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DOE OSTI2022

Modeling rarefied gas chemistry with QuiPS, a novel quasi-particle method

The goal of this work is to build up the capability of quasi-particle simulation (QuiPS), a novel flow solver, such that it can adequately model the rarefied portion of an atmospheric reentry trajectory. Direct simulation Monte Carlo (DSMC) is the conventional solver for such conditions, but struggles to resolve transient flows, trace species, and high-level internal energy states due to stochastic noise. Quasi-particle simulation (QuiPS) is a novel Boltzmann solver that describes a system with a discretized, truncated velocity distribution function. The resulting fixed-velocity, variable weight quasi-particles enable smooth variation of macroscopic properties. The distribution function description enables the use of a variance-reduced collision model, greatly minimizing expense near equilibrium. This work presents the addition of a neutral air chemistry model to QuiPS and some demonstrative 0D simulations. The explicit representation of internal distributions in QuiPS reveals some of the flaws in existing physics models. Overall, variance reduction, a key feature of QuiPS, can greatly reduce expense of multi-dimensional calculations, but is only cheaper when the gas composition is near chemical equilibrium.

42 ENGINEERING↗

DOE OSTI2022

Comparison and calibration of dose delivered by 137 Cs and x-ray irradiators in mice

Objective. The Office of Radiological Security, U.S. Department of Energy's National Nuclear Security Administration, is implementing a radiological risk reduction program which seeks to minimize or eliminate the use of high activity radiological sources, including 137 Cs, by replacing them with non-radioisotopic technologies, such as x-ray irradiators. The main goal of this paper is to evaluate the equivalence of the dose delivered by gamma- and x-ray irradiators in mice using experimental measurements and Monte Carlo simulations. We also propose a novel biophantom as an in situ dose calibration method. Approach. We irradiated mouse carcasses and 3D-printed mouse biophantoms in a 137 Cs irradiator (Mark I-68) and an x-ray irradiator (X-Rad320) at three voltages (160 kVp, 225 kVp and 320 kVp) and measured the delivered radiation dose. A Geant4-based Monte Carlo model was developed and validated to provide a comprehensive picture of gamma- and x-ray irradiation in mice. Main Results. Our Monte Carlo model predicts a uniform dose delivered in soft-tissue for all the explored irradiation programs and in agreement with the absolute dose measurements. Our Monte Carlo model shows an energy-dependent difference between dose in bone and in soft tissue that decreases as photon energy increases. Dose rate depends on irradiator and photon energy. We observed a deviation of the measured dose from the target value of up to –9% for the Mark I-68, and up to 35% for the X-Rad320. The dose measured in the 3D-printed phantoms are equivalent to that in the carcasses within 6% uncertainty. Significance. Our results suggest that 320 kVp irradiation is a good candidate to substitute 137 Cs irradiation barring a few caveats. There is a significant difference between measured and targeted doses for x-ray irradiation that suggests a strong need for in situ calibration, which can be achieved with 3D-printed mouse biophantoms. A dose correction is necessary for bone doses, which can be provided by a Monte Carlo calculation. Lastly, the biological implications of the differences in dose rates and dose per photon for the different irradiation methods should be carefully assessed for each small-animal irradiation experiment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

Numerical-heating effects in atmospheric pressure streamer discharges simulated with a PIC code

Artificial heating in plasma simulations is a well-known phenomenon which occurs when, among other things, the Debye length is poorly resolved by the simulation mesh. Here, in this work, the degree to which numerical-heating occurs during a simulation of a nanosecond atmospheric pressure streamer discharge is examined. The streamer is simulated using a two-dimensional finite-element, particle-in-cell code Empire, which uses direct simulation Monte Carlo for binary particle interactions. Initially, an estimate of the numerical-heating rate applied to Empire is performed using a simple plasma model. Second, a positive atmospheric pressure streamer discharge simulation is performed to study the effects of numerical heating on plasma density, electron temperature, and streamer velocity. The nominal Debye length is approximately 1 μm and the amount of numerical heating introduced in the simulation is varied by using mesh sizes ranging from 2 μm to 20 μm. A measurable numerical heating quantity is proposed that can be used to estimate the appropriate element size and quantify the numerical-heating that can be expected over the simulation time for an atmospheric pressure streamer. In conclusion while Δx/λ D violations can be an issue it is not likely to be an issue with streamer discharges that are temporally short and occur in environments where collision frequencies are high. This result validates the rationale of grid size choices for a large amount of previously published works where Δx/λ D violation was not clearly addressed. Primary finding of this work is that numerical heating is of minor concern for plasma simulations where electron–neutral collisions are numerous such that multiple collisions can occur within a single plasma period.

Nikic, Dejan [University of New Mexico, Albuquerqu↗

DOE OSTI2021

Idealized simulation study of the relationship of disdrometer sampling statistics with the precision of precipitation rate measurement

Due to the discretized nature of rain, the measurement of a continuous precipitation rate by disdrometers is subject to statistical sampling errors. Here, Monte Carlo simulations are employed to obtain the precision of rain detection and rate as a function of disdrometer collection area and compared with World Meteorological Organization guidelines for a 1 min sample interval and 95 % probability. To meet these requirements, simulations suggest that measurements of light rain with rain rates R ≤ 0.50 mm h-1 require a collection area of at least 6 cm × 6 cm, and for R = 1 mm h -1 , the minimum collection area is 13 cm × 13 cm. For R = 0.01 mm h -1 , a collection area of 2 cm × 2 cm is sufficient to detect a single drop. Simulations are compared with field measurements using a new hotplate device, the Differential Emissivity Imaging Disdrometer. The field results suggest an even larger plate may be required to meet the stated accuracy, likely in part due to non-Poissonian hydrometeor clustering.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Simulating quantum mechanics with a $\theta$-term and an ’t Hooft anomaly on a synthetic dimension

A topological θ-term in gauge theories, including quantum chromodynamics in 3 +1 dimensions, gives rise to a sign problem that makes classical Monte Carlo simulations impractical. Quantum simulations are not subject to such sign problems and are a promising approach to studying these theories in the future. In the near term, it is interesting to study simpler models that retain some of the physical phenomena of interest and their implementation on quantum hardware. For example, dimensionally reducing gauge theories on small spatial tori produces quantum mechanical models which, despite being relatively simple to solve, retain interesting vacuum and symmetry structures from the parent gauge theories. We consider quantum mechanical particle-on-a-circle models, related by dimensional reduction to the 1 + 1d Schwinger model, that possess a θ-term and realize an ’t Hooft anomaly or global inconsistency at θ = π. These models also exhibit the related phenomena of spontaneous symmetry breaking and instanton-anti-instanton interference in real time. We propose an experimental scheme for the real-time simulation of a particle on a circle with a θ-term and a $\mathbb{Z}_n$ potential using a synthetic dimension encoded in a Rydberg atom. Simulating the Rydberg atom with realistic experimental parameters, we demonstrate that the essential physics can be well captured by the experiment, with expected behavior in the tunneling rate as a function of θ. Similar phenomena and observables can also arise in more complex quantum mechanical models connected to higher-dimensional non-Abelian gauge theories by dimensional reduction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

COG User's Manual: A Multiparticle Monte Carlo Transport Code (Sixth Edition)

COG is a high-resolution code for the Monte Carlo simulation of coupled particle transport in arbitrary 3-D geometry. COG will transport neutrons, protons, deuterons, alpha particles with energies up to hundreds of GeV, and photons with energy ranges limited by the available cross section sets and physics models. Electrons can be transported via the EGS5 electron transport kernel, electrons can also be transported. The COG code is a significant upgrade from earlier Monte Carlo transport codes and has been written specifically to make it more versatile, accurate, and easy to use. COG has provisions for calculating deep penetration (shielding) problems, criticality problems, and neutron activation problems while retains all of the standard capabilities found in other Monte Carlo transport codes. COG uses high-resolution pointwise cross-section databases and makes no compromises in the transport physics, so that the results of a COG run are limited only by the accuracy of the databases used. COG runs primarily on Linux Operating System workstations with MPICH software installed – currently, Red Hat 7 & 8, Windows 10 (Windows Subsystem for Linux –WSL), Ubuntu 16, 18 & 20, OpenSUSE Leap 15.2, Fedora 32, Apple Power Mac with Intel CPU (with MacPorts installed) workstations, and LLNL LC supercomputer CTS-1 cluster with TOSS 3 are supported.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

COG11.3 Abstract

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials

As a corollary of the rapid advances in computing, ab initio simulation is playing an increasingly important role in modeling materials at the atomic scale. Two strategies are possible, ab initio Monte Carlo (AIMC) and molecular dynamics (AIMD) simulation. The former benefits from exact sampling from the correct thermodynamic distribution, while the latter is typically more efficient with its collective all-atom coordinate updates. In this study, using a relatively simple test model comprised of helium and argon, we show that AIMC can be brought up to, and even above, the performance levels of AIMD via a hybrid nested sampling/machine learning (ML) strategy. Here, ML provides an accurate classical reference potential (up to three-body explicit interactions) that can pilot long collective Monte Carlo moves that are accepted or rejected in toto à la nested Monte Carlo (NMC); this is in contrast to the single move nature of a naive implementation. Our proposed method only requires a small up front expense from evaluating the ab initio energies and forces of (100) random configurations for training. Importantly, our method does not totally rely on the trained, assuredly imperfect, interaction. We show that high performance and exact sampling at the desired level of theory can be realized even when the trained interaction has appreciable differences from the ab initio potential. Remarkably, at the highest levels of performance realized via our approach, a pair of statistically uncorrelated atomic configurations can be generated with (1) ab initio calculations.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

Entropy-Driven Porous Liquids Allowing Gas Solubility in Solvent-Filled Imine-Based Porous Organic Cages

Porous liquids offer a promising platform for gas separation by combining fluid processability with intrinsic molecular porosity. Traditional Type II porous liquids are formed by dissolving porous molecular cages in size-excluded solvents, limiting solvent options and practical applications. In this work, we introduce a novel method of creating Type II porous liquids using common small solvents, where intrinsic porosity is achieved at elevated pressures due to the selective displacement of solvent molecules by gas molecules within the cage structures. Using molecular simulations, we investigate the behavior of CO 2 in solutions of the imine-based porous organic cage CC13 dissolved in small molecular solvents such as chloroform and 1,2-dimethoxyethane (DME). Grand canonical Monte Carlo simulations of solid-state CC13 reveal that selectivity reversal, where smaller CO 2 molecules displace larger solvent molecules inside the cage, is achievable at sufficiently high pressures. Temperature quench molecular dynamics simulations confirm that while CO 2 displacement within chloroform-filled cages is limited, DME enables entropy-driven cage CO 2 occupancy at pressures as low as ∼23 bar, setting up the foundation of an alternative way of forming Type II porous liquids.

adsorption↗

DOE OSTI2020

A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration

A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, here we propose a cost-effective, data-driven approach to assessing a power system's load margin probabilistically. Using actual wind data, a kernel density estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of traditional Monte-Carlo simulations that process a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian-learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate, which serves as a highly efficient, parametric Bayesian prior, is developed. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Results of simulations that are performed on several test systems reveal the impressive performance of the proposed method in the probabilistic load-margin assessment.

17 WIND ENERGY↗

DOE OSTI2021

Development of a Pulsed Slowing-Down-Time Benchmark of Neutron Thermalization in Graphite

Graphite is a classic neutronic material that has been used as both a reactor reflector and moderator in various nuclear reactor systems. The ability to accurately predict the slowing down and thermalization of neutrons in graphite can have significant implications on the safety and operation of such reactor systems. In reactors, the neutron thermalization process is quantified using the thermal scattering law (TSL) and related cross sections for a given moderator. An ideal approach to assess the validity of TSL data is using benchmark measurement based on the pulsed Slowing-Down-Time technique and its comparison with the appropriate graphite nuclear library. In this work, experimental measurement and computational Monte Carlo simulations were performed to benchmark the slowing down characteristics and thermalization of neutrons in nuclear (reactor-grade) graphite. Given the density of graphite, various graphite libraries were selected for the benchmark analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Single particle detection system for strong-field QED experiments

Abstract Measuring signatures of strong-field quantum electrodynamics (SF-QED) processes in an intense laser field is an experimental challenge: it requires detectors to be highly sensitive to single electrons and positrons in the presence of the typically very strong x-ray and γ -photon background levels. In this paper, we describe a particle detector capable of diagnosing single leptons from SF-QED interactions and discuss the background level simulations for the upcoming Experiment-320 at FACET-II (SLAC National Accelerator Laboratory). The single particle detection system described here combines pixelated scintillation LYSO screens and a Cherenkov calorimeter. We detail the performance of the system using simulations and a calibration of the Cherenkov detector at the ELBE accelerator. Single 3 GeV leptons are expected to produce approximately 537 detectable photons in a single calorimeter channel. This signal is compared to Monte-Carlo simulations of the experiment. A signal-to-noise ratio of 18 in a single Cherenkov calorimeter detector is expected and a spectral resolution of 2% is achieved using the pixelated LYSO screens.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Trivializing Flow in 2D-O(3) model

The two dimensional O(3) sigma model, just as quantum chromodynamics, is an asymptotically free theory with a mass gap. Therefore, it is an interesting and simple toy model to investigate algorithms for Markov Chain Monte Carlo simulations of quantum chromodynamics. In this talk, we discuss the construction of a trivializing map, a field transformation from a given theory to a trivial one, through a suitably chosen gradient flow. An analytic solution for the generating functional of this trivializing flow has been obtained by a perturbative expansion in the flow time. Utilizing this solution allows for new approaches to be considered when proposing updates for a Markov Chain Monte Carlo algorithm.

Chamness, Christopher↗

DOE OSTI2025

Lattice Effective Field Theory Simulations of Nuclei

Lattice effective field theory applies the principles of effective field theory in a lattice framework where space and time are discretized. Nucleons are placed on the lattice sites, and the interactions are tuned to replicate the observed features of the nuclear force. Monte Carlo simulations are then employed to predict the properties of nuclear few- and many-body systems. Here, we review the basic methods and several theoretical and algorithmic advances that have been used to further our understanding of atomic nuclei.

nuclear lattice effective field theory↗

DOE OSTI2020

Magnetic multilayer edges in Bernal-stacked hexagonal boron nitride

Single-layer h-BN is known to have edges with unique magnetism; however, in the commonly fabricated multilayer AA'-h-BN, edge relaxations occur that create interlayer bonds and eliminate the unpaired electrons at the edge. Recently, a robust method of growing the unconventional Bernal-stacked h-BN (AB-h-BN) has been reported. Here, we use theoretical approaches to investigate the nitrogen-terminated zigzag edges in AB-h-BN that can be formed in a controlled fashion using a high-energy electron beam. We find that these "open"edges remain intact in bilayer and multilayer AB-h-BN, enabling researchers potentially to investigate these edge states experimentally. We also investigate the thermodynamics of the spin configurations at the edge by constructing a lattice model that is based on parameters extracted from a set of first-principles calculations. We find that the edge spins in neighboring layers interact very weakly, resulting in a sequence of independent spin chains in multilayer samples. By solving this model using Monte Carlo simulations, we can determine nm-scale correlation lengths at liquid-N 2 temperatures and lower. At low temperatures, these edges may be utilized in magnetoresistance and spintronics applications.

1-dimensional spin chains↗

DOE OSTI2022

Essential role of magnetic frustration in the phase diagrams of doped cobaltites

Doped perovskite cobaltites (e.g., La 1–x Sr x CoO 3 ) have been extensively studied for their spin-state physics, electronic inhomogeneity, and insulator-metal transitions. Ferromagnetically interacting spin-state polarons emerge at low x in the phase diagram of these compounds, eventually yielding long-range ferromagnetism. The onset of long-range ferromagnetism (x≈0.18) is substantially delayed relative to polaron percolation (x≈0.05), however, generating a troubling inconsistency. Here, in this study, Monte Carlo simulations of a disordered classical spin model are used to establish that previously ignored magnetic frustration is responsible for this effect, enabling faithful reproduction of the magnetic phase diagram.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Computational investigation of the impact of metal–organic framework topology on hydrogen storage capacity

Metal–organic frameworks (MOFs) are promising, tunable materials for hydrogen storage. For application under cryogenic operating conditions, past work has run into a ceiling on performance due to a trade-off in the volumetric deliverable capacity (VDC) versus the gravimetric deliverable capacity (GDC). In this study, we computationally constructed and screened 105 230 MOF structures based on 529 nets to explore the effect of underlying topology on the hydrogen storage performance of the resulting materials. A machine learning model was developed based on simulated hydrogen uptake to facilitate screening of the entire dataset, and it successfully identified the top 10% of materials with a root-mean-square error of approximately 1 g L −1 as validated by subsequent grand canonical Monte Carlo simulations. We identified a promising structure based on the tsx topology that exhibits both VDC and GDC higher than the current benchmark material, MOF-5. Our data-driven analysis indicates that nets with higher net density yield MOFs with enhanced volumetric and gravimetric surface areas, thereby improving maximum VDC while shifting the capacity trade-off toward higher GDC.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Deconvolving the components of the sign problem

Auxiliary field quantum Monte Carlo simulations of interacting fermions require sampling over a Hubbard-Stratonovich field h introduced to decouple the interactions. The weight for a given configuration involves the products of the determinant of matrices $M_σ(h)$ where σ labels the species, and hence is typically not positive definite. Indeed, the average sign $\langle \mathscr {L} \rangle$ of the determinants goes to zero exponentially with increasing spatial size and decreasing temperature for most Hamiltonians of interest. This statement, however, does not explicitly separate two possible origins for the vanishing of $\langle \mathscr {L} \rangle$. Does $\langle \mathscr {L} \rangle$ → 0 because randomly chosen field configurations have det[M(h)] < 0, or does the sign problem arise because the specific subset of configurations chosen by the weighting function have a greater preponderance of negative values? In the latter case, the process of weighting the configurations with |det[M(h)]| might steer the simulation to a region of configuration space of h where positive and negative determinants are equally likely, even though randomly chosen h would preferentially have determinants with a single dominant sign. Here in this paper, we address the relative importance of these two mechanisms for the vanishing of $\langle \mathscr {L} \rangle$ in quantum simulations.

36 MATERIALS SCIENCE↗