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At least 739 records · Page 41

Causality, unitarity, and the weak gravity conjecture

We consider the shift of charge-to-mass ratio for extremal black holes in the context of effective field theory, motivated by the Weak Gravity Conjecture. We constrain extremality corrections in different regimes subject to unitarity and causality constraints. In the asymptotic IR, we demonstrate that for any supersymmetric theory in flat space, and for all minimally coupled theories, logarithmic running at one loop pushes the Wilson coefficient of certain four-derivative operators to be larger at lower energies, guaranteeing the existence of sufficiently large black holes with Q > M. We identify two exceptional cases of nonsupersymmetric theories involving large numbers of light states and Planck-scale nonminimal couplings, in which the sign of the running is reversed, leading to black holes with negative corrections to Q/M in the deep IR, but argue that these do not rule out extremal black holes as the requisite charged states for the WGC. We separately show that causality and unitarity imply that the leading threshold corrections to the effective action from integrating out massive states, in any weakly coupled theory, can be written as a sum of squares and is manifestly positive for black hole backgrounds. Quite beautifully, the shift in the extremal Q/M ratio is directly proportional to the shift in the on-shell action, guaranteeing that these threshold corrections push Q > M in compliance with the WGC. Our results apply for black holes with or without dilatonic coupling and charged under any number of U(1)s.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A multi-institution collaboration to study copper wire hybrid and laser-cut foil X-pinches

X-pinches are a pulsed power wire-array configuration that produce nanosecond and micron-scale X-ray sources with numerous applications. The earliest embodiment of the X-pinch inspired its name, as it is typically composed of two or more fine (5-50 µm) wires crossed into the shape of an X (Fig. 1a). The ‘X’ ablates when subjected to large (10 1 -10 4 kA), fast-rising (~1 kA/ns) currents, and extreme magnetic pressure at the cross-point constricts the ablated plasma, which develops instabilities and then pinches to near-zero radius, localized ‘hot spots’, emitting X-rays from a sub-nanosecond and 1 µm scale source characteristic of a hot (~1 keV), dense (10% solid density) plasma. A subsequent gap forms where the hot spot(s) occurred, across which substantial potential accelerates electron beams (e-beams) that generate larger, longer-lasting X-ray bursts composed of harder X-rays.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Software For Advanced Large-scale Analysis Of Magnetic Confinement For Numerical Design, Engineering & Research (salamander)

As magnetic confinement fusion energy gains traction internationally to enable abundant energy production, designing components for fusion systems is a pressing challenge. During the planned lifetime of a fusion device, components evolve in extreme environments and must withstand large, repeated thermal loads and bombardment by 14 MeV neutrons, plasma ions, and neutral particles (deuterium, tritium, and helium), corrosive conditions, etc. All these physical processes take place simultaneously, interact in intricate ways, and impose important constraints that can affect performance. Experimental data is rare and costly to obtain, making design particularly challenging. Predictive computational frameworks must be an integral part of an accelerated and cost-effective design process by modeling fusion system performance in simulated environments. To better understand component degradation and operational impacts on their performance, the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER) is designed as an open-source, fully integrated, multiphysics, multiscale, NQA-1 compliant framework facilitating 3D, high-fidelity fusion system modeling. To that end, SALAMANDER is a MOOSE-based framework, and therefore leverages MOOSE upstream libraries such as PETSc and libMesh to deliver sophisticated finite element, finite volume, and nonlinear solver technology for fusion energy simulations. SALAMANDER couples MOOSE physics module capabilities—such as thermal hydraulics, heat conduction, Navier-Stokes, and thermomechanics—with tritium transport via TMAP8, neutronics via Cardinal, and nascent particle-in-cell capabilities. Direct simulation Monte Carlo methods will be used to address neutral transport near the walls. By coupling all these physics in an integrated application, SALAMANDER will enable high-fidelity modeling of irradiation levels and plasma exposure conditions of plasma facing components and their impact on heat and tritium distributions, as well as the resulting mechanical constraints experienced by the plasma facing components and performance of blanket systems. Furthermore, SALAMANDER will be particularly suited for engineering studies thanks to the stochastic tool module readily available in MOOSE, allowing for extended uncertainty quantification and risk analysis studies. It is also able to use computer-aided design (CAD) meshes to model complex geometries, which is indispensable for fusion systems. SALAMANDER therefore supports design, safety, engineering, and research projects for magnetic confinement fusion systems

Simon, Pierre-Clement [Idaho National Laboratory (↗

Mechanical quantum sensing in the search for dark matter

Numerous astrophysical and cosmological observations are best explained by the existence of dark matter, a mass density which interacts only very weakly with visible, baryonic matter. Here, searching for the extremely weak signals produced by this dark matter strongly motivate the development of new, ultra-sensitive detector technologies. Paradigmatic advances in the control and readout of massive mechanical systems, in both the classical and quantum regimes, have enabled unprecedented levels of sensitivity. In this white paper, we outline recent ideas in the potential use of a range of solid-state mechanical sensing technologies to aid in the search for dark matter in a number of energy scales and with a variety of coupling mechanisms.

79 ASTRONOMY AND ASTROPHYSICS↗

Helical dynamo growth and saturation at modest versus extreme magnetic Reynolds numbers

Understanding magnetic field growth in astrophysical objects is a persistent challenge. In stars and galaxies, turbulent flows with net kinetic helicity are believed to be responsible for driving large-scale magnetic fields. However, numerical simulations have demonstrated that such helical dynamos in closed volumes saturate at lower magnetic field strengths when increasing the magnetic Reynolds number Rm . This would imply that helical large-scale dynamos cannot be efficient in astrophysical bodies without the help of helicity outflows such as stellar winds. But do these implications actually apply for very large Rm ? Here we tackle the long-standing question of how much helical large-scale dynamo growth occurs independent of Rm in a closed volume. We analyze data from numerical simulations with a new method that tracks resistive versus nonresistive drivers of helical field growth. We identify a presaturation regime when the large-scale field grows at a rate independent of Rm , but to an Rm -dependent magnitude. The latter Rm dependence is due to a dominant resistive contribution, but whose fractional contribution to the large-scale magnetic energy decreases with increasing Rm . We argue that the resistive contribution would become negligible at large Rm and an Rm -independent dynamical contribution would dominate if the current helicity spectrum in the inertial range is steeper than k 0 . As such helicity spectra are plausible, this renews optimism for the relevance of closed dynamos. Our work pinpoints how modest Rm simulations can cause misapprehension of the Rm → ∞ behavior. Published by the American Physical Society 2024

79 ASTRONOMY AND ASTROPHYSICS↗

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

54 ENVIRONMENTAL SCIENCES↗

Data Federation Challenges in Remote Near-Real-Time Fusion Experiment Data Processing

Fusion energy experiments and simulations provide critical information needed to plan future fusion reactors. As next-generation devices like ITER move toward long-pulse experiments, analyses, including AI and ML, should be performed in a wide range of time and computing constraints, from near-real-time constraints, between-shot analysis, and to campaign-wide long-term analysis. However, the data volume, velocity, and variety make it extremely challenging for analyses using only local computational resources. Researchers need the ability to compose and execute workflows spanning edge resources to large-scale high-performance computing facilities.We present Delta, a system to address data analysis challenges, including AI/ML, in fusion science, by leveraging the ADIOS I/O library and middleware, to support executing science workflows over the wide area network for near-real-time streaming. We discuss the data federation challenges in performing remote workflows, focusing on on-going research work in (1) managing, reducing, and streaming data to minimize I/O and data movement overheads, (2) decompressing and reorganizing data for analysis, and (3) executing workflows for automated data analysis. We introduce examples for deep-learning based data analysis for the fusion domain and demonstrate how we use Delta to construct end-to-end workflows for a fusion device in Korea, connecting a remote DOE facility in the USA. The capability demonstrated by this project is the basis for improving the state of the art for near-real-time data federation amongst remote facilities.

Choi, Jong Youl↗

Characteristics and Variability of the Nearshore Wave Resource on the U.S. West Coast

Characterizing the nearshore wave resource at a regional scale poses a great challenge because high-quality wave data are required. This paper presents a detailed analysis of nearshore wave climate and resource characterization on the U.S. West Coast based on a 32-year, regional wave hindcast. Resource characterization closely followed the International Electrotechnical Commission standards. The wave hindcast data were generated from an unstructured-grid SWAN model with approximately 300 m grid resolution in the nearshore region. Statistics of six IEC wave resource parameters at the 50 m water depth were calculated for four sub-regions along the coast, including Washington, Oregon, and Northern and Southern California. Results indicate that the Washington and Oregon coast has a similar nearshore wave resource. The wave resource in Southern California is significantly less than that of the Washington and Oregon coast. Alongshore and cross-shore variabilities of wave power were assessed. Temporal variability, at seasonal, inter-annual, and decadal scales were analyzed. Overall, strong seasonal variation is observed; high wave energy occurs in the winter months and calm seas in the summer. Finally, extreme environmental conditions, i.e., the 100-year extreme environmental contours, for different locations along the West Coast were investigated.

wave resource characterization, nearshore, west co↗

Brittle-to-Ductile Transition Temperature Decreases at the Microscale in Muscovite Mica

Characterizing the structural and chemical transitions of muscovite mica across various scales and thermal profiles is essential for ensuring the reliability of high-voltage power insulation, aerospace thermal barriers, and nanoscale semiconductor substrates under extreme operational stress. Here, we investigate notch fracture toughness $K^{n}_{Ic}$ of muscovite mica as a function of feature size w and temperature T with microscale shear testing. $K^{n}_{Ic}$ decreased and then stayed constant as w increased, likely from fabrication-induced changes to the microstructure and roughness. Moreover, $K^{n}_{Ic}$ initially decreased and then increased as temperature T increased, with the reversal in trend indicative of a brittle-to-ductile transition. Complementary Raman spectroscopy and molecular dynamics showed that the observed trends were due to an increase in bond lengths and a decrease in critical stresses/strains. In comparing the microscale results to macroscale counterparts, it was surmised that the decrease in brittle-to-ductile transition temperature was due to an increase in the dehydroxylation rate.

DelRio, Frank W. [Sandia National Laboratories (SN↗

Thermoelectric power of Sachdev-Ye-Kitaev islands: Probing Bekenstein-Hawking entropy in quantum matter experiments

The Sachdev-Ye-Kitaev (SYK) model describes electrons with random and all-to-all interactions and realizes a many-body state without quasiparticle excitations and a nonvanishing extensive entropy $S_0$ in the zero-temperature limit. Its low-energy theory coincides with the low-energy theory of near-extremal charged black holes with Bekenstein-Hawking entropy $S_0$. Several mesoscopic experimental configurations realizing SYK quantum dynamics over a significant intermediate temperature scale have been proposed. We investigate quantum thermoelectric transport in such configurations and describe low-temperature crossovers out of SYK criticality into regimes either with Fermi liquid behavior, with a Coulomb blockade, or criticality associated with Schwarzian quantum gravity fluctuations. In this work, our results show that thermopower measurements can serve as a direct probe for $S_0$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Classical and quantum evolution in a simple coherent neutrino problem

The extraordinary neutrino flux produced in extreme astrophysical environments like the early Universe, core-collapse supernovae and neutron star mergers may produce coherent quantum neutrino oscillations on macroscopic length scales. The Hamiltonian describing this evolution can be mapped into quantum spin models with all-to-all couplings arising from neutrino-neutrino forward scattering. To date many studies of these oscillations have been performed in a mean-field limit where the neutrinos time evolve in a product state. In this paper we examine a simple two-beam model evolving from an initial product state and compare the mean-field and many-body evolution. The symmetries in this model allow us to solve the real-time evolution for the quantum many-body system for hundreds or thousands of spins, far beyond what would be possible in a more general case with an exponential number (2 N ) of quantum states. We compare mean-field and many-body solutions for different initial product states and ratios of one- and two-body couplings, and find that in all cases in the limit of infinite spins the mean-field (product state) and many-body solutions coincide for simple observables. This agreement can be understood as a consequence of the fact that the typical initial condition represents a very local but dense distribution about a mean energy in the spectrum of the Hamiltonian. We explore quantum information measures like entanglement entropy and purity of the many-body solutions, finding intriguing relationships between the quantum information measures and the dynamical behavior of simple physical observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Scalable production of recombinant three-finger proteins: from inclusion bodies to high quality molecular probes

The three-finger proteins are a collection of disulfide bond rich proteins of great biomedical interests. Scalable recombinant expression and purification of bioactive three-finger proteins is quite difficult. We introduce a working pipeline for expression, purification and validation of disulfide-bond rich three-finger proteins using E. coli as the expression host. With this pipeline, we have successfully obtained highly purified and bioactive recombinant α-Βungarotoxin, k-Bungarotoxin, Hannalgesin, Mambalgin-1, α-Cobratoxin, MTα, Slurp1, Pate B etc. Milligrams to hundreds of milligrams of recombinant three finger proteins were obtained within weeks in the lab. The recombinant proteins showed specificity in binding assay and six of them were crystallized and structurally validated using X-ray diffraction protein crystallography. Our pipeline allows refolding and purifying recombinant three finger proteins under optimized conditions and can be scaled up for massive production of three finger proteins. As many three finger proteins have attractive therapeutic or research interests and due to the extremely high quality of the recombinant three finger proteins we obtained, our method provides a competitive alternative to either their native counterparts or chemically synthetic ones and should facilitate related research and applications.

59 BASIC BIOLOGICAL SCIENCES↗

Far-field thermal imaging below diffraction limit

Non-uniform self-heating and temperature hotspots are major concerns compromising the performance and reliability of submicron electronic and optoelectronic devices. At deep submicron scales where effects such as contact-related artifacts and diffraction limits accurate measurements of temperature hotspots, non-contact thermal characterization can be extremely valuable. In this work, we use a Bayesian optimization framework with generalized Gaussian Markov random field (GGMRF) prior model to obtain accurate full-field temperature distribution of self-heated metal interconnects from their thermoreflectance thermal images (TRI) with spatial resolution 2.5 times below Rayleigh limit for 530nm illumination. Finite element simulations along with TRI experimental data were used to characterize the point spread function of the optical imaging system. In addition, unlike iterative reconstruction algorithms that use ad hoc regularization parameters in their prior models to obtain the best quality image, we used numerical experiments and finite element modeling to estimate the regularization parameter for solving a real experimental inverse problem.

42 ENGINEERING↗

Revolutionizing observations and predictability of Arctic system dynamics through next-generation dense, heterogeneous and intelligent wireless sensor networks with embedded AI

The Arctic region is a complex and dynamic system, where changes in temperature and partitioning between water in solid, liquid and vapor phase are fundamentally reshaping hydro-biogeochemical fluxes from the bedrock to the atmosphere. The increasing temperature and frequency of extreme heat, precipitation and fire events (Wang et al., 2017; Meredith et al., 2019) have critical consequences for the Arctic ecosystems and people, as well as for the global climate system. Although many observational and remote sensing activities have been on-going, detailed ecosystem processes – particularly related to controls on and impacts of permafrost thaw – are difficult to detect or measure. To improve the understanding and prediction of these extreme events and their consequences, we need a revolution in the way we collect ground-based observations from hillslope to pan-Arctic scales and couple them with satellite imagery and models to deliver rich, actionable, and scale-appropriate data.

54 ENVIRONMENTAL SCIENCES↗

Wind Turbine Gust Load Alleviation with Active Flow Control

Large wind turbine rotors are becoming more common in utility-scale wind power, especially for offshore wind plants. However, the trend toward large rotors can be limited by their ability to manage dynamic and extreme loads. To provide a safety margin for the rotor design and avoid catastrophic events such as tower strikes, extreme loads need to be controlled. The objective of this study is to develop and evaluate a feedback control system to alleviate extreme loads and reduce blade deflections under gust events using active flow control devices. We also propose a modification in the turbine controller to achieve further reduction in extreme loads. The extreme load reductions are evaluated under gust wind conditions with direction changes according to the IEC standard. The effects of the gust alleviation controller on turbine performance and fatigue loads are investigated as well. With the deployment of the gust alleviation controller and modified turbine baseline controller, the extreme loads and deflections reduce by up to 23%. The energy captured by the turbine is not affected by the proposed gust alleviation controller. The fatigue loads of various wind turbine components are either reduced or remain unchanged.

17 WIND ENERGY↗

The effect of temperature on photosystem II efficiency across plant functional types and climate

Modeling terrestrial gross primary productivity (GPP) is central to predicting the global carbon cycle. Much interest has been focused on the environmentally induced dynamics of photosystem energy partitioning and how improvements in the description of such dynamics assist the prediction of light reactions of photosynthesis and therefore GPP. The maximum quantum yield of photosystem II (Φ PSIImax ) is a key parameter of the light reactions that influence the electron transport rate needed for supporting the biochemical reactions of photosynthesis. Φ PSIImax is generally treated as a constant in biochemical photosynthetic models even though a constant Φ PSIImax is expected only for non-stressed plants. We synthesized reported Φ PSIImax values from pulse-amplitude-modulated fluorometry measurements in response to variable temperatures across the globe. We found that Φ PSIImax is strongly affected by prevailing temperature regimes with declined values in both hot and cold conditions. To understand the spatiotemporal variability in Φ PSIImax , we analyzed the temperature effect on Φ PSIImax across plant functional type (PFT) and habitat climatology. The analysis showed that temperature's impact on Φ PSIImax is shaped more by climate than by PFT for plants with broad latitudinal distributions or in regions with extreme temperature variability. There is a trade-off between the temperature range within which Φ PSIImax remains maximal and the overall rate of decline of Φ PSIImax outside the temperature range such that species cannot be simultaneously tolerant and resilient to extreme temperatures. Our study points to a quantitative approach for improving electron transport and photosynthetic productivity modeling under changing climates at regional and global scales.

54 ENVIRONMENTAL SCIENCES↗

New Evidence for the Importance of Non-Stomatal Pathways in Ozone Deposition During Extreme Heat and Dry Anomalies

Dry deposition could partially explain the observed response in ambient ozone to extreme hot and dry episodes. We examine the response of ozone deposition to heat and dry anomalies using three long-term co-located ecosystem-scale carbon dioxide, water vapor and ozone flux measurement records. We find that, as expected, canopy stomatal conductance generally decreases during days with dry air or soil. However, during hot days, concurrent increases in non-stomatal conductance are inferred at all three sites, which may be related to several temperature-sensitive processes not represented in the current generation of big-leaf models. This may offset the reduction in stomatal conductance, leading to smaller net reduction, or even net increase, in total deposition velocity. We find the response of deposition velocity to soil dryness may be related to its impact on photosynthetic activity, though considerable variability exists. Our findings emphasize the need for better understanding and representation of non-stomatal ozone deposition.

54 ENVIRONMENTAL SCIENCES↗

Ultra-high energy density relativistic plasmas from nanostructures: scaling to ultra-high intensities (Final Report)

This project investigated the ultra-high energy density (UHED) regime of matter found in the center of stars, using a compact PW-class laser. These extreme conditions are typically only obtained in the laboratory in the central hot-spot of spherically imploded capsules in inertial confinement fusion experiments driven by the world’s largest lasers. We have shown that near-solid density arrays of aligned nanostructures can be volumetrically heated to multi-keV temperatures by irradiation at relativistic intensity with ultrafast laser pulses of modest energy, opening a path for the generation of UHED plasmas with compact lasers. This new UHED plasma generation approach promises to create an environment with extreme energy densities and degrees of ionization, record conversion of optical laser light into ultrafast x-ray pulses, gigantic magnetic fields and pressures, and directed beams of high energy particles . In this project we achieved record degree of ionization in volumetric heating solid density and near-solid density plasmas. Gold plasmas which spectra is characterized by L shell transition emission from ions with charge up to the Ne-like state, Au+ 72 were generated using ultrafast laser pulses of less than 10 J of energy from a compact laser focused to an intensity of ~ 3x10 21 Wcm -2 . We also conducted measurements to determine the heat penetration depth in Ni nanowire arrays as compared to Ni foil targets by monitoring the line emission of a Co buried tracer underneath a variable amount of Ni. The measurements revealed that the nanowire plasmas are roughly six times larger in depth than the solid density target plasmas. A result of this increased plasma in nanowire arrays is a greatly increased conversion of optical laser light into > 1 KeV x-rays, a record conversion efficiency of 20 %. Critical to the realization of the proposed experiments was the generation of ultrafast laser pulses with ultra-high contrast that can deposit the energy deep into the nanowire arrays before the nanowires explode to create a continuous plasma. Supporting the proposed experiments was the recent demonstration at Colorado State University of a Petawatt-class laser that emits 30 fs pulses at high repetition rates. The experiments combined this unique laser tool with a variety of tailored nanowire arrays fabricated in house and with an extensive suite of diagnostics. The experiments were accompanied by 3-dimensional particle-in-cell simulations and detailed atomic physics simulations with transient kinetics and radiation transport. The proposed research allowed us to continue training Ph.D students and post-docs with broad experience in HEDP Science.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗