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Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory simulations without appreciably sacrificing accuracy of material property prediction.
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The vibration isolation problem is formulated as a 1D kinematic problem. The geometry of the stochastic wall trajectories arising from the stroke constraint is defined in terms of their significant extrema. An optimal control solution for the minimum acceleration return path determines a lower bound on platform mean square acceleration. This bound is expressed in terms of the probability density function on the significant maxima and the conditional fourth moment of the first passage time inverse. The first of these is found analytically while the second is found using a Monte Carlo simulation. The rms acceleration lower bound as a function of available space is then determined through numerical quadrature.
Abstract We report on the results of point-projection ion deflectometry measurements from a mid-size university z-pinch experiment. A 1 MA 8 kJ LTD generator at the University of Michigan (called MAIZE) drove a hybrid x-pinch (HXP) with a deuterated polyethylene fiber load to produce a point-like source of MeV ions for backlighting. In these experiments, 2.7 MeV protons were generated by DD beam-target fusion reactions. Due to the kinematics of beam-target fusion, the proton energies were down-shifted from the more standard 3.02 MeV proton energy that is released from the center-of-mass rest frame of a DD reaction. In addition to the 2.7 MeV protons, strongly anisotropic beams of 3 MeV accelerated deuterons were detected by ion diagnostics placed at a radial distance of 90 mm from the x-pinch. Numerical reconstruction of experimental data generated by deflected hydrogen ion trajectories evaluated the total current in the vacuum load region. Numerical ion-tracking simulations show that accelerated deuteron beams exited the ion source region at large angles with respect to the pinch current direction.
The thermo-mechanical analysis and the mechanical design of the accelerator of MITICA (i.e. the full size prototype of the ITER neutral beam injector under construction at RFX [1]) are based on the calculation of the power deposition induced by particle impacts. This calculation is performed by EAMCC [2], a relativistic particle tracking code based on the Monte-Carlo method for describing collisions inside the accelerator, under prescribed electric and magnetic fields. The magnetic field maps are produced by 3D codes, while the electric field maps come from the 2D axi-symmetric code SLACCAD [3]. In order to perform a multi-beamlet analysis, which allows to take into account the beamlet-beamlet repulsion, and to consider other effects neglected under the hypothesis of axi-symmetric beam, a fully 3D version of the code is required. In this paper, a modified version of EAMCC, fully 3D, capable of modifying the mesh of the 3D maps and of dealing with uneven meshes is presented. A finer mesh is used just in the regions where a more detailed description of the fields is required, for a more realistic simulation. A comparison between the original code and the modified version is presented at first, as a validation of the modifications introduced in the latter. Subsequently, the main results of a single-beamlet analysis performed with the two versions of the code are shown and the differences between the 2D and the 3D simulations discussed. The last part is dedicated to the multi-beamlet simulation of the accelerator.
Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.
Recent analysis of the Spitzer Photometry and Accurate Rotation Curve (SPARC) galaxy sample found a surprisingly tight relation between the radial acceleration inferred from the rotation curves and the acceleration due to the baryonic components of the disk. It has been suggested that this relation may be evidence for new physics, beyond Λ CDM . In this Letter, we show that 32 galaxies from the MUGS2 match the SPARC acceleration relation. These cosmological simulations of star-forming, rotationally supported disks were simulated with a WMAP3 Λ CDM cosmology, and match the SPARC acceleration relation with less scatter than the observational data. These results show that this acceleration relation is a consequence of dissipative collapse of baryons, rather than being evidence for exotic dark-sector physics or new dynamical laws.
Sandia National Laboratories (SNL) is assisting Jet Propulsion Laboratory in undertaking feasibility studies and performance assessments for the Planetary Protection aspect of the Europa Lander mission. The specific areas of interest for this project are described by task number. This white paper presents the evaluation results for Task 2, Radiation Testing, which was stated as follows: Survey SNL facilities and capabilities for simulating the Europan radiation environment and assess suitability for: A. Testing batteries, electronics, and other component and subsystems B. Exposing biological organisms to assess their survivability metrics. The radiation environment the Europa Lander will encounter on route and in orbit upon arrival at its destination consists primarily of charged particles, energetic protons and electrons with the energies up to 1 GeV. The charged particle environments can be simulated using the accelerators at the Ion Beam Laboratory. The Gamma Irradiation Facility and its annex, the Low Dose Rate Irradiation Facility, offer irradiations using Co-60 gamma sources (1.17 and 1.33 MeV), as well as Cs-137 gamma (0.661 MeV) AmBe neutron (0-10 MeV) sources.
Particle-in-cell simulations of laser- ion acceleration demonstrate marked discrepancies in the acceleration experienced by the different ion species in complex target compositions, especially when the target becomes relativistically transparent to the pulse during irradiation. Beginning with proton contaminants in a carbon target, we show how the higher charge-to-mass ratio of the protons results in species stratification and late-time suppression of the carbon acceleration. The target normal sheath acceleration (TNSA) primarily experienced by the protons can be exploited to mitigate this tamping by using a shaped rear surface of the target, leaving the break-out afterburner-driven carbons to accelerate close to the laser axis and then experience less tamping during a late-time TNSA phase. Here, we then explore preferentially accelerating the lighter species in a mixed composition target, particularly focusing on deuteron beam applications. We examine three different target compositions with the same areal electron density, CD 2 , CH 2 , and 7 LiD, and propose an alternative shaping of the rear surface of 7 LiD to increase the number of high-energy deuterons in the beam.
Projection based dimensionality reduction is used to approximate the full discretiza tion of a PDE problem in order to reduce the cost of its numerical simulation while staying faithful to the full dynamics. For nonlinear problems, hyperreduction methods are additionally needed to remove the dependence of the nonlinear terms on the full problem’s size. In this project, we develop an energy conserving hyperreduction method and apply it to Eulerian-Lagrangian hydrodynamics problems, demonstrating that the reduced model successfully accelerates the numerical simulations while conserving the problem’s total energy with high accuracy.
Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.
We perform simulations for future Cherenkov Telescope Array (CTA) observations of RX J1713.7−3946, a young supernova remnant (SNR) and one of the brightest sources ever discovered in very high energy (VHE) gamma rays. Special attention is paid to exploring possible spatial (anti)correlations of gamma rays with emission at other wavelengths, in particular X-rays and CO/H i emission. We present a series of simulated images of RX J1713.7−3946 for CTA based on a set of observationally motivated models for the gamma-ray emission. In these models, VHE gamma rays produced by high-energy electrons are assumed to trace the nonthermal X-ray emission observed by XMM-Newton , whereas those originating from relativistic protons delineate the local gas distributions. The local atomic and molecular gas distributions are deduced by the NANTEN team from CO and H i observations. Our primary goal is to show how one can distinguish the emission mechanism(s) of the gamma rays (i.e., hadronic versus leptonic, or a mixture of the two) through information provided by their spatial distribution, spectra, and time variation. This work is the first attempt to quantitatively evaluate the capabilities of CTA to achieve various proposed scientific goals by observing this important cosmic particle accelerator.