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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

PHASE: Personalized Head-based Automatic Simulation for Electromagnetic properties in 7T MRI

Accurate and individualized human head models are becoming increasingly important for electromagnetic (EM) simulations. These simulations depend on precise anatomical representations to realistically model electric and magnetic field distributions, particularly when evaluating Specific Absorption Rate (SAR) within safety guidelines. State of the art simulations use the Virtual Population due to limited public resources and the impracticality of manually annotating patient data at scale. Here, this paper introduces Personalized Head-based Automatic Simulation for EM properties (PHASE), an automated open-source toolbox that generates high-resolution, patient-specific head models for EM simulations using paired T1-weighted (T1w) magnetic resonance imaging (MRI) and computed tomography (CT) scans with 14 tissue labels. To evaluate the performance of PHASE models, we conduct semi-automated segmentation and EM simulations on 15 real human patients, serving as the gold standard reference. The PHASE model achieved comparable global SAR and localized SAR averaged over 10 grams of tissue (SAR-10g), demonstrating its potential as a promising tool for generating large-scale human model datasets in the future. The code and models of PHASE toolbox have been made publicly available: https://github.com/hrlblab/PHASE.

Deep learning↗
DOE OSTI2018

Long-term simulation of space-charge effects

The long-term macroparticle tracking simulation is computationally challenging but needed in order to study space-charge effects in high intensity circular accelerators. To address the challenge, in this paper, we proposed using a fully symplectic particle-in-cell model for the long-term space-charge simulation. We analyzed the artificial numerical emittance growth in the simulation and suggested using threshold numerical filtering in frequency domain to mitigate the emittance growth in the simulation. Furthermore, we also explored alternative frozen space-charge simulations and observed qualitative agreement with the self-consistent simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Recent progress on coarse graining simulations

We focus on coarse graining simulations based on the primary conservation equations, effectively codesigned physics and algorithms, and low-Mach-number corrected (LMC) hydrodynamics. Simulation methods involve LANL’s x-Radiation-Adaptive-Grid-Eulerian Large-Eddy Simulation, Besnard-Harlow-Rauenzahn (BHR) Reynolds-Averaged Navier-Stokes (RANS) approach, and Dynamic BHR – a paradigm bridging RANS and LES. A relevant question addressed relates to whether 3D RANS and RANS/LES hybrids – the industry standards for aerospace and automotive research, are presently relevant for practical variable-density applications involving shocked and accelerated interface instabilities. Furthermore, recent simulations of the GaTECH inclined mixing-layer shock-tube and NIF ICF-capsule experiments are used to demonstrate issues, challenges, and potential for 3D coarse grained LMC simulation strategies for robustly simulating complex transitional and coupled hydrodynamics-multiphysics with coarser resolution. Present LES readiness to provide accurate predictions at scale is demonstrated – whereas 3D RANS and RANS/LES bridging do not appear impactful in this context.

42 ENGINEERING↗
DOE OSTI2022

Influence of NOx chemistry on the prediction of natural gas end-gas autoignition in CFD engine simulations

Natural gas (NG) represents a promising low-cost/low-emission alternative to diesel fuel when used in high-efficiency internal combustion engines. Advanced combustion strategies utilizing high EGR rates and controlled end-gas autoignition can be implemented with NG to achieve diesel-like efficiencies; however, to support the design of these next-generation NG ICEs, computational tools, including single- and multi-dimensional simulation packages will need to account for the complex chemistry that can occur between the reactive species found in EGR (including NOx) and the fuel. Research has shown that NOx plays an important role in the promotion/inhibition of large hydrocarbon autoignition and when accounted for in CFD engine simulations, can significantly improve the prediction of end-gas autoignition for these fuels. However, reduced NOx-enabled NG mechanisms for use in CFD engine simulations are lacking, and as a result, the influence of NOx chemistry on NG engine operation remains unknown. Here, we analyze the effects of NOx chemistry on the prediction of NG/oxidizer/EGR autoignition and generate a reduced mechanism of a suitable size to be used in engine simulations. Results indicate that NG ignition is sensitive to NOx chemistry, where it was observed that the addition of EGR, which included NOx, promoted NG autoignition. The modified mechanism captured well all trends and closely matched experimentally measured ignition delay times for a wide range of EGR rates and NG compositions. Here, the importance of C2-C3 chemistry is noted, especially for wet NG compositions containing high fractions of ethane and propane. Finally, when utilized in CFD simulations of a Cooperative Fuels Research (CFR) engine, the new reduced mechanism was able to predict the knock onset crank angle (KOCA) to within one crank angle degree of experimental data, a significant improvement compared to previous simulations without NOx chemistry.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Assessing the Quality of Molecular Simulations for Vapor–Liquid Equilibria: An Analysis of the TraPPE Database

As molecular modeling and simulation techniques become increasingly important sources of thermophysical property and phase equilibrium data, the ability to assess the robustness of that data becomes more critical. Recently, the use of the compressibility factor (Z) has been suggested as a metric for testing the quality of simulation data for vapor–liquid equilibria (VLE). Int his paper, we analyze predicted VLE data from the transferable potentials for phase equilibria (TraPPE) database and show that, apart from data entry or typographical errors, Z will always be well-behaved in Gibbs ensemble Monte Carlo (GEMC) simulations even when the simulations are not sufficiently equilibrated. However, this is not true for grand canonical Monte Carlo simulations. When the pressure is calculated from the internal forces, then pressure and density are strongly correlated for the vapor phase and, for GEMC simulations, it is recommended to treat Z as an instantaneous mechanical property. From analysis of the TraPPE VLE data, we propose a complementary metric based on the predicted vapor pressures at three neighboring temperatures and their deviation from a local Clausius–Clapeyron fit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Molecular-Level Insights into the NMR Relaxivity of Gadobutrol Using Quantum and Classical Molecular Simulations

MRI is an indispensable diagnostic tool in modern medicine; however, understanding the molecular-level processes governing NMR relaxation of water in the presence of MRI contrast agents remains a challenge, hindering the molecular-guided development of more effective contrast agents. By using quantum-based polarizable force fields, the first-of-its-kind molecular dynamics (MD) simulations of Gadobutrol are reported where the 1 H NMR longitudinal relaxivity r 1 of the aqueous phase is determined without any adjustable parameters. The MD simulations of r 1 dispersion (i.e., frequency dependence) show good agreement with measurements at frequencies of interest in clinical MRI. Importantly, the simulations reveal key insights into the molecular level processes leading to r 1 dispersion by decomposing the NMR dipole–dipole autocorrelation function G(t) into a discrete set of molecular modes, analogous to the eigenmodes of a quantum harmonic oscillator. The molecular modes reveal important aspects of the underlying mechanisms governing r 1 , such as its multiexponential nature and the importance of the second eigenmodal decay. By simply analyzing the MD trajectories on a parameter-free approach, the Gadobutrol simulations show that the outer-shell water contributes ∼50% of the total relaxivity r 1 compared to the inner-shell water, in contrast to simulations of (nonchelated) gadolinium-aqua where the outer shell contributes only ∼15% of r 1 . The deviation between simulations and measurements of r 1 below clinical MRI frequencies is used to determine the low-frequency electron-spin relaxation time for Gadobutrol, in good agreement with independent studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Simulations of drift resistive ballooning L-mode turbulence in the edge plasma of the DIII-D tokamak

Results from simulations of electromagnetic drift-resistive ballooning turbulence for tokamak edge turbulence in realistic single-null geometry are reported. The calculations are undertaken with the BOUT three-dimensional fluid code that solves Braginskii-based fluid equations [X. Q. Xu and R. H. Cohen, Contrib. Plasma Phys. 36, 158 (1998)]. The simulation setup models L-mode edge plasma parameters in the actual magnetic geometry of the DIII-D tokamak [J. L. Luxon et al., Fusion Sci. Technol. 48, 807 (2002)]. The computations track the development of drift-resistive ballooning turbulence in the edge region to saturation. Fluctuation amplitudes, fluctuation spectra, and particle and thermal fluxes are compared to experimental data near the outer midplane from Langmuir probe and beam-emission-spectroscopy for a few well-characterized L-mode discharges in DIII-D. The simulations are comprised of a suite of runs in which the physics model is varied to include more fluid fields and physics terms. The simulations yield results for fluctuation amplitudes, correlation lengths, particle and energy fluxes, and diffusivities that agree with measurements within an order of magnitude and within factors of 2 or better for some of the data. The agreement of the simulations with the experimental measurements varies with respect to including more physics in the model equations within the suite of models investigated. The simulations show stabilizing effects of sheared E × B poloidal rotation (imposed zonal flow) and of lower edge electron temperature and density.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Multi-scale gyrokinetic simulation of Alcator C-Mod tokamak discharges

Alcator C-Mod tokamak discharges have been studied with nonlinear gyrokinetic simulation simultaneously spanning both ion and electron spatiotemporal scales. These multi-scale simulations utilized the gyrokinetic model implemented by GYRO code [J. Candy and R. E. Waltz, J. Comput. Phys. 186, 545 (2003)] and the approximation of reduced electron mass (μ = (m{sub D}/m{sub e}){sup .5} = 20.0) to qualitatively study a pair of Alcator C-Mod discharges: a low-power discharge, previously demonstrated (using realistic mass, ion-scale simulation) to display an under-prediction of the electron heat flux and a high-power discharge displaying agreement with both ion and electron heat flux channels [N. T. Howard et al., Nucl. Fusion 53, 123011 (2013)]. These multi-scale simulations demonstrate the importance of electron-scale turbulence in the core of conventional tokamak discharges and suggest it is a viable candidate for explaining the observed under-prediction of electron heat flux. In this paper, we investigate the coupling of turbulence at the ion (k{sub θ}ρ{sub s}∼O(1.0)) and electron (k{sub θ}ρ{sub e}∼O(1.0)) scales for experimental plasma conditions both exhibiting strong (high-power) and marginally stable (low-power) low-k (k{sub θ}ρ{sub s} < 1.0) turbulence. It is found that reduced mass simulation of the plasma exhibiting marginally stable low-k turbulence fails to provide even qualitative insight into the turbulence present in the realistic plasma conditions. In contrast, multi-scale simulation of the plasma condition exhibiting strong turbulence provides valuable insight into the coupling of the ion and electron scales.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Enhanced stopping of macro-particles in particle-in-cell simulations

We derive an equation for energy transfer from relativistic charged particles to a cold background plasma appropriate for finite-size particles that are used in particle-in-cell simulation codes. Expressions for one-, two-, and three-dimensional particles are presented, with special attention given to the two-dimensional case. This energy transfer is due to the electric field of the wake set up in the background plasma by the relativistic particle. The enhanced stopping is dependent on the q{sup 2}/m, where q is the charge and m is the mass of the relativistic particle, and therefore simulation macro-particles with large charge but identical q/m will stop more rapidly. The stopping power also depends on the effective particle shape of the macro-particle. These conclusions are verified in particle-in-cell simulations. We present 2D simulations of test particles, relaxation of high-energy tails, and integrated fast ignition simulations showing that the enhanced drag on macro-particles may adversely affect the results of these simulations in a wide range of high-energy density plasma scenarios. We also describe a particle splitting algorithm which can potentially overcome this problem and show its effect in controlling the stopping of macro-particles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Lazy Updating of hubs can enable more realistic models by speeding up stochastic simulations

To respect the nature of discrete parts in a system, stochastic simulation algorithms (SSAs) must update for each action (i) all part counts and (ii) each action's probability of occurring next and its timing. This makes it expensive to simulate biological networks with well-connected “hubs” such as ATP that affect many actions. Temperature and volume also affect many actions and may be changed significantly in small steps by the network itself during fever and cell growth, respectively. Such trends matter for evolutionary questions, as cell volume determines doubling times and fever may affect survival, both key traits for biological evolution. Yet simulations often ignore such trends and assume constant environments to avoid many costly probability updates. Such computational convenience precludes analyses of important aspects of evolution. Here we present “Lazy Updating,” an add-on for SSAs designed to reduce the cost of simulating hubs. When a hub changes, Lazy Updating postpones all probability updates for reactions depending on this hub, until a threshold is crossed. Speedup is substantial if most computing time is spent on such updates. We implemented Lazy Updating for the Sorting Direct Method and it is easily integrated into other SSAs such as Gillespie's Direct Method or the Next Reaction Method. Testing on several toy models and a cellular metabolism model showed >10× faster simulations for its use-cases—with a small loss of accuracy. Thus we see Lazy Updating as a valuable tool for some special but important simulation problems that are difficult to address efficiently otherwise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Two-dimensional numerical simulation of boron diffusion for pyramidally textured silicon

Multidimensional numerical simulation of boron diffusion is of great relevance for the improvement of industrial n-type crystalline silicon wafer solar cells. However, surface passivation of boron diffused area is typically studied in one dimension on planar lifetime samples. This approach neglects the effects of the solar cell pyramidal texture on the boron doping process and resulting doping profile. In this work, we present a theoretical study using a two-dimensional surface morphology for pyramidally textured samples. The boron diffusivity and segregation coefficient between oxide and silicon in simulation are determined by reproducing measured one-dimensional boron depth profiles prepared using different boron diffusion recipes on planar samples. The established parameters are subsequently used to simulate the boron diffusion process on textured samples. The simulated junction depth is found to agree quantitatively well with electron beam induced current measurements. Finally, chemical passivation on planar and textured samples is compared in device simulation. Particularly, a two-dimensional approach is adopted for textured samples to evaluate chemical passivation. The intrinsic emitter saturation current density, which is only related to Auger and radiative recombination, is also simulated for both planar and textured samples. The differences between planar and textured samples are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Oxygen transport properties estimation by DSMC-CT simulations

Coupling DSMC simulations with classical trajectories calculations is emerging as a powerful tool to improve predictive capabilities of computational rarefied gas dynamics. The considerable increase of computational effort outlined in the early application of the method (Koura,1997) can be compensated by running simulations on massively parallel computers. In particular, GPU acceleration has been found quite effective in reducing computing time (Ferrigni,2012; Norman et al.,2013) of DSMC-CT simulations. The aim of the present work is to study rarefied Oxygen flows by modeling binary collisions through an accurate potential energy surface, obtained by molecular beams scattering (Aquilanti, et al.,1999). The accuracy of the method is assessed by calculating molecular Oxygen shear viscosity and heat conductivity following three different DSMC-CT simulation methods. In the first one, transport properties are obtained from DSMC-CT simulations of spontaneous fluctuation of an equilibrium state (Bruno et al, Phys. Fluids, 23, 093104, 2011). In the second method, the collision trajectory calculation is incorporated in a Monte Carlo integration procedure to evaluate the Taxman’s expressions for the transport properties of polyatomic gases (Taxman,1959). In the third, non-equilibrium zero and one-dimensional rarefied gas dynamic simulations are adopted and the transport properties are computed from the non-equilibrium fluxes of momentum and energy. The three methods provide close values of the transport properties, their estimated statistical error not exceeding 3%. The experimental values are slightly underestimated, the percentage deviation being, again, few percent.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Thermo-mechanical simulation of liquid-supported stretch blow molding

Stretch blow molding is the well-established plastics forming method to produce Polyehtylene therephtalate (PET) bottles. An injection molded preform is heated up above the PET glass transition temperature (Tg∼85°C) and subsequently inflated by pressurized air into a closed cavity. In the follow-up filling process, the resulting bottle is filled with the final product. A recently developed modification of the process combines the blowing and filling stages by directly using the final liquid product to inflate the preform. In a previously published paper, a mechanical simulation and successful evaluation of this liquid-driven stretch blow molding process was presented. In this way, a realistic process parameter dependent simulation of the preform deformation throughout the forming process was enabled, whereas the preform temperature evolution during forming was neglected. However, the formability of the preform is highly reduced when the temperature sinks below Tg during forming. Experimental investigations show temperature-induced failure cases due to the fast heat transfer between hot preform and cold liquid. Therefore, in this paper, a process dependent simulation of the temperature evolution during processing to avoid preform failure is presented. For this purpose, the previously developed mechanical model is used to extract the time dependent thickness evolution. This information serves as input for the heat transfer simulation. The required material parameters are calibrated from preform cooling experiments recorded with an infrared-camera. Furthermore, the high deformation ratios during processing lead to strain induced crystallization. This exothermal reaction is included into the simulation by extracting data from preform measurements at different stages of deformation via Differential Scanning Calorimetry (DSC). Finally, the thermal simulation model is evaluated by free forming experiments, recorded by a high-speed infrared camera.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Communication: Multiple atomistic force fields in a single enhanced sampling simulation

The main concerns of biomolecular dynamics simulations are the convergence of the conformational sampling and the dependence of the results on the force fields. While the first issue can be addressed by employing enhanced sampling techniques such as simulated tempering or replica exchange molecular dynamics, repeating these simulations with different force fields is very time consuming. Here, we propose an automatic method that includes different force fields into a single advanced sampling simulation. Conformational sampling using three all-atom force fields is enhanced by simulated tempering and by formulating the weight parameters of the simulated tempering method in terms of the energy fluctuations, the system is able to perform random walk in both temperature and force field spaces. The method is first demonstrated on a 1D system and then validated by the folding of the 10-residue chignolin peptide in explicit water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Investigating wind turbine impacts on near-wake flow using profiling Lidar data and large-eddy simulations with an actuator disk model

In this study, wind turbine impacts on the atmospheric flow are investigated using data from the Crop Wind Energy Experiment (CWEX-11) and large-eddy simulations (LESs) utilizing a generalized actuator disk (GAD) wind turbine model. CWEX-11 employed velocity-azimuth display (VAD) data from two Doppler lidar systems to sample vertical profiles of flow parameters across the rotor depth both upstream and in the wake of an operating 1.5 MW wind turbine. Lidar and surface observations obtained during four days of July 2011 are analyzed to characterize the turbine impacts on wind speed and flow variability, and to examine the sensitivity of these changes to atmospheric stability. Significant velocity deficits (VD) are observed at the downstream location during both convective and stable portions of four diurnal cycles, with large, sustained deficits occurring during stable conditions. Variances of the streamwise velocity component, σ u , likewise show large increases downstream during both stable and unstable conditions, with stable conditions supporting sustained small increases of σ u , while convective conditions featured both larger magnitudes and increased variability, due to the large coherent structures in the background flow. Two representative case studies, one stable and one convective, are simulated using LES with a GAD model at 6 m resolution to evaluate the compatibility of the simulation framework with validation using vertically profiling lidar data in the near wake region. Virtual lidars were employed to sample the simulated flow field in a manner consistent with the VAD technique. Simulations reasonably reproduced aggregated wake VD characteristics, albeit with smaller magnitudes than observed, while σu values in the wake are more significantly underestimated. The results illuminate the limitations of using a GAD in combination with coarse model resolution in the simulation of near wake physics, and validation thereof using VAD data.

17 WIND ENERGY↗
DOE OSTI2015

The effect of voxel size on dose distribution in Varian Clinac iX 6 MV photon beam using Monte Carlo simulation

Recently, Monte Carlo (MC) calculation method has reported as the most accurate method of predicting dose distributions in radiotherapy. The MC code system (especially DOSXYZnrc) has been used to investigate the different voxel (volume elements) sizes effect on the accuracy of dose distributions. To investigate this effect on dosimetry parameters, calculations were made with three different voxel sizes. The effects were investigated with dose distribution calculations for seven voxel sizes: 1 × 1 × 0.1 cm{sup 3}, 1 × 1 × 0.5 cm{sup 3}, and 1 × 1 × 0.8 cm{sup 3}. The 1 × 10{sup 9} histories were simulated in order to get statistical uncertainties of 2%. This simulation takes about 9-10 hours to complete. Measurements are made with field sizes 10 × 10 cm2 for the 6 MV photon beams with Gaussian intensity distribution FWHM 0.1 cm and SSD 100.1 cm. MC simulated and measured dose distributions in a water phantom. The output of this simulation i.e. the percent depth dose and dose profile in d{sub max} from the three sets of calculations are presented and comparisons are made with the experiment data from TTSH (Tan Tock Seng Hospital, Singapore) in 0-5 cm depth. Dose that scored in voxels is a volume averaged estimate of the dose at the center of a voxel. The results in this study show that the difference between Monte Carlo simulation and experiment data depend on the voxel size both for percent depth dose (PDD) and profile dose. PDD scan on Z axis (depth) of water phantom, the big difference obtain in the voxel size 1 × 1 × 0.8 cm{sup 3} about 17%. In this study, the profile dose focused on high gradient dose area. Profile dose scan on Y axis and the big difference get in the voxel size 1 × 1 × 0.1 cm{sup 3} about 12%. This study demonstrated that the arrange voxel in Monte Carlo simulation becomes important.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences

We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of protein sequences with block-copolymeric architectures. Our algorithm, named CAMELOT for Coarse-grained simulations Aided by MachinE Learning Optimization and Training, leverages information from converged all atom simulations that is used to determine a suitable resolution and parameterize the coarse-grained model. To parameterize a system-specific coarse-grained model, we use a combination of Boltzmann inversion, non-linear regression, and a Gaussian process Bayesian optimization approach. The accuracy of the coarse-grained model is demonstrated through direct comparisons to results from all atom simulations. We demonstrate the utility of our coarse-graining approach using the block-copolymeric sequence from the exon 1 encoded sequence of the huntingtin protein. This sequence comprises of 17 residues from the N-terminal end of huntingtin (N17) followed by a polyglutamine (polyQ) tract. Simulations based on the CAMELOT approach are used to show that the adsorption and unfolding of the wild type N17 and its sequence variants on the surface of polyQ tracts engender a patchy colloid like architecture that promotes the formation of linear aggregates. These results provide a plausible explanation for experimental observations, which show that N17 accelerates the formation of linear aggregates in block-copolymeric N17-polyQ sequences. The CAMELOT approach is versatile and is generalizable for simulating the aggregation and phase behavior of a range of block-copolymeric protein sequences.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Combining molecular dynamics with mesoscopic Green’s function reaction dynamics simulations

In many reaction-diffusion processes, ranging from biochemical networks, catalysis, to complex self-assembly, the spatial distribution of the reactants and the stochastic character of their interactions are crucial for the macroscopic behavior. The recently developed mesoscopic Green’s Function Reaction Dynamics (GFRD) method enables efficient simulation at the particle level provided the microscopic dynamics can be integrated out. Yet, many processes exhibit non-trivial microscopic dynamics that can qualitatively change the macroscopic behavior, calling for an atomistic, microscopic description. We propose a novel approach that combines GFRD for simulating the system at the mesoscopic scale where particles are far apart, with a microscopic technique such as Langevin dynamics or Molecular Dynamics (MD), for simulating the system at the microscopic scale where reactants are in close proximity. This scheme defines the regions where the particles are close together and simulated with high microscopic resolution and those where they are far apart and simulated with lower mesoscopic resolution, adaptively on the fly. The new multi-scale scheme, called MD-GFRD, is generic and can be used to efficiently simulate reaction-diffusion systems at the particle level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗