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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Sources of stochasticity in the growth of cloud droplets: supersaturation fluctuations versus turbulent transport

The role played by fluctuations of supersaturation in the growth of cloud droplets is examined in this study. The stochastic condensation framework and the three regimes of activation of cloud droplets – namely, mean-dominant, fluctuation-influenced, and fluctuation-dominant, are used for analyzing the data from high-resolution large-eddy simulations of the Pi convection-cloud chamber. Based on a detailed budget analysis the significance of all the terms in the evolution of the droplet size distribution equation is evaluated in all three regimes. The analysis indicates that the mean-growth rate is a dominant process in shaping the droplet size distribution in all three regimes. Turbulence introduces two sources of stochasticity, turbulent transport and particle lifetime, and supersaturation fluctuations. The transport of cloud droplets plays an important role in all three regimes, whereas the direct effect of supersaturation fluctuations is primarily related to the activation and growth of the small droplets in the fluctuation-influenced and fluctuation-dominant regimes. Here, we compare our results against the previous studies (experimental and theory) of the Pi chamber, and discuss the limitations of the existing models based on the stochastic condensation framework. Furthermore, we extend the discussion of our results to atmospheric clouds, and in particular focus on recent adiabatic turbulent cloud parcel simulations based on the stochastic condensation framework, and emphasize the importance of entrainment/mixing and turbulent transport in shaping the droplet size distribution.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Evaluation of Galvanic Vestibular Stimulation System

Microgravity exposure results in an adaptive central reinterpretation of information from multiple sensory sources to produce a sensorimotor state appropriate for motor actions in this unique environment, but this new adaptive state is no longer appropriate for the 1-g gravitational environment on Earth. During these gravitational transitions, astronauts experience deficits in both perceptual and motor functions including impaired postural control, disruption in spatial orientation, impaired control of locomotion that include alterations in muscle activation variability, modified lower limb kinematics, alterations in head-trunk coordination as well as reduced dynamic visual acuity. Post-flight changes in postural and locomotor control might have adverse consequences if a rapid egress was required following a long-duration mission, where support personnel may not be available to aid crewmembers. The act of emergency egress includes, but is not limited to standing, walking, climbing a ladder, jumping down, monitoring displays, actuating discrete controls, operating auxiliary equipment, and communicating with Mission Control and recovery teams while maintaining spatial orientation, mobility and postural stability in order to escape safely. The average time to recover impaired postural control and functional mobility to preflight levels of performance has been shown to be approximately two weeks after long-duration spaceflight. The postflight alterations are due in part to central reinterpretation of vestibular information caused by exposure to microgravity. In this study we will use a commonly used technique of transcutaneous electrical stimulation applied across the vestibular end organs (galvanic vestibular stimulation, GVS) to disrupt vestibular function as a simulation of post-flight disturbances. The goal of this project is an engineering human-in-the-loop evaluation of a device that can degrade performance of functional tasks (e.g. to maintain upright balance) similar to what astronauts experience during transitions to new gravitational environments. Stochastic electrical stimulation can be applied to the vestibular system through electrodes placed over the mastoid process behind the ears in the binaural configuration resulting in stimulation in the mediolateral (side-to-side) plane. An additional electrode can be placed over the bony landmark of the tip of the c7 spinous process for the double monaural configuration, which will cause stimulation in the anteroposterior (forward-backward) plane. A portable constant current bipolar stimulator with subject isolation was designed and built to deliver the stimulus. The unit is powered using a 3.7 V battery pack and designed to produce currents up to 5 mA. The stimulator, controlled by a Raspberry Pi 3 computer, offers several stimulus signal generation options including a standalone mode, which uses onboard signal files stored on the flash memory card. Stochastic stimulation signals will be generated in 0-30 Hz frequency bandwidth. Stimulation amplitude can be increased incrementally to a maximum amplitude of 5.0 mA (e.g., 0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0 mA). In control trials, subjects will be experiencing vestibular stimulation with 0-mA current applied through the electrodes. The system will be evaluated at various levels of stimulation and in both the binaural and double monaural electrode configurations. One of the objectives is to identify stimulation levels producing effects most comparable to the post-flight disturbances. This is a pilot study that will set the stage for a larger, more comprehensive study that will investigate wider aspects of post-flight sensorimotor dysfunction and set sensorimotor standards for crew health.

Kofman, I. S.↗
DOE OSTI2021

A modular methodology for time-domain stochastic seismic wave propagation

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. In this work, the first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM). Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.

58 GEOSCIENCES↗
DOE OSTI2022

Coalescence dynamics of prismatic dislocation loops due to vacancy supersaturation

Recovery processes in metals with a high defect concentration are at the heart of a structural alloy's usability in engineering applications. However, such processes take place via nonconservative mechanisms mediated by point defect diffusion and are governed by their interactions with the underlying microstructure. Indeed, one of the main challenges in studying recovery processes is their intrinsic multiscale complexity, which often negates the direct use of experimental techniques. Here, in this Research Letter, we study the mechanism of dislocation loop coalescence in metals by simulating the interaction dynamics between vacancies and prismatic loops using a stochastic framework that seamlessly bridges point defect kinetics with dislocation mechanics. Our simulations reveal two fundamental discoveries: first, that climb processes are governed by local fluctuations that are not captured by continuum elastodiffusion models, and, second, that prismatic loops create internal traps for vacancies, leading to the formation of rings around the perimeter of the loops that affect how coalescence takes place. Our results show an excellent agreement with in situ experimental observations of loop coalescence in Fe under irradiation.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Vestibular Stochastic Resonance as a Method to Improve Balance Function: Optimization of Stimulus Characteristics

Stochastic resonance (SR) is a mechanism by which noise can assist and enhance the response of neural systems to relevant sensory signals. Application of imperceptible SR noise coupled with sensory input through the proprioceptive, visual, or vestibular sensory systems has been shown to improve motor function. Specifically, studies have shown that that vestibular electrical stimulation by imperceptible stochastic noise, when applied to normal young and elderly subjects, significantly improved their ocular stabilization reflexes in response to whole-body tilt as well as balance performance during postural disturbances. The goal of this study was to optimize the characteristics of the stochastic vestibular signals for balance performance during standing on an unstable surface. Subjects performed a standardized balance task of standing on a block of 10 cm thick medium density foam with their eyes closed for a total of 40 seconds. Stochastic electrical stimulation was applied to the vestibular system through electrodes placed over the mastoid process behind the ears during the last 20 seconds of the test period. A custom built constant current stimulator with subject isolation delivered the stimulus. Stimulation signals were generated with frequencies in the bandwidth of 1-2 Hz and 0.01-30 Hz. Amplitude of the signals were varied in the range of 0- +/-700 micro amperes with the RMS of the signal increased by 30 micro amperes for each 100 micro amperes increase in the current range. Balance performance was measured using a force plate under the foam block and inertial motion sensors placed on the torso and head segments. Preliminary results indicate that balance performance is improved in the range of 10-25% compared to no stimulation conditions. Subjects improved their performance consistently across the blocks of stimulation. Further the signal amplitude at which the performance was maximized was different in the two frequency ranges. Optimization of the frequency and amplitude of the signal characteristics of the stochastic noise signals on maximizing balance performance will have a significant impact in its development as a unique system to aid recovery of function in astronauts after long duration space flight or for people with balance disorders.

Mulavara, Ajitkumar↗
NASA NTRS2019

Ongoing Breakthroughs in Convective Parameterization

While the increase of computer power mobilizes a part of the community towards models with explicit convection or based on machine learning, we review the part of the literature dedicated to convective parameterization development for large-scale forecast and climate models. Recent findings: Many developments are underway to overcome endemic limitations of traditional convective parameterizations, either in unified or multi-object frameworks: scale-aware and stochastic approaches, new prognostic equations or representations of new components such as cold pools. Understanding their impact on the emergent properties of a model remains challenging, due to subsequent tuning of parameters and the limited understanding given by traditional metrics. Summary: Further effort still needs to be dedicated to the representation of the life cycle of convective systems, in particular their mesoscale organization and associated cloud cover. The development of more process-oriented metrics based on new observations is also needed to help quantify model improvement and better understand the mechanisms of climate change.

parameterizations for large-scale models↗
DOE OSTI2021

Convective and turbulent motions in non-precipitating Cu. Part 1: Method of separation of convective and turbulent motions

Atmospheric motions in clouds and cloud surroundings have a wide range of scales, from several kilometers to centimeters. These motions have different impacts on cloud dynamics and microphysics. Larger-scale motions (hereafter referred to as convective motions) are responsible for mass transport over distances comparable with cloud scale, while motions of smaller scales (hereafter referred to as turbulent motions) are stochastic and responsible for mixing and cloud dilution. This distinction substantially simplifies the analysis of dynamic and microphysical processes in clouds. The present research is Part I of the study aimed at describing the method for separating the motion scale into a convective component and a turbulent component. An idealized flow is constructed, which is a sum of an initially prescribed field of the convective velocity with updrafts in the cloud core and downdrafts outside the core, and a stochastic turbulent velocity field obeying the turbulent properties, including the -5/3 law and the 2/3 structure function law. A wavelet method is developed allowing separation of the velocity field into the convective and turbulent components, with parameter values being in a good agreement with those prescribed initially. The efficiency of the method is demonstrated by an example of a vertical velocity field of a cumulus cloud simulated using the System for Atmospheric Modeling (SAM) with bin microphysics and resolution of 10 m. It is shown that vertical velocity in clouds indeed can be represented as a sum of convective velocity (forming zone of cloud updrafts and subsiding shell) and a stochastic velocity obeying laws of homogeneous and isotropic turbulence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations

Abstract In this paper, we analyze the neutrino-driven winds that emerge in 12 unprecedentedly long-duration 3D core-collapse supernova simulations done using the code F ornax . The 12 models cover progenitors with zero-age main-sequence mass between 9 and 60 solar masses. In all our models, we see transonic outflows that are at least 2 times as fast as the surrounding ejecta and that originate generically from a proto−neutron star surface atmosphere that is turbulent and rotating. We find that winds are common features of 3D simulations, even if there is anisotropic early infall. We find that the basic dynamical properties of 3D winds behave qualitatively similarly to those inferred in the past using simpler 1D models, but that the shape of the emergent wind can be deformed, very aspherical, and channeled by its environment. The thermal properties of winds for less massive progenitors very approximately recapitulate the 1D stationary solutions, while for more massive progenitors they deviate significantly owing to aspherical accretion. The Y e temporal evolution in winds is stochastic, and there can be some neutron-rich phases. Though no strong r -process is seen in any model, a weak r -process can be produced, and isotopes up to 90 Zr are synthesized in some models. Finally, we find that there is at most a few percent of a solar mass in the integrated wind component, while the energy carried by the wind itself can be as much as 10%–20% of the total explosion energy.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1982

On-line methods for rotorcraft aeroelastic mode identification

The requirements for the on-line identification of rotorcraft aeroelastic blade modes from random response test data are presented. A recursive maximum likelihood (RML) technique is used in conjunction with a bandpass filter to identify isolated blade mode damping and frequency. The RML technique is demonstrated to have excellent convergence characteristics in random measurement noise and random process noise excitation. The RML identification technique uses an ARMA representation for the aeroelastic stochastic system and requires virtually no user interaction while providing accurate confidence bands on the parameter estimates. Comparisons are made with an off-line Newton type maximum likelihood algorithm which uses a state variable model representation. Results are presented from simulation random response data which quantify the identifed parameter convergence behavior for various levels of random excitation which is typical of wind tunnel turbulence levels. The RML technique is applied to hingless rotor test data from the NASA Langley Research Center Helicopter Hover Facility.

Molusis, J. A.↗
NASA NTRS1985

Dynamic features of combustion

The dynamic features of combustion are discussed for four important cases: ignition, inflammation, explosion, and detonation. Ignition, the initiation of a self-sustained exothermic process, is considered in the simplest case of a closed thermodynamic system and its stochastic distribution. Inflammation, the initiation and propagation of self-sustained flames, is presented for turbulent flow. Explosion, the dynamic effects caused by the deposition of exothermic energy in a compressible medium, is illustrated by self-similar blast waves with energy deposition at the front and the adiabatic non-self-similar wave. Detonation, the most comprehensive illustration of all the dynamic effects of combustion, is discussed with a phenomenological account of the development and structure of the wave.

Oppenheim, A. K.↗
NASA NTRS1987

Combined optimization of image-gathering and image-processing systems for scene feature detection

The relationship between the image gathering and image processing systems for minimum mean squared error estimation of scene characteristics is investigated. A stochastic optimization problem is formulated where the objective is to determine a spatial characteristic of the scene rather than a feature of the already blurred, sampled and noisy image data. An analytical solution for the optimal characteristic image processor is developed. The Wiener filter for the sampled image case is obtained as a special case, where the desired characteristic is scene restoration. Optimal edge detection is investigated using the Laplacian operator x G as the desired characteristic, where G is a two dimensional Gaussian distribution function. It is shown that the optimal edge detector compensates for the blurring introduced by the image gathering optics, and notably, that it is not circularly symmetric. The lack of circular symmetry is largely due to the geometric effects of the sampling lattice used in image acquisition. The optimal image gathering optical transfer function is also investigated and the results of a sensitivity analysis are shown.

Halyo, Nesim↗
NASA NTRS2005

Introduction to Particle Acceleration in the Cosmos

Accelerated charged particles have been used on Earth since 1930 to explore the very essence of matter, for industrial applications, and for medical treatments. Throughout the universe nature employs a dizzying array of acceleration processes to produce particles spanning twenty orders of magnitude in energy range, while shaping our cosmic environment. Here, we introduce and review the basic physical processes causing particle acceleration, in astrophysical plasmas from geospace to the outer reaches of the cosmos. These processes are chiefly divided into four categories: adiabatic and other forms of non-stochastic acceleration, magnetic energy storage and stochastic acceleration, shock acceleration, and plasma wave and turbulent acceleration. The purpose of this introduction is to set the stage and context for the individual papers comprising this monograph.

Gallagher, D. L.↗
NASA NTRS1999

Physical Model of Immune Inspired Computing

Artificial Immune System is a rapidly growing field of information processing and computing based upon immune inspired paradigns of nonlinear dynamics. This paper introduces stochastic models which mimic the phenomenology of basic functions of immune systems such as self-nonself discrimination, self-repair, predator-prey pursuit, and reproduction.

Immune↗
NASA NTRS2021

Drought Relief and Reversal Over North America from 1500 to 2016

Season-to-season persistence of soil moisture drought varies across North America. Such inter-seasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool (December-April) and warm (May-July) season precipitation series and offers new insights on the intra-annual changes in drought for up to 2,000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and three-month long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the 20th century and are used to estimate relief and reversals for the most skillfully reconstructed past 500-years. Sub-continental to continental scale reversals of cool season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the Mid-Atlantic region and the US Southwest. Drought relief at the continental scale may arise in part from macroscale ocean-atmosphere processes, while the smaller scale regional reversals may reflect land-surface feedbacks and stochastic variability.

Drought↗
DOE OSTI2020

A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models

Bayesian experimental design (BED) aims at designing an experiment to maximize the information gathering from the collected data. The optimal design is usually achieved by maximizing the mutual information (MI) between the data and the model parameters. When the analytical expression of the MI is unavailable, e.g.,having implicit models with intractable data distributions, a neural network-based lower bound of the MI was recently proposed and a gradient ascent method was used to maximize the lower bound [1]. However, the approach in [1] requires a pathwise sampling path to compute the gradient of the MI lower bound with respect to the design variables, and such a pathwise sampling path is usually inaccessible for implicit models. In this work, we propose a hybrid gradient approach that leverages recent advances in variational MI estimator and evolution strategies (ES)combined with black-box stochastic gradient ascent (SGA) to maximize the MI lower bound. This allows the design process to be achieved through a unified scalable procedure for implicit models without sampling path gradients. Several experiments demonstrate that our approach significantly improves the scalability of BED for implicit models in high-dimensional design space.

Zhang, Jiaxin↗
DOE OSTI2021

Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty

The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. Here we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints.

42 ENGINEERING↗
DOE OSTI2022

Mesh objective stochastic simulations of quasibrittle fracture

Continuum finite element (FE) modeling of damage and failure of quasibrittle structures suffers from the spurious mesh sensitivity due to strain localization. Here this issue has been addressed for deterministic analysis through the development of localization limiters. Here this study proposes a mechanism-based model to mitigate the mesh sensitivity in stochastic FE simulations of quasibrittle fracture. The interest is placed on the analysis of large-size structures, where the mesh size is conveniently chosen to be larger than the width of the fracture process zone as well as the correlation length of the random fields of constitutive properties. The present model is formulated within the framework of continuum damage mechanics. Two localization parameters are introduced to describe the evolution of the damage pattern of each finite element. These parameters are used to guide the energy regularization of the constitutive law, as well as to formulate the mesh-dependent probability distributions of constitutive properties. Depending on the prevailing damage pattern, different energy regularization schemes and mesh dependence of the probability distribution functions are used in the constitutive law. The model is applied to simulate the stochastic failure behavior of quasibrittle structures of different geometries featuring different failure processes including damage initiation, localization, and propagation. It is shown that using fixed probability distribution functions of constitutive properties could lead to strong mesh dependence of the prediction of the mean and variance of the structural load capacity. The probability distribution functions of constitutive properties must be linked to the damage pattern, which may evolve during the failure process. Such a mechanism-based modeling of the probability distributions of constitutive properties is essential for mitigating the spurious mesh sensitivity in stochastic FE analysis of quasibrittle fracture.

42 ENGINEERING↗
NASA NTRS2014

NASA Tech Briefs, March 2014

Topics include: Data Fusion for Global Estimation of Forest Characteristics From Sparse Lidar Data; Debris and Ice Mapping Analysis Tool - Database; Data Acquisition and Processing Software - DAPS; Metal-Assisted Fabrication of Biodegradable Porous Silicon Nanostructures; Post-Growth, In Situ Adhesion of Carbon Nanotubes to a Substrate for Robust CNT Cathodes; Integrated PEMFC Flow Field Design for Gravity-Independent Passive Water Removal; Thermal Mechanical Preparation of Glass Spheres; Mechanistic-Based Multiaxial-Stochastic-Strength Model for Transversely-Isotropic Brittle Materials; Methods for Mitigating Space Radiation Effects, Fault Detection and Correction, and Processing Sensor Data; Compact Ka-Band Antenna Feed with Double Circularly Polarized Capability; Dual-Leadframe Transient Liquid Phase Bonded Power Semiconductor Module Assembly and Bonding Process; Quad First Stage Processor: A Four-Channel Digitizer and Digital Beam-Forming Processor; Protective Sleeve for a Pyrotechnic Reefing Line Cutter; Metabolic Heat Regenerated Temperature Swing Adsorption; CubeSat Deployable Log Periodic Dipole Array; Re-entry Vehicle Shape for Enhanced Performance; NanoRacks-Scale MEMS Gas Chromatograph System; Variable Camber Aerodynamic Control Surfaces and Active Wing Shaping Control; Spacecraft Line-of-Sight Stabilization Using LWIR Earth Signature; Technique for Finding Retro-Reflectors in Flash LIDAR Imagery; Novel Hemispherical Dynamic Camera for EVAs; 360 deg Visual Detection and Object Tracking on an Autonomous Surface Vehicle; Simulation of Charge Carrier Mobility in Conducting Polymers; Observational Data Formatter Using CMOR for CMIP5; Propellant Loading Physics Model for Fault Detection Isolation and Recovery; Probabilistic Guidance for Swarms of Autonomous Agents; Reducing Drift in Stereo Visual Odometry; Future Air-Traffic Management Concepts Evaluation Tool; Examination and A Priori Analysis of a Direct Numerical Simulation Database for High-Pressure Turbulent Flows; and Resource-Constrained Application of Support Vector Machines to Imagery.

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