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A Monte Carlo radiative transfer model is used to simulate the atmospheric spreading effects. The spreading functions for several vertical aerosol profiles are obtained. The dependence of atmospheric conditions and aerosol properties are investigated, and the importance of the effect on MSS and TM measurements are assessed.
In this Letter we report Ising model simulations of the growth of alloys which predict quite different behavior near and far from equilibrium. Our simulations reproduce the phenomenon which has been termed 'solute trapping,' where concentrations of solute, which are far in excess of the equilibrium concentrations, are observed in the crystal after rapid crystallization. This phenomenon plays an important role in many processes which involve first order phase changes which take place under conditions far from equilibrium. The underlying physical basis for it has not been understood, but these Monte Carlo simulations provide a powerful means for investigating it.
Here, we present a scalable machine learning (ML) framework for large-scale kinetic Monte Carlo (kMC) simulations of itinerant electron Ising systems. As the effective interactions between Ising spins in such itinerant magnets are mediated by conducting electrons, the calculation of energy change due to a local spin update requires solving an electronic structure problem. Such repeated electronic structure calculations could be overwhelmingly prohibitive for large systems. Assuming the locality principle, a convolutional neural network (CNN) model is developed to directly predict the effective local field and the corresponding energy change associated with a given spin update based on Ising configuration in a finite neighborhood. As the kernel size of the CNN is fixed at a constant, the model can be directly scalable to kMC simulations of large lattices. Our approach is reminiscent of the ML force field models widely used in first-principles molecular dynamics simulations. Applying our ML framework to a square-lattice double-exchange Ising model, we uncover unusual coarsening of ferromagnetic domains at low temperatures. Our work highlights the potential of ML methods for large-scale modeling of similar itinerant systems with discrete dynamical variables.
A case is made for including anisotropy in a Monte Carlo flight simulation scheme of low-altitude wind-shear turbulence by means of power spectral density. This study attempts to eliminate all flight simulation-induced deficiencies in the basic turbulence model. A full-scale low-altitude wind-shear turbulence simulation scheme is proposed with particular emphasis on low cost and practicality for near-ground flight. The power spectral density statistic is used to highlight the need for realistic estimates of energy transfer associated with low-altitude wind-shear turbulence. The simulation of a particular anisotropic turbulence model is shown to be a relatively simple extension from that of traditional isotropic (Dryden) turbulence.
The simulation of flowfields in the transition flow regime is notoriously difficult with high demands on computer resources (CPU time and storage) and user expertise/labor. This paper describes a new, efficient code which has been developed to simulate high Knudsen number flowfields in three dimensions about bodies of arbitrarily complex geometry. The algorithm has been tested over a wide range of conditions, from free molecular to near-continuum flow regimes, for slender and blunt bodies, for re-entry vehicles and spacecraft. A series of validation tests have been conducted using both wind-tunnel measurements and flight data.
Low-density carbon fiber preforms, used as thermal protection systems (TPS) materials for planetary entry systems, have permeable, highly porous microstructures consisting of interlaced fibers. Internal gas transport in TPS is important in modeling the penetration of hot boundary-layer gases and the in-depth transport of pyrolysis and ablation products. The gas effective diffusion coefficient of a porous material must be known before the gas transport can be modeled in material response solvers; however, there are very little available data for rigid fibrous insulators used in heritage TPS.The tortuosity factor, which reflects the efficiency of the percolation paths, can be computed from the effective diffusion coefficient of a gas inside a porous material and is based on the micro-structure of the material. It is well known, that the tortuosity factor is a strong function of the Knudsen number. Due to the small characteristic scales of porous media used in TPS applications (typical pore size of the order of 50 micron), the transport of gases can occur in the rarefied and transitional regimes, at Knudsen numbers above 1. A proper way to model the gas dynamics at these conditions consists in solving the Boltzmann equation using particle-based methods that account for movement and collisions of atoms and molecules.In this work we adopt, for the first time, the Direct Simulation Monte Carlo (DSMC) method to compute the tortuosity factor of fibrous media in the rarefied regime. To enable realistic simulations of the actual transport of gases in the porous medium, digitized computational grids are obtained from X-ray micro-tomography imaging of real TPS materials. The SPARTA DSMC solver is used for simulations. Effective diffusion coefficients and tortuosity factors are obtained by computing the mean-square displacement of diffusing particles.We first apply the method to compute the tortuosity factors as a function of the Knudsen number for computationally designed materials such as random cylindrical fibers and packed bed of spheres with prescribed porosity. Results are compared to literature values obtained using random walk methods in the rarefied and transitional regime and a finite-volume method for the continuum regime. We then compute tortuosity factors for a real carbon fiber material with a transverse isotropic structure (FiberForm), quantifying differences between through-thickness and in-plain tortuosities at various Knudsen regimes.
Ablative thermal protection system (TPS) materials for spacecraft are composites that are often made out of carbon-based reinforcement and a polymeric matrix. They endure high-temperature oxidation and surface recession when re-entering Earth’s atmosphere. Ablation is the result of many coupled and competing thermal, mechanical, and chemical phenomena, and it is difficult to isolate the role of each on the overall degradation of the TPS. Here we develop an ablation model for material recession coupled explicitly to finite rate carbon oxidation in complex microstructures. In this work, Stochastic PArallel Rarified-gas Time-accurate Analyzer (SPARTA), a direct-simulation Monte Carlo (DSMC) code, is modified to allow oxidation-driven ablation of implicitly defined carbon surfaces. In SPARTA, implicit surfaces are generated from the grid corner point values via a marching cubes algorithm, therefore creating a new set of surface elements every time ablation is performed. The finite-rate oxidation model developed by Gopalan et. al, was adapted to tally surface reactions and other surface data on a per-grid cell basis. The ablation functionality was also adjusted so once the reactions have occurred, the number of reactions leading to CO formation can be converted to corner point reduction values; therefore, carbon removal is directly proportional to surface recession. We also develop robust algorithms which handle the evolution of the flow cells and solid material regions, including split cells (flow cell divided in two by a solid surface). Finally, we demonstrate our implicit chemistry model for 2D and 3D geometries by producing reaction statistics and detailed visualization of oxidation-induced material recession at the microscale.
A preliminary comparative evaluation of dispersed solar thermal power plants utilizing advanced technologies available in 1985-2000 time frame is under way at JPL. The solar power plants of 50 KWe to 10 MWe size are equipped with two axis tracking parabolic dish concentrator systems operating at temperatures in excess of 1000 F. The energy conversion schemes under consideration include advanced steam, open and closed cycle gas turbines, stirling, and combined cycle. The energy storage systems include advanced batteries, liquid metal, and chemical. This paper outlines a simple methodology for a probabilistic assessment of such systems. Sources of uncertainty in the development of advanced systems are identified, and a computer Monte Carlo simulation is exercised to permit an analysis of the tradeoffs of the risk of failure versus the potential for large gains. Frequency distribution of energy cost for several alternatives are presented.
Warm dense matter (WDM) is an active field of research, with applications ranging from astrophysics to inertial confinement fusion. Ionization degree and continuum lowering are important quantities to understand how materials behave under these conditions, but can be difficult to diagnose since experimental campaigns are limited and often require model-dependent analysis. This is especially true for hydrogen, which has a comparably low scattering cross section, making high-quality data particularly difficult to obtain. Consequently, building equation of state tables often relies on simulations in combination with untested approximations to extract properties from experiments. Here, we investigate an approach for extracting the ionization potential depression and ionization degree—quantities which are otherwise not directly accessible from the physical model—from first-principles path integral Monte Carlo (PIMC) simulations utilizing a chemical model. In contrast to experimental measurements, where noise and nonequilibrium effects add to the uncertainty of the inferred parameters, PIMC simulations provide a clean signal with well-defined thermodynamic conditions. Comparisons against commonly used models show a qualitative agreement, but we find deviations primarily for the high-density and high-temperature cases. We also demonstrate the decreasing sensitivity of the dynamic structure factor with respect to both ionization and continuum lowering for increasing scattering angles in x-ray Thomson scattering experiments. Our work has important implications for the design of future experiments, but also offers qualitative understanding of structure factors and the imaginary-time correlation function obtained from first-principles quantum Monte Carlo simulations.
The shock interaction produced when an incident shock impinges on an inlet cowl lip is investigated for a cowl lip radius of 0.1 in. and flight conditions of Mach 15 and 35-km altitude. This problem is of interest in the design of hypersonic flight vehicles because the interference heating at the cowl lip surface from such a shock interaction is expected to be very high and the design of adequate thermal protection in this region will be challenging. Noncontinuum effects may be significant at this combination of altitude and small dimensions. If this is so, a computational solution based on the Navier-Stokes equations could significantly overpredict the surface heating on the cowl lip. Therefore, a kinetic theory solution technique, direct simulation Monte Carlo (DSMC), is used to predict the flowfield and surface heating. The details of modeling this problem and some preliminary results are presented. The same qualitative flow patterns are seen with DSMC and a continuum solution of the shock interference case, but the peak heating predicted with DSMC is somewhat lower.
INTRODUCTION: The radiation track structure is of crucial importance to understand radiation damage to molecules and subsequent biological effects. Of a particular importance in radiobiology is the induction of double-strand breaks (DSBs) by ionizing radiation, which are caused by clusters of lesions in DNA, and oxidative damage to cellular constituents leading to aberrant signaling cascades. DSB can be visualized within cell nuclei with gamma-H2AX experiments. MATERIAL AND METHODS: In DSB induction models, the DSB probability is usually calculated by the local dose obtained from a radial dose profile of HZE tracks. In this work, the local dose imparted by HZE ions is calculated directly from the 3D Monte-Carlo simulation code RITRACKS. A cubic volume of 5 micron edge (Figure 1) is irradiated by a (Fe26+)-56 ion of 1 GeV/amu (LET approx.150 keV/micron) and by a fluence of 450 H+ ions, 300 MeV/amu (LET approx. 0.3 keV/micron). In both cases, the dose deposited in the volume is approx.1 Gy. The dose is then calculated into each 3D pixels (voxels) of 20 nm edge and visualized in 3D. RESULTS AND DISCUSSION: The dose is deposited uniformly in the volume by the H+ ions. The voxels which receive a high dose (orange) corresponds to electron track ends. The dose is deposited differently by the 56Fe26+ ion. Very high dose (red) is deposited in voxels with direct ion traversal. Voxels with electron track ends (orange) are also found distributed around the path of the track. In both cases, the appearance of the dose distribution looks very similar to DSBs seen in gammaH2AX experiments, particularly when the visualization threshold is applied. CONCLUSION: The refinement of the dose calculation to the nanometer scale has revealed important differences in the energy deposition between high- and low-LET ions. Voxels of very high dose are only found in the path of high-LET ions. Interestingly, experiments have shown that DSB induced by high-LET radiation are more difficult to repair. Therefore, this new approach may be useful to understand the nature of DSB and oxidative damage induced by ionizing radiation.
Cryopump-based direct internal recycling (DIR) of fusion fuel is an attractive prospect because it provides pumping of helium ash from a reactor along with separation of helium and other impurities from the fuel. Previous studies have demonstrated a continuously regenerating cryopump, referred to as the Snail pump, that separates helium ash from fusion fuel for reactor-relevant flow rates. Here, in this study, a conceptual cryopump was designed as a proof-of-principle study to target other impurities, besides helium ash, from the fusion exhaust that would complement the Snail pump. The impurity-removal cryopump consists of four sets of chevron fins, two sets operating at 80 K and the other two at 30 K. A computational study using direct simulation Monte Carlo (DSMC) was performed with a gas mixture of 96.5% D2, 2.0% He, and 0.5% each of CO2, N2, and CH4. In this configuration, 80 K chevron fin sets are capable of capturing CO2 and 30 K sets capable of capturing CO2, N2, and CH4. The computational study showed that the cryopump is capable of reducing the impurity content by more than two orders of magnitude from the flow.
Metal hydrides are important across diverse applications, such as hydrogen storage, batteries, gas sensors, nuclear reactions, and high-temperature superconductivity. Previous computational studies of metal hydrides under extreme pressures, e.g., 𝑂(10 2 ) GPa, usually treat them as stoichiometric compounds without considering interstitial lattice disorder. As pressures become more moderate in the 𝑂(10 0 ) GPa and below range, hydrogen disorder at interstitial lattice sites becomes prominent, e.g., in the N-doped Lu hydride that was recently claimed superconducting near 1 GPa. Further adding compositional complexity from alloying and/or multielement interstitial occupation makes elucidating pressure- and temperature-dependent observables intractable by first-principles calculations alone. We therefore propose a lattice graph neural-network surrogate modeling approach to predict configuration- and pressure-dependent equation-of-state properties. Their efficiency permits Monte Carlo simulations to calculate Gibbs energies and pressure-dependent phase diagrams, thereby revealing insights into the synthesis conditions required for achieving desired phase equilibria. We demonstrate this concept for the compositionally complex cubic Lu(H,N,Va) 3 system where three constituents (hydrogen, nitrogen and vacancy) have disordered multielement interstitial occupancies and insights into pressure-dependent phase equilibria are critically needed, e.g., N-doping levels can significantly lower dehydrogenation temperatures and provide a new strategy to optimize hydrogen-storage alloys. This work can improve the thermodynamic understanding of the Lu-H-N system and help rational synthesis of N-doped Lu hydrides, but more generally demonstrates an efficient approach to model pressure-dependent thermodynamics of multicomponent solid solutions.
ICARUS (Imaging Cosmic and Rare Underground Signals) serves as the Far Detector in the Short Baseline Neutrino (SBN) program at Fermilab, playing a central role in investigating the potential existence of sterile neutrinos in the eV squared mass range. The detector consists of two large Liquid Argon Time Projection Chambers (LArTPCs) with a total capacity of 760 tons of liquid Argon. A key component of the system is its array of 360 Photo-Multiplier Tubes (PMTs), which detect the scintillation light produced by charged particles in liquid Argon; the fast scintillation signal enables accurate event timing, triggering, and reconstruction. Together with the TPC and CRT systems, the PMTs ensure precise interaction time measurements, which are crucial for distinguishing neutrino interactions from cosmic-ray backgrounds. ICARUS uses Hamamatsu R5912-MOD PMTs, optimized for cryogenic temperatures, with high quantum efficiency, excellent timing resolution, low dark current (around 10 nA at 1500 V), and broad spectral sensitivity (300–650 nm). These characteristics are crucial for the efficient detection of scintillation light. Analyzing the waveforms of PMT signals allows for a detailed comparison between experimental data and Monte Carlo simulations. This analysis is fundamental for improving the accuracy of neutrino event reconstruction, enhancing detector calibration, and optimizing the detector's performance for current and future operations.
This report describes initial efforts to model the endlinking reaction of phenylethynyl-terminated oligomers. Several different molecular weights were simulated using the Bond Fluctuation Monte Carlo technique on a 20 x 20 x 20 unit lattice with periodic boundary conditions. After a monodisperse "melt" was equilibrated, chain ends were linked whenever they came within the allowed bond distance. Ends remained reactive throughout, so that multiple links were permitted. Even under these very liberal crosslinking assumptions, geometrical factors limited the degree of crosslinking. Average crosslink functionalities were 2.3 to 2.6; surprisingly, they did not depend strongly on the chain length. These results agreed well with the degrees of crosslinking inferred from experiment in a cured phenylethynyl-terminated polyimide oligomer.
The combined effects of nutrient diffusion and surface kinetics on the crystal morphology were investigated using a Monte Carlo model to simulate the evolving morphology of a crystal growing from a two-component gaseous nutrient phase. The model combines nutrient diffusion, based on a modified diffusion-limited aggregation process, with anisotropic surface-attachment kinetics and surface diffusion. A variety of conditions, ranging from kinetic-controlled to diffusion-controlled growth, were examined. Successive transitions from compact faceted (dominant surface kinetics) to open dendritic morphologies (dominant volume diffusion) were obtained.
A Monte-Carlo soil mixing computer code was used to evaluate isotopic effects induced by galactic cosmic rays in samples on the top surface of the lunar regolith and in lunar core tubes. For surface samples, good agreement was found between theoretical predictions of Ne-21 concentrations, neutron fluences, and Xe-131/Xe-126 ratios, and the corresponding observations.
A general form of the integral equation of exospheric transport on moon-like bodies is derived in a form that permits arbitrary specification of time varying physical processes affecting atom creation and annihilation, atom-regolith collisions, adsorption and desorption, and nonplanetocentric acceleration. Because these processes usually defy analytic representation, the Monte Carlo method of solution of the transport equation, the only viable alternative, is described in detail, with separate discussions of the methods of specification of physical processes as probabalistic functions. Proof of the validity of the Monte Carlo exosphere simulation method is provided in the form of a comparison of analytic and Monte Carlo solutions to three classical, and analytically tractable, exosphere problems. One of the key phenomena in moonlike exosphere simulations, the distribution of velocities of the atoms leaving a regolith, depends mainly on the nature of collisions of free atoms with rocks. It is shown that on the moon and Mercury, elastic collisions of helium atoms with a Maxwellian distribution of vibrating, bound atoms produce a nearly Maxwellian distribution of helium velocities, despite the absence of speeds in excess of escape in the impinging helium velocity distribution.