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Multi-scale nonlinear stress analysis of Nb 3 Sn superconducting accelerator magnets
A multi-scale nonlinear procedure to analyze the stress in Nb 3 Sn superconducting accelerator magnets is presented to address one of the most challenging obstacles currently facing the successful development of high-field superconducting magnets—the issue of stress management. The study demonstrates that gaskets (special nonlinear materials) are capable of modeling the complex nonlinear deformation behavior of insulation layers within the Nb 3 Sn coil block and that Hill materials (orthotropic materials utilizing the Hill yield criterion) are suitable to enable homogenization of the filamentary regions and the resin-impregnated Nb 3 Sn Rutherford cables. With the whole magnet under preload, cool-down, and Lorentz forces, the nonlinear behavior of the Nb 3 Sn coil was simulated, in three orthongonal axes, by the combined properties of the gaskets (insulation layers) and Hill materials (resin-impreganted cable). The procedure entailes minimal material assumptions because it incorporated measured stress-strain curves in the analysis. The coil was simulated to a high level of detail consisting of the insulation layers and resin-impregnated cables. The computed compressive azimuthal stresses of the cables were used to assess the stress-induced performance degradation. Through submodeling, the area-weighted average axial strains of the strands were computed and employed to evaluate the strain-induced performance degradation. The overall performance degradation of the Nb 3 Sn coil was thus obtained, and this information was subsequently used to guide the design of the overall magnet. Besides Nb 3 Sn magnets, this procedure can also be employed in the design of LTS, HTS, and room temperature magnets or in any composite structures.
Redshift space power spectrum beyond Einstein-de Sitter kernels
Abstract We develop a framework to compute the redshift space power spectrum (PS), with kernels beyond Einstein-de Sitter (EdS), that can be applied to a wide variety of generalized cosmologies. We build upon a formalism that was recently employed for standard cosmology in Chen, Vlah & White (2020), and utilize an expansion of the density-weighted velocity moment generating function that explicitly separates the magnitude of the k-modes and their angle to the line-of-sight direction dependencies. We compute the PS for matter and biased tracers to 1-loop Perturbation Theory (PT) and show that the expansion has a correct infrared and ultraviolet behavior, free of unwanted divergences. We also add Effective Field Theory (EFT) counterterms, necessary to account for small-scale contributions to PT, and employ an IR-resummation prescription to properly model the smearing of the BAO due to large scale bulk flows within Standard-PT. To demonstrate the applicability of our formalism, we apply it on the ΛCDM and the Hu-Sawicki f(R) models, and compare our numerical results against the elephant suite of N-body simulations, finding very good agreement up to k = 0.27 Mpc -1 h at z = 0.5 for the first three non-vanishing Legendre multipoles of the PS. To our knowledge, the model presented in this work is the most accurate theoretical EFT-PT for modified gravity to date, being the only one that accounts for beyond linear local biasing in redshift-space. Hence, we argue our RSD modeling is a promising tool to construct theoretical templates in order to test deviations from ΛCDM using real data obtained from the next stage of cosmological surveys such as DESI and LSST.
Realistic inflation in no-scale U(1) R symmetric flipped SU(5)
We have realized non-minimal Higgs inflation and standard hybrid inflation in the supersymmetric flipped SU(5) model with U(1) R symmetry using the no-scale form of the Kähler potential. In non-minimal Higgs inflation the waterfall Higgs field plays the role of inflaton, and in standard hybrid inflation the gauge singlet field S is employed as an inflaton. The predictions of both models are in good agreement with the Planck 2018 data. For numerical calculations we have fixed the gauge symmetry breaking scale, M, around 2 × 10 16 GeV. In both models the inflaton field values are constrained below m P . The tensor to scalar ratio r in non-minimal inflation is of the order of 10 -3 and for standard hybrid inflation r is tiny, of order 10 -15 –10 -4 . The scalar spectral index in both cases lie within the Planck 1-σ bounds, and the running of the scalar spectral index lies in the range, -dn s /d ln k ~ 6 × 10 -4 for non-minimal model and 10 -9 –10 -3 for the standard hybrid model. A realistic scenario of reheating and non-thermal leptogenesis is employed with reheat temperature T r ~ 10 9 GeV for non-minimal model and 10 6 –10 10 GeV for standard hybrid model. The R-symmetry plays a vital role in forbidding rapid proton decay, but at the same time it also suppresses terms responsible for generating right handed neutrino masses. A realistic scenario of right handed neutrino masses is obtained by considering effective R symmetry breaking at the nonrenormalizable level with adequate suppression of rapid proton decay.
Current profile and normalized beta control via feedback linearization and Lyapunov techniques
Abstract Simultaneous control of the current profile and normalized plasma beta is an essential control problem in the development of advanced tokamak scenarios. However, this control problem is especially challenging due to the nonlinear nature of the current, heat, and particle transport dynamics, as well as the difficulty to understand and accurately model such processes. In this work, a nonlinear, robust, model-based controller for the simultaneous regulation of the current profile and normalized beta has been designed using feedback linearization and Lyapunov redesign techniques. Feedback linearization avoids approximate linearization of the plasma dynamics, retaining the original physics content of the model. Moreover, the use of Lyapunov redesign techniques makes the controller robust against the uncertainties arising during the modeling process. The controller’s performance in the presence of unknown dynamics is tested in nonlinear, one-dimensional simulations using the Control Oriented Transport SIMulator (COTSIM) code, which employs plasma models that are significantly more complex than those employed for control synthesis.
Energetic particle marginal stability profile for HL-2M integrated simulation based on neural network module
Abstract A critical gradient model is employed to develop a module of energetic particle (EP) marginal stability profiles in OMFIT integrated simulations for studying EP transport. Currently, each iteration of transport evolution is approximately 10 min in the integrated simulation, whereas, the EP marginal stability profile, which serves as an input in the integrated simulation could take much longer; the reason being a combination of the TGLFEP and EPtran codes is employed in our previous investigation. To reduce the simulation time, the critical gradient is predicted by a neural network instead of the TGLFEP code, and the EPtran code is revised with parallel computing, so that the running time of this module can be controlled to within 5 min. The predictions are in good agreement with previous approaches. The integrated simulation of HL-2M with Alfven eigenmodes transported by neutral beam EP profiles indicates that EP transport reduces the total pressure and current as expected, but could also under some conditions raise the safety factor in the core, which is favorable for reversed magnetic shear and high-performance plasmas.
Recent advances in plasma control and physics research in the Large Helical Device
The Large Helical Device (LHD), the largest superconducting helical system in the world, is equipped with advanced heating and diagnostic tools, facilitating plasma control and physics research. Data assimilation was employed for electron temperature control using a real-time Thomson scattering system and real time prediction code. A virtual LHD environment enabled visualization of escaping high-energy tritium ions and demonstrated that these ions impact the rear side of the divertor plate. Pioneering results crucial to plasma control have also been achieved. Real-time wall conditioning using Lithium granule dropping improved bulk ion energy and particle transport while simultaneously enhancing the heavy impurity transport. Progress has also been made in the investigation of turbulence-driven transport. At the confinement bifurcation, ion-scale turbulence decreased, while electron-scale turbulence increased. A change in the anisotropy of turbulent eddies was also observed at the confinement bifurcation. Coexistence of local and non-local turbulence was identified in electron-scale turbulence. Non-local turbulence exhibited the rapid spatial propagation of perturbations throughout the plasma, while local turbulence followed the temperature gradient. A transition between drift-wave turbulence and magnetohydrodynamics (MHD) turbulence was observed with the turbulence minimized at the transition condition. Machine learning analysis was employed to evaluate the temperate and density conditions of this turbulence transition. Then, real-time control of fueling and heating was applied to maintain the turbulence transition condition, improving the energy confinement enhancement factor by 20%. In addition, evidence was obtained for collisionless ion heating by energetic-ion-driven geodesic acoustic modes and MHD bursts. These achievements represent unique contributions to the development of fusion reactors.
Reconstruction and Measurement of $\mathcal{O}$(100) MeV Energy Electromagnetic Activity from $\pi^0 \rightarrow \gamma\gamma$ Decays in the MicroBooNE LArTPC
We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νμ interactions with final state π0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of &calO;(100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant π0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of νμ + Ar → μ + π0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of π0 kinematics.
Landau Damping with Electron Lenses in Space-Charge Dominated Beams
Further progress of fundamental physics requires accelerated beams of high intensity. The intensities, however, are limited by many factors, including coherent beam instabilities. Usual methods to control the instabilities, such as octupole magnets, beam feedback dampers and employment of chromatic effects, may be ineffective or insufficient. In this study, electron lenses were proposed as a means to provide stabilizing spread in the beam betatron frequencies. It was shown that electron lenses are uniquely effective for Landau damping of transverse beam instabilities in high energy particle accelerators, and that their employment does not compromise incoherent (single particle) stability, dynamic aperture and the beam lifetime. Here we consider effectiveness of the Landau damping with electron lenses when the space charge tune shift cannot be neglected. We demonstrate that the desired stability can be assured with proper choice of the electron beam parameters and current distributions.
Development and construction status of the Mu2e electromagnetic calorimeter mechanical structures
Abstract The Mu2e experiment at Fermilab will search for the CLFV neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the evidence of physics beyond the Standard Model. Mu2e comprises a straw-tracker, an electromagnetic calorimeter and an external veto for cosmic rays. The calorimeter provides electron identification, a fast trigger and aids track reconstruction. It is a state-of-the-art crystal calorimeter and employs 1340 pure CsI crystals readout by UV-extended SiPM and fast electronics. The design consists of two identical annular disks positioned at the relative distance of 70 cm downstream the target.The hostile Mu2e conditions (total ionizing dose of 12 krad and a neutron fluence of 5 × 10 10 n/cm 2 @ 1 MeVeq (Si)/y, 1 T magnetic field and vacuum level of 10 −4 Torr) posed tight constraints on the mechanical structures and materials choice. The support structure of the two crystal matrices employs two aluminum hollow rings and parts made of open-cell vacuum-compatible carbon fiber. SiPMs and front-end electronics for each crystal are assembled in one mechanical unit inserted in a machined copper holder. The units are supported by a plate made of vacuum-compatible material. The plate integrates the cooling system made of a network of copper lines flowing a low temperature fluid and placed in thermal contact with the copper holders. The DAQ is hosted in aluminum crates positioned on the lateral surface of the disks. The crates also integrate the DAQ electronics cooling system. We review the constraints on the calorimeter structures design, the development of all the structural components, including the simulations that have determined the materials and technological choices and the specifications of the cooling station, components production and quality assurance tests, the procedures for detector assembly, transportation and installation in the experimental area.
Characterization of a SiPM-based monolithic neutron scatter camera using dark counts
The Single Volume Scatter Camera (SVSC) Collaboration aims to develop portable neutron imaging systems for a variety of applications in nuclear non-proliferation. Conventional double-scatter neutron imagers are composed of several separate detector volumes organized in at least two planes. A neutron must scatter in two of these detector volumes for its initial trajectory to be reconstructed. As such, these systems typically have a large footprint and poor geometric efficiency. We report on the design and characterization of a prototype monolithic neutron scatter camera that is intended to significantly improve upon the geometrical shortcomings of conventional neutron cameras. The detector consists of a 50 mm×56 mm× 60 mm monolithic block of EJ-204 plastic scintillator instrumented on two faces with arrays of 64 Hamamatsu S13360-6075PE silicon photomultipliers (SiPMs). The electronic crosstalk is limited to < 5% between adjacent channels and < 0.1% between all other channel pairs. SiPMs introduce a significantly elevated dark count rate over PMTs, as well as correlated noise from after-pulsing and optical crosstalk. In this article, we characterize the dark count rate and optical crosstalk and present a modified event reconstruction likelihood function that accounts for them. We find that the average dark count rate per SiPM is 4.3 MHz with a standard deviation of 1.5 MHz among devices. The analysis method we employ to measure internal optical crosstalk also naturally yields the mean and width of the single-electron pulse height. Here, we calculate separate contributions to the width of the single-electron pulse-height from electronic noise and avalanche fluctuations. We demonstrate a timing resolution for a single-photon pulse to be (128 ± 4) ps. Finally, coincidence analysis is employed to measure external (pixel-to-pixel) optical crosstalk. We present a map of the average external crosstalk probability between 2×4 groups of SiPMs, as well as the in-situ timing characteristics extracted from the coincidence analysis. Further work is needed to characterize the performance of the camera at reconstructing single- and double-site interactions, as well as image reconstruction.
The DUNE Far Detector Vertical Drift Technology. Technical Design Report
DUNE is an international experiment dedicated to addressing some of the questions at the forefront of particle physics and astrophysics, including the mystifying preponderance of matter over antimatter in the early universe. The dual-site experiment will employ an intense neutrino beam focused on a near and a far detector as it aims to determine the neutrino mass hierarchy and to make high-precision measurements of the PMNS matrix parameters, including the CP-violating phase. It will also stand ready to observe supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector implements liquid argon time-projection chamber (LArTPC) technology, and combines the many tens-of-kiloton fiducial mass necessary for rare event searches with the sub-centimeter spatial resolution required to image those events with high precision. The addition of a photon detection system enhances physics capabilities for all DUNE physics drivers and opens prospects for further physics explorations. Given its size, the far detector will be implemented as a set of modules, with LArTPC designs that differ from one another as newer technologies arise. In the vertical drift LArTPC design, a horizontal cathode bisects the detector, creating two stacked drift volumes in which ionization charges drift towards anodes at either the top or bottom. The anodes are composed of perforated PCB layers with conductive strips, enabling reconstruction in 3D. Light-trap-style photon detection modules are placed both on the cryostat's side walls and on the central cathode where they are optically powered. This Technical Design Report describes in detail the technical implementations of each subsystem of this LArTPC that, together with the other far detector modules and the near detector, will enable DUNE to achieve its physics goals.
Estimated impacts of forest restoration scenarios on smoke exposures among outdoor agricultural workers in California
Abstract As wildfires continue to worsen across western United States, forest managers are increasingly employing prescribed burns as a way to reduce excess fuels and future wildfire risk. While the ecological benefits of these fuel treatments are clear, little is known about the smoke exposure tradeoffs of using prescribed burns to mitigate wildfires, particularly among at-risk populations. Outdoor agricultural workers are a population at increased risk of smoke exposure because of their time spent outside and the physical demands of their work. Here, we assess the smoke exposure impacts among outdoor agricultural workers resulting from the implementation of six forest management scenarios proposed for a landscape in the Central Sierra, California. We leverage emissions estimates from LANDIS-II to model daily PM 2.5 concentrations with the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and link those to agricultural employment data from the Bureau of Labor Statistics. We find a u-shaped result, in that moderate amounts of prescribed burning result in the greatest reduction in total smoke exposure among outdoor agricultural workers, particularly during months of peak agricultural activity due to wildfire-specific smoke reductions. The reduction in total smoke exposure, relative to scenarios with the least amount of management, decreases as more prescribed burning is applied to the landscape due to the contributions of the fuel treatments themselves to overall smoke burden. The results of this analysis may contribute to preparedness efforts aimed at reducing smoke exposures among outdoor agricultural workers, while also informing forest management planning for this specific landscape.
The role of hydrogen as long-duration energy storage and as an international energy carrier for electricity sector decarbonization
With countries and economies around the globe increasingly relying on non-dispatchable variable renewable energy (VRE), the need for effective energy storage and international carriers of low-carbon energy has intensified. This study delves into hydrogen's prospective, multifaceted contribution to decarbonizing the electricity sector, with emphasis on its utilization as a scalable technology for long-duration energy storage and as an international energy carrier. Using Japan as a case study, based on its ambitious national hydrogen strategy and plans to import liquefied hydrogen as a low-carbon fuel source, we employ advanced models encompassing capacity expansion and hourly dispatch. We explore diverse policy scenarios to unravel the timing, quantity, and operational intricacies of hydrogen deployment within a power system. Our findings highlight the essential role of hydrogen in providing a reliable power supply by balancing mismatches in VRE generation and load over several weeks and months and reducing the costs of achieving a zero-emission power system. The study recommends prioritizing domestically produced hydrogen, leveraging renewables for cost reduction, and strategically employing imported hydrogen as a risk hedge against potential spikes in battery storage and renewable energy costs. Furthermore, the strategic incorporation of hydrogen mitigates system costs and enhances energy self-sufficiency, informing policy design and investment strategies aligned with the dynamic global energy landscape.
Excitons in strained and suspended monolayer WSe 2
Here, we study suspended membranes of atomically thin WSe 2 as hosts of excitons. We perform optical reflectance measurements to probe the exciton physics and obtain the peak energies for the 1$s$, 2$s$, and 3$s$ states of the $A$ exciton in suspended WSe 2 and consider supported membranes as a reference. We find that elimination of the influence of the dielectric environment enables a strong electron–hole interaction and a concomitant increase in the exciton binding energy in suspended monolayer (1L) WSe 2 . Based on the experimental results, we calculate the excitonic binding energies by employing the recently developed quantum electrostatic heterostructure model and the commonly employed Rytova–Keldysh potential model. We see that the binding energy of the ground state $A$ exciton increases from about 0.3 eV (on a substrate) to above 0.4 eV (suspended). We also exploit the tunability of the excitons in suspended samples via mechanical strain. By applying external gas pressure of 2.72 atm to a 1L suspended over a circular hole of 8 μm diameter, we strain the WSe 2 and obtain a reversible 0.15 eV redshift in the exciton resonance. The linewidth of the $A$ exciton decreases by more than half, from about 50 to 20 meV under 1.5% biaxial strain at room temperature. This line narrowing is due to the suppression of intervalley exciton–phonon scattering. By making use of the observed strain-dependent optical signatures, we infer the two-dimensional (2D) elastic moduli of 1L and 2L WSe 2 . Our results exemplify the use of suspended 2D materials as novel systems for fundamental studies, as well as for strong and dynamic tuning of their optical properties.
Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing
To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.
Network science can improve the sustainable development of solar energy
Abstract The recent emergence of agrivoltaic and ecovoltaic approaches to ground-mounted photovoltaic (PV) energy provides a much-needed alternative to the environmentally disruptive practices employed in utility-scale solar development. Research on such land-sharing approaches has grown rapidly, with an emphasis on characterizing how PV arrays impact ecosystem processes and agricultural productivity. Although these studies have done well to quantify a variety of dual-use solar practices by employing site-specific sampling designs, this approach has limited our ability to synthesize results across sites, regions, and globally. We call for a network science approach for improved cross-site synthesis of dual-use solar research. We contend that a common approach for data collection and synthesis will facilitate a more rigorous investigation of the agricultural and ecological impacts of PV development across space and over time. The products of this scientifically informed approach can be directly applied to improve sustainable land management.
Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes
In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They can transform the solving of general nonlinear PDEs into solving quadratic optimization problems with nonlinear, PDE-induced constraints. However, the complexity bottleneck lies in computing with dense kernel matrices obtained from pointwise evaluations of the covariance kernel, and its partial derivatives, a result of the PDE constraint and for which fast algorithms are scarce. The primary goal of this paper is to provide a near-linear complexity algorithm for working with such kernel matrices. We present a sparse Cholesky factorization algorithm for these matrices based on the near-sparsity of the Cholesky factor under a novel ordering of pointwise and derivative measurements. The near-sparsity is rigorously justified by directly connecting the factor to GP regression and exponential decay of basis functions in numerical homogenization. We then employ the Vecchia approximation of GPs, which is optimal in the Kullback-Leibler divergence, to compute the approximate factor. This enables us to compute ϵ-approximate inverse Cholesky factors of the kernel matrices with complexity O(N log d (N/ϵ)) in space and O(N log 2d (N/ϵ)) in time. We integrate sparse Cholesky factorizations into optimization algorithms to obtain fast solvers of the nonlinear PDE. We numerically illustrate our algorithm’s near-linear space/time complexity for a broad class of nonlinear PDEs such as the nonlinear elliptic, Burgers, and Monge-Ampère equations. In summary, we provide a fast, scalable, and accurate method for solving general PDEs with GPs and kernel methods.