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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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Quantum quench dynamics of geometrically frustrated Ising models
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EGS Collab: Modeling and Simulation Working Group Teleconference Series (99-128)
This submission contains the presentation slides and recordings from EGS Collab Modeling and Simulation Working Group (MSWG) teleconferences number 99 through 128. These teleconferences served three objectives for the project: 1) share simulation results, 2) communicate field activities and results to the simulation teams, and 3) hold open scientific discussions on EGS topics.
Intermolecular Network Theory:A General Approach for Understanding the Structural and Dynamic Properties of Liquids and Solutions
Graph theory applications within the physical sciences have a long history. However, it has never moved to the forefront of analytical techniques employed by the computational chemistry community. Though much of the earliest work leveraged percolation theory as a backdrop for understanding critical phenomena and phase transitions within statistical mechanical simulations, modern data science has much to contribute. Here in this paper, we focus on the realm of intermolecular networks, where vertices represent molecules or particles, and an edge represents an intermolecular or interparticle interaction. These interactions are widely understood to dictate many of the physical properties of soft matter, and for liquids in particular, understanding this network is essential. Consider the sheer volume of ~70 years of literature associated with the hydrogen bond network of water, a topic that remains an active area of research even today. The aim of this work is twofold: first, to put into context prior algorithms and analyses that employed “connectedness theory” or lattice-based models to understand the intermolecular networks of chemical systems; and second to discuss a more general strategy for analyzing the multiscale structural and dynamic properties of chemical systems—intermolecular network theory. This approach encompasses foundational methods such as percolation theory, but also utilizes contemporary graph theoretical analyses that have evolved alongside the development of the World Wide Web, cloud computing, and big data. It is the realm of intermolecular network theory to determine the relationships between the essential physics of a system and the topological properties of the network and to derive new techniques in graph theory that can be related to the underlying physico-chemical or reactive properties of a system. Examples are used throughout this chapter that demonstrate applications to a wide variety of complex chemical systems, including ion association within electrolytes, mass transport and the properties of liquid–liquid phase boundaries, and multicomponent solutions.
Resonant diffraction gratings for spatial differentiation of optical beams
Diffraction of a two-dimensional optical beam from a resonant diffraction grating is considered. It is shown that at certain resonance parameters the diffraction grating allows for spatial differentiation and integration of the incident beam. The parameters of the diffraction grating for spatial differentiation of optical beams in the transmission geometry are calculated. It is shown that the differentiating diffraction grating allows the conversion of the two-dimensional beam into the two-dimensional Hermite – Gaussian mode. The presented results of numerical modelling are in good agreement with the proposed theoretical description. The use of the considered resonant diffraction gratings is promising for solving the problems of all-optical data processing. (laser applications and other topics in quantum electronics)
Magnetism and superconductivity in Fe 1+y Te 1-x Se x
Neutron scattering has played a significant role in characterizing magnetic and structural correlations in Fe$_{1+y}$Te$_{1-x}$Se$_x$ and their connections with superconductivity. Here we review several key aspects of the physics of iron chalcogenide superconductors where neutron studies played a key role. These topics include the phase diagram of Fe$_{1+y}$Te$_{1-x}$Se$_{x}$, where the doping-dependence of structural transitions can be understood from a mapping to the anisotropic random field Ising model. We then discuss orbital-selective Mott physics in the Fe chalcogenide series, where temperature-dependent magnetism in the parent material provided one of the earliest cases for orbital-selective correlation effects in a Hund's metal. Finally, we elaborate on the character of local magnetic correlations revealed by neutron scattering, its dependence on temperature and composition, and the connections to nematicity and superconductivity.
The SAGEX review on scattering amplitudes Chapter 2: An invitation to color-kinematics duality and the double copy
Abstract Advances in scattering amplitudes have exposed previously-hidden color-kinematics and double-copy structures in theories ranging from gauge and gravity theories to effective field theories such as chiral perturbation theory and the Born–Infeld model. These novel structures both simplify higher-order calculations and pose tantalizing questions related to a unified framework underlying relativistic quantum theories. This introductory mini-review article invites further exploration of these topics. After a brief introduction to color-kinematics duality and the double copy as they emerge at tree and loop-level in gauge and gravity theories, we present two distinct examples: (1) an introduction to the web of double-copy-constructible theories, and (2) a discussion of the application of the double copy to calculation relevant to gravitational-wave physics.
THERMAL STRESS ANALYSIS OF A SPENT NUCLEAR FUEL CANISTER
The potential for chloride induced stress corrosion cracking (CISCC) in spent nuclear fuel dry storage canisters is a current topic of research by the US Department of Energy Spent Fuel and Waste Science and Technology program. One of the important prerequisites for CISCC is a tensile residual stress state. This study utilizes computational models to provide an initial analysis of thermal stresses in a generic spent nuclear fuel canister. A STAR-CCM+ thermal fluid model provides a temperature profile analysis of the canister based on four different ambient temperature conditions. An ANSYS APDL finite element model incorporates the temperature profiles to analyze the thermal stresses in the canister. The calculated thermal stress magnitudes are in the range of 10 MPa to 80 MPa, which could be significant for crack propagation through the canister wall. The maximum thermal stress is not a concern for the structural failure of the canister, but it is high enough that it is a significant contribution to the total stress state of the canister wall, which also includes residual stress from fabrication, internal pressure from helium cover gas, and potential transient mechanical loads, such as earthquakes. This paper describes the initial finite element thermal stress analysis that was completed in 2020, reports the initial findings, and identifies areas where model refinement is still needed to complete this work in the future.
Designing an Optimal Sensor Network via Minimizing Information Loss
Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We observe that recent advancements in computational sciences often yield large datasets based on physics-based simulations, which are rarely leveraged in experimental design. We introduce a novel model-based sensor placement criterion, along with a highly-efficient optimization algorithm, which integrates physics-based simulations and Bayesian experimental design principles to identify sensor networks that “minimize information loss” from simulated data. Our technique relies on sparse variational inference and (separable) Gauss-Markov priors, and thus may adapt many techniques from Bayesian experimental design. We validate our method through a case study monitoring air temperature in Phoenix, Arizona, using state-of-the-art physics-based simulations. Our results show our framework to be superior to random or quasi-random sampling, particularly with a limited number of sensors. We conclude by discussing practical considerations and implications of our framework, including more complex modeling tools and real-world deployments.
AmeriFlux Measurement Network: Science Team Research
Research involves analysis and field direction of AmeriFlux operations, and the PI provides scientific leadership of the AmeriFlux network. Activities include the coordination and quality assurance of measurements across AmeriFlux network sites, synthesis of results across the network, organizing and supporting the annual Science Team Meeting, and communicating AmeriFlux results to the scientific community and other users. Objectives of measurement research include (i) coordination of flux and biometric measurement protocols (ii) timely data delivery to the Carbon Dioxide Information and Analysis Center (CDIAC); and (iii) assurance of data quality of flux and ecosystem measurements contributed by AmeriFlux sites. Objectives of integration and synthesis activities include (i) integration of site data into network-wide synthesis products; and (ii) participation in the analysis, modeling and interpretation of network data products. Communications objectives include (i) organizing an annual meeting of AmeriFlux investigators for reporting annual flux measurements and exchanging scientific information on ecosystem carbon budgets; (ii) developing focused topics for analysis and publication; and (iii) developing data reporting protocols in support of AmeriFlux network goals.
International Workshop on Comparing Ice Nucleation Measuring Systems 2014
The relationship of ambient aerosol particles to the formation of ice-containing clouds is one of the largest uncertainties in understanding the Earth’s climate. The uncertainty is due to several poorly understood processes and measurements including, but not limited to: (1) the microphysics of how particles nucleate ice, (2) the number of ice forming particles as a function of atmospheric properties such as temperature and relative humidity, (3) the atmospheric distribution of ice forming particles and (4) the role of anthropogenic activities in producing or changing the behavior of ice forming particles. The ways in which ice forming particles can impact climate is also multi-faceted. More ice forming particles can lead to clouds with more ice crystals and different optical properties than clouds with less ice forming particles. More effective ice forming particles can lead to ice at higher temperature and/or lower saturation, resulting in clouds at lower altitude or latitude which also changes the Earth’s radiative balance. Ice nucleation also initiates most of the Earth’s precipitation, even in the mid- and low-latitudes, since cloud-top temperatures are often below freezing. The limited measurements and lack of understanding directly translates to restrictions in our ability to model atmospheric ice formation and project changes into the future. The importance of ice nucleation research is further exemplified by Figure 1 which shows the publications per decade and citations per year on the topic of ice nucleation [DeMott et al., 2011]. After a lull at the end of the last century, there has been a dramatic increase in both publications and citations related to ice nucleation; this directly corresponds to the importance of ice nucleation on the Earth’s climate and the uncertainty in this area noted by the Solomon [2007].
Quantification of Land-Use/Land Cover Change as Driver of Earth System Dynamics (Final Technical Report)
Human land-use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth surface, with impacts on climate. In the future, land-use activities are likely to expand and/or intensify further to meet future demands for food, feed, fiber, and energy. The proposal has three major objectives: 1: Develop metrics to quantify climate sensitivity to land-use/land-cover change using DOE-ESM/CESM; 2: develop new essential input data products and algorithms for land-use in DOE-ESM/CESM (past-future); and 3: design model experiments to isolate and quantify the effects of land-use on climate in DOE- ESM/CESM. The science supported by this award has generated numerous accomplishments, and is generating major impacts on the topic of land-use/climate more broadly.
Assessing Global Ocean Circulation Sensitivity to Changes in Antarctic Ice Discharge
The principal goal of this research is to assess how projected increases in meltwater discharge and iceberg calving from the Antarctic Ice Sheet will impact climate in the next 250 years. In short, the sensitivity of the global ocean circulation and climate, and in particular the Atlantic Meridional Overturning Circulation (AMOC), to large increases in iceberg calving and meltwater discharges to the Southern Ocean are poorly understood. The requirement to investigate this topic is heightened by growing evidence that the West Antarctic Ice Sheet (WAIS) is vulnerable to rapid retreat and collapse through marine-ice-sheet-instability (MISI) on multidecadal-to-centennial timescales. Future model simulations of the AIS show the potential for rapid ice sheet retreat in the next 50 – 300 years, with such a collapse associated with the discharge of enormous volumes of ice and meltwater to the Southern Ocean. This project uses a combination of an ice sheet/shelf model and a fully coupled (ocean-atmosphere-seaice) model (CESM 1.2) to explore the sensitive of global climate to future changes in runoff from the Antarctic continent under future greenhouse gas emissions scenarios IPCC RCP4.5 and 8.5. Overall, accounting for Antarctic meltwater reduces the projected global mean anthropogenic greenhouse warming by up to 2 deg C. during peak WAIS collapse and by ~1 deg. for most of the simulation. In addition, the increased meltwater contribution raises sub-surface ocean temperatures at the Antarctic ice sheet margin by more than 1ºC, such that the ice shelf basal melt rates increase beyond those currently projected. In contrast, the model predicts a strong cooling of both the surface air and ocean temperatures over the Southern Ocean that results in a massive expansion and thickening of the perennial sea-ice. The slow-down in anthropogenic warming also results in the onset of ice-free conditions in the Arctic being delayed in the model by several decades, and the projected slowdown in the AMOC to reduce.
Cosmic Signatures of Fundamental Physics (Final Technical Report)
Cosmology is the study of the history of the Universe. Major topics of current cosmology research include the nature of the dark energy which today is causing the Universe to accelerate, and the nature of cosmic inflation which is our best theory of the very early evolution of the Universe. Each of these topics can be explored with modern cosmological data, and rely on our theories of fundamental physics to operate. Research supported by this grant has led to advances in all these areas. We developed specific models of cosmic inflation and explored potential observations signatures. We explored observational constraints on the number of dimensions of space, which in certain modern theories can take in different values, and developed the specific “brane theories” of dark energy. The possibility that novel quantum effects (“quantum entanglement”) during cosmic inflation could lead to interesting observable phenomena was also explored.
Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission
Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is due not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but, perhaps most crucially, the multitude of epidemiological, ecological, and socioeconomic factors that drive VBD transmission, and this complexity has generated considerable debate over the last 10-15 years. In this article, and Theme Issue, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions, and, crucially, offer some solutions for the field moving forwards. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector-pathogen systems. This Theme Issue seeks to cover both, reflected in the breadth and depth of the topics and VBD-systems considered, itself strongly indicative of the challenging, but necessary, multidisciplinary nature of this research field.
ARM Aerosol Measurement Science Group 2019 Strategic Planning Workshop Report
This report summarizes the results of a U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility workshop held in November 2019 to advance a science-based strategy for ARM’s aerosol measurement program. This was the second such workshop since the Aerosol Measurement Science Group (AMSG) was chartered in 2015 to enhance coordination of ARM observations of aerosols and atmospheric trace gases with the needs of ARM users. The results presented here reflect the AMSG’s focus in recent years on science-based strategies that will contribute to the increased use of ARM data to fulfill its mission of improving process representations and predictability in climate models. Sessions held during the workshop range from interfacing with models through aerosol sampling strategies to calibration protocols and data products. The AMSG workshops have been designed to recommend actions that will enable ARM to evolve and continue to meet its mission. To that end, the AMSG will develop an actionable plan from the recommendations outlined here. Some are well defined and can reasonably be accomplished in the short term. Others are less definite or of a larger scope that calls for a longer-term implementation. Further discussion will be required to develop and prioritize actionable items related to such areas. Task teams comprising the appropriate expertise and perspective from the AMSG and other members of the community will be formed to achieve this outcome. Some particular topics are recognized as high priority, so plans are underway to develop task teams and to hold follow-on discussions to address them. Four areas currently being considered for short, focused discussion are 1) aerosol measurements on the North Slope of Alaska, 2) improving data usability for modeling, 3) strategies for advancing remote sensing, vertical profiling, and distributed measurements of aerosols, and 4) aerosol sampling strategies at existing ARM sites to provide intensive modes of operation to promote data usage for process and modeling studies.
Monte Carlo/CFD Coupling for Accurate Modeling of the Delayed Neutron Precursors and Compressibility Effects in Molten Salt Reactors
Multiphysics reactor analysis has become one of the leading activities of most nuclear engineering research groups. Coupled neutronics and thermal/hydraulics (T/H) simulations have traditionally been performed by adopting deterministic transport tools and reactor system T/H codes. The main reason for avoiding higher fidelity numerical methods like continuous-energy Monte Carlo analysis has been the high computational cost of coupled, full-scale simulations. Nowadays, high fidelity multiphysics simulations represent a hot research topic, with several applications involving advanced nuclear systems. Liquid fuel Molten Salt Reactors (MSRs) feature peculiar physical phenomena that are not present in solid fuel reactors. Some of these phenomena are not correctly resolved by legacy reactor physics tools, and require specific treatments. This work focuses in particular on the analysis of the effect of the delayed neutron precursors drift and the impact of liquid fluid thermal expansion and compressibility in postulated criticality excursion transients in fast-spectrum MSRs. A new modeling tool based on a Monte Carlo approach for neutronics (Serpent code) coupled to a Computational Fluid Dynamic (CFD) analysis (OpenFOAM) was developed to obtain accurate modeling and simulation of these phenomena. The development of the Serpent Monte Carlo code [1] at VTT in the past few years has made this code particularly attractive for performing multiphysics studies. The availability of a flexible multiphysics interface, on-the-fly Doppler broadening and density-based collision rejection, and Delta-tracking, proved of paramount importance for the computationally efficient performance of coupled Monte Carlo/CFD calculations [2]. OpenFOAM is a C++ multiphysics toolkit featured by automatic matrix construction and solution capabilities for scalar and vector partial differential equations (PDEs), adopting several possible differencing and interpolation schemes, based on the Finite-Volume method. The ease of implementing the constitutive equations of different physical phenomena makes OpenFOAM particularly suitable for the modeling of complex coupled phenomena. In the past, several research activities proposed the adoption of both Serpent and OpenFOAM for the modeling and simulation of MSRs and the associated fuel cycles. In this work, these tools have been internally coupled for the steady-state and transient analysis of fast-spectrum systems, accounting for the effect of delayed neutron precursors transport and the salt thermal expansion and compressibility.