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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 145 records · Page 8
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DOE OSTI2017

Stochastic Community Assembly: Does It Matter in Microbial Ecology?

Understanding the mechanisms controlling community diversity, functions, succession, and biogeography is a central, but poorly understood, topic in ecology, particularly in microbial ecology. Although stochastic processes are believed to play nonnegligible roles in shaping community structure, their importance relative to deterministic processes is hotly debated. The importance of ecological stochasticity in shaping microbial community structure is far less appreciated. Some of the main reasons for such heavy debates are the difficulty in defining stochasticity and the diverse methods used for delineating stochasticity. Here in this paper, we provide a critical review and synthesis of data from the most recent studies on stochastic community assembly in microbial ecology. We then describe both stochastic and deterministic components embedded in various ecological processes, including selection, dispersal, diversification, and drift. We also describe different approaches for inferring stochasticity from observational diversity patterns and highlight experimental approaches for delineating ecological stochasticity in microbial communities. In addition, we highlight research challenges, gaps, and future directions for microbial community assembly research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Statistical properties of solar flares and coronal mass ejections through the solar cycle

Waiting Time Distributions (WTDs) of solar flares are investigated all through the solar cycle. The same approach applied to Coronal Mass Ejections (CMEs) in a previous work is considered here for flare occurrence. Our analysis reveals that flares and CMEs share some common statistical properties, which result dependent on the level of solar activity. Both flares and CMEs seem to independently occur during minimum solar activity phases, whilst their WTDs significantly deviate from a Poisson function at solar maximum, thus suggesting that these events are correlated. The characteristics of WTDs are constrained by the physical processes generating those eruptions associated with flares and CMEs. A scenario may be drawn in which different mechanisms are actively at work during different phases of the solar cycle. Stochastic processes, most likely related to random magnetic reconnections of the field lines, seem to play a key role during solar minimum periods. On the other hand, persistent processes, like sympathetic eruptions associated to the variability of the photospheric magnetism, are suggested to dominate during periods of high solar activity. Moreover, despite the similar statistical properties shown by flares and CMEs, as it was mentioned above, their WTDs appear different in some aspects. During solar minimum periods, the flare occurrence randomness seems to be more evident than for CMEs. Those persistent mechanisms generating interdependent events during maximum periods of solar activity can be suggested to play a more important role for CMEs than for flares, thus mitigating the competitive action of the random processes, which seem instead strong enough to weaken the correlations among flare event occurrence during solar minimum periods. However, it cannot be excluded that the physical processes at the basis of the origin of the temporal correlation between solar events are different for flares and CMEs, or that, more likely, more sophisticated effects are at work at the same time leading to an even more complex picture. This work represents a first step for further investigations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Parametric models for samples of random functions

A new class of parametric models, referred to as sample parametric models, is developed for random elements that match sample rather than the first two moments and/or other global properties of these elements. The models can be used to characterize, e.g., material properties at small scale in which case their samples represent microstructures of material specimens selected at random from a population. The samples of the proposed models are elements of finite-dimensional vector spaces spanned by samples, eigenfunctions of Karhunen–Loève (KL) representations, or modes of singular value decompositions (SVDs). The implementation of sample parametric models requires knowledge of the probability laws of target random elements. Numerical examples including stochastic processes and random fields are used to demonstrate the construction of sample parametric models, assess their accuracy, and illustrate how these models can be used to solve efficiently stochastic equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM)

The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) program has developed a framework for sequential design of experiments (SDoE) that aims to maximize knowledge gained from budget- and schedule-limited pilot scale testing. SDoE was applied to the planning and execution of campaigns for testing CO2 capture systems at pilot-scale in order to optimally allocate resources available for the testing. In this methodology, a stochastic process model is developed by quantifying the parametric uncertainty in submodels of interest; for a solvent-based CO2 capture system, these may include physical properties and equipment performance submodels (e.g., mass transfer, interfacial area). This uncertainty is propagated through the full process model, over variable operating conditions, for estimating the resulting uncertainty in key model outputs (e.g., percentage of CO2 capture, solvent regeneration energy requirement). In developing a data collection plan, the predicted output uncertainty is incorporated into an algorithm that seeks simultaneously to select process operating conditions for which the predicted uncertainty is relatively high and to ensure that the entire space of operation is well represented. This test plan is then used to guide operation of the pilot plant at varying steady-state conditions, with resulting process data incorporated into the existing model using Bayesian inference to refine parameter distributions. The updated stochastic model, with reduced parametric uncertainty from data collected, is then used to guide additional data collection, thus the sequential nature of the experimental design. The SDoE process was implemented at the pilot test unit (12 MWe in scale) at Norway’s Technology Centre Mongstad (TCM) in a summer 2018 test campaign with aqueous monoethanolamine (MEA). During the test campaign, the varied operating conditions included the flowrates of circulated solvent, flue gas, and reboiler steam and the CO2 concentration in the flue gas. The process data were used to update probability distributions of mass transfer and interfacial area parameters of a stochastic process model developed by the CCSI2 team. Two iterations of the SDoE process were executed, resulting in the uncertainty in model predicted CO2 capture percentage decreasing by an average of 58.0 ± 4.7% over the full input space of interest. This work demonstrates the potential of the SDoE process for model refinement through reduction in process model parametric uncertainty, and ultimately risk in scale-up, in CO2 capture technology performance.

carbon capture↗
DOE OSTI2018

A Functional Central Limit Theorem for the Becker–Döring Model

We investigate the fluctuations of the stochastic Becker–Döring model of polymerization when the initial size of the system converges to infinity. A functional central limit problem is proved for the vector of the number of polymers of a given size. It is shown that the stochastic process associated to fluctuations is converging to the strong solution of an infinite dimensional stochastic differential equation (SDE) in a Hilbert space. We also prove that, at equilibrium, the solution of this SDE is a Gaussian process. The proofs are based on a specific representation of the evolution equations, the introduction of a convenient Hilbert space and several technical estimates to control the fluctuations, especially of the first coordinate which interacts with all components of the infinite dimensional vector representing the state of the process.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

A uniform history for galaxy evolution

Recent observations indicate a remarkable similarity in the properties of evolving galaxies at fixed mass and redshift, prompting us to consider the possibility that most galaxies may evolve with a common history encompassing star formation, quasar accretion, and eventual quiescence. We quantify this by defining a synchronization timescale for galaxies as a function of mass and redshift that characterizes the extent to which different galaxies of a common mass are evolving in the same manner at various cosmic epochs. We measure this synchronization timescale using nine different star-forming galaxy observations from the literature and Sloan Digital Sky Survey quasar observations spanning 0 < z ≲ 6. Surprisingly, this synchronization timescale is a constant, approximately 1.5 Gyr for all combinations of mass and time. We also find that the ratio between the stellar mass of galaxies turning off star formation and black hole mass of turnoff quasars is approximately 30:1, much lower than the 500:1 for quiescent galaxies at low redshift. As a result, we propose a model in which the star-forming 'main sequence', analogous quasar behavior, and other observations form a galactic evolution 'main sequence', in which star formation occurs earliest, followed by supermassive black hole accretion, and feedback between the two are dominated by deterministic rather than stochastic processes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The use of imprecise processing to improve accuracy in weather and climate prediction

The use of stochastic processing hardware and low precision arithmetic in atmospheric models is investigated. Stochastic processors allow hardware-induced faults in calculations, sacrificing bit-reproducibility and precision in exchange for improvements in performance and potentially accuracy of forecasts, due to a reduction in power consumption that could allow higher resolution. A similar trade-off is achieved using low precision arithmetic, with improvements in computation and communication speed and savings in storage and memory requirements. As high-performance computing becomes more massively parallel and power intensive, these two approaches may be important stepping stones in the pursuit of global cloud-resolving atmospheric modelling. The impact of both hardware induced faults and low precision arithmetic is tested using the Lorenz '96 model and the dynamical core of a global atmosphere model. In the Lorenz '96 model there is a natural scale separation; the spectral discretisation used in the dynamical core also allows large and small scale dynamics to be treated separately within the code. Such scale separation allows the impact of lower-accuracy arithmetic to be restricted to components close to the truncation scales and hence close to the necessarily inexact parametrised representations of unresolved processes. By contrast, the larger scales are calculated using high precision deterministic arithmetic. Hardware faults from stochastic processors are emulated using a bit-flip model with different fault rates. Our simulations show that both approaches to inexact calculations do not substantially affect the large scale behaviour, provided they are restricted to act only on smaller scales. By contrast, results from the Lorenz '96 simulations are superior when small scales are calculated on an emulated stochastic processor than when those small scales are parametrised. This suggests that inexact calculations at the small scale could reduce computation and power costs without adversely affecting the quality of the simulations. This would allow higher resolution models to be run at the same computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

One-dimensional Turbulence Models of Type I X-ray Bursts

Type I X-ray bursts are caused by thermonuclear explosions occurring on the surface of an accreting neutron star in a binary star system. Observations and simulations of these phenomena are of great importance for understanding the fundamental properties of neutron stars and dense matter because the equation of state for cold dense matter can be constrained by the mass-radius relationship of neutron stars. During the bursts, turbulence plays a key role in mixing the fuels and driving the unstable nuclear burning process. This dissertation presents one-dimensional models of photospheric radius expansion bursts with a new approach to simulate turbulent advection. Compared with the traditional mixing length theory, the one-dimensional turbulence (ODT) model represents turbulent motions by a sequence of maps that are generated according to a stochastic process. The light curves I obtained with the ODT models are in good agreement with those of the KEPLER model in which the mixing length theory and various diffusive processes are applied. The abundance comparison, however, indicates that the differences in turbulent regions and turbulent diffusivities result in more 12 C survival during the bursts in the ODT models, which can make a difference in the superbursts phenomena triggered by unstable carbon burning.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

First passage times in homogeneous nucleation: Dependence on the total number of particles

Motivated by nucleation and molecular aggregation in physical, chemical, and biological settings, we present an extension to a thorough analysis of the stochastic self-assembly of a fixed number of identical particles in a finite volume. We study the statistics of times required for maximal clusters to be completed, starting from a pure-monomeric particle configuration. For finite volumes, we extend previous analytical approaches to the case of arbitrary size-dependent aggregation and fragmentation kinetic rates. For larger volumes, we develop a scaling framework to study the first assembly time behavior as a function of the total quantity of particles. We find that the mean time to first completion of a maximum-sized cluster may have a surprisingly weak dependence on the total number of particles. We highlight how higher statistics (variance, distribution) of the first passage time may nevertheless help to infer key parameters, such as the size of the maximum cluster. Finally, we present a framework to quantify formation of macroscopic sized clusters, which are (asymptotically) very unlikely and occur as a large deviation phenomenon from the mean-field limit. We argue that this framework is suitable to describe phase transition phenomena, as inherent infrequent stochastic processes, in contrast to classical nucleation theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

WE-H-BRA-03: Development of a Model to Include the Evolution of Resistant Tumor Subpopulations Into the Treatment Optimization Process for Schedules Involving Targeted Agents in Chemoradiation Therapy

Purpose: To develop a model that includes the process of resistance development into the treatment optimization process for schedules that include targeted therapies. Further, to validate the approach using clinical data and to apply the model to assess the optimal induction period with targeted agents before curative treatment with chemo-radiation in stage III lung cancer. Methods: Growth of the tumor and its subpopulations is modeled by Gompertzian growth dynamics, resistance induction as a stochastic process. Chemotherapy induced cell kill is modeled by log-cell kill dynamics, targeted agents similarly but restricted to the sensitive population. Radiation induced cell kill is assumed to follow the linear-quadratic model. The validation patient data consist of a cohort of lung cancer patients treated with tyrosine kinase inhibitors that had longitudinal imaging data available. Results: The resistance induction model was successfully validated using clinical trial data from 49 patients treated with targeted agents. The observed recurrence kinetics, with tumors progressing from 1.4–63 months, result in tumor growth equaling a median volume doubling time of 92 days [34–248] and a median fraction of pre-existing resistance of 0.035 [0–0.22], in agreement with previous clinical studies. The model revealed widely varying optimal time points for the use of curative therapy, reaching from ∼1m to >6m depending on the patient’s growth rate and amount of pre-existing resistance. This demonstrates the importance of patient-specific treatment schedules when targeted agents are incorporated into the treatment. Conclusion: We developed a model including evolutionary dynamics of resistant sub-populations with traditional chemotherapy and radiation cell kill models. Fitting to clinical data yielded patient specific growth rates and resistant fraction in agreement with previous studies. Further application of the model demonstrated how proper timing of chemo-radiation could minimize the probability of resistance, increasing tumor control significantly.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Electrostatic turbulence intermittence driven by biasing in Texas Helimak

We investigate changes in the intermittent sequence of bursts in the electrostatic turbulence due to imposed positive bias voltage applied to control the plasma radial electric field in Texas Helimak [K. W. Gentle and H. He, Plasma Sci. Technol. 10, 284 (2008)]—a toroidal plasma device with a one-dimensional equilibrium, magnetic curvature, and shear. We identify the burst characteristics by analyzing ion saturation current fluctuations collected in a large set of Langmuir probes. The number of bursts increase with positive biasing, giving rise to a long tailed skewed turbulence probability distribution function. The burst shape does not change much with the applied bias voltage, while their vertical velocity increases monotonically. For high values of bias voltage, the bursts propagate mainly in the vertical direction which is perpendicular to the radial density gradient and the toroidal magnetic field. Moreover, in contrast with the bursts in tokamaks, the burst velocity agrees with the phase velocity of the overall turbulence in both vertical and radial directions. For a fixed bias voltage, the time interval between bursts and their amplitudes follows exponential distributions. Altogether, these burst characteristics indicate that their production can be modelled by a stochastic process.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Time-resolved observation of thermally activated rupture of a capillary-condensed water nanobridge

The capillary-condensed liquid bridge is one of the most ubiquitous forms of liquid in nature and contributes significantly to adhesion and friction of biological molecules as well as microscopic objects. Despite its important role in nanoscience and technology, the rupture process of the bridge is not well understood and needs more experimental works. Here, we report real-time observation of rupture of a capillary-condensed water nanobridge in ambient condition. During slow and stepwise stretch of the nanobridge, we measured the activation time for rupture, or the latency time required for the bridge breakup. By statistical analysis of the time-resolved distribution of activation time, we show that rupture is a thermally activated stochastic process and follows the Poisson statistics. In particular, from the Arrhenius law that the rupture rate satisfies, we estimate the position-dependent activation energies for the capillary-bridge rupture.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗