Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “accelerator simulation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Ion motion in the wake driven by long particle bunches in plasmas

In this paper, we explore the role of the background plasma ion motion in self-modulated plasma wakefield accelerators. We employ Dawson's plasma sheet model to derive expressions for the transverse plasma electric field and ponderomotive force in the narrow bunch limit. We use these results to determine the on-set of the ion dynamics and demonstrate that the ion motion could occur in self-modulated plasma wakefield accelerators. Simulations show the motion of the plasma ions can lead to the early suppression of the self-modulation instability and of the accelerating fields. The background plasma ion motion can nevertheless be fully mitigated by using plasmas with heavier plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Warp-X: A new exascale computing platform for beam–plasma simulations

Turning the current experimental plasma accelerator state-of-the-art from a promising technology into mainstream scientific tools depends critically on high-performance, high-fidelity modeling of complex processes that develop over a wide range of space and time scales. As part of the U.S. Department of Energy's Exascale Computing Project, a team from Lawrence Berkeley National Laboratory, in collaboration with teams from SLAC National Accelerator Laboratory and Lawrence Livermore National Laboratory, is developing a new plasma accelerator simulation tool that will harness the power of future exascale supercomputers for high-performance modeling of plasma accelerators. We present the various components of the codes such as the new Particle-In-Cell Scalable Application Resource (PICSAR) and the redesigned adaptive mesh refinement library AMReX, which are combined with redesigned elements of the Warp code, in the new WarpX software. Lastly, the code structure, status, early examples of applications and plans are discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

Monte Carlo calculations for reference dosimetry of electron beams with the PTW Roos and NE2571 ion chambers

Purpose: To investigate recommendations for reference dosimetry of electron beams and gradient effects for the NE2571 chamber and to provide beam quality conversion factors using Monte Carlo simulations of the PTW Roos and NE2571 ion chambers. Methods: The EGSnrc code system is used to calculate the absorbed dose-to-water and the dose to the gas in fully modeled ion chambers as a function of depth in water. Electron beams are modeled using realistic accelerator simulations as well as beams modeled as collimated point sources from realistic electron beam spectra or monoenergetic electrons. Beam quality conversion factors are calculated with ratios of the doses to water and to the air in the ion chamber in electron beams and a cobalt-60 reference field. The overall ion chamber correction factor is studied using calculations of water-to-air stopping power ratios. Results: The use of an effective point of measurement shift of 1.55 mm from the front face of the PTW Roos chamber, which places the point of measurement inside the chamber cavity, minimizes the difference betweenR{sub 50}, the beam quality specifier, calculated from chamber simulations compared to that obtained using depth-dose calculations in water. A similar shift minimizes the variation of the overall ion chamber correction factor with depth to the practical range and reduces the root-mean-square deviation of a fit to calculated beam quality conversion factors at the reference depth as a function of R{sub 50}. Similarly, an upstream shift of 0.34 r{sub cav} allows a more accurate determination of R{sub 50} from NE2571 chamber calculations and reduces the variation of the overall ion chamber correction factor with depth. The determination of the gradient correction using a shift of 0.22 r{sub cav} optimizes the root-mean-square deviation of a fit to calculated beam quality conversion factors if all beams investigated are considered. However, if only clinical beams are considered, a good fit to results for beam quality conversion factors is obtained without explicitly correcting for gradient effects. The inadequacy of R{sub 50} to uniquely specify beam quality for the accurate selection of k{sub Q} factors is discussed. Systematic uncertainties in beam quality conversion factors are analyzed for the NE2571 chamber and amount to between 0.4% and 1.2% depending on assumptions used. Conclusions: The calculated beam quality conversion factors for the PTW Roos chamber obtained here are in good agreement with literature data. These results characterize the use of an NE2571 ion chamber for reference dosimetry of electron beams even in low-energy beams.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Accelerating magnonic simulations with the pseudospectral Landau-Lifshitz equation

The pseudospectral Landau-Lifshitz (PS-LL) model can describe atomic-scale magnetic exchange interactions within a continuum framework. This is achieved by employing a convolution kernel that models the nonlocal interaction in a grid-independent manner. Even though the PS-LL was originally introduced to address atomic exchange, any nonlocal kernel can be modeled. In the field of magnonics, the dipole field is fundamental to describe the dispersion relation of magnons, the quasiparticle representation of angular momentum. Because dipole-dipole interactions are long-range, numerical approaches typically rely on convolutions. Here, we demonstrate that the PS-LL model can be used to perform magnonic simulations with a single convolution kernel derived from analytical solutions. We demonstrate a twofold increase in computational speed compared with the full dipole calculation. This approach is valid insofar as the excitations are linear, which is typically the case for magnons. Our results have the potential to accelerate magnonic research, particularly for the inverse design method, where several simulations must be performed to achieve the desired outcome.

Mathematics and computing↗
DOE OSTI2014

Feature-Based Analysis of Plasma-Based Particle Acceleration Data

Plasma-based particle accelerators can produce and sustain thousands of times stronger acceleration fields than conventional particle accelerators, providing a potential solution to the problem of the growing size and cost of conventional particle accelerators. To facilitate scientific knowledge discovery from the ever growing collections of accelerator simulation data generated by accelerator physicists to investigate next-generation plasma-based particle accelerator designs, we describe a novel approach for automatic detection and classification of particle beams and beam substructures due to temporal differences in the acceleration process, here called acceleration features. The automatic feature detection in combination with a novel visualization tool for fast, intuitive, query-based exploration of acceleration features enables an effective top-down data exploration process, starting from a high-level, feature-based view down to the level of individual particles. We describe the application of our analysis in practice to analyze simulations of single pulse and dual and triple colliding pulse accelerator designs, and to study the formation and evolution of particle beams, to compare substructures of a beam and to investigate transverse particle loss.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

From Cell to System: Accelerated hpc Simulations of BESS Aging under Frequency Regulation and Arbitrage use cases

Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.

Ayalasomayajula, Surya [ORNL] (ORCID:0009000860788↗
DOE OSTI2022

Measuring Critical Fuel Properties for Simulations to Accelerate Fuel Qualification

NREL's sustainable aviation fuel (SAF) research in the area of fuel properties and end use is focused on accelerating SAF development and deployment. This will be achieved by close collaboration with BETO program researchers on fuel property measurement and development of structure-property relationships. Additionally, fuel properties measured at often extreme engine operating conditions will be measured, and will be used as inputs to exascale ready simulations of aviation combustion turbines to reveal impacts of properties on turbine operation. This research will potentially lead to a widening of the allowable fuel property range for SAF, expanding SAF production process options and production volumes.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

SU‐C‐105‐05: Reference Dosimetry of High‐Energy Electron Beams with a Farmer‐Type Ionization Chamber

Purpose: To investigate gradient effects and provide Monte Carlo calculated beam quality conversion factors to characterize the Farmer‐type NE2571 ion chamber for high‐energy reference dosimetry of clinical electron beams. Methods: The EGSnrc code system is used to calculate the absorbed dose to water and to the gas in a fully modeled NE2571 chamber as a function of depth in a water phantom. Electron beams incident on the surface of the phantom are modeled using realistic BEAMnrc accelerator simulations and electron beam spectra. Beam quality conversion factors are determined using calculated doses to water and to air in the chamber in high‐energy electron beams and in a cobalt‐60 reference field. Calculated water‐to‐air stopping power ratios are employed for investigation of the overall ion chamber perturbation factor. Results: An upstream shift of 0.3–0.4 multiplied by the chamber radius, r-cav, both minimizes the variation of the overall ion chamber perturbation factor with depth and reduces the difference between the beam quality specifier (R{sub 5} {sub 0}) calculated using ion chamber simulations and that obtained with simulations of dose‐to‐water in the phantom. Beam quality conversion factors are obtained at the reference depth and gradient effects are optimized using a shift of 0.2r-cav. The photon‐electron conversion factor, k-ecal, amounts to 0.906 when gradient effects are minimized using the shift established here and 0.903 if no shift of the data is used. Systematic uncertainties in beam quality conversion factors are investigated and amount to between 0.4 to 1.1% depending on assumptions used. Conclusion: The calculations obtained in this work characterize the use of an NE2571 ion chamber for reference dosimetry of high‐energy electron beams. These results will be useful as the AAPM continues to review their reference dosimetry protocols.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Denoising Convolutional Networks to Accelerate Detector Simulation [Poster]

The high accuracy of detector simulation is crucial for modern particle physics experiments. However, this accuracy comes with a high computational cost, which will be exacerbated by the large datasets and complex detector upgrades associated with next-generation facilities such as the High Luminosity LHC. We explore the viability of regression-based machine learning (ML) approaches using convolutional neural networks (CNN) to ``denoise'' faster, lower-quality detector simulations, augmenting them to produce a higher-quality final result with a reduced computational burden. The denoising CNN works in concert with classical detector simulation software rather than replacing it entirely, increasing its reliability compared to other ML approaches to simulation. We obtain promising results from a prototype based on photon showers in the CMS electromagnetic calorimeter. Future directions are also discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Accelerating detector simulations with Celeritas: Profiling and performance optimizations

Celeritas is a GPU-optimized Monte Carlo (MC) particle transport code designed to meet the growing computational demands of next-generation high energy physics (HEP) experiments. It provides efficient simulation of electromagnetic (EM) physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

Lund, Amanda [Argonne National Laboratory (ANL)]↗
DOE OSTI2021

A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design

A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. In this work, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An Open-Source Parallel EMT Simulation Framework

As the integration level of inverter-based resources (IBRs) increases, ensuring the reliable operation of the bulk power systems requires the use of electromagnetic transient (EMT) simulation tools to identify and mitigate system-wide stability risks. Conducting EMT studies for large-scale, IBR-rich grids, however, is challenging due to the inherent computational bottleneck caused by the underlying high-fidelity models and required small time steps. This paper introduces ParaEMT: an open-source, generic EMT simulation framework designed to accelerate simulations by leveraging advanced parallel computational technologies, such as high-performance computers. This paper presents a comprehensive exposition of ParaEMT, covering its modeling library, simulation strategy, framework structure, operational procedures, and auxiliary features, alongside its extensible parallel computational architecture. Notably, ParaEMT is a publicly accessible and modularized framework written in Python, thereby facilitating future development and the integration of new models and algorithms. The accuracy and efficiency of ParaEMT are demonstrated by rigorous validations via multiple case studies.

electromagnetic transient simulation↗
DOE OSTI2017

On the use of reverse Brownian motion to accelerate hybrid simulations

Multiscale and multiphysics simulations are two rapidly developing fields of scientific computing. Efficient coupling of continuum (deterministic or stochastic) constitutive solvers with their discrete (stochastic, particle-based) counterparts is a common challenge in both kinds of simulations. We focus on interfacial, tightly coupled simulations of diffusion that combine continuum and particle-based solvers. The latter employs the reverse Brownian motion (rBm), a Monte Carlo approach that allows one to enforce inhomogeneous Dirichlet, Neumann, or Robin boundary conditions and is trivially parallelizable. We discuss numerical approaches for improving the accuracy of rBm in the presence of inhomogeneous Neumann boundary conditions and alternative strategies for coupling the rBm solver with its continuum counterpart. Numerical experiments are used to investigate the convergence, stability, and computational efficiency of the proposed hybrid algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

36 MATERIALS SCIENCE↗
DOE OSTI2014

A virtual squeeze on chemistry: Accelerated reaction simulations [Nanoreactor Simulations]

We describe how molecular simulations have the potential to give valuable insights into experimental results, but can be limited by the time- and length-scales they can simulate. Now, reactive chemistry can be driven through a novel simulation approach, which could have ramifications for many research areas, including astrobiology and the origins of life.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗