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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 sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An Evaluation of The Dynamic Physical Security Risk Assessment Methodology for Fleet-Wide Applications

The requirements for U.S. nuclear power plants to maintain a large onsite physical security force contribute to their high operational costs. The cost of maintaining the current physical security posture is approximately 10% of the overall operation and maintenance budget for commercial nuclear power plants. The goal of the Light Water Reactor Sustainability (LWRS) program’s physical security pathway is to develop tools, methods, and technologies and provide the technical basis for an optimized physical security posture. The conservatisms built into current security postures may be analyzed and minimized to reduce security costs while still ensuring adequate security and operational safety. The research performed at Idaho National Laboratory within LWRS program’s physical security pathway has successfully developed a dynamic force-on-force modeling framework using various computer simulation tools and integrating them with the dynamic assessment Event Modeling Risk Assessment using Linked Diagrams (EMRALD) tool. This integrated process for physical security analysis is named Modeling and Analysis for Safety Security using Dynamic EMRALD Framework (MASS-DEF). This document provides an update on the progress in applying the MASS-DEF process to an operating commercial nuclear power plant as well as additional industry feedback regarding use of the tool for other physical security risk-informed topics. This report is only a summary of the progress and does not contain specific modeling results as those contain sensitive security information. Previous reports described how a user could integrate their plant-specific force-on-force models with the dynamic simulation tool EMRALD, model operator actions, and integrate with probabilistic risk assessment tools, such as CAFTA (Computer Aided Fault Tree Analysis System) or SAPHIRE (Systems Analysis Programs for Hands-on Integrated Reliability Evaluations), and with thermal-hydraulic tools, such as RELAP-5 or MAAP. Previous reports applied various combinations of available simulations codes with EMRALD using generic plant models to demonstrate how to perform the analysis. This report is an update the progress of applying the dynamic computational framework to an actual nuclear facility using their security scenarios and timelines. This report also provides an update to the procedural guidance for the MASS-DEF process and an overview of the generic models available for use by utilities. This report does not contain any plant’s sensitive information and/or safeguards information. This study’s purpose was to verify that the results achieved using generic models are similar to actual plant results and refine our guidance on the use of the framework. This assessment enables further analysis, such as what-if scenarios and staff-reduction evaluation, thereby optimizing physical security at plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Data Driven Approach to Public Opinion Mining on Autonomous Vehicles: Sentiment Analysis of Social Media Comments Using Large Language Models

In the realm of online identity, social media has emerged as a rich and dynamic source of user-generated content, making it an invaluable resource for understanding public sentiment on a wide range of topics. Individuals often share their raw emotions and candid opinions on these platforms without fear of judgment or backlash. In this study, we conduct a sentiment analysis on user comments collected from various online platforms, with a specific focus on discussions surrounding autonomous vehicles. Leveraging the capabilities of large language models (LLMs), we classify each comment into one of five sentiment categories: Very Negative, Negative, Neutral, Positive, and Very Positive. Our approach demonstrates the effectiveness of LLMs in capturing nuanced contextual sentiment, offering a scalable and state-of-the-art alternative to traditional manual annotation methods. The results reveal key trends and insights into public perception, enabling a deeper understanding of how autonomous vehicle technologies are received by the online community. Our findings underscore the dynamic nature of public sentiment, which is shaped not only by advances in autonomous vehicle technology but also by contextual events such as regulatory developments, political adjustment and safety incidents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Determination of broken KAM surfaces for particle orbits in toroidal confinement systems

Here, the destruction of Kolmogorov–Arnold–Moser surfaces in a Hamiltonian system is an important topic in nonlinear dynamics, and in particular in the theory of particle orbits in toroidal magnetic confinement systems. Analytic models for transport due to mode-particle resonances are not sufficiently correct to give the effect of these resonances on transport. In this paper we compare three different methods for the detection of the loss of stability of orbits in the dynamics of charged particles in a toroidal magnetic confinement device in the presence of time dependent magnetic perturbations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Efficient prediction of time- and angle-resolved photoemission spectroscopy measurements on a nonequilibrium BCS superconductor

We study how time- and angle-resolved photoemission (tr-ARPES) reveals the dynamics of BCS-type, s -wave superconducting systems with time-varying order parameters. Approximate methods are discussed, based on previous approaches to either optical conductivity or quantum dot transport, to enable computationally efficient prediction of photoemission spectra. One use of such predictions is to enable extraction of the underlying order parameter dynamics from experimental data, which is topical given the rapidly growing use of tr-ARPES in studying unconventional superconductivity. The methods considered model the two-time lesser Green's functions with an approximated lesser self-energy that describes relaxation by coupling of the system to two types of baths. The approach primarily used here also takes into consideration the relaxation of the excited states into equilibrium by explicitly including the level-broadening of the retarded and advanced Green's functions. Here, we present equilibrium and nonequilibrium calculations of tr-ARPES spectrum from our model and discuss the signatures of different types of superconducting dynamics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Molecular simulation of structure and diffusion at smectite-water interfaces: Using expanded clay interlayers as model nanopores

In geologic settings relevant to a number of extraction and potential sequestration processes, nanopores bounded by clay mineral surfaces play a critical role in the transport of aqueous species. Solution structure and dynamics at clay–water interfaces are quite different from their bulk values, and the spatial extent of this disruption remains a topic of current interest. We have used molecular dynamics simulations to investigate the structure and diffusion of aqueous solutions in clay nanopores approximately 6 nm thick, comparing the effect of clay composition with model Na-hectorite and Na-montmorillonite surfaces. In addition to structural properties at the interface, water and ion diffusion coefficients were calculated within each aqueous layer at the interface, as well as in the central bulk-like region of the nanopore. The results show similar solution structure and diffusion properties at each surface, with subtle differences in sodium adsorption complexes and water structure in the first adsorbed layer due to different arrangements of layer hydroxyl groups in the two clay models. Interestingly, the extent of surface disruption on bulk-like solution structure and diffusion extends to only a few water layers. Additionally, a comparison of sodium ion residence times confirms similar behavior of inner-sphere and outer-sphere surface complexes at each clay surface, but ~1% of sodium ions adsorb in ditrigonal cavities on the hectorite surface. Thus, the presence of these anhydrous ions is consistent with highly immobile anhydrous ions seen in previous nuclear magnetic resonance spectroscopic measurements of hectorite pastes.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Semiclassical approaches to nuclear dynamics

The extended Gutzwiller trajectory approach is presented for the semiclassical description of nuclear collective dynamics, in line with the main topics of the fruitful activity of V.G. Solovjov. Within the Fermi-liquid droplet model, the leptodermous effective surface approximation was applied to calculations of energies, sum rules, and transition densities for the neutron–proton asymmetry of the isovector giant-dipole resonance and found to be in good agreement with the experimental data. By using the Strutinsky shell correction method, the semiclassical collective transport coefficients, such as nuclear inertia, friction, stiffness, and moments of inertia, can be derived beyond the quantum perturbation approximation of the response function theory and the cranking model. The averaged particle-number dependences of the low-lying collective vibrational states are described in good agreement with the basic experimental data, mainly due to the enhancement of the collective inertia as compared to its irrotational flow value. Shell components of the moment of inertia are derived in terms of the periodic-orbit free-energy shell corrections. A good agreement between the semiclassical extended Thomas–Fermi moments of inertia with shell corrections and the quantum results is obtained for different nuclear deformations and particle numbers. Shell effects are shown to be exponentially dampted out with increasing temperature in all the transport coefficients.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Random Quantum Circuits

Quantum circuits—built from local unitary gates and local measurements—are a new playground for quantum many-body physics and a tractable setting to explore universal collective phenomena far from equilibrium. These models have shed light on longstanding questions about thermalization and chaos, and on the underlying universal dynamics of quantum information and entanglement. In addition, such models generate new sets of questions and give rise to phenomena with no traditional analog, such as dynamical phase transitions in quantum systems that are monitored by an external observer. Quantum circuit dynamics is also topical in view of experimental progress in building digital quantum simulators that allow control of precisely these ingredients. Randomness in the circuit elements allows a high level of theoretical control, with a key theme being mappings between real-time quantum dynamics and effective classical lattice models or dynamical processes. Many of the universal phenomena that can be identified in this tractable setting apply to much wider classes of more structured many-body dynamics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

A unified framework for polycrystal plasticity with grain boundary evolution

Plastic deformation in polycrystals is governed by the interplay between intra-granular slip and grain boundary-mediated plasticity. However, while the role played by bulk dislocations is relatively well-understood, the contribution of grain boundaries (GBs) has only recently begun to be studied. GB plasticity is known to play a key role along with bulk plasticity under a wide range of conditions, such as dynamic recovery, superplasticity, severe plastic deformation, etc., and developing models capable of simultaneously capturing GB and bulk plasticity has become a topic of high relevance. In this paper we develop a thermodynamically-consistent polycrystal plasticity model capable of simulating a variety of grain boundary-mediated plastic processes in conjunction with bulk dislocation slip. The model starts from the description of a single crystal and creates lattice strain-free polycrystalline configurations by using a specially-designed multiplicative decomposition developed by the authors. This leads to the introduction of a particular class of geometrically necessary dislocations (GND) that define fundamental GB features such as misorientation and inclination. The evolution of the system is based on an energy functional that uses a non-standard function of the GND tensor to account for the grain boundary energy, as well as for the standard elastic energy. Our implementation builds on smooth descriptions of GBs inspired on diffuse-interface models of grain evolution for numerical convenience. Here we demonstrate the generality and potential of the methodology by simulating a wide variety of phenomena such as shear-induced GB sliding, coupled GB motion, curvature-induced grain rotation and shrinkage, and polygonization via dislocation sub-grain formation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Time–Concentration Superposition for Linear Viscoelasticity of Polymer Solutions

The concentration dependence of linear viscoelastic properties of polymer solutions is a well-studied topic in polymer physics. Dynamic scaling theories allow qualitative predictions of polymer solution rheology, but quantitative predictions are still limited to model polymers. Meanwhile, the scaling properties of non-model polymer solutions must be determined experimentally. In present paper, the time–concentration superposition (TCS) of experimental data is shown to be a robust procedure for studying the concentration scaling properties of binary and ternary polymer solutions. TCS can not only identify whether power law scaling may exist or not, and over which concentration range, but also unambiguously estimate the concentration scaling exponents of linear viscoelastic properties for a range of non-model polymer solutions.

42 ENGINEERING↗
DOE OSTI2021

Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison

The replacement of classical force fields (FFs) with novel neural-network-based frameworks is an emergent topic in molecular dynamics (MD) simulations. In contrast to classical FFs, which have proven their capability to provide insights into complex soft matter systems at an atomistic resolution, the machine learning (ML) potentials have yet to demonstrate their applicability for soft materials. However, the underlying philosophy, which is learning the energy of an atom in its surrounding chemical environment, makes this approach a promising tool. In particular for the exploration of novel chemical compounds, which have not been considered in the original parametrization of classical FFs. In this article, we study the performance of the ANI-2x ML model and compare the results with those of two classical FFs, namely, CHARMM27 and the GROMOS96 43a1 FF. We explore the performance of these FFs for bulk water and two model peptides, trialanine and a 9-mer of the α-aminoisobutyric acid, in vacuum and water. The results for water describe a highly ordered water structure, with a structure similar to those using ab initio molecular dynamics simulations. The energy landscape of the peptides described by Ramachandran maps show secondary structure basins similar to those of the classical FFs but differ in the position and relative stability of the basins. Details of the sampled structures show a divergent performance of the different models, which can be related either to the short-ranged nature of the ML potentials or to shortcomings of the underlying data set used for training. These findings highlight the current state of the applicability of ANI-2x ML potential for MD simulations of soft matter systems. Simultaneously, they provide insights for future improvements of current ML potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to purely data-driven methods, {PIML} models can be trained from additional information obtained by enforcing physical laws such as energy and mass conservation. More broadly, {PIML} models can include abstract properties and conditions such as stability, convexity, or invariance. The basic premise of {PIML} is that the integration of ML and physics can yield more effective, physically consistent, and data-efficient models. This paper aims to provide a tutorial-like overview of the recent advances in {PIML} for dynamical system modeling and control. Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of {PIML} models; and 4) physics-informed digital twins. The paper is concluded with a perspective on open challenges and future research opportunities.

Nghiem, Truong↗
DOE OSTI2025

Model Calibration with Markov Chain Monte Carlo Tutorial

The purpose of this tutorial is to demonstrate how to use Markov chain Monte Carlo (MCMC) to calibrate a model. By calibration, we mean the selection of model parameters (and, when relevant, structures). A common goal in model development and diagnostics is calibration, or the identification of model structures and parameters which are consistent with data. While models can be calibrated through hand-tuning parameters or minimizing simple error metrics such as root-mean-square-error (RMSE), these approaches can underrepresent the probabilistic nature of the data-generating process, as well as the potential for multiple model configurations to be consistent with the data. Probabilistic uncertainty quantification, which is the topic of this notebook, can address these concerns. This tutorial is presented as an appendix to the e-book: Addressing Uncertainty in MultiSector Dynamics Research.

Markov chain Monte Carlo↗
DOE OSTI2023

A Study of Wake Characteristics of Marine Turbine Arrays: Preprint

As the marine energy industry continues to develop and installations grow from single turbines to small arrays and larger farms, understanding and predicting wake behavior using modeling tools is necessary for array design and optimization. The wake characteristics of upstream turbines, including velocity deficit, wake swirling, tip vortices, wake shape and direction, and flow recovery, influence the inflow conditions experienced by downstream turbines in an array and, subsequently, affect the individual turbine loading and power generated by the array. These wake characteristics are influenced by many factors, including site geography, inflow shear profile, turbulence, waves, the strength of the current resource, gravitational forces, storm surges, and seasonal effects like temperature and wind conditions. Efficient array modeling tools that capture these wake effects and resulting turbine-to-turbine interactions need to be developed to optimize farm layouts and design individual turbines for operation within arrays. Unlike the wind industry, the marine energy research community is largely limited to the use of either numerical tools or tank/flume testing to understand marine turbine wake interactions. This paper will investigate recent advances in numerical and experimental marine turbine wake modeling, with a focus on recent publications and on both axial-flow and cross-flow marine turbines. A summary of recent research on these topics is provided. Additionally, a demonstration of turbine wake interactions using computational fluid dynamics is presented.

marine hydrokinetic turbine↗
DOE OSTI2014

A two-fluid model for relativistic heat conduction

Three years ago it was presented in these proceedings the relativistic dynamics of a multi-fluid system together with various applications to a set of topical problems [1]. In this talk, I will start from such dynamics and present a covariant formulation of relativistic thermodynamics which provides us with a causal constitutive equation for the propagation of heat in a relativistic setting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Subalpine and alpine species range shifts with climate change: temperature and soil moisture manipulations to test species and population responses (Final Report)

The Alpine Treeline Warming Experiment (ATWE) was established in 2008-2009 to experimentally test how climate change would affect species ranges at and beyond the cold edge limits of forest growth. One key finding was that warming can hinder upslope tree migration by increasing seedling water stress and reducing seedling survival. We found evidence for more conservative traits in seedlings from high elevation tree populations, which may further slow tree expansion into the current alpine. In the alpine, warming may only increase productivity if summer rain increases, as dominant alpine life forms were negatively affected by heating unless water was added. The ATWE experimental design and focus on dynamic vegetation were novel, and the setting at the mountain treeline transition was important to understanding effects of climate variability and change on water resources, carbon cycling and biodiversity. We ended climate manipulations and removed associated infrastructure from the field in 2016 following 8 years of data collection. To date, 5 MS theses and 18 peer-reviewed papers have been published or accepted (including one that yielded the cover image in Global Change Biology, left), and 1 PhD thesis and at least 5 papers are in review or active development. Numerous past seasonal and long-term field staff have gone on to graduate school, and subsequently to research positions as university faculty or agency scientists. PI Kueppers continues collecting data on soil microclimate and seedling survival for long-lived tree species to fill a critical gap in knowledge regarding the seedling-to-sapling transition across species and elevations. Primary project data is housed on the project’s virtual server, organized according to topic. Published data and model code is available either through the project website or via other permanent archives. Our results are influencing understanding and modeling of climate and genetic constraints on dynamic vegetation change across the landscape, particularly in the mountains of the Western U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Modeling used fuel response to normal conditions of transportation package drops to access geometric sensitivities

The US Department of Energy Used Fuel Disposition program is performing research to determine the loading conditions that used nuclear fuel (UNF) experiences during normal conditions of transportation (NCT). This loading environment includes mechanical shock and vibration as the UNF is transported by rail or truck, and it also includes potential package free drop conditions, which are the topic of this study. NCT drop test requirements for UNF package designs are defined in 10 CFR 71.71. The free drop test height is 30 cm for the UNF packages considered in this study. Explicit dynamic finite element models of generic rail and truck UNF packages containing a highly resolved 17x17 PWR fuel assembly are used to estimate the potential range of loading experienced by UNF during NCT horizontal package drops onto an unyielding surface. There are many variables and uncertainties that can affect the loading of UNF during this type of free drop scenario. This study focuses on geometric variables, such as gap conditions and small adjustments to the angle of impact, in a number of analysis cases. The LS-DYNA finite element analyses calculate detailed results for all the components of the fuel assembly. The results show that the peak localized axial strain in the fuel cladding is estimated to be 0.006421 (nominal) with an upper bound of 0.014511, based on the peak instantaneous bending moment of 63.65 N-m and other uncertainty factors. For the assumed low burnup condition of the UNF, the cladding yield strain is 0.011027 and the failure strain is 0.015574, which means that the calculated upper bound strain approaches the cladding failure limit without exceeding it. The peak shear force on the cladding was calculated to be 7879 N. This could be significant to cladding integrity if applied to the UNF in a situation where the fuel pellets do not support the shear transfer. Further evaluation is needed to determine if this is a credible threat to cladding integrity for these 30 cm free drop scenarios.

42 ENGINEERING↗
DOE OSTI2017

Phase-breaking effect on polaron transport in organic conjugated polymers

Despite intense investigations and many accepted viewpoints on theory and experiment, the coherent and incoherent carrier transport in organic semiconductors remains an unsettled topic due to the strong electron-phonon coupling. Based on the tight-binding Su-Schrieffer-Heeger (SSH) model combined with a non-adiabatic dynamics method, we study the effect of phase-breaking on polaron transport by introducing a group of phase-breaking factors into π-electron wave-functions in organic conjugated polymers. Two approaches are applied: the modification of the transfer integral and the phase-breaking addition to the wave-function. Within the former, it is found that a single site phase-breaking can trap a polaron. However, with a larger regular phase-breaking a polaron becomes more delocalized and lighter. Additionally, a group of disordered phase-breaking factors can make the polaron disperse in transport process. Within the latter approach, we show that the phase-breaking can render the delocalized state in valence band discrete and the state in the gap more localized. Consequently, the phase-breaking frequency and intensity can reduce the stability of a polaron. Furthermore, the phase-breaking in organic systems is the main factor that degrades the coherent transport and destroys the carrier stability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi-scale modeling of fuel fragmentation and microstructural evolution

An overview of NEAMS program activities to model fuel fragmentation and microstructual evolution to a group of industry attendees at a meeting hosted by EPRI. Topics include atomistic methods to determine grain boundary strength, phase-field fracture modeling to determine critical pressure at grain boundaries, and coupled phase-field-cluster dynamics to model microstructural evolution.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗