock of modern society, and an attack on this infrastructure could prove disastrous. In this project we were tasked to investigate the use of distribution synchrophasors as an independent isolated sensor network with which we can corroborate, or flag potentially spoofed,Supervisory Control And Data Acquisition (SCADA) data. We adapted an approach to marry the underlying physical properties of power systems with the network communications used by power systems in order to offer insights unattainable by either data stream isolation. While the concept of intrusion detection systems (IDS) is well understood for monitoring network traffic and traditional IT computing systems, the approach discussed in this report is motivated by several key notions: first, current SCADA communications alone presents an incomplete view of the grid. Second, the power grid, and the equipment controlling it, is grounded by laws of physics. Given this, we leverage high-frequency physical grid measurements to understand the physical condition of the grid, and combine this with SCADA. While high-frequency physical grid measurements and SCADA communication over Internet Protocol (IP) networks are fundamentally disparate information sources, when collectively examined through appropriate lenses, they offer a much more nuanced depiction of the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Validating a Dynamic PWR Safety and Security Model?

Nuclear power plants (NPPs) are assessed for safety and security using separate models that cannot capture how an attacker's decisions and a plant's response unfold together in real time, leaving regulators and operators without a complete picture of true plant vulnerability. Traditional probabilistic risk assessment (PRA) methods treat adversarial events as fixed initiators with predetermined outcomes, and are structurally incapable of representing the time-dependent interplay between physical security events, safety system response, and operator mitigative actions. At Idaho National Laboratory (INL), I contributed to the development and validation of Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF). Where static PRA relies on event-tree logic that cannot evolve mid-scenario, MASS-DEF couples a time-dependent dynamic PRA tool EMRALD (Event Modeling Risk Assessment using Linked Diagrams) with attack simulation software, allowing attacker behavior, plant system states, and operator actions to interact across time. My work focused on validating a general Pressurized Water Reactor (PWR) model. I traced model logic against PWR plant to identified errors in logic and confirm accuracy. I then built and tested attack scenarios against a general PWR model to verify that the model produced expected outcomes across all logical pathways. I also contributed a section to a related technical paper applying the same EMRALD platform to radiation dose modeling. Results show that MASS-DEF can quantitatively demonstrate that many plants exceed their regulatory security thresholds. This demonstrated margin provides a technically defensible basis for reducing the number of guards without compromising regulatory compliance. Physical security costs represent roughly 10% of annual operating budgets, making such reductions directly meaningful to INL's mission of sustaining existing commercial NPPs. This internship strengthened my understanding of nuclear systems, probabilistic modeling, and technical writing, and has solidified my pursuit of a career at a national laboratory.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2025

Computational Modeling of a 3D Printed Recuperator and Subsequent Experimental Loop for Supercritical Carbon Dioxide Cycles

Oak Ridge National Laboratory (ORNL), in collaboration with mechanical-thermal energy storage (mTES) provider EarthEn, a US Department of Energy (DOE) Lab-Embedded Entrepreneurship Program (LEEP) recipient at ORNL’s Innovation Crossroads 2023, is utilizing a state-of-the-art patented 3D printing technique to design an additively manufactured (AM) supercritical CO2 (sCO2) recuperator (REC) for EarthEn’s charge/discharge cycle. The AM REC will be printed at ORNL’s Manufacturing Demonstration Facility using Inconel Alloy 718 and tested on a closed-loop, ∼100 kW scale experimental facility that is under construction. The testing will compare the printed design against a commercial-off-the-shelf Printed Circuit Heat Exchanger (PCHE) REC. The design of the sCO2 facility is guided by a Modelica-based system model which is primarily dependent on the open-source TRANSFORM library developed at ORNL and uses the open-source CoolProp library for thermophysical properties of sCO2 via the External Media library. It is envisioned that an iterative process will be followed between the physical loop and the system model wherein the initial experimental data will be used to tune the model, which in turn will be used to guide future loop operation. Simultaneously, the AM REC is being designed using computer-aided design models, and it is also being analyzed for hydraulic and thermomechanical response using commercial computational fluid dynamics software, Simcenter STAR-CCM+, on highperformance computing resources.1

See, Nate [ORNL] (ORCID:0000000178581202)↗
DOE OSTI2022

Enhanced Atmospheric Research at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) (Final Report)

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was an international scientific expedition into the central Arctic sea ice to study the changing atmosphere-ice-ocean system and its key physical, chemical, and biological processes in a way that will support improved understanding and modeling of Arctic climate. The US Department of Energy’s Atmospheric Research Measurement (ARM) Program operated its second ARM Mobile Facility (AMF2) as a major contribution to the expedition, and the Atmospheric System Research (ASR) program supported this project to maximize the success of DOE’s involvement in MOSAiC. This project has enabled the successful collection of data through support of field operations plans, in-field operational implementation, oversight of real-time data quality, and representing DOE’s interests within the broader international MOSAiC endeavor. The project has promoted scientific research by facilitating and leading a number of efforts to develop value-added products that will support research, including products related to aerosol properties, trace gases, cloud properties, snowfall, the surface energy budget, and a merged product designed for model assessment. These products and related analyses have also contributed to numerous scientific publications on related themes as well as direct model evaluation. Lastly, the project has engaged in a great deal of communication and outreach activities that help to publicize and promote both MOSAiC and DOE’s specific contribution to a broad audience that includes the scientific community, program management, policy makers, and the general public.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Chapter 18: Learning and Tracking Ad Hoc Fiducial Markers in Spatial Augmented Reality

We describe a spatial augmented reality system with a tangible user interface used to control computer simulations of complex systems. In spatial augmented reality, the user’s physical space is augmented with projected imagery, blending real objects with projected information, and a tangible user interface enables users to manipulate physical objects as controllers for interactive visualizations. Our system learns ad hoc objects in the user’s environment as fiducial markers (i.e., objects that are visually recognized and tracked). When combined with simulation and visualization tools, these interfaces allow the user to control simulations or ensembles of simulations via physical objects using apt metaphors. While other research has leveraged the use of depth cameras, our system enables the use of standard cameras in readily available smartphones and webcams and has an implementation that runs completely in JavaScript in the web browser. We discuss the prerequisite object-recognition requirements for such tangible user interfaces and describe computer-vision and machine-learning algorithms meeting those requirements. We conclude by presenting example applications, which are also available online.

energy distribution↗
DOE OSTI2024

Elucidating the complex interplay between thermodynamics, kinetics, and electrochemistry in battery electrodes through phase-field modeling

Abstract This article highlights applications of phase-field modeling to electrochemical systems, with a focus on battery electrodes. We first provide an overview on the physical processes involved in electrochemical systems and applications of the phase-field approach to understand the thermodynamic and kinetic mechanisms underlying these processes. We employ two examples to highlight how realistic thermodynamics and kinetics can naturally be incorporated into phase-field modeling of electrochemical processes. One is a composite battery cathode with an intercalation compound (Li x FePO 4 ) as the electrochemically active material, and the other is a displacement reaction compound (Li–Cu–TiS 2 ). With the input parameters mostly from atomistic calculations and experimental measurements, phase-field simulations allowed us to untangle the interactions among transport, reaction, electricity, chemistry, and thermodynamics that lead to highly complex evolution of the materials within battery electrodes. The implications of these observations for battery performance and degradation are discussed. Graphical abstract

Andrews, W. Beck (ORCID:0000000287824621)↗
DOE OSTI2025

Managing technical debt across large-scale control systems

This presentation provides a comprehensive overview of technical debt in the context of control systems for large-scale physics facilities. We will explore its various forms, common causes, and potential consequences on system reliability, maintainability, and upgradability. Drawing on experiences from multiple projects, we will present a range of strategies for proactively managing technical debt, including best practices in design, development, testing, and documentation, as well as reactive approaches for identifying and mitigating existing issues. The presentation will also emphasize the importance of a collaborative approach and the use of modern tools, like fault tracking systems, to ensure the long-term health and success of control systems in our field.

Watts, Adam [Fermilab]↗
DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗
DOE OSTI2024

The ion-gas-neutral interactions with surfaces-2 (IGNIS-2) facility for the study of plasma–material interactions

The Ion-Gas-Neutral Interactions with Surfaces-2 (IGNIS-2) surface science facility has been designed at the Pennsylvania State University with the specific purpose of enabling experiments to study plasma–material interactions. This in situ surface modification and characterization facility consists of multiple reconfigurable substations that are connected through a central transfer chamber. This fully connected vacuum system ensures that the physical and chemical properties of samples are not altered between surface modification and analysis. The modification techniques in IGNIS-2 include a low-energy (<300 eV), high-flux (up to 1016 cm−2 s−1) broad-beam ion source, a liquid metal dropper, a lithium injection system, an RF sputter source, and an evaporator. Its characterization techniques include charged particle-based techniques, such as low-energy ion scattering (enabled by two <5 keV ion sources) and x-ray photoelectron spectroscopy, and photon and light-based techniques, such as x-ray fluorescence, multi-beam optical stress sensors, and optical cameras. All of these techniques can be utilized up to mTorr pressures, allowing both in situ and in operando studies to be conducted. Results are presented on lithium wetting experiments of argon-irradiated tungsten-based composites, surface stress measurements of tungsten films during deuterium ion irradiation, and temperature-programmed desorption of deuterium-irradiated graphite to demonstrate the in situ capabilities of this new facility.

Instruments & Instrumentation↗
DOE OSTI2025

Multiscale Thermal-hydraulic analysis of the MARVEL micro-reactor using coupled MOOSE Subchannel (SCM) and SAM

MARVEL is a natural-convection-cooled sodium-potassium microreactor that is anticipated to generate 85 kilowatts of thermal energy. It will operate within Idaho National Laboratory’s Transient Reactor Test Facility and is being developed by the DOE Microreactor Program. MARVEL will be used to test microreactor applications, evaluate systems for remote monitoring, and develop autonomous control technologies. A thermal-hydraulic computational model of this facility is a valuable tool to study important transients and calculate the safety limits of the micro-reactor design. For this purpose, the authors have chosen to use a multiscale coupled simulation: SCM for modeling the reactor core and SAM for the reactor’s primary cooling system. SCM is MOOSE physics module for subchannel analysis, which was designed to model single-phase flows through liquid-metal cooled, wire-wrapped fuel pin sub-assemblies, ordered in a triangular lattice. The SCM code was modified to be able to model MARVEL’s unique geometry. SAM is a systems analysis module based on the MOOSE framework. It aims to provide fast-running, whole-plant transient analyses capability with improved-fidelity for various advanced reactor types. The coupling between the two SCM and SAM for MARVEL modeling is done implementing a domain over-lapping approach. The resulting coupled simulation can model transients such as reactor startup/shutdown and provide an intermediate fidelity picture of the temperature field and other variables, in the core. Results for the steady-state simulations are presented in the article as well as flow blockage transient.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗