Results for “Monte Carlo Methods” — Page 21

DOE OSTI2019

Lattice Monte Carlo for quantum Hall states on a torus

The Monte Carlo method is very useful for studying various model states proposed for the fractional quantum Hall systems. In this paper, we introduce a lattice Monte Carlo method based on an exact lattice formalism for quantum Hall problems defined on a torus. This “lattice representation” is applied to study the many body Berry phase of the composite-Fermi liquid phase in a half-filled Landau level. Here, the Monte Carlo result agrees with the exact numerical result found on small systems sizes and is consistent with the prediction from the Dirac fermion low-energy effective theory. Many other aspects of other model states, including the structure factor, Coulomb energy and particle-hole symmetry, are studied and discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Energy Gap Closure of Crystalline Molecular Hydrogen with Pressure

Here, we study the gap closure with pressure of crystalline molecular hydrogen. The gaps are obtained from grand-canonical quantum Monte Carlo methods properly extended to quantum and thermal crystals, simulated by coupled electron ion Monte Carlo methods. Nuclear zero point effects cause a large reduction in the gap (~2 eV). Depending on the structure, the fundamental indirect gap closes between 380 and 530 GPa for ideal crystals and 330–380 GPa for quantum crystals. Beyond this pressure the system enters into a bad metal phase where the density of states at the Fermi level increases with pressure up to ~450–500 GPa when the direct gap closes. Our work partially supports the interpretation of recent experiments in high pressure hydrogen.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2016

GPU Acceleration of History-Based Multigroup Monte Carlo

Accurate solutions of the radiation transport equation play an important role in the analysis of many nuclear systems. Because there is no need to discretize the problem, Monte Carlo methods offer the most accurate possible solutions to the transport equation. However, significant computational effort is required to reduce the stochastic noise inherent to Monte Carlo methods. Current trends in high performance computing are moving towards vectorized, single instruction multiple data (SIMD), architectures such as Intel Xeon Phis and graphics processing units (GPUs) due to their favorable ratio of performance to power consumption. The challenge of adapting Monte Carlo transport to vectorized computing architectures was first addressed by Brown with an algorithm known as event-based Monte Carlo. In this approach, rather than simulating individual particle histories from birth to death, groups of particles are processed one event at a time. This allows each event batch to perform the same operations in lockstep, enabling use of the vector hardware. Although modern vectorized hardware is more versatile than the early vector machines, reducing so-called thread divergence is nonetheless still an important consideration for algorithm design. Many of the algorithms proposed so far for Monte Carlo transport on advanced architectures have focused on variants of event-based transport. Some evidence, however, suggests that low-level thread divergence, resulting from short-lived branching statements, may not be detrimental to overall performance on GPUs as long as the effect on achievable memory bandwidth is considered. This paper characterizes the performance of a traditional history-based Monte Carlo algorithm on GPUs. We show that with only minor modifications to traditional algorithms, significant performance gains relative to CPUs are possible. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2014

Spin decoherence in n-type GaAs: The effectiveness of the third-body rejection method for electron-electron scattering

We study the spin decoherence in n-type bulk GaAs for moderate electronic densities at room temperature using the Ensemble Monte Carlo method. We demonstrate that a technique called “third-body rejection method” devised by B. K. Ridley, J. Phys. C: Solid State Phys. 10, 1589 (1977) can be successfully adapted to Ensemble Monte Carlo method and used to tackle the problem of the electron-electron contribution to spin decoherence in the parameter region under study, where the electron-electron interaction can be reasonably described by a Yukawa potential. This scattering technique is employed in a doping region where one can expect that multiple collisions may play a role in carrier dynamics. By this technique, we are able to calculate spin relaxation times which are in very good agreement with the experimental results found by Oertel et al., Appl. Phys. Lett. 93, 13 (2008). Through this method, we show that the electron-electron scattering is overestimated in Born approximation, in agreement with previous results obtained by C. A. Kukkonen and H. Smith, Phys. Rev. B 8, 4601 (1973).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2016

Uncertainty Analysis of the NASA Glenn 8x6 Supersonic Wind Tunnel

This paper presents methods and results of a detailed measurement uncertainty analysis that was performed for the 8- by 6-foot Supersonic Wind Tunnel located at the NASA Glenn Research Center. The statistical methods and engineering judgments used to estimate elemental uncertainties are described. The Monte Carlo method of propagating uncertainty was selected to determine the uncertainty of calculated variables of interest. A detailed description of the Monte Carlo method as applied for this analysis is provided. Detailed uncertainty results for the uncertainty in average free stream Mach number as well as other variables of interest are provided. All results are presented as random (variation in observed values about a true value), systematic (potential offset between observed and true value), and total (random and systematic combined) uncertainty. The largest sources contributing to uncertainty are determined and potential improvement opportunities for the facility are investigated.

Error↗

NASA NTRS1967

Electron flow in low-density argon gas including space-charge and elastic collisions.

Electron flow in low density argon gas diode analyzed by Monte Carlo method

ARGON↗

NASA NTRS1977

Determination of three-dimensional radiative exchange factors for the Space Shuttle by Monte Carlo

A program was developed to predict radiative exchange factors in three-dimensional configurations with curved, specularly-reflecting surfaces using the Monte Carlo method. It was shown that this can be done efficiently by using the normal Monte Carlo method for closed configurations and/or high surface emissivities, while Energy Partitioning should be used for open configurations and/or low surface emissivities. Comparison with experiments on the Space Shuttle radiator panels showed that the simple model of gray, diffuse emission is sufficient for the prediction of exchange factors between actual (silver-coated teflon) surfaces. For solar-irradiation exchange factors on the Space Shuttle, on the other hand, spectral and directional variations of surface properties must be taken into account.

Modest, M. F.↗

DOE OSTI2025

MCCCS-MN

The MCCCS‒MN (Monte Carlo for Complex Chemical Systems‒Minnesota) software is developed by the Siepmann research group at the University of Minnesota. MCCCS‒MN allows for the simulation of multi-component molecular systems in the canonical, isobaric-isothermal (including constant stress for solids), grand-canonical, semi-grand, and Gibbs (NVT, NPT, and more than two simulation boxes) ensembles. It uses the configurational-bias Monte Carlo method to efficiently sample phase space for linear, branched and cyclic chain molecules, the adiabatic nuclear and electronic sampling Monte Carlo method to treat many-body polarization effects, and the aggregation-volume-bias Monte Carlo algorithm to efficiently sample the spatial distribution of associating molecules. MCCCS-MN employs a molecular representation of the system where force fields contain bonded and non-bonded terms. Funding for the development of MCCCS-MN through grants from the National Science Foundation (simulation of fluid phase equilibria and chromatography) and the Department of Energy (simulation of adsorption equilibria) is gratefully acknowledged.

Siepmann, J.Ilja [University of Minnesota - Twin C↗

DOE OSTI2016

SU-F-T-408: On the Determination of Equivalent Squares for Rectangular Small MV Photon Fields

Purpose: It is common practice to tabulate dosimetric data like output factors, scatter factors and detector signal correction factors for a set of square fields. In order to get the data for an arbitrary field, it is mapped to an equivalent square, having the same scatter as the field of interest. For rectangular fields both, tabulated data and empiric formula exist. We tested the applicability of such rules for very small fields. Methods: Using the Monte-Carlo method (EGSnrc-doseRZ), the dose to a point in 10cm depth in water was calculated for cylindrical impinging fluence distributions. Radii were from 0.5mm to 11.5mm with 1mm thickness of the rings. Different photon energies were investigated. With these data a matrix was constructed assigning the amount of dose to the field center to each matrix element. By summing up the elements belonging to a certain field, the dose for an arbitrary point in 10cm depth could be determined. This was done for rectangles up to 21mm side length. Comparing the dose to square field results, equivalent squares could be assigned. The results were compared to using the geometrical mean and the 4Xperimeter/area rule. Results: For side length differences less than 2mm, the difference between all methods was in general less than 0.2mm. For more elongated fields, relevant differences of more than 1mm and up to 3mm for the fields investigated occurred. The mean square side length calculated from both empiric formulas fitted much better, deviating hardly more than 1mm and for the very elongated fields only. Conclusion: For small rectangular photon fields, deviating only moderately from square both investigated empiric methods are sufficiently accurate. As the deviations often differ regarding their sign, using the mean improves the accuracy and the useable elongation range. For ratios larger than 2, Monte-Carlo generated data are recommended. SW is funded by Deutsche Forschungsgemeinschaft (SA481/10-1)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Implementation and testing of the on-the-fly thermal scattering Monte Carlo sampling method for graphite and light water in MCNP6

Here, a proper treatment of thermal neutron scattering requires accounting for chemical binding through a scattering law S(α,β,T). Monte Carlo codes sample the secondary neutron energy and angle after a thermal scattering event from probability tables generated from S(α,β,T) tables at discrete temperatures, requiring a large amount of data for multiscale and multiphysics problems with detailed temperature gradients. We have previously developed a method to handle this temperature dependence on-the-fly during the Monte Carlo random walk using polynomial expansions in 1/T to directly sample the secondary energy and angle. In this paper, the on-the-fly method is implemented into MCNP6 and tested in both graphite-moderated and light water-moderated systems. The on-the-fly method is compared with the thermal ACE libraries that come standard with MCNP6, yielding good agreement with integral reactor quantities like k-eigenvalue and differential quantities like single-scatter secondary energy and angle distributions. The simulation runtimes are comparable between the two methods (on the order of 5–15% difference for the problems tested) and the on-the-fly fit coefficients only require 5–15 MB of total data storage.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2014

Spectroscopic accuracy directly from quantum chemistry: Application to ground and excited states of beryllium dimer

We combine explicit correlation via the canonical transcorrelation approach with the density matrix renormalization group and initiator full configuration interaction quantum Monte Carlo methods to compute a near-exact beryllium dimer curve, without the use of composite methods. In particular, our direct density matrix renormalization group calculations produce a well-depth of D{sub e} = 931.2 cm{sup −1} which agrees very well with recent experimentally derived estimates D{sub e} = 929.7±2 cm{sup −1} [J. M. Merritt, V. E. Bondybey, and M. C. Heaven, Science 324, 1548 (2009)] and D{sub e}= 934.6 cm{sup −1} [K. Patkowski, V. Špirko, and K. Szalewicz, Science 326, 1382 (2009)], as well the best composite theoretical estimates, D{sub e} = 938±15 cm{sup −1} [K. Patkowski, R. Podeszwa, and K. Szalewicz, J. Phys. Chem. A 111, 12822 (2007)] and D{sub e}=935.1±10 cm{sup −1} [J. Koput, Phys. Chem. Chem. Phys. 13, 20311 (2011)]. Our results suggest possible inaccuracies in the functional form of the potential used at shorter bond lengths to fit the experimental data [J. M. Merritt, V. E. Bondybey, and M. C. Heaven, Science 324, 1548 (2009)]. With the density matrix renormalization group we also compute near-exact vertical excitation energies at the equilibrium geometry. These provide non-trivial benchmarks for quantum chemical methods for excited states, and illustrate the surprisingly large error that remains for 1 {sup 1}Σ{sub g}{sup −} state with approximate multi-reference configuration interaction and equation-of-motion coupled cluster methods. Overall, we demonstrate that explicitly correlated density matrix renormalization group and initiator full configuration interaction quantum Monte Carlo methods allow us to fully converge to the basis set and correlation limit of the non-relativistic Schrödinger equation in small molecules.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

reinforcement learning↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRS2023

Uncertainty Analysis of the NASA Glenn 8- by 6-Foot Supersonic Wind Tunnel 2019 Characterization Test 14-Foot, 5.8 Percent Test Section Porosity

This paper presents methods and results of a measurement uncertainty analysis that was performed for the 8- by 6-foot Supersonic Wind Tunnel located at the NASA Glenn Research Center. The uncertainty analysis is based on data collected during a characterization test following major structural modifications to the facility and upgrades to the facility’s data and control systems. The statistical methods and engineering judgments used to estimate elemental uncertainties are described in this report. The Monte Carlo method of propagating uncertainty was selected to determine the uncertainty of calculated variables of interest. A description of the Monte Carlo method as applied for this analysis is provided. The primary variable of interest (VOI) for this facility is free stream Mach number. In addition to determining the uncertainty in Mach number, the uncertainty in free stream values of static pressure, total pressure, dynamic pressure, total temperature, static temperature, Reynolds number, and air speed were also calculated. Uncertainty results are presented as random (unpredictable variation in repeated measurements), systematic (potential offset between observed and true value), and total (random and systematic combined) uncertainty for Mach number. Systematic uncertainty results are presented for the other VOIs. Individual uncertainty sources are presented both dimensionally and as percent contributions to uncertainty in all VOIs, to aid in the identification of primary uncertainty sources.

Uncertainty Analysis↗

DOE OSTI2017

Formation and structural phase transition in Co atomic chains on a Cu(775) surface

The formation of Co atomic chains on a Cu(775) surface is investigated by the kinetic Monte Carlo method. It is found that the length of Co atomic chains formed as a result of self-organization during epitaxial growth is a random quantity and its mean value depends on the parameters of the experiment. The existence of two structural phases in atomic chains is detected using the density functional theory. In the first phase, the separations between an atom and its two nearest neighbors in a chain are 0.230 and 0.280 nm. In the second phase, an atomic chain has identical atomic spacings of 0.255 nm. It is shown that the temperature of the structural phase transition depends on the length of the atomic chain.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

Low Z target switching to increase tumor endothelial cell dose enhancement during gold nanoparticle-aided radiation therapy

Purpose: Previous studies have introduced gold nanoparticles as vascular-disrupting agents during radiation therapy. Crucial to this concept is the low energy photon content of the therapy radiation beam. The authors introduce a new mode of delivery including a linear accelerator target that can toggle between low Z and high Z targets during beam delivery. In this study, the authors examine the potential increase in tumor blood vessel endothelial cell radiation dose enhancement with the low Z target. Methods: The authors use Monte Carlo methods to simulate delivery of three different clinical photon beams: (1) a 6 MV standard (Cu/W) beam, (2) a 6 MV flattening filter free (Cu/W), and (3) a 6 MV (carbon) beam. The photon energy spectra for each scenario are generated for depths in tissue-equivalent material: 2, 10, and 20 cm. The endothelial dose enhancement for each target and depth is calculated using a previously published analytic method. Results: It is found that the carbon target increases the proportion of low energy (<150 keV) photons at 10 cm depth to 28% from 8% for the 6 MV standard (Cu/W) beam. This nearly quadrupling of the low energy photon content incident on a gold nanoparticle results in 7.7 times the endothelial dose enhancement as a 6 MV standard (Cu/W) beam at this depth. Increased surface dose from the low Z target can be mitigated by well-spaced beam arrangements. Conclusions: By using the fast-switching target, one can modulate the photon beam during delivery, producing a customized photon energy spectrum for each specific situation.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2014

Elucidating the effects of adsorbent flexibility on fluid adsorption using simple models and flat-histogram sampling methods

Using flat-histogram Monte Carlo methods, we investigate the adsorptive behavior of the square-well fluid in two simple slit-pore-like models intended to capture fundamental characteristics of flexible adsorbent materials. Both models require as input thermodynamic information about the flexible adsorbent material itself. An important component of this work involves formulating the flexible pore models in the appropriate thermodynamic (statistical mechanical) ensembles, namely, the osmotic ensemble and a variant of the grand-canonical ensemble. Two-dimensional probability distributions, which are calculated using flat-histogram methods, provide the information necessary to determine adsorption thermodynamics. For example, we are able to determine precisely adsorption isotherms, (equilibrium) phase transition conditions, limits of stability, and free energies for a number of different flexible adsorbent materials, distinguishable as different inputs into the models. While the models used in this work are relatively simple from a geometric perspective, they yield non-trivial adsorptive behavior, including adsorption-desorption hysteresis solely due to material flexibility and so-called “breathing” of the adsorbent. The observed effects can in turn be tied to the inherent properties of the bare adsorbent. Some of the effects are expected on physical grounds while others arise from a subtle balance of thermodynamic and mechanical driving forces. In addition, the computational strategy presented here can be easily applied to more complex models for flexible adsorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗