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To present a Monte Carlo (MC) beam model and its implementation in a clinical treatment planning system (TPS, Varian Eclipse) for a modified ultrahigh dose-rate electron FLASH radiation therapy (eFLASH-RT) linear accelerator (LINAC) using clinical accessories and geometry.
Under the U.S. Department of Energy Office of Nuclear Energy’s Advanced Modeling and Simulation (NEAMS) Program, an integrated multiphysics approach is being developed to model the core bowing phenomena important to liquid metal-cooled fast reactors. Core bowing is an important passive safety mechanism whereby increased power (which leads to temperature and flux gradients) influences the core to bow into less reactive configurations when the restraint system is properly designed. The phenomenon includes a complex interplay of radiation transport, duct temperature calculations involving fluid flow and heat transfer, and thermo-mechanical responses to the induced temperature and flux gradients. Structural material properties are also important to determining inelastic response to longer term flux gradients which cause irradiation creep and swelling. While core bowing provides a strong negative reactivity feedback when the restraint system is designed properly, it also results in additional forces between assemblies which increase the loads required to extricate them during refueling or control rod movement. Therefore, the restraint system must be designed with these tradeoffs in mind. The first stage of the work, which commenced in FY21 and continues through FY22, assesses thermo-mechanical modeling tools for producing core bowing predictions consistent with conventional tools. The Multiphysics Object Oriented Simulation Environment (MOOSE) Tensor Mechanics and Contact Modules are employed. This status report describes work on additional thermo-mechanical benchmark verification problems with increased complexity from the examples demonstrated in FY21. Several benchmark verification examples were selected from the IAEA verification and validation report. These examples involve clusters of ducts representative of a sector of a hexagonal reactor core which bow into each other and cause contact and load pad elevations, as well as single ducts subjected to irradiation fields undergoing swelling and subsequent bowing. The MOOSE-based results were compared to both IAEA benchmark participants’ results, analytic equations as available, and NUBOW-3D, a beam model code developed by Argonne National Laboratory. In every case, the MOOSE results agreed with other simulations results, providing additional verification basis of the tools for this particular physics application.
Herein, this paper describes modifications to the Matrix Power Control Algorithm (MPCA) to improve convergence for Random Vibration Control (RVC) testing. In particular, this paper presents Multiple-Input Multiple-Output (MIMO) implementations of MPCA in simulation and experiment. An Euler–Bernoulli beam model was simulated with applied base excitations and the Box Assembly with Removable Component (BARC) was used in experiment to validate results. The Bayesian optimization package Dragonfly was used to optimize control parameters. Additionally, a moving-average was employed and optimized to improve the measured response feedback for MPCA, reduce the number of averages needed to be taken between control updates, and further improve convergence. The key results of this paper show that the performance of MPCA can be improved by tuning the control parameters and by applying an optimized moving-average. Furthermore, it is demonstrated that convergence can be achieved within 12 drive-frames, which greatly enhances vibration control capability.
The phenomenon of focusing of microwave beams in a plasma near a turning-point caustic is discussed by exploiting the analytical solution to the Gaussian beam-tracing equations in the two-dimensional (2-D) linear-layer problem. The location of maximum beam focusing and the beam width at that location are studied in terms of the beam initial conditions. This focusing must be taken into account to interpret Doppler backscattering (DBS) measurements. We find that the filter function that characterises the scattering intensity contribution along the beam path through the plasma is inversely proportional to the beam width, predicting enhanced scattering from the beam focusing region. We show that the DBS signal enhancement for decreasing incident angles between the beam path and the density gradient is due to beam focusing and not due to forward scattering, as was originally proposed by (Gusakov et al., (Plasma Phys. Contr. Fusion, vol. 56, 2014, p. 0250092014, 2017); Plasma Phys. Rep. vol. 43(6), 2017, pp. 605–613). The analytic beam model is used to predict the measurement of the k y density-fluctuation wavenumber power spectrum via DBS, showing that, in an NSTX-inspired example, the spectral exponent of the turbulent, intermediate-to-high k y density-fluctuation spectrum might be quantitatively measurable via DBS, but not the spectral peak corresponding to the driving scale of the turbulent cascade.
En-route charging infrastructure for electric vehicles is critical to support transportation needs. These charging stations are likely to have high loads and especially sharp peak loads given fast charging capabilities needed to meet transportation schedules. In order to reduce both strain on distribution grid infrastructure and charging station operational costs, many stations are likely to employ behind the meter storage. This paper demonstrates a behind the meter storage sizing optimization that employs an open-source agent-based vehicle behavior model (BEAM) to determine the best sizing across many scenarios. This optimization and analysis is novel in that it examines how storage size impacts not only charging station cost and peak load, but also vehicle queue times. The optimization is also applied across a wide analysis region with sufficient diversity and numbers to provide novel statistical analysis of optimal sizes.
The intrabeam scattering is an important collective effect that can deteriorate the property of a high-intensity beam and electron cooling is a method to mitigate the IBS effect. JSPEC (JLab Simulation Package on Electron Cooling) is an open-source C++ program developed at Jefferson Lab, which simulates the evolution of the ion beam under the IBS and/or the electron cooling effect. The Python wrapper of the C++ code, pyJSPEC, for Python 3.x environment has been recently developed and released. It allows the users to run JSPEC simulations in a Python environment. It also makes it possible for JSPEC to collaborate with other accelerator and beam modeling programs as well as plentiful python tools in data visualization, optimization, machine learning, etc. In this paper, we will introduce the features of pyJSPEC and demonstrate how to use it with sample codes and numerical results.
Modern cosmic microwave background (CMB) analysis pipelines regularly employ complex time-domain filters, beam models, masking, and other techniques during the production of sky maps and their corresponding angular power spectra. However, these processes can generate couplings between multipoles from the same spectrum and from different spectra, in addition to the typical power attenuation. Within the context of pseudo-C ℓ based, MASTER-style analyses, the net effect of the time-domain filtering is commonly approximated by a multiplicative transfer function, F ℓ , that can fail to capture mode mixing and is dependent on the spectrum of the signal. To address these shortcomings, we have developed a simulation-based spectral correction approach that constructs a two-dimensional transfer matrix, ${J}_{{\ell }{\ell }^{\prime} }$, which contains information about mode mixing in addition to mode attenuation. We demonstrate the application of this approach on data from the first flight of the Spider balloon-borne CMB experiment.
Broadband lasers have the potential to mitigate cross-beam energy transfer (CBET) in direct-drive inertial confinement fusion (ICF) experiments. Furthermore, a quantitative assessment of the bandwidth required for CBET mitigation necessitates the development of broadband ray-based CBET models that can be implemented in the radiation-hydrodynamic codes that are used to design ICF experiments. Two different approaches to broadband ray-based CBET modeling (discrete and fixed spectrum) are developed and compared to wave-based calculations. Both approaches give good agreement with wave-based calculations in ICF-relevant configurations. Full-scale 3D calculations show that the bandwidth required for adequate CBET mitigation increases with increasing scale and drive intensity.
Together with Realta Fusion we developed advanced simulations using WarpX for magnetic confinement fusion devices and advanced neutral beam systems for plasma heating.
Together with Realta Fusion, we set up several simulation scenarios and ran simulations using the WarpX code which is developed by LBNL and especially by members of the LBNL team. We investigated beam transport and beam neutralization and ran optimization and benchmark simulations. To achieve this, several new features were added to the warpX code. These code additions will also be beneficial to other users of warpX including several fusion companies and researchers at National Laboratories. Furthermore, we supported Realta Fusion in setting up their own instances of WarpX. We presented our results at the American Physical Society Division of Plasma Physics meeting and are in the process of writing a publication that will be submitted to a peer-reviewed journal to share our results with the broader community.
Plasmas generated using energetic electron beams are well known for their low electron temperature (T e ) and plasma potential, which makes them attractive for atomic-precision plasma processing applications such as atomic layer etch and deposition. A 2-dimensional particle-in-cell model for an electron beam-generated plasma in argon confined by a constant applied magnetic field is described here in this article. Plasma production primarily occurs in the path of the beam electrons in the center of the chamber. The resulting plasma spreads out in the chamber through non-ambipolar diffusion with a short-circuiting effect allowing unequal electron and ion fluxes to different regions of the bounding conductive chamber walls. The cross-field transport of the electrons (and thus the steady-state characteristics of the plasma) are strongly impacted by the magnetic field. T e is anisotropic in the electron beam region, but low and isotropic away from the plasma production zone. The plasma density increases and the plasma becomes more confined near the region of production when the magnetic field strengthens. The magnetic field reduces both electron physical and energy transport perpendicular to the magnetic field. T e is uniform along the magnetic field lines and slowly decreases perpendicular to it. Electrons are less energetic in the sheath regions where the sheath electric field repels and confines the low-energy electrons from the bulk plasma. Even though electron and ion densities are similar in the bulk plasma due to quasi-neutrality, electron and ion fluxes on the grounded chamber walls are unequal at most locations. Electron confinement by the magnetic field weakens with increasing pressure, and the plasma spread out farther from the electron beam region.
The SNS beam test facility is a model of the SNS front end (source through medium-energy transport). On-going work at the BTF focuses on accurate modeling of the beam distribution to enable the prediction of halo losses (>100 parts per million). This presentation will discuss the latest progress towards this goal, including recent results after a reconfiguration of the test beamline. Good agreement within the 90% beam core is shown for a 30 mA beam at 2.5 MeV.
The neutral beam injection (NBI) system in EAST produces energetic neutral particles, which collide with electrons and ions in tokamak plasmas and heat the plasmas through Coulomb collisions. Moreover, it drives a non-inductive source of current, due to the charge-exchange collision between neutral particles and ions, and injects toroidal torque, which generates a toroidal rotation of the plasma. The effect caused by the NBI system, such as plasma heating, current drive, total neutron rate, momentum transfer, and shine-through, are modeled by a comprehensive module called NUBEAM. However, NUBEAM is computationally intensive since it relies on Monte Carlo methods. In this work, a neural network model has been developed as a surrogate model for NUBEAM in EAST. The database for neural-network model training, validation and testing is generated by running TRANSP for experimental discharges from recent EAST campaigns (after the latest NBI upgrade) while using the NUBEAM module. Simulation results illustrate that the trained neural network has the capability of replicating the predictions made by NUBEAM while demanding a significantly shorter execution time. Finally, these results indicate that surrogate models like the one proposed in this work could enable fast transport simulations for EAST after integrating them into a control-oriented predictive code such as COTSIM.
Recent experimental studies suggest the use of spatially extended laser beam profiles as a strategy to control the melt pool during laser powder bed fusion (LPBF) additive manufacturing. However, linkages connecting laser beam profiles to thermal fields and resultant microstructures have not been established. Herein, we employ a coupled thermal transport-Monte Carlo model to predict the evolution of temperature fields and grain microstructures during LPBF using Gaussian, ring, and Bessel beam profiles. In addition, simulation results reveal that the ring-shaped beam yields lower temperatures compared with the Gaussian beam. Owing to the small melt pool size when using the Bessel beam, the grains are smaller in size and more equiaxed compared to those using the Gaussian and ring beams. Our approach provides future avenues to predict the impact of laser beam shaping on microstructure development during LPBF.
Solenoid scans are often used to determine the beam emittance of an intense relativistic electron beam (IREB). In a solenoid scan, the beam size is measured at a location downstream of the solenoid as a function of the strength of the solenoid magnetic field. The emittance is then inferred using a beam optics model of the transport of the beam through the solenoid magnet to the imaging location. This requires precise knowledge of the field strength and shape as a function of the current supplied to the solenoid. The beam optics model for an IREB needs to include an accurate model of the magnetic field. Unfortunately, most beam optics models do not include the effect of spherical aberrations which are inherent in a solenoid magnet. Spherical aberrations are manifested as an essentially quadratic increase in the solenoid magnetic field as a function of radius. One consequence of this is that the focal length will be dependent on the beam radius. This note examines the sensitivity of a typical solenoid scan performed on DARHT axis 2 to spherical aberrations. The sensitivity to spherical aberrations is examined as a function of the beam envelope size and the drift distance from the solenoid magnet to the imaging station.
The PIP-II superconducting linac at Fermilab is designed to deliver multi-megawatt proton beams for neutrino physics and other high-intensity applications. To expedite commissioning and enhance operational reliability, we have developed an EPICS-based data flow framework that seamlessly integrates digital twins (DT) with physical twins (PT). These digital twins comprise high-fidelity beam dynamics models or data-driven surrogate models connected to their physical counterparts through real-time diagnostics and advanced machine-learning algorithms.Central to this framework is Linac_Gen, an accelerated simulation tool that incorporates convolutional neural networks, random forests, and genetic algorithms to provide up to a tenfold speedup in optimizing the accelerator geometry model. An EPICS translator layer ensures interoperability by efficiently mapping lattice parameters across diverse simulation platforms.Our EPICS-based framework supports multiple operational modes—monitoring, passive learning, closed-loop control, and online learning—covering the entire machine lifecycle. By leveraging HPC resources and multi-objective optimization techniques, the digital twin enables adaptive trajectory correction, real-time fault detection, and predictive modeling of beam stability. This comprehensive approach paves the way for robust, high-intensity operation and data-driven accelerator R&D at Fermilab.
Abstract Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.
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