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DOE OSTI2020

De Sitter diagrammar and the resummation of time

Light scalars in inflationary spacetimes suffer from logarithmic infrared divergences at every order in perturbation theory. This corresponds to the scalar field values in different Hubble patches undergoing a random walk of quantum fluctuations, leading to a simple toy “landscape” on superhorizon scales, in which we can explore questions relevant to eternal inflation. However, for a sufficiently long period of inflation, the infrared divergences appear to spoil computability. Some form of renormalization group approach is thus motivated to resum the log divergences of conformal time. Such a resummation may provide insight into De Sitter holography. We present here a novel diagrammatic analysis of these infrared divergences and their resummation. Basic graph theory observations and momen- tum power counting for the in-in propagators allow a simple and insightful determination of the leading-log contributions. One thus sees diagrammatically how the superhorizon sector consists of a semiclassical theory with quantum noise evolved by a first-order, interacting classical equation of motion. This rigorously leads to the “Stochastic Inflation” ansatz developed by Starobinsky to cure the scalar infrared pathology nonperturbatively. Our approach is a controlled approximation of the underlying quantum field theory and is systematically improvable.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Development of the self-consistent approximation and its application to the problem of magnetoelastic resonance in an inhomogeneous medium

Our previously proposed approximation involving both the first and second terms of the expansion of the vertex function is generalized to the system of two interacting wavefields of different physical nature. A system of self-consistent equations for the matrix Green’s function and matrix vertex function is derived. On the basis of this matrix generalization of the new self-consistent approximation, a theory of magnetoelastic resonance is developed for a ferromagnetic model, where the magnetoelastic coupling parameter ε(x) is inhomogeneous. Equations for magnetoelastic resonance are analyzed for one-dimensional inhomogeneities of the coupling parameter. The diagonal and off-diagonal elements of the matrix Green’s function of the system of coupled spin and elastic waves are calculated with the change in the ratio between the average value ε and rms fluctuation Δε of the coupling parameter between waves from the homogeneous case (ε ≠ 0, Δε = 0) to the extremely randomized case (ε = 0, Δε ≠ 0) at various correlation wavenumbers of inhomogeneities k{sub c}. For the limiting case of infinite correlation radius (k{sub c} = 0), in addition to approximate expressions, exact analytical expressions corresponding to the summation of all diagrams of elements of the matrix Green’s function are obtained. The results calculated for an arbitrary k{sub c} value in the new self-consistent approximation are compared to the results obtained in the standard self-consistent approximation, where only the first term of the expansion of the vertex function is taken into account. It is shown that the new approximation corrects disadvantages of the Green’s functions calculated in the standard approximation such as the dome shape of resonances and bends on the sides of resonance peaks. The appearance of a fine structure of the spectrum in the form of a narrow resonance on the Green’s function of spin waves and a narrow antiresonance on the Green’s function of elastic waves, which was previously predicted in the standard self-consistent approximation, is confirmed. With an increase in the parameter k{sub c}, the Green’s functions calculated in the standard and new approximations approach each other and almost coincide with each other at k{sub c}/k ≥ 0.5. At the same time, the results of this work indicate that the new self-consistent approximation has a certain advantage for studying the problems of stochastic radiophysics in media with long-wavelength inhomogeneities (small k{sub c} values), because it describes both the shape and width of peaks much better than the standard approximation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Nucleon-induced inelastic scattering with statistical strength functions and the ECIS direct reaction code

Modern theoretical descriptions of inelastic scattering make use of multi-step direct reaction approaches together with transition potentials obtained from sophisticated nuclear structure models. Here we demonstrate how the complexity of such calculations can be reduced to permit simpler ones, also using the ECIS code, but providing an almost equally precise alternative to a much more detailed calculation. We have studied the transition form factors within the random phase approximation (RPA), where these are obtained as linear combinations of particle–hole states. At moderate to high excitation energies, where interference effects tend to disappear, we have proposed an independent particle–hole formalism in which particle–hole states are spread in energy with an appropriate strength function obtained from the RPA. The effects of more complex modes, such as 2p–2h ones, are simulated with widths calculated in a semi-classical context. In this work, we verify the validity of our approximations for pre-equilibrium proton-induced reactions on 90 Zr target. Our calculations provide a good description of the reaction data and point toward a simplification of the description of nucleon-induced reactions based on averages of microscopic details of the projectile–target interaction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1980

Proton upsets in LSI memories in space

Two types of large scale integrated dynamic random access memory devices were tested and found to be subject to soft errors when exposed to protons incident at energies between 18 and 130 MeV. These errors are shown to differ significantly from those induced in the same devices by alphas from an Am-241 source. There is considerable variation among devices in their sensitivity to proton-induced soft errors, even among devices of the same type. For protons incident at 130 MeV, the soft error cross sections measured in these experiments varied from 10 to the -8th to 10 to the -6th sq cm/proton. For individual devices, however, the soft error cross section consistently increased with beam energy from 18-130 MeV. Analysis indicates that the soft errors induced by energetic protons result from spallation interactions between the incident protons and the nuclei of the atoms comprising the device. Because energetic protons are the most numerous of both the galactic and solar cosmic rays and form the inner radiation belt, proton-induced soft errors have potentially serious implications for many electronic systems flown in space.

Mcnulty, P. J.↗

DOE OSTI2019

Learning Interactions in Complex Biological Systems

In this project, we developed novel methods for integrating simulation and data-driven methods to accelerate biomedical discovery. In particular, we exploit agent-based models of disease processes and deep reinforcement learning to identify potential novel multi-drug, patient-specific, and adaptive therapeutic strategies. We use the Innate Immune Response agent-based model (IIRABM) to model sepsis as a demonstration of our approach. We first calibrate the agent-based model using novel stochastic optimization algorithms develop in this project, then use the calibrated model to identify optimal cytokine mediation strategies using deep reinforcement learning (DRL). The learned policy achieves a dramatic reduction in mortality over a set of 500 simulated patients relative to standalone antibiotic therapy. Advantages of our approach are three-fold: 1) the use of simulation allows exploring therapeutic strategies beyond clinical practice and available data, 2) advances in DRL accommodate learning complex therapeutic strategies for complex biological systems, and 3) optimized treatments respond to a patient’s individual disease progression over time, therefore capturing both differences across patients and the inherent randomness of disease progression within a single patient. We hope that this work motivates both considering adaptive, personalized multi-cytokine mediation therapy for sepsis and exploiting simulation with DRL for precision medicine more broadly.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

The PENELOPE Physics Models and Transport Mechanics. Implementation into Geant4

A translation of the PENELOPE physics subroutines to C++, designed as an extension of the GEANT4 toolkit, is presented. The Fortran code system PENELOPE performs Monte Carlo simulation of coupled electron-photon transport in arbitrary materials for a wide energy range, nominally from 50 eV up to 1 GeV. PENELOPE implements the most reliable interaction models that are currently available, limited only by the required generality of the code. In addition, the transport of electrons and positrons is simulated by means of an elaborate class II scheme in which hard interactions (involving deflection angles or energy transfers larger than pre-defined cutoffs) are simulated from the associated restricted differential cross sections. After a brief description of the interaction models adopted for photons and electrons/positrons, we describe the details of the class-II algorithm used for tracking electrons and positrons. The C++ classes are adapted to the specific code structure of GEANT4. They provide a complete description of the interactions and transport mechanics of electrons/positrons and photons in arbitrary materials, which can be activated from the G4ProcessManager to produce simulation results equivalent to those from the original PENELOPE programs. The combined code, named PENG4, benefits from the multi-threading capabilities and advanced geometry and statistical tools of GEANT4.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Atomic-level heterogeneity and defect dynamics in concentrated solid-solution alloys

Performance enhancement of structural materials in extreme radiation environments has been activelyinvestigated for many decades. Traditional alloys, such as steel, brass and aluminum alloys, normallycontain one or two principal element(s) with a low concentration of other elements. While these existin either a mixture of metallic phases (multiple phases) or in a solid solution (single phase), limited orlocalized chemical disorder is a common characteristic of the main matrix. Fundamentally different fromtraditional alloys, recently developed single-phase concentrated solid-solutio n alloys (CSAs) contain mul-tiple elemental species in equiatomic or high concentrations with different elements randomly arrangedon a crystalline lattice. Due to the lack of ordered elemental arrangement in these CSAs, they exhibit sig-nificant chemical disorder and unique site-to-site lattice distortion. While it is well recognized intraditional alloys that minor additions lead to enhanced radiation resistance, it remains unclear inCSAs how atomic-level heterogeneity affects defect formation, damage accumulation, and microstruc-tural evolution. These knowledge gaps have acted as roadblocks to the development of future-generation energy technology. CSAs with a simple crystal structure, but complex chemical disorder,are unique systems that allow us, through replacing principal alloying elements and modifying concen-trations, to study how compositional complexity influences defect dynamics, and to bridge the knowl-edge gaps through understanding intricate electronic- and atomic-level interactions, mass and energytransfer processes, and radiation resistance performance. Recent advances in defect dynamics and irradi-ation performance of CSAs are reviewed, intrinsic chemical effects on radiation performance arediscussed, and direction for future studies is suggested.

Zhang, Yanwen↗

DOE OSTI2024

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

van der Waals Correction to the Physisorption of Graphene on Metal Surfaces

Adsorption is a scientifically and technologically important interfacial phenomenon, which however presents challenges to conventional density functional theory (DFT) due to the long-range van der Waals (vdW) interactions. We have developed a model of long-range vdW correction for physisorption of graphene (G) on metals with the Lifshitz–Zaremba–Kohn second-order perturbation theory, by incorporating dipole- and quadrupole-surface interactions and screening effects. The physisorption energies calculated by the model between graphene and eight metal surfaces (Al, Ni, Co, Pd, Pt, Cu, Ag, and Au), and the adsorption energies for the same G/metal structures from self-consistent DFT PBE (Perdew–Burke–Ernzerhof) calculations, are obtained in a range of distances between G and the metal surfaces. The sum of these two parts is the total adsorption energy as a function of the distance, from which the equilibrium distance and the binding energy are determined simultaneously. The results show high accuracy, with the mean absolute error (MAE) of binding energy of 7 meV and the MAE of equilibrium distance of 0.2 Å, significantly improving upon other vdW methods. Here, the PBE + vdW binding energy curves give better fits to the random phase approximation curves around the equilibrium distances than do the curves of other methods considered here. The higher-order quadrupole-surface correction is important and accounts for about 30% of the total vdW correction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Urbanization and malaria have a contextual relationship in endemic areas: A temporal and spatial study in Ghana

In West Africa, malaria is one of the leading causes of disease-induced deaths. Existing studies indicate that as urbanization increases, there is corresponding decrease in malaria prevalence. However, in malaria-endemic areas, the prevalence in some rural areas is sometimes lower than in some peri-urban and urban areas. Therefore, the relationship between the degree of urbanization, the impact of living in urban areas, and the prevalence of malaria remains unclear. This study explores this association in Ghana, using epidemiological data at the district level (2015–2018) and data on health, hygiene, and education. We applied a multilevel model and time series decomposition to understand the epidemiological pattern of malaria in Ghana. Then we classified the districts of Ghana into rural, peri-urban, and urban areas using administratively defined urbanization, total built areas, and built intensity. We converted the prevalence time series into cross-sectional data for each district by extracting features from the data. To predict the determinant most impacting according to the degree of urbanization, we used a cluster-specific random forest. We find that prevalence is impacted by seasonality, but the trend of the seasonal signature is not noticeable in urban and peri-urban areas. While urban districts have a slightly lower prevalence, there are still pockets with higher rates within these regions. These areas of high prevalence are linked to proximity to water bodies and waterways, but the rise in these same variables is not associated with the increase of prevalence in peri-urban areas. The increase in nightlight reflectance in rural areas is associated with an increased prevalence. We conclude that urbanization is not the main factor driving the decline in malaria. However, the data indicate that understanding and managing malaria prevalence in urbanization will necessitate a focus on these contextual factors. Finally, we design an interactive tool, ’malDecision’ that allows data-supported decision-making.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2007

Os-186 and Os-187 Enrichments and High-He-3/He-4 sources in the Earth's Mantle: Evidence from Icelandic Picrites

Picrites from the neovolcanic zones in Iceland display a range in Os-187/Os-188O from 0.1297 to 0.1381 ((gamma)Os = 0.0 to 6.5) and uniform Os-186/Os-188 of 0.1198375+/-32 (2 (sigma)). The value for Os-186/Os-188 is within uncertainty of the present-day value for the primitive upper mantle of 0.1198398+/-16. These Os isotope systematics are best explained by ancient recycled crust or melt enrichment in the mantle source region. If so, then the coupled enrichments displayed in Os-186/Os-188 and Os-187/Os-188 from lavas of other plume systems must result from an independent process, the most viable candidate at present remains core-mantle interaction. While some plumes with high He-3/He-4, such as Hawaii, appear to have been subjected to detectable addition of Os (and possibly He) from the outer core, others such as Iceland do not. A positive correlation between Os-187/Os-188 and He-3/He-4 from 9.6 to 19 RA in Iceland picrites is best modeled as mixtures of 500 Ma or older ancient recycled crust mixed with primitive mantle, creating a hybrid source region that subsequently mixes with the convecting MORB mantle during ascent and melting. This multistage mechanism to explain these isotope systematics is consistent with ancient recycled crust juxtaposed with more primitive, relatively He-rich mantle, in convective isolation from the upper mantle, most likely in the lowermost mantle. This is inconsistent with models that propose random mixing between heterogeneities in the convecting upper mantle as a mechanism to explain the observed isotopic variation in oceanic lavas or models that produce a high He-3/He-4 signature in melt depleted and strongly outgassed, He-poor mantle. Instead these systematics require a deep mantle source to explain the 3He/4He signature in Iceland lavas. The He-3/He-4 of lavas derived from the Iceland plume changed over time, from a maximum of 50 RA at 60 Ma, to approximately 25-27 RA at present. The changes are coupled with distinct compositional gaps between the different aged lavas when H-3/He-4 is plotted versus various geochemical parameters such as Nd-143/Nd-144 and La/Sm. These relationships can be interpreted as an increase in the proportion of ancient recycled crust in the upwelling plume over this time period.

Brandon, Alan D.↗

NASA NTRS2014

Statistical Mechanics of Turbulent Dynamos

Incompressible magnetohydrodynamic (MHD) turbulence and magnetic dynamos, which occur in magnetofluids with large fluid and magnetic Reynolds numbers, will be discussed. When Reynolds numbers are large and energy decays slowly, the distribution of energy with respect to length scale becomes quasi-stationary and MHD turbulence can be described statistically. In the limit of infinite Reynolds numbers, viscosity and resistivity become zero and if these values are used in the MHD equations ab initio, a model system called ideal MHD turbulence results. This model system is typically confined in simple geometries with some form of homogeneous boundary conditions, allowing for velocity and magnetic field to be represented by orthogonal function expansions. One advantage to this is that the coefficients of the expansions form a set of nonlinearly interacting variables whose behavior can be described by equilibrium statistical mechanics, i.e., by a canonical ensemble theory based on the global invariants (energy, cross helicity and magnetic helicity) of ideal MHD turbulence. Another advantage is that truncated expansions provide a finite dynamical system whose time evolution can be numerically simulated to test the predictions of the associated statistical mechanics. If ensemble predictions are the same as time averages, then the system is said to be ergodic; if not, the system is nonergodic. Although it had been implicitly assumed in the early days of ideal MHD statistical theory development that these finite dynamical systems were ergodic, numerical simulations provided sufficient evidence that they were, in fact, nonergodic. Specifically, while canonical ensemble theory predicted that expansion coefficients would be (i) zero-mean random variables with (ii) energy that decreased with length scale, it was found that although (ii) was correct, (i) was not and the expected ergodicity was broken. The exact cause of this broken ergodicity was explained, after much investigation, by greatly extending the statistical theory of ideal MHD turbulence. The mathematical details of broken ergodicity, in fact, give a quantitative explanation of how coherent structure, dynamic alignment and force-free states appear in turbulent magnetofluids. The relevance of these ideal results to real MHD turbulence occurs because broken ergodicity is most manifest in the ideal case at the largest length scales and it is in these largest scales that a real magnetofluid has the least dissipation, i.e., most closely approaches the behavior of an ideal magnetofluid. Furthermore, the effects grow stronger when cross and magnetic helicities grow large with respect to energy, and this is exactly what occurs with time in a real magnetofluid, where it is called selective decay. The relevance of these results found in ideal MHD turbulence theory to the real world is that they provide at least a qualitative explanation of why confined turbulent magnetofluids, such as the liquid iron that fills the Earth's outer core, produce stationary, large-scale magnetic fields, i.e., the geomagnetic field. These results should also apply to other planets as well as to plasma confinement devices on Earth and in space, and the effects should be manifest if Reynolds numbers are high enough and there is enough time for stationarity to occur, at least approximately. In the presentation, details will be given for both theoretical and numerical results, and references will be provided.

Shebalin, John V.↗

NASA NTRS2014

MHD Turbulence and Magnetic Dynamos

Shebalin, John V↗

DOE OSTI2018

Molecular Simulations of the Hydration Behavior of a Zwitterion Brush Array and Its Antifouling Property in an Aqueous Environment

We carried out umbrella sampling and molecular dynamics (MD) simulations to investigate molecular interactions between sulfobetaine zwitterions or between sulfobetaine brushes in different media. Simulation results show that it is more energetically favorable for the two sulfobetaine zwitterions or brushes to be fully hydrated in aqueous solutions than in vacuum where strong ion pairs are formed. Structural properties of the hydrated sulfobetaine brush array and its antifouling behavior against a foulant gel are subsequently studied through steered MD simulations. We find that sulfobetaine brush arrays with different grafting densities have different structures and antifouling mechanisms. At a comparably higher grafting density, the sulfobetaine brush array exhibits a more organized structure which can hold a tightly bound hydration water layer at the interface. Compression of this hydration layer results in a strong repulsive force. However, at a comparably lower grafting density, the brush array exhibits a randomly oriented structure in which the antifouling of the brush array is through the deformation of the sulfobetaine branches.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Final Report on CFD and Thermal-Mechanical Stress Analysis of PWR Surge Line under Transient Condition Thermal Stratification and an Evolutionary Cyclic Plasticity Based Transformative Fatigue Evaluation Approach without Using S~N Curve: Rev. 1

This report presents an evolutionary cyclic plasticity based transformative approach for fatigue evaluation of safety critical components. This approach is generic which can be used not only for the present generation light water reactor components (which is our focus) but also for advanced reactor components. The approach is based on the fundamental concept of time-evolution of progressive fatigue damage rather than the end-of-life data based conventional S~N curve approaches. Series of fatigue tests on 316 stainless steel uniaxial specimens were conducted under strain-controlled and stress-controlled cyclic loading in two different environment such as in-air at 300 °C and PWR coolant water condition at 300 °C. Material models are developed to capture the uniaxial test data and are subsequently programmed into commercially available ABAQUS code to transform the uniaxial material behavior to multiaxial loading domain. The developed evolutionary cyclic-plasticity approach is verified by both analytical and 3D-FE modeling of uniaxial fatigue test specimens. Results show that the developed material models can capture the time-dependence of material hardening and softening with great accuracy. In addition, results show that the material models not only can capture the material behavior under constant amplitude loading but also can capture the load-sequence effect under variable and random amplitude loading. The ANL developed approach is one of its kind, which shows first in the fatigue research community that fatigue evaluation of a component (in our case verified for laboratory scale fatigue test specimens with great accuracy) can be performed for its entire fatigue life, including the hardening and softening behavior, without using a conventional S~N curve based approach. Additionally results are presented related to fatigue evaluation of real reactor components such as of a pressurized water reactor (PWR) surge line (SL), under upset transient conditions. SLs of PWR experience complex thermal stratification due to the mixing of differential temperature flow from hot leg and pressurizer. A detailed parametric study of thermal stratification in SL is performed through both standalone computational fluid dynamics (CFD) analysis and fluid-structure-interaction based coupled CFD and heat transfer analysis. Results show that the mass flow rate in SL substantially affects the extent of its thermal stratification. The resulting thermal boundaries conditioned are used as inputs for thermal-mechanical stress analysis and component level fatigue evaluation. Fatigue evaluation of SL pipe under example loading conditions was performed using the above-mentioned evolutionary-plasticity based approach and ASME code (for in-air life) and NUREG-6909 (for PWR environment life) based approaches. Results show that the evolutionary plasticity based approach estimate substantially different life compared to the conventional ASME based approach. To show the entirety of the proposed evolutionary fatigue-modeling framework, this report includes the results presented in semi-annual March report (ANL/LWRS-17/01). Sections 2-5 of March report are directly included as Sections 2-5 in this report, without any significant changes. However, Sections 6-9 of this report present the work performed during March-September of 2017. Whereas, Sections 1 and 10 present the organization of this report and the summary and future work, respectively. This report (ANL/LWRS-17/03 Rev. 1) is a slightly revised version of the earlier published report (ANL/LWRS-17/03). In this revised version, some of the results and associated discussion have been corrected/updated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

The Heating of Solar Coronal Loops by Alfvén Wave Turbulence

In this paper we further develop a model for the heating of coronal loops by Alfvén wave turbulence (AWT). The Alfvén waves are assumed to be launched from a collection of kilogauss flux tubes in the photosphere at the two ends of the loop. Using a three-dimensional magnetohydrodynamic model for an active-region loop, we investigate how the waves from neighboring flux tubes interact in the chromosphere and corona. For a particular combination of model parameters we find that AWT can produce enough heat to maintain a peak temperature of about 2.5 MK, somewhat lower than the temperatures of 3–4 MK observed in the cores of active regions. The heating rates vary strongly in space and time, but the simulated heating events have durations less than 1 minute and are unlikely to reproduce the observed broad differential emission measure distributions of active regions. The simulated spectral line nonthermal widths are predicted to be about 27 km s{sup −1}, which is high compared to the observed values. Therefore, the present AWT model does not satisfy the observational constraints. An alternative “magnetic braiding” model is considered in which the coronal field lines are subject to slow random footpoint motions, but we find that such long-period motions produce much less heating than the shorter-period waves launched within the flux tubes. We discuss several possibilities for resolving the problem of producing sufficiently hot loops in active regions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Arctic Tipping Points Triggering Global Change (LDRD Final Report)

The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).

54 ENVIRONMENTAL SCIENCES↗