Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “direct simulation Monte Carlo methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Anisotropic angular scattering models of elastic electron-neutral collisions for Monte Carlo plasma simulations

Many laboratory and industrial plasma applications require accurate modeling techniques to understand the interplay between microscopic and macroscopic processes. A prime example of this interplay is how particle and Monte Carlo (MC) simulation codes describe angular scattering of electrons following elastic scattering events. The forward peaked nature of high energy electron elastic scattering is relatively trivial to accurately describe in plasma simulations. However, for lower energy collisions, which produce near isotropic or backward peaked differential cross sections, there is not a strong consensus among the plasma modeling community on how to best describe these angular scattering trends. Here, in this study, we propose a systematic method to approximate the aforementioned non-trivial angular scattering behavior with a formula that can be readily implemented in particle-in-cell (PIC) and/or MC plasma simulation codes. The present approach is specifically applied to fusion relevant atomic hydrogen and helium, as well as for molecular hydrogen, and results are also applicable to the atomic isotopes and homonuclear molecular isotopologues of these species. Comparisons between the present angular distribution function and benchmark scattering data were used to validate the proposed models. In addition, two-term Boltzmann calculations and PIC direct simulation MC simulations revealed that the proposed angular distribution function is accurate, agreeing very well with benchmark convergent close-coupling scattering calculations, and electron transport measurements. These studies confirmed that the present angular distribution function model can be utilized without the need of renormalization to the momentum transfer cross section (as opposed to using the elastic scattering integrated cross section), which has been suggested by several studies in order to correct for deficient angular scattering models, and to agree with transport measurements. Hence, the present anisotropic angular scattering model can be utilized to accurately model the momentum transfer as well as the electron trajectories of elastic collisions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

AMSR-2 Daily Snow Depth Data Product Using a Neural Network Algorithm Trained by Collocated ICESat-2 Measurements

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu 1 et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu 2 et al. (2022,2024) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, a neural network (NN) algorithm, employing several channels from AMSR-2 and the humidity vertical profiles GMAO GEOS-IT, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained NN snow-depth was applied to 2014-2020 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. This paper used AMSR-2 data, a passive microwave instrument to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
DOE OSTI2022

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↗
NASA NTRS2012

Low Average Sidelobe Slot Array Antennas for Radiometer Applications

In radiometer applications, it is required to design antennas that meet low average sidelobe levels and low average return loss over a specified frequency bandwidth. It is a challenge to meet such specifications over a frequency range when one uses resonant elements such as waveguide feed slots. In addition to their inherent narrow frequency band performance, the problem is exacerbated due to modeling errors and manufacturing tolerances. There was a need to develop a design methodology to solve the problem. An iterative design procedure was developed by starting with an array architecture, lattice spacing, aperture distribution, waveguide dimensions, etc. The array was designed using Elliott s technique with appropriate values of the total slot conductance in each radiating waveguide, and the total resistance in each feed waveguide. Subsequently, the array performance was analyzed by the full wave method of moments solution to the pertinent integral equations. Monte Carlo simulations were also carried out to account for amplitude and phase errors introduced for the aperture distribution due to modeling errors as well as manufacturing tolerances. If the design margins for the average sidelobe level and the average return loss were not adequate, array architecture, lattice spacing, aperture distribution, and waveguide dimensions were varied in subsequent iterations. Once the design margins were found to be adequate, the iteration was stopped and a good design was achieved. A symmetric array architecture was found to meet the design specification with adequate margin. The specifications were near 40 dB for angular regions beyond 30 degrees from broadside. Separable Taylor distribution with nbar=4 and 35 dB sidelobe specification was chosen for each principal plane. A non-separable distribution obtained by the genetic algorithm was found to have similar characteristics. The element spacing was obtained to provide the required beamwidth and close to a null in the E-plane end-fire direction. Because of the alternating slot offsets, grating lobes called butterfly lobes are produced in non-principal planes close to the H-plane. An attempt to reduce the influence of such grating lobes resulted in a symmetric design.

Rengarajan, Sembiam↗
DOE OSTI2022

Methods for estimating X-ray machine output through measurement and simulation

We report ball grid arrays are increasingly being applied in the electronics industry and may require X-ray inspection to ensure the integrity and correct placement of solder pins. However, as the architecture of integrated circuits continues to narrow while simultaneously growing more complex, the risk of electronic failure due to radiation damage increases. While medical X-ray devices have been held to high standards and are repeatedly shown to be well characterized, devices used for electronic inspection are often lacking detailed characterization. This study presents unique methods to solve for important properties in X-ray inspection devices such as source to object distance and energy spectrum. This information can then be applied to Monte Carlo models to achieve better overall dose estimates to electronics, which will lead to superior manufactured products. Since X-ray devices can vary greatly in source characteristics, this work investigates spectral measurement and Monte Carlo representation of three X-ray devices. For a Philips SRO 33 100 medical diagnostic device, the spectral output followed expected trends given by the prediction software SpekCalc and Spektr. For the Dage XD7500NT, direct measurement showed a spectral artifact that through the use of Gafchromic films, was shown to be a contributing effect in the dose output. For the Rad Source RS1800, a high powered irradiation device, direct spectral measurement was not achieved. However, a Monte Carlo model using an assumed spectra was found to match ion chamber measurements to a high degree.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

On the Statistical Uncertainty of Monte Carlo-Calculated Scattering Sensitivities

Sensitivity coefficients calculated with Monte Carlo codes are widely used for nuclear data uncertainty quantification in the modeling and simulation of complex 3D reactor systems. This study systematically compares sensitivity coefficients and associated statistical uncertainties for the multiplication factor and fuel temperature reactivity across multiple Monte Carlo codes (SCALE/KENO, SCALE/Shift, MCNP, and Serpent) using simple models representing light-water reactors and advanced reactor concepts. For multiplication factor sensitivities, statistical uncertainties are generally acceptable, although scattering sensitivities show significantly larger statistical uncertainties than, for example, fission and capture reactions. Fuel temperature reactivity sensitivities show significantly larger statistical uncertainties across all reactions. Elastic scattering sensitivities are the most problematic: all Monte Carlo codes fail to resolve energy-dependent coefficients, and they produce dramatically different energy-collapsed values. Critically, the use of these sensitivity coefficients in nuclear data uncertainty propagation leads to reduced statistical uncertainties in individual uncertainty contributions. This can lead to the masking of unusable sensitivity coefficients and producing misleading uncertainty results. The findings of this study show that new or enhanced methods are needed to improve Monte Carlo elastic scattering sensitivity calculations. Additionally, this study shows the relevance of verifying sensitivity coefficients through direct perturbation calculations for individual nuclide reactions, instead of only for total cross sections as commonly done.

Bostelmann, Rike [ORNL] (ORCID:0000000165968088)↗
NASA NTRS2016

Landing Gear Noise Prediction and Analysis for Tube-and-Wing and Hybrid-Wing-Body Aircraft

Improvements and extensions to landing gear noise prediction methods are developed. New features include installation effects such as reflection from the aircraft, gear truck angle effect, local flow calculation at the landing gear locations, gear size effect, and directivity for various gear designs. These new features have not only significantly improved the accuracy and robustness of the prediction tools, but also have enabled applications to unconventional aircraft designs and installations. Systematic validations of the improved prediction capability are then presented, including parametric validations in functional trends as well as validations in absolute amplitudes, covering a wide variety of landing gear designs, sizes, and testing conditions. The new method is then applied to selected concept aircraft configurations in the portfolio of the NASA Environmentally Responsible Aviation Project envisioned for the timeframe of 2025. The landing gear noise levels are on the order of 2 to 4 dB higher than previously reported predictions due to increased fidelity in accounting for installation effects and gear design details. With the new method, it is now possible to reveal and assess the unique noise characteristics of landing gear systems for each type of aircraft. To address the inevitable uncertainties in predictions of landing gear noise models for future aircraft, an uncertainty analysis is given, using the method of Monte Carlo simulation. The standard deviation of the uncertainty in predicting the absolute level of landing gear noise is quantified and determined to be 1.4 EPNL dB.

Guo, Yueping↗
DOE OSTI2025

Simulation Center for Runaway Electron Avoidance and Mitigation (SCREAM SciDAC) (Technical Final Report)

Runaway electrons can severely damage the plasma facing components on ITER during a major disruption and pose a major risk for tokamak fusion. It has been recognized that an adequate disruption mitigation system (DMS) is essential for the safe operation of ITER. The United States is responsible for the design and implementation of the disruption mitigation system on ITER, and in July 2016 the Simulation Center for Runaway Electron Avoidance and Mitigation (SCREAM) was launched by DOE, in a joint Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) collaboration. SCREAM was a comprehensive theory and simulation SciDAC center that provided physics guidance in the avoidance and mitigation of runaway electrons, and in tandem with domestic and international experiments, helped establish the qualitative and quantitative bases for safe operational scenarios and viable mitigation techniques. The SCREAM center assembled a national team of experts in runaway electron physics, tokamak disruptions, magnetohydrodynamic (MHD) simulation, and advanced algorithms and computing. The team combined advanced simulation and analysis capability facilitated by direct participation of ASCR SciDAC institutes with theoretical models and code development by FES scientists to focus on the runaway risk for ITER and tokamaks in general. The research scope was focussed on integrated simulations of kinetic runaway electrons, including MHD and fluid models of impurity transport, within a research plan guided by theory. The specific research tasks were (1) establish the fundamental physics of runaway generation, saturation, and dynamical evolution in a tokamak; (2) examine the critical path toward runaway avoidance; and (3) investigate the viability and effectiveness of the leading candidate schemes for runaway mitigation. In all three areas, members of the team carried out scoping studies that established the readiness for rapid and critical advances, especially in the deployment and further development of large-to extreme-scale simulation tools. Our multi-pronged computational approach included (1) relativistic Fokker-Planck solvers with discretization in phase space, (2) self-consistent particle-in-cell techniques, (3) particle-based Monte-Carlo, and (4) MHD-particle hybrid simulations. Cross-check between these different methods provided an additional means for verification and further bolstered the fidelity of our physics prediction. Validation against experimental results brings confidence to the predictive capability for ITER and frequently leads to new ideas for understanding and mitigating the thermal quench driven runaway electron phenomenon.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Decoding Golden Eagle Movement Behavior from High-Resolution, Variable-Rate Telemetry Data Through Bayesian Filtering

The recent advances in animal tracking technology have enabled the collection of a vast amount of in situ data regarding the movement of wildlife at high spatiotemporal resolution. These data are usually available at variable time resolutions and contains noise (error) originating from GPS fixes. Decoding movement characteristics, particularly of flying animals, from telemetry data while handling these factors is a challenging yet important task for conservation purposes. Typically, this task is broken into two subtasks: resampling, and model calibration. The resampling subtask converts the variable rate positional data into a constant time interval data, while the model calibration subtask uses the resampled data to tune time-invariant parameters of the proposed models. For telemetry data at high temporal resolutions (order of 1 second), it is very challenging to decouple noise from actual movements using interpolation-based resampling techniques. Any errors introduced during resampling can significantly alter the the calibration and prediction attributes of the movement model. We address this problem through a unified Bayesian state-space framework that can handle both the resampling and calibration tasks in a single step. In addition, we use the speed and heading of the bird from telemetry data to regularize the position information of the bird. We use a Kalman filtering approach to include these nonlinearly related motion parameters within the state space framework. We cross-validated to quantify how this inclusion affects the model performance in estimating true bird movements. The relationship between the true state of the bird and environmental and topographical covariates is then represented parametrically. These parameters are then tuned using stochastic sampling strategies like Markov Chain Monte Carlo (MCMC). We use the telemetry data collected from golden eagles in the western USA to demonstrate the applicability of this approach to build a predictive, probabilistic movement model. Our preliminary results show that this approach provides improved predictive performance in terms of capturing higher-order motion parameters such as angular and horizontal accelerations, which may have simpler and more direct relationships with environmental covariates than corresponding speeds. In this talk, we will demonstrate how this state-space approach benefits the prediction capabilities of a movement model in simulating golden eagle paths through a wind power plant in Wyoming given certain atmospheric conditions. The model outcomes are aimed at informing mitigation strategies that can minimize the potential for collisions of golden eagles with wind turbines.

Bayesian methods↗
DOE OSTI2021

An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System

The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, we, in this paper, propose a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning

Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts. To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U, allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An all-digital associated particle imaging system for the 3D determination of isotopic distributions

Associated particle imaging (API) is a non-destructive nuclear technique for the 3D determination of isotopic distributions. By detecting the alpha particle associated with the emitted neutron in the deuterium–tritium fusion reaction with a position- and time-resolving detector, the direction of the 14.1 MeV neutron and its time of emission can be determined. Employing this method, isotope characteristic gamma rays emitted in inelastic neutron scattering events can be correlated with the neutron interaction location. Here, an API system consisting of a sealed-type neutron generator, gamma detectors, and a position-sensitive alpha detector was designed, constructed, and characterized. The system was tested with common soil elements and shown to be sensitive to 12 C, 16 O, 28 Si, 27 Al, and 56 Fe. New aspects of our approach are the use of a yttrium–aluminum–perovskite scintillator, using a sapphire window instead of a fiber-optic faceplate for light transport to the photomultiplier, and the all-digital data acquisition system. We present a description of the system with simulations and experimental results that show a position resolution on the alpha detector of 1 mm, a depth resolution using a LaBr 3 detector of 6.2 cm, and an angular resolution of 4.5°. Additionally, we present single-element gamma response measurements for the elements mentioned above together with a comparison to Monte Carlo simulations (MCNP6).

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Reaching the equilibrium state of the frustrated triangular Ising magnet Ca 3 Co 2 O 6

Ca 3 Co 2 O 6 is a frustrated magnet consisting of a triangular arrangement of chains of Ising spins. It shows regular magnetization steps versus magnetic field every 1.2 T that are metastable with very slow dynamics. This has puzzled the community for many years and given rise to numerous potential theories. Here we approach the problem by seeking the elusive magnetic equilibrium state at T=2 K. To this end, we explore two approaches: (1) bypassing the slow dynamics produced by changing fields by instead field-cooling directly to the target temperature and (2) quantum annealing in transverse magnetic fields. While we observe no measurable effect of the quantum annealing in fields up to 7 T, which is likely due to the large Ising anisotropy of Co spins in this material, we find that for the field cooling in longitudinal fields we achieve the predicted equilibrium 1/3 magnetization. We perform Monte Carlo simulations of the ground state phase diagram and we also simulate the quantum annealing process and find good agreement between experiment and theory. Thus we present an investigation of the elusive ground state properties of the canonical frustrated triangular system Ca 3 Co 2 O 6 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Investigation of Best-Practices and Computationally Inexpensive Radiative Exchange Models for Discrete Element Method Modeling of Aluminosilicate Particles in Concentrating Solar Power Environments

Chemically inert, aluminosilicate based particles have been investigated as both a thermal transport and sensible energy storage medium for concentrating solar power facilities. These particles will experience a wide range of operating temperatures (300-1000 K) and handling conditions (dense to dilute falling particle curtains, dense granular flows, or dense structures), requiring specially-designed and optimized infrastructures. The relative influence of collisional and frictional interactions between particles varies based on temperature-dependent particulate properties and greatly impacts the bulk, granular flow behavior. These underlying physics are captured using discrete element method modeling tools. However, this modeling method is computationally expensive as each particle position and interaction is tracked during the simulation. These modeling methods are further complicated by introducing temperature-dependent particle properties, high-temperature radiative exchange, and directional irradiation sources experienced by granular flows in concentrating solar power environments. In this study, coupled experimental and numerical slump testing of aluminosilicate particles was performed and computationally efficient radiative exchange models were evaluated to establish best-practices for discrete element method models for concentrating solar power environments. The three particle types investigated included Carbobead HSP 30 /60, Carbobead CP 30/60, and Granusil 4030. Existing modeling limitations and computationally-efficient multi-modal heat transfer models were evaluated using Aspherix®, a commercial discrete element method software. High-temperature (< 1073 K) slump testing of aluminosilicate particles was performed to investigate the deviation between experimentally-observed and numerically-predicted angles of repose introduced by computation-time reduction practices including the relaxation of the particle elastic modulus and coarse-graining. Coarse-graining is used to use a single modeled particle that is representative of a collection of smaller particles, decreasing the computational cost at the expense of geometric accuracy. Additionally, relaxation of the elastic modulus is used to reduce computational time at the expense of an increased, modeled particle overlap. Prior studies have determined that aluminosilicate particles retain a high elastic modulus at high temperatures (< 1073 K), requiring small simulation timesteps to ensure resolved contact forces resemble appropriate solid mechanics. A parametric study was performed to evaluate the influence of computation time improvements on the deviation between experimental and modeled angle of repose across high temperatures < 1073 K. Additionally, numerical case studies were performed on candidate particle systems at varying porosities and temperatures. These studies were performed to investigate the influence of computationally-efficient radiative-exchange modeling methods coupled to Aspherix® on modeled accuracy and computation time. The recently-developed distance-based approximation was evaluated in estimating radiative exchange between particles and participating surfaces located in close proximity. The distance based approximation was developed to use tabulated estimates of the radiative distribution factor between individual particles and surfaces in close proximity (< 40 particle radii). These methods were expanded to the aluminosilicate particles of interest, including the influence of particle size distributions. To capture radiative exchange between particles and surfaces not in close proximity (> 40 particle radii) and to capture the absorption of directional irradiation from concentrating solar resources, a volumetrically-averaged radiative distribution factor was calculated between the modeled granular flow and surfaces using Monte Carlo ray-tracing for participating media. Volume-averaged absorption and scattering coefficients were predicted using a volumetric discretization of the modeled domain with monodisperse approximations based on geometric optics and experimentally-determined scattering phase functions for aluminosilicate particles.

14 SOLAR ENERGY↗
DOE OSTI2021

Neuroevolutionary Learning of Particles and Protocols for Self-Assembly

Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without input from physical concepts such as thermal equilibrium or mechanical stability and without prior knowledge of candidate or competing structures. Furthermore, the learning algorithm is capable of both directed and exploratory design: it can assemble a material with a user-defined property, or search for novelty in the space of specified order parameters. In the latter mode it explores the space of what can be made, rather than the space of structures that are low in energy but not necessarily kinetically accessible.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Understanding Reentrance in Frustrated Magnets: The Case of the Er 2 Sn 2 O 7 Pyrochlore

Reentrance, the return of a system from an ordered phase to a previously encountered less-ordered one as a controlled parameter is continuously varied, is a recurring theme found in disparate physical systems, yet its microscopic cause is often not investigated thoroughly. Here, through detailed characterization and theoretical modeling, we uncover the microscopic mechanism behind reentrance in the strongly frustrated pyrochlore antiferromagnet Er 2 Sn 2 O 7 . We use single crystal heat capacity measurements to expose that Er 2 Sn 2 O 7 exhibits multiple instances of reentrance in its magnetic field B vs temperature T phase diagram for magnetic fields along three cubic high symmetry directions. Through classical Monte Carlo simulations, mean field theory, and classical linear spin-wave expansions, we argue that the origins of the multiple occurrences of reentrance observed in Er 2 Sn 2 O 7 are linked to soft modes. These soft modes arise from phase competition and enhance thermal fluctuations that entropically stabilize a specific ordered phase, resulting in an increased transition temperature for certain field values and thus the reentrant behavior. Our work represents a detailed examination into the mechanisms responsible for reentrance in a frustrated magnet and may serve as a template for the interpretation of reentrant phenomena in other physical systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Correlating isothermal compressibility to nucleon fluctuations in the inner crust of neutron stars

The question of how and which physical observables or thermodynamic parameters can best predict the onset of a possible phase transition in the inner crust of neutron stars remains largely unresolved. Here, using semiclassical Monte Carlo simulations, we investigate the isothermal compressibility and density fluctuations in a region of relevance to the dynamics of the inner crust. We show that the isothermal compressibility serves as a robust observable to characterize the transition from the nonuniform crust to the uniform core for proton fractions over 0.2. Moreover, we show explicitly how the two-component isothermal compressibility, computed using the Kirkwood-Buff theory, is directly connected to the fluctuations in the number density, recorded in the grand canonical ensemble by monitoring the number of particles in a small volume located at the center of the simulation box. That is, we compute mean-square particle fluctuations and compare them against the isothermal compressibility for different proton fractions. Although our results show that the mean-square particle fluctuations are proportional to the isothermal compressibility, the lack of a perfect correlation is attributed to the relatively small number of particles included in the simulations. The nonunity slope observed in the dimensionless isothermal compressibility—total nucleon fluctuation variance relationship suggests that the inner crust of neutron stars is composed of anisotropic and inhomogeneous matter.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Beyond Magic Barrels: Digital manufacturing for crystallization, process development and optimization of explosive materials: Part II Resveratrol Exemplar

This SAND report summarizes work supported by an Engineering Sciences Research Foundation (ESRF) Lab Directed Research and Development (LDRD) project entitled “Beyond Magic Barrels: Digital manufacturing for crystallization, process development and optimization of explosive materials.” This SAND report is written in two parts with Part 1 discusses recrystallization of our explosive exemplar and Part 2 summarizing our work with recrystallization of resveratrol. We have studied resveratrol recrystallization with a multiscale approach combining experiments, modeling and simulation. At the single crystal scale, microscopy experiments illuminate crystal time-dependent growth rates using advanced image analysis. Bench scale experiments were carried out to look at growth of multiple particles in a small reactor creating thousands of particles and analyzing the results with microscopy and μCT. For the modeling we combine kinetic Monte Carlo (kMC) models with subscale information from density functional theory (DFT) or molecular dynamics. This work is discussed in Part 1 and can also be found in a paper from the project discussing a coarse-grained kMC model specifically developed for resveratrol. For well-mixed systems, we have population balance equations (PBE) linked with species mass conservation forming a set of ordinary differential equations that can be solved quickly. For more complicated geometries, such as the vat crystallization used throughout the complex, a coupled computational fluid dynamic (CFD)/PBE method was developed to account for gradients in temperature and concentration and differences in crystallization rates throughout the domain. These simulations are more complex and require high performance computing. We present results for two cases: 5% seed fast cool with parameters fit to the well-mixed case and 5% seed slow cool using the same parameters. We show reasonable agreement with experiments though are particles are significantly larger than the experiments.

36 MATERIALS SCIENCE↗