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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.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Recurrent dynamics in an epidemic model due to stimulated bifurcation crossovers

Epidemics are known to persist in the form of recurrence cycles. Despite intervention efforts through vaccination and targeted social distancing, peaks of activity for infectious diseases like influenza reappear over time. Analysis of a stochastic model is here undertaken to explore a proposed cycle-generating mechanism – the bifurcation crossover. Time series from simulations of the model exhibit oscillations similar to the temporal signature of influenza activity. Power-spectral density indicates a resonant frequency, which corresponds to the annual seasonality of influenza in temperate zones. The study finds that intervention actions influence the extinguishability of epidemic activity. Asymptotic solution to a backward Kolmogorov equation corresponds to a mean extinction time that is a function of both intervention efficacy and population size. Intervention efficacy must be greater than a certain threshold to increase the chances of extinguishing the epidemic. Agreement of the model with several phenomenological features of epidemic cycles lends to it a tractability that may serve as early warning of imminent outbreaks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Stochastic modeling of phosphorus transport in the Three Gorges Reservoir by incorporating variability associated with the phosphorus partition coefficient

Phosphorus (P) fate and transport plays a crucial role in the ecology of rivers and reservoirs in which eutrophication is limited by P. A key uncertainty in models used to help manage P in such systems is the partitioning of P to suspended and bed sediments. By analyzing data from field and laboratory experiments, we stochastically characterize the variability of the partition coefficient (Kd) and derive spatio-temporal solutions for P transport in the Three Gorges Reservoir (TGR). We formulate a set of stochastic partial different equations (SPDEs) to simulate P transport by randomly sampling Kd from the measured distributions, to obtain statistical descriptions of the P concentration and retention in the TGR. The correspondence between predicted and observed P concentrations and P retention in the TGR combined with the ability to effectively characterize uncertainty suggests that a model that incorporates the observed variability can better describe P dynamics and more effectively serve as a tool for P management in the system. This study highlights the importance of considering parametric uncertainty in estimating uncertainty/variability associated with simulated P transport.

Partition coefficient↗
DOE OSTI2016

Emulation of dynamic simulators with application to hydrology

Many simulation-intensive tasks in the applied sciences, such as sensitivity analysis, parameter inference or real time control, are hampered by slow simulators. Emulators provide the opportunity of speeding up simulations at the cost of introducing some inaccuracy. An emulator is a fast approximation to a simulator that interpolates between design input–output pairs of the simulator. Increasing the number of design data sets is a computationally demanding way of improving the accuracy of emulation. We investigate the complementary approach of increasing emulation accuracy by including knowledge about the mechanisms of the simulator into the formulation of the emulator. To approximately reproduce the output of dynamic simulators, we consider emulators that are based on a system of linear, ordinary or partial stochastic differential equations with a noise term formulated as a Gaussian process of the parameters to be emulated. This stochastic model is then conditioned to the design data so that it mimics the behavior of the nonlinear simulator as a function of the parameters. The drift terms of the linear model are designed to provide a simplified description of the simulator as a function of its key parameters so that the required corrections by the conditioned Gaussian process noise are as small as possible. The goal of this paper is to compare the gain in accuracy of these emulators by enlarging the design data set and by varying the degree of simplification of the linear model. We apply this framework to a simulator for the shallow water equations in a channel and compare emulation accuracy for emulators based on different spatial discretization levels of the channel and for a standard non-mechanistic emulator. Our results indicate that we have a large gain in accuracy already when using the simplest mechanistic description by a single linear reservoir to formulate the drift term of the linear model. Adding some more reservoirs does not lead to a significant improvement in accuracy. However, the transition to a spatially continuous linear model leads again to a similarly large gain in accuracy as the transition from the non-mechanistic emulator to that based on one reservoir.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Brownian aggregation rate of colloid particles with several active sites

We theoretically analyze the aggregation kinetics of colloid particles with several active sites. Such particles (so-called “patchy particles”) are well known as chemically anisotropic reactants, but the corresponding rate constant of their aggregation has not yet been established in a convenient analytical form. Using kinematic approximation for the diffusion problem, we derived an analytical formula for the diffusion-controlled reaction rate constant between two colloid particles (or clusters) with several small active sites under the following assumptions: the relative translational motion is Brownian diffusion, and the isotropic stochastic reorientation of each particle is Markovian and arbitrarily correlated. This formula was shown to produce accurate results in comparison with more sophisticated approaches. Also, to account for the case of a low number of active sites per particle we used Monte Carlo stochastic algorithm based on Gillespie method. Simulations showed that such discrete model is required when this number is less than 10. Finally, we applied the developed approach to the simulation of immunoagglutination, assuming that the formed clusters have fractal structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Catalytic conversion in nanoporous materials: Concentration oscillations and spatial correlations due to inhibited transport and intermolecular interactions

We show that steady-state catalytic conversion in nanoporous materials can occur in a quasi-counter-diffusion mode with the reactant (product) concentration strongly decaying (growing) into the pore, but also with oscillations in the total concentration. These oscillations reflect the response of the fluid to the transition from an extended to a confined environment near the pore opening. We focus on the regime of strongly inhibited transport in narrow pores corresponding to single-file diffusion. Here, limited penetration of the reactant into the pores and the associated low reaction yield is impacted by strong spatial correlations induced by both reaction (non-equilibrium correlations) and also by intermolecular interactions (thermodynamic correlations). We develop a generalized hydrodynamic formulation to effectively describe inhibited transport accounting for the effect of these correlations, and incorporate this description of transport into appropriate reaction-diffusion equations. These equations accurately describe both shorter-range concentration oscillations near the pore opening and the longer-range mesoscale variation of concentration profiles in the pore (and thus also describe reaction yield). Success of the analytic theory is validated by comparison with a precise kinetic Monte Carlo simulation of an appropriate molecular-level stochastic reaction-diffusion model. As a result, this work elucidates unconventional chemical kinetics in interacting confined systems.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Probabilistic high cycle fatigue failure analysis with application to liquid propellant rocket engines

A probabilistic high cycle fatigue (HCF) failure analysis of a welded duct in a rocket engine of the Space Shuttle main engine class is described. A state-of-the-art HCF failure prediction method was used in a Monte Carlo simulation to generate a distribution of failure lives. A stochastic stress/life model is used for material characterization, and a composite stress history is generated for accurately deriving the stress cycles for the fatigue-damage calculations. The HCF failure model expresses fatigue life as a function of stochastic parameters including environment, loads, material properties, geometry, and model specification errors. A series of HCF failure life analyses were performed to study the impact of a fixed parameter and to assess the importance of each stochastic input parameter through marginal analyses.

Sutharshana, S.↗
NASA NTRS1995

A UT positioning approach for dynamic satellite constellations

In this paper, we discuss a new positioning scheme which is thought to be applicable for dynamic satellite constellations. We begin with the introduction of our filter model which is based on stochastic process and filtering theory. Then, simulation results of the technique are presented based on a LEO constellation and some of the IRIDIUM system parameters. Performance of this algorithm is investigated under various noise conditions. Finally, several applications of this UT (user terminal) positioning algorithm are discussed.

Zhao, W.↗
DOE OSTI2018

Progress Summary: Simulating stochastic and additively manufactured foams at large strains and high strain rates

Motivation: Understanding the mechanical response of polymer foams is essential in developing system material models and in designing replacements for legacy pads and cushions. Simulations can help elucidate structure-property relationships and compare local stress distributions to understand aging behavior and improve designs. In many applications,these materials experience significant loadings and computations need the capability to reproduce those conditions. Significance: In designing the compressive and shear response of additively manufactured foams, we expect the curvature of the substrate to influence their mechanical behavior. Progress:We have the capability of mapping planar structures to arbitrary curved surfaces and are currently developing the software and procedures to translate the mapped pattern into a geometrical model that can be imported to either finite element or material point software to evaluate the mechanical response. Figure 1 depicts a four-layer face-centered tetragonal pattern (FCT) mapped to a hemispherical surface. Because the mapping cannot exactly preserve both distances and angles, we expect some local variation in mechanical properties and porosity. These models will help determine the sensitivity of these variations to structure and curvature, optimize the mapping procedure, and inform the design of foams for non-planar surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Reconstruction of Galaxy Star Formation Histories through SED Fitting:The Dense Basis Approach

We introduce the dense basis method for Spectral Energy Distribution (SED) fitting. It accurately recovers traditional SED parameters, including M {sub *}, SFR, and dust attenuation, and reveals previously inaccessible information about the number and duration of star formation episodes and the timing of stellar mass assembly, as well as uncertainties in these quantities. This is done using basis star formation histories (SFHs) chosen by comparing the goodness-of-fit of mock galaxy SEDs to the goodness-of-reconstruction of their SFHs. We train and validate the method using a sample of realistic SFHs at z = 1 drawn from stochastic realizations, semi-analytic models, and a cosmological hydrodynamical galaxy formation simulation. The method is then applied to a sample of 1100 CANDELS GOODS-S galaxies at 1 10{sup 9.5} M{sub ⊙}. About 40% of the CANDELS galaxies have SFHs whose maximum occurs at or near the epoch of observation. The dense basis method is scalable and offers a general approach to a broad class of data-science problems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Discrete ion stochastic continuum overdamped solvent algorithm for modeling electrolytes

In this paper we develop a methodology for the mesoscale simulation of strong electrolytes. The methodology is an extension of the fluctuating immersed-boundary approach that treats a solute as discrete Lagrangian particles that interact with Eulerian hydrodynamic and electrostatic fields. In both algorithms the immersed-boundary method of Peskin is used for particle-field coupling. Hydrodynamic interactions are taken to be overdamped, with thermal noise incorporated using the fluctuating Stokes equation, including a "dry diffusion" Brownian motion to account for scales not resolved by the coarse-grained model of the solvent. Long-range electrostatic interactions are computed by solving the Poisson equation, with short-range corrections included using an immersed-boundary variant of the classical particle-particle particle-mesh technique. Also included is a short-range repulsive force based on the Weeks-Chandler-Andersen potential. This methodology is validated by comparison to Debye-Hückel theory for ion-ion pair correlation functions, and Debye-Hückel-Onsager theory for conductivity, including the Wien effect for strong electric fields. In each case, good agreement is observed, provided that hydrodynamic interactions at the typical ion-ion separation are resolved by the fluid grid.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Stochastic generation of hourly rainstorm events in Johor

Engineers and researchers in water-related studies are often faced with the problem of having insufficient and long rainfall record. Practical and effective methods must be developed to generate unavailable data from limited available data. Therefore, this paper presents a Monte-Carlo based stochastic hourly rainfall generation model to complement the unavailable data. The Monte Carlo simulation used in this study is based on the best fit of storm characteristics. Hence, by using the Maximum Likelihood Estimation (MLE) and Anderson Darling goodness-of-fit test, lognormal appeared to be the best rainfall distribution. Therefore, the Monte Carlo simulation based on lognormal distribution was used in the study. The proposed model was verified by comparing the statistical moments of rainstorm characteristics from the combination of the observed rainstorm events under 10 years and simulated rainstorm events under 30 years of rainfall records with those under the entire 40 years of observed rainfall data based on the hourly rainfall data at the station J1 in Johor over the period of 1972–2011. The absolute percentage error of the duration-depth, duration-inter-event time and depth-inter-event time will be used as the accuracy test. The results showed the first four product-moments of the observed rainstorm characteristics were close with the simulated rainstorm characteristics. The proposed model can be used as a basis to derive rainfall intensity-duration frequency in Johor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

235-F GoldSim Fate and Transport Model: Uncertainty Quantification

Building 235-F was configured with two missions in mind: Actinide Billet Line (ABL) and the fabrication of Pu-238 oxide for space program applications. ABL produced Np-237 billets for use in SRS reactors, whereas the design process, fabrication, and examination of Pu-238 oxide powder occurred within the following areas, respectively: Plutonium Experimental Facility (PEF), Plutonium Fuel Form (PuFF), and Old Metallography Lab (OML). By 1990 production ceased and by 2006 de-inventory occurred; however, assays have shown significant holdup remains within ABL and PuFF. As a result, 235-F is a Category 2 nuclear facility, with plans to undergo deactivation and decommission (D and D) via In-Situ Disposal (ISD). The purpose of this project is to ensure United States Environmental Protection Agency (USEPA) groundwater radiation maximum contaminant level (MCL) and dosage standards are met during the D and D of 235-F by quantifying uncertainty through probabilistic modeling and evaluation of various ISD alternatives. GoldSim is a dynamic modeling software package with a graphical, object-oriented interface capable of capturing the influence of complex system input variability on probabilistic system outcomes. A GoldSim stochastic fate and transport model for 235-F was developed and matched with a PORFLOW deterministic model to simulate probabilistic release and flow of radionuclides from ABL and PuFF into the vadose zone, the Upper Three Runs (UTR) Aquifer, and UTR Creek. The GoldSim model was used to probabilistically evaluate four ISD scenarios against USEPA groundwater radiation MCLs and dosage standards. The deterministic 235-F GoldSim fate and transport model continues to be refined to match the results of the PORFLOW deterministic model to ensure the model accurately represents radionuclide movement through the groundwater system. The stochastic variables that are utilized within the GoldSim model are founded on the most current data; a conservative perspective is taken where needed. Alignment with the PORFLOW deterministic model, coupled with input stochastic variability, allows the probabilistic 235-F GoldSim model to capture the conservative breadth of possible outcomes for radionuclide fate within this particular system. This ensures that the USEPA MCLs and dosage limits hold even in the worst case scenarios.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Simulating stochastic and additively manufactured foams at large strains and high strain rates (Progress Summary)

Understanding the mechanical response of polymer foams is essential in developing system material models and in designing replacements for legacy pads and cushions. Simulations can help elucidate structure-property relationships and compare local stress distributions to understand aging behavior and improve designs. In many applications, these materials experience significant loadings and computations need the capability to reproduce those conditions. We are applying are previously developed material point method (MPM) simulation capability to understand the aging properties of loaded direct ink write resins. Significant particle loading of a stiffer material in siloxane networks exhibit different compression set behavior than unloaded specimens. We are applying our computational mechanics capabilities to understand this difference at the continuum level. Figure 1 highlights preliminary results, depicting renderings of an unloaded DIW filament alongside one containing a 20% volume loading of spherical particles. Also, the stress distribution of a neat filament at 20% compression is shown. We are planning to study this behavior for a range of loading fractions, particle sizes, and material stiffness ratios. We will explore the sensitivity of the stress response to these variables which could inform aging models of these materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Managing uncertainty in data-driven simulation-based optimization

Optimization using data from complex simulations has become an attractive decision-making option, due to ability to embed high-fidelity, non-linear understanding of processes within the search for optimal values. Due to lack of tractable algebraic equations, the link between simulations and optimization is oftentimes a surrogate metamodel. However, several forms of uncertainty exist within the cycle that links simulation data, to metamodels, to optimization. Uncertainty may originate from parameters of the simulation, or the form and fitted parameters of the metamodel. This paper reviews different literatures that are relevant to surrogate-based optimization and proposes different strategies for handling uncertainty, by combining machine learning with stochastic programming, robust optimization, and discrepancy modeling. We show that incorporating uncertainty management within simulation-based optimization leads to more robust solutions, which protect the decision-maker from infeasible solutions. Here, we present the results of our proposed approaches through a case study for direct-air capture through temperature swing adsorption.

42 ENGINEERING↗
DOE OSTI2011

Exploring the performance of spatial stochastic simulation algorithms

Since the publication of Gillespie's direct method, diverse methods have been developed to improve the performance of stochastic simulation methods and to enter the spatial realm. In this paper we discuss a spatial {tau}-leaping variant (S{tau}) that extends the basic leap method. S{tau} takes reaction and both outgoing and incoming diffusion events into account when calculating a leap candidate. A performance analysis shall reveal details on the achieved success in balancing speed and accuracy in comparison to other methods. However, performance analysis of spatial stochastic algorithms requires significant effort - it is crucial to choose suitable (benchmark) models and to carefully define model and simulation setups that take problem and simulation design spaces into account.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A stochastic extension of the explicit algebraic subgrid-scale models

The explicit algebraic subgrid-scale (SGS) stress model (EASM) of Marstorp et al. [“Explicit algebraic subgrid stress models with application to rotating channel flow,” J. Fluid Mech. 639, 403–432 (2009)] and explicit algebraic SGS scalar flux model (EASFM) of Rasam et al. [“An explicit algebraic model for the subgrid-scale passive scalar flux,” J. Fluid Mech. 721, 541–577 (2013)] are extended with stochastic terms based on the Langevin equation formalism for the subgrid-scales by Marstorp et al. [“A stochastic subgrid model with application to turbulent flow and scalar mixing,” Phys. Fluids 19, 035107 (2007)]. The EASM and EASFM are nonlinear mixed and tensor eddy-diffusivity models, which improve large eddy simulation (LES) predictions of the mean flow, Reynolds stresses, and scalar fluxes of wall-bounded flows compared to isotropic eddy-viscosity and eddy-diffusivity SGS models, especially at coarse resolutions. The purpose of the stochastic extension of the explicit algebraic SGS models is to further improve the characteristics of the kinetic energy and scalar variance SGS dissipation, which are key quantities that govern the small-scale mixing and dispersion dynamics. LES of turbulent channel flow with passive scalar transport shows that the stochastic terms enhance SGS dissipation statistics such as length scale, variance, and probability density functions and introduce a significant amount of backscatter of energy from the subgrid to the resolved scales without causing numerical stability problems. The improvements in the SGS dissipation predictions in turn enhances the predicted resolved statistics such as the mean scalar, scalar fluxes, Reynolds stresses, and correlation lengths. Moreover, the nonalignment between the SGS stress and resolved strain-rate tensors predicted by the EASM with stochastic extension is in much closer agreement with direct numerical simulation data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗