Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “stochastic simulation”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Neuromorphic heat transport effects in a molecular junction

Understanding energy transport at the nanoscale is an open and fundamental challenge in the molecular sciences with direct implications for the design of new electronics, computing devices, and materials. While nanoscale energy transport under steady-state conditions has been studied extensively, there is much less known about energy transport under time-dependent driving forces, particularly in the far-from-equilibrium regime. In this work, we use nonequilibrium molecular dynamics simulations and stochastic thermodynamics to investigate energy transport in a well-studied nanoscale system—a molecular junction—subjected to a time-periodic temperature gradient. The primary observation is that molecular junctions can exhibit heat transport hysteresis, a phenomenon in which the heat flux through a system depends not only on the instantaneous value of a time-dependent temperature bias but also on the temporal history of that bias. The presented findings illustrate that molecular junctions can exhibit the specific memory effect—heat transport hysteresis—that is essential for the design of thermal neuromorphic computers. This work elucidates a potential pathway toward the realization of such devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Constraining quenching time-scales in galaxy clusters by forward-modelling stellar ages and quiescent fractions in projected phase space

We forward-model mass-weighted stellar ages (MWAs) and quiescent fractions (f Q ) in projected phase space (PPS), using data from the Sloan Digital Sky Survey, to jointly constrain an infall quenching model for galaxies in log (M vir /M ⊙ ) > 14 galaxy clusters at z ∼ 0. We find the average deviation in MWA from the MWA–M⋆ relation depends on position in PPS, with a maximum difference between the inner cluster and infalling interloper galaxies of ∼1 Gyr. Our model employs infall information from N-body simulations and stochastic star-formation histories from the universemachine model. We find total quenching times of t Q = 3.7 ± 0.4 Gyr and t Q = 4.0 ± 0.2 Gyr after first pericentre, for 9 < log (M ⋆ /M ⊙ ) < 10 and 10 < log (M ⋆ /M ⊙ ) < 10.5 galaxies, respectively. By using MWAs, we break the degeneracy in time of quenching onset and time-scale of star formation rate (SFR) decline. We find that time of quenching onset relative to pericentre is $t_{\mathrm{delay}}=3.5^{+0.6}_{-0.9}$ Gyr and $t_{\mathrm{delay}}=-0.3^{+0.8}_{-1.0}$ Gyr for 9 < log (M ⋆ /M ⊙ ) < 10 and 10 < log (M ⋆ /M ⊙ ) < 10.5 galaxies, respectively, and exponential SFR suppression time-scales are τenv ≤ 1.0 Gyr for 9 < log (M ⋆ /M ⊙ ) < 10 galaxies and τenv ∼ 2.3 Gyr for 10 < log (M ⋆ /M ⊙ ) < 10.5 galaxies. Stochastic star formation histories remove the need for rapid infall quenching to maintain the bimodality in the SFR of cluster galaxies; the depth of the green valley prefers quenching onsets close to first pericentre and a longer quenching envelope, in slight tension with the MWA-driven results. Taken together these results suggest that quenching begins close to, or just after pericentre, but the time-scale for quenching to be fully complete is much longer and therefore ram-pressure stripping is not complete on first pericentric passage.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Differentiable stochastic halo occupation distribution

ABSTRACT In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep reinforcement learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models. As a particular example, we focus on the problem of estimating parameters of halo occupation distribution (HOD) models that are used to connect galaxies with their dark matter haloes. Using a combination of continuous relaxation and gradient re-parametrization techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogue realizations. Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte Carlo, and greatly speed up parameter inference. We demonstrate our technique on a mock galaxy catalogue generated from the Bolshoi simulation using a standard HOD model and find near-identical posteriors as standard Markov chain Monte Carlo techniques with an increase of ∼8× in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Quenching through the QCD chiral phase transition

We present a detailed numerical and analytical study of the out-of-equilibrium dynamics of Model G, the dynamical universality class relevant to the chiral phase transition. We perform numerical 3D stochastic (Langevin) simulations of the 𝑂⁡(4) critical point for large lattices in the chiral limit. We quench the system from the high-temperature unbroken phase to the broken phase and study the nonequilibrium dynamics of pion fields. Strikingly, the nonequilibrium evolution of the two-point functions exhibits a regime of growth, a parametrically large enhancement, and a subsequent slow relaxation to equilibrium. We analyze our numerical results using dynamic critical scaling and mean-field theory. The growth of the two-point functions is determined by the nonlinear dynamics of an ideal non-Abelian superfluid, which is a limit of Model G that reflects the broken chiral symmetry. We also relate the nonequilibrium two-point functions to a long-lived parametric enhancement of soft pion yields relative to thermal equilibrium following a quench.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Code for Experiment in Publication “Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations”

Following the Open Research requirements for AGU journals, we must release the code used to perform the experiment described in our recent publication, posted at (https://arxiv.org/abs/2312.04722). This code fits a joint emulator to data from a Discrete Fracture Network (DFN) simulation, performed using the open-source software DFNworks (https://dfnworks.lanl.gov/). All code to be released implements existing methods; there are no novel algorithms nor any major innovations to existing software.

Murph, Alexander↗
DOE OSTI2022

LigninGraphs: lignin structure determination with multiscale graph modeling

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Search for Resonant WZ Production in the Fully Leptonic Final State at 13 TeV With the CMS Detector

A search for an electrically charged, narrow-width diboson (WZ) resonance in the fully leptonic final state using 137:65 fb?1 of data collected by the CMS experiment during the 2016-2018 run period, at the LHC and a center of mass energy of p s = 13 TeV is presented. The search focuses on a channel where the resonance decays to a pair of standard model vector bosons: W and Z. The Z boson subsequently decays to a pair of leptons, either electrons (e) or muons (?). While the W boson decays to a lepton and a neutrino (?). As ? escapes undetected, the neutrino is reconstructed from the missing transverse momentum (Pmisst ) of the event. The analysis is based on a bump-hunt search strategy with background estimation based on stochastically generated simulation events. No significant excess of events is observed above the expected background from the Standard Model and limits are set on the production cross-section of the diboson resonance as a function of the resonance mass at 95% confidence level. Results are interpreted in the context of the Heavy Vector Triplet (HVT) framework.

Hernandez, Andres Vargas↗
DOE OSTI2023

Stochastic and mixed density functional theory within the projector augmented wave formalism for simulation of warm dense matter

Stochastic density functional theory (DFT) and mixed stochastic-deterministic DFT are burgeoning approaches for the calculation of the equation of state and transport properties in materials under extreme conditions. In the intermediate warm dense matter regime, a state between correlated condensed matter and kinetic plasma, electrons can range from being highly localized around nuclei to delocalized over the whole simulation cell. The plane-wave basis pseudopotential approach is thus the typical tool of choice for modeling such systems at the DFT level. Unfortunately, stochastic DFT methods scale as the square of the maximum plane-wave energy in this basis. To reduce the effect of this scaling and improve the overall description of the electrons within the pseudopotential approximation, we present stochastic and mixed DFT approaches developed and implemented within the projector augmented wave formalism. In conclusion, we compare results between the different DFT approaches for both single-point and molecular dynamics trajectories and present calculations of self-diffusion coefficients of solid density carbon from 1 to 50 eV.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Numerical simulations of laser-driven experiments of ion acceleration in stochastic magnetic fields

We present numerical simulations used to interpret laser-driven plasma experiments at the GSI Helmholtz Centre for Heavy Ion Research. The mechanisms by which non-thermal particles are accelerated in astrophysical environments, e.g., the solar wind, supernova remnants, and gamma ray bursts, is a topic of intense study. When shocks are present, the primary acceleration mechanism is believed to be first-order Fermi, which accelerates particles as they cross a shock. Second-order Fermi acceleration can also contribute, utilizing magnetic mirrors for particle energization. Despite this mechanism being less efficient, the ubiquity of magnetized turbulence in the universe necessitates its consideration. Another acceleration mechanism is the lower-hybrid drift instability, arising from gradients of both density and magnetic field, which produce lower-hybrid waves with an electric field that energizes particles as they cross these waves. With the combination of high-powered laser systems and particle accelerators, it is possible to study the mechanisms behind cosmic-ray acceleration in the laboratory. In this work, we combine experimental results and high-fidelity three-dimensional simulations to estimate the efficiency of ion acceleration in a weakly magnetized interaction region. We validate the FLASH magneto-hydrodynamic code with experimental results and use OSIRIS particle-in-cell code to verify the initial formation of the interaction region, showing good agreement between codes and experimental results. We find that the plasma conditions in the experiment are conducive to the lower-hybrid drift instability, yielding an increase in energy ΔE of ~ 264 keV for 242 MeV calcium ions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Evaluation of a Stochastic Mixing Scheme in the Deep Convective Gray Zone Using a Tropical Oceanic Deep Convection Case Study

Abstract A stochastic horizontal subgrid‐scale mixing scheme is evaluated in ensemble simulations of a tropical oceanic deep convection case using a horizontal grid spacing (Δ h ) of 3 km. The stochastic scheme, which perturbs the horizontal mixing coefficient according to a prescribed spatiotemporal autocorrelation scale, is found to generally increase mesoscale organization and convective intensity relative to a non‐stochastic control simulation. Perturbations applied at relatively short autocorrelation scales induce differences relative to the control that are more systematic than those from perturbations applied at relatively long scales that yield more variable outcomes. A simulation with mixing enhanced by a constant factor of 4 significantly increases mesoscale organization and convective intensity, while turning off horizontal subgrid‐scale mixing decreases both. Total rainfall is modulated by a combination of mesoscale organization, areal coverage of convection, and convective intensity. The stochastic simulations tend to behave more similarly to the constant enhanced mixing simulation owing to greater impacts from enhanced mixing as compared to reduced mixing. The impacts of stochastic mixing are robust, ascertained by comparing the stochastic mixing ensembles with a non‐stochastic mixing ensemble that has grid‐scale noise added to the initial thermodynamic field. Compared to radar observations and a higher resolution Δ h = 1 km simulation, stochastic mixing seemingly degrades the simulation performance. These results imply that stochastic mixing produces non‐negligible impacts on convective system properties and evolution but does not lead to an improved representation of convective cloud characteristics in the case studied here.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Validation of the stochastic inversion algorithm for acoustic travel-time tomography: a large eddy simulation study

Acoustic tomography (AT) is explored as a remote sensing technique to obtain instantaneous snapshots of temperature and velocity fluctuations for wind energy applications. This study integrates Large Eddy Simulation (LES) with the Stochastic Inversion (SI) method to validate the algorithm’s capacity for accurate reconstruction of atmospheric fluctuations. The initial findings demonstrate the efficacy of the method in accurately capturing the predominant flow structures. Normalized L2 error evaluations further inform the algorithm’s precision, with errors accentuated in less sampled peripheral regions. The results underscore the method’s promise as a non-intrusive observational tool, with ongoing development poised to improve its precision and reliability.

17 WIND ENERGY↗
DOE OSTI2024

Stochastic Error Cancellation in Analog Quantum Simulation

Analog quantum simulation is a promising path towards solving classically intractable problems in many-body physics on near-term quantum devices. However, the presence of noise limits the size of the system and the length of time that can be simulated. In our work, we consider an error model in which the actual Hamiltonian of the simulator differs from the target Hamiltonian we want to simulate by small local perturbations, which are assumed to be random and unbiased. We analyze the error accumulated in observables in this setting and show that, due to stochastic error cancellation, with high probability the error scales as the square root of the number of qubits instead of linearly. We explore the concentration phenomenon of this error as well as its implications for local observables in the thermodynamic limit. Moreover, we show that stochastic error cancellation also manifests in the fidelity between the target state at the end of time-evolution and the actual state we obtain in the presence of noise. This indicates that, to reach a certain fidelity, more noise can be tolerated than implied by the worst-case bound if the noise comes from many statistically independent sources.

Analog quantum simulation↗
DOE OSTI2022

Gaussian Process Emulators for Volcanic Ash Dispersion Model Tephra2

It is necessary to predict volcanic ash deposition since falling ash is harmful to human activities. Because simulators built for this purpose are computationally expensive, it is popular to use statistical emulators for geophysical hazard analyses, where a large number of simulations are required. Gaussian stochastic process emulators are able to approximate expensive simulations in an accurate and efficient way. Using a relatively small number of simulation runs, a well-trained emulator can accurately predict simulation outputs at massive new input points in a few seconds. Under different explosive eruption conditions, we constructed Gaussian stochastic process emulators for Tephra2, a simulation tool for estimating the accumulation of volcanic ash over a region. Historical wind records are used as input wind data, without assuming a Gaussian wind speed profile or common wind direction among elevations. While there are several inputs for Tephra2, we use no more than three physically motivated variables as emulator inputs to reduce the computational cost of emulations. The emulator outputs predict the mass of tephra per unit area at 50281 grid points around a predetermined vent location.

58 GEOSCIENCES↗
DOE OSTI2024

Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data

We consider a continuous-valued simulation optimization (SO) problem, where a simulator is built to optimize an expected performance measure of a real-world system while parameters of the simulator are estimated from streaming data collected periodically from the system. At each period, a new batch of data is combined with the cumulative data and the parameters are re-estimated with higher precision. The system requires the decision variable to be selected in all periods. Therefore, it is sensible for the decision-maker to update the decision variable at each period by solving a more precise SO problem with the updated parameter estimate to reduce the performance loss with respect to the target system. We define this decision-making process as the multi-period SO problem and introduce a multi-period stochastic approximation (SA) framework that generates a sequence of solutions. Two algorithms are proposed: Re-start SA (ReSA) reinitializes the stepsize sequence in each period, whereas Warm-start SA (WaSA) carefully tunes the stepsizes, taking both fewer and shorter gradient-descent steps in later periods as parameter estimates become increasingly more precise. We show that under suitable strong convexity and regularity conditions, ReSA and WaSA achieve the best possible convergence rate in expected sub-optimality either when an unbiased or a simultaneous perturbation gradient estimator is employed, while WaSA accrues significantly lower computational cost as the number of periods increases. In addition, we present the regularized ReSA, which obviates the need to know the strong convexity constant and achieves the same convergence rate at the expense of additional computation.

Computer Science↗
DOE OSTI2020

Simulating the optical properties of soot using a stochastic soot model

KL extinction measurements are common in literature concerning soot producing sprays because of their nonintrusiveness. Unfortunately, these measurements often rely on bold assumptions of uniform and monodisperse spherical particles. In this work, a Spray A case is simulated using a highly detailed 3D stochastic soot model and the measured and simulated soot volume fraction are compared. Although the simulated volume fraction magnitude is approximately correct, features are much sharper than in the measured data. Optical models based on particle shape are applied to the simulated soot particles to nd the local optical thickness. The modeled optical thickness aligns with the measured volume fraction much more closely than the simulated volume fraction indicating KL extinction measurements are not simply related to soot volume fraction.

Strickland, Tyler↗