Computation of electron diode characteristics by Monte Carlo method including effect of collisions.
Consistent-field Monte Carlo method used to calculate electron and ion diode characteristics, including effect of collisions
MONTE CARLO METHOD↗
SEARCH · Engineering Papers
Results for “Monte Carlo”
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.
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Hybrid computer Monte-Carlo techniques
Hybrid analog-digital computer systems for Monte Carlo method applications
ANALOG COMPUTER↗
A Monte Carlo program for transmission probability calculations including mass motions
Monte Carlo computer program for transmission probability for gas molecular flow through two coaxial cylinders in series
MASS FLOW↗
A Monte Carlo approach to transition and free molecular flow problems.
Monte Carlo approach to transition and free molecular flow problems having mass as well as thermal motion of molecules
HEAT TRANSFER↗
Monte-carlo maneuver analysis for the microwave anisotropy probe
The results of this trajectory replication are presented, followed by the results of monte-carlo maneuver simulations. The results are subject to two important assumptions: that linearization is valid (as the software used for the monte-carlo simulation, LAMBIC, is based on a linearization of the trajectory about the nominal) and that the planned human-in-the-loop lunar targeting may be approximated by targeting to a fixed aimpoint.
monte-carlo↗
Renovating Monte Carlo Methods and Codebases with Generative Models
The code will implement a standardized interface for Monte Carlo sampling methods, including conventional techniques, and going beyond current available packages to also incorporate generative model-enabled Monte Carlo sampling to provide a unified framewor
Garcia-Cardona, Cristina↗
Determination of the critical manifold tangent space and curvature with Monte Carlo renormalization group
We show that the critical manifold of a statistical mechanical system in the vicinity of a critical point is locally accessible through correlation functions at that point. A practical numerical method is presented to determine the tangent space and the curvature to the critical manifold with variational Monte Carlo renormalization group. Because of the use of a variational bias potential of the coarse-grained variables, critical slowing down is greatly alleviated in the Monte Carlo simulation. In addition, this method is free of truncation error. Furthermore, we study the isotropic Ising model on square and cubic lattices, the anisotropic Ising model, and the tricritical Ising model on square lattices to illustrate the method.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A path integral ground state Monte Carlo algorithm for entanglement of lattice bosons
A ground state path integral quantum Monte Carlo algorithm is introduced that allows for the study of entanglement in lattice bosons at zero temperature. The Rényi entanglement entropy between spatial subregions is explored across the phase diagram of the one dimensional Bose-Hubbard model for systems consisting of up to L=256 L = 256 sites at unit-filling without any restrictions on site occupancy, far beyond the reach of exact diagonalization. The favorable scaling of the algorithm is demonstrated through a further measurement of the Rényi entanglement entropy at the two dimensional superfluid-insulator critical point for large system sizes, confirming the existence of the expected entanglement boundary law in the ground state. The Rényi estimator is extended to measure the symmetry resolved entanglement that is operationally accessible as a resource for experimentally relevant lattice gases with fixed total particle number.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A study of microkinetic adjustments required to match shock wave experiments and Monte Carlo Direct Simulation for a wide Mach number range
Shock wave thickness predictions from Monte Carlo Direct Simulations, using differential scattering and the Maitland-Smith-Aziz interatomic potential, underpredict experiments as shock Mach numbers increase above about 4. Examination of several sources of data has indicated that at relatively high energies the repulsive portion of accepted potentials such as the Maitland-Smith-Aziz may be too steep. An Exponential-6 potential due to Ross, based on high energy molecular beam scattering data and shock velocity measurements in liquid argon, has been combined with the lower energy portion of the Maitland-Smith-Aziz potential. When this hybrid potential is used in Monte Carlo Direct Simulations, agreement with experiments is improved over the previous predictions using the pure Maitland-Smith-Aziz form.
Pham-Van-diep, Gerald C.↗
A triple junction energy study using an inclination-dependent anisotropic Monte Carlo Potts grain growth model
This work presents a Monte Carlo Potts grain growth model in which the grain boundary (GB) energies depend on the GB inclination. The inclination is calculated using a linear smoothing approach developed by the authors. In bicrystal simulations with a shrinking grain, the grain changes shape to prefer low energy GB inclinations. However, in polycrystal simulations the preferred inclinations depend on the approach used to assign the triple junction (TJ) energies. Approaches that produce unimodal TJ energy distributions result in the expected behavior of preferring low energy GB inclinations. However, approaches that produce bimodal TJ energy distributions result in medium energy or even high energy inclinations being preferred. Overall, this study underscores the importance of TJs in anisotropic grain growth.
Grain boundary inclination↗
Energy-preserving coupling of explicit particle-in-cell with Monte Carlo collisions
The particle-in-cell (PIC) and Monte Carlo collisions (MCC) methods are workhorses of many numerical simulations of physical systems. Recently, it was pointed out that, while the two methods can be exactly—or nearly—energy-conserving independently, combining the two leads to anomalous numerical heating. This paper reviews the standard explicit PIC-MCC algorithm, elucidates the origins of the anomalous numerical heating, and explains how to couple the two methods such that the anomalous numerical heating is avoided.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Emulators for Scarce and Noisy Data: Application to Auxiliary-Field Diffusion Monte Carlo for Neutron Matter
Understanding the equation of state (EOS) of pure neutron matter is necessary for interpreting multimessenger observations of neutron stars. Reliable data analyses of these observations require well-quantified uncertainties for the EOS input, ideally propagating uncertainties from nuclear interactions directly to the EOS. This, however, requires calculations of the EOS for a prohibitively larger number of nuclear Hamiltonians, solving the nuclear many-body problem for each one. Quantum Monte Carlo methods, such as auxiliary-field diffusion Monte Carlo (AFDMC), provide precise and accurate results for the neutron matter EOS, but they are very computationally expensive, making them unsuitable for the fast evaluations necessary for uncertainty propagation. Here, we employ parametric matrix models to develop fast emulators for AFDMC calculations of neutron matter and use them to directly propagate uncertainties of coupling constants in the Hamiltonian to the EOS. As these uncertainties include estimates of the effective field theory truncation uncertainty, this approach provides robust uncertainty estimates for use in astrophysical data analyses. In conclusion, this Letter will enable novel applications such as using astrophysical observations to put constraints on coupling constants for nuclear interactions.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Monte Carlo neutronics benchmarks on nuclear fuel depletion: A review
Monte Carlo (MC) neutronics codes are used widely for academic and industrial needs. Several schemes of coupling MC neutronics code with isotope generation and depletion code exist, which are used for performing nuclear fuel depletion simulations. These simulations can estimate the inventory of isotopes in neutron irradiated nuclear reactor fuel. However, the accuracy of these simulations shall be validated through experiments. MC codes are seldom validated by isotopic benchmarks compared to criticality benchmarks. This work compiles and analyzes the fuel depletion benchmarks and validations used to analyze the performance of MC-based fuel depletion neutronics codes. Analyses of these benchmarks and validations showed that the computed concentrations of 133 Cs, 135 Cs, 137 Cs, 148 Nd, 239 Pu, 240 Pu, and 241 Pu in the irradiated fuel by the depletion codes agreed with the measured values within 10% error. However, the computed concentrations of 125 Sb, 242 Cm, 243 Cm, 244 Cm, 245 Cm, and 246 Cm had errors more than 15% compared to the measured values. As a result, Ventina depletion code showed the most accurate predictions for the greatest number of isotope concentrations compared to ORIGEN2 and CINDER90.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo
In this work we develop a new hierarchical multilevel approach to generate Gaussian random field realizations in an algorithmically scalable manner that is well suited to incorporating into multilevel Markov chain Monte Carlo (MCMC) algorithms. This approach builds off of other partial differential equation (PDE) approaches for generating Gaussian random field realizations; in particular, a single field realization may be formed by solving a reaction-diffusion PDE with a spatial white noise source function as the right-hand side. While these approaches have been explored to accelerate forward uncertainty quantification tasks, e.g., multilevel Monte Carlo, the previous constructions are not directly applicable to multilevel MCMC frameworks which build fine-scale random fields in a hierarchical fashion from coarse-scale random fields. Our new hierarchical multilevel method relies on a hierarchical decomposition of the white noise source function in $L^2$ which allows us to form Gaussian random field realizations across multiple levels of discretization in a way that fits into multilevel MCMC algorithmic frameworks. After presenting our main theoretical results and numerical scaling results to showcase the utility of this new hierarchical PDE method for generating Gaussian random field realizations, this method is tested on a four-level MCMC algorithm to explore its feasibility.
algebraic multigrid↗
Hybridizing pseudo-Hamiltonians and non-local pseudopotentials in diffusion Monte Carlo
An accurate treatment of effective core potentials (ECPs) requires care in continuum quantum Monte Carlo (QMC) methods. While most QMC studies have settled on the use of familiar non-local (NL) pseudopotentials with additional localization approximations, these approaches have been shown to result in moderate residual errors for some classes of molecular and solid state applications. Here, we revisit an idea proposed early in the history of QMC ECPs that does not require localization approximations, namely, a differential class of potentials referred to as pseudo-Hamiltonians. We propose to hybridize NL potentials and pseudo-Hamiltonians to reduce residual non-locality of existing potentials. We derive an approach to recast pseudopotentials for 3d elements as hybrid pseudo-Hamiltonians with optimally reduced NL energy. We demonstrate the fidelity of the hybrid potentials by studying atomic ionization potentials of Ti and Fe and the binding properties of TiO and FeO molecules with diffusion Monte Carlo (DMC). We show that localization errors have been reduced relative to potentials with the same NL channels for Sc–Zn by considering the DMC energy change with respect to the choice of approximate localization. While localization error decreases proportionate to the reduced NL energy without a Jastrow, with a Jastrow, the degree of reduction decreases at higher filling of the d-shell. Our results suggest that a subset of existing ECPs may be recast in this hybrid form to reduce the DMC localization error. They also point to the prospect of further reducing this error by generating ECPs within this hybrid form from the start.
36 MATERIALS SCIENCE↗
Validation of the TOPAS Monte Carlo toolkit for HDR brachytherapy simulations
The goal of this work is to validate the user-friendly Geant4-based Monte Carlo toolkit TOol for PArticle Simulation (TOPAS) for brachytherapy applications. Brachytherapy simulations performed with TOPAS were systematically compared with published TG-186 reference data. The photon emission energy spectrum, the air-kerma strength, and the dose-rate constant of the model-based dose calculation algorithm (MBDCA)-WG generic Ir-192 source were extracted. For dose calculations, a track-length estimator was implemented. Additionally, the four Joint AAPM/ESTRO/ABG MBDCA-WG test cases were evaluated through histograms of the local and global dose difference volumes. A prostate, a palliative lung, and a breast case were simulated. For each case, the dose ratio map, the histogram of the global dose difference volume, and cumulative dose-volume histograms were calculated. The air-kerma strength was (9.772 ± 0.001) × 10 -8 U Bq -1 (within 0.3% of the reference value). The dose-rate constant was 1.1107 ± 0.0005 cGy h -1 U -1 (within 0.01% of the reference value). For all cases, at least 96.9% of voxels had a local dose difference within [-1%, 1%] and at least 99.9% of voxels had a global dose difference within [-0.1%, 0.1%]. The implemented track-length estimator scorer was more efficient than the default analog dose scorer by a factor of 237. For all clinical cases, at least 97.5% of voxels had a global dose difference within [-1%, 1%]. Dose-volume histograms were consistent with the reference data. TOPAS was validated for high-dose-rate brachytherapy simulations following the TG-186 recommended approach for MBDCAs. Built on top of Geant4, TOPAS provides broad access to a state-of-the-art Monte Carlo code for brachytherapy simulations.
59 BASIC BIOLOGICAL SCIENCES↗
Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation
The combination of neural networks and quantum Monte Carlo methods has arisen as a promising path forward for highly accurate electronic structure calculations. Previous proposals have combined equivariant neural network layers with a final antisymmetric layer in order to satisfy the antisymmetry requirements of the electronic wavefunction. However, to date it is unclear if one can represent antisymmetric functions of physical interest, and it is difficult to precisely measure the expressiveness of the antisymmetric layer. Here, this work attempts to address this problem by introducing explicitly antisymmetrized universal neural network layers. This approach has a computational cost which increases factorially with respect to the system size, but we are nonetheless able to apply it to small systems to better understand how the structure of the antisymmetric layer affects its performance. We first introduce a generic antisymmetric (GA) neural network layer, which we use to replace the entire antisymmetric layer of the highly accurate ansatz known as the FermiNet. We demonstrate that the resulting FermiNet-GA architecture can yield effectively the exact ground state energy for small atoms and molecules. We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing the products of determinants with products of antisymmetrized neural networks. We find, interestingly, that the resulting FermiNet-FA architecture does not significantly outperform the FermiNet. This strongly suggests that the sum of products of antisymmetries is a key limiting aspect of the FermiNet architecture. To explore this further, we investigate a slight modification of the FermiNet, called the full determinant mode, which replaces each product of determinants with a single combined determinant. We find that the full single-determinant FermiNet closes a large part of the gap between the standard single-determinant FermiNet and FermiNet-GA on small atomic and molecular problems. Surprisingly, on the nitrogen molecule at a dissociating bond length of 4.0 Bohr, the full single-determinant FermiNet can outperform the largest standard FermiNet calculation with 64 determinants.
97 MATHEMATICS AND COMPUTING↗
Using Importance Sampling Monte Carlo to Analyze Aircraft Takeoff and Landing
Aircraft have achieved high levels of reliability, so the probability of undesirable dynamics is very low. The low probability of a bad takeoff or landing challenges nondeterministic methods to quantify the uncertainty of these improbable events. In the present work, a Monte Carlo method is defined to more effectively quantify these unlikely events that is suitable for uncertainty analysis of a mature design with many uncertainty parameters. The proposed Monte Carlo method has been applied to the takeoff and landing of the X-59 Quiet SuperSonic Technology (QueSST). Takeoff and landing simulation profiles were defined using a combination of industry standards and piloted simulation experience. The proposed method is shown to be able to quantify events with significantly fewer samples than would be required with conventional Monte Carlo. These results improve understanding of the sensitivity of the takeoff and landing dynamics to inform further studies and test planning.
Jeffrey Ouellette↗