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Project cost risk is the uncertainty in final project cost, contingent on initial budget, requirements and schedule. For a proposed mission, a dynamic simulation model relying for some of its input on a simple risk elicitation is used to identify and quantify systemic cost risk.
The disregistry of holes drilled with a pulse laser beam in 330-micron-thick single-crystal silicon-on-sapphire wafers is examined. The exit positions of the holes were displaced from the hole entrance positions on the opposing face of the wafer, and this random displacement increased with the number of laser pulses required. A model in which the bottom of the drill hole experiences small random displacements during each laser pulse is used to describe the experimental observations. It is shown that the average random displacement caused by each pulse is only a few percent of the hole diameter and can be reduced by using as few laser pulses as necessary while avoiding the cracking and spalling of the wafer that occur with a hole drilled with a single pulse.
Using the architectural plan views of our future computer science building as test objects, we have completed the first stage of a Building walkthrough system. The inputs to our system are AutoCAD files. An AutoCAD converter translates the geometrical information in these files into a format suitable for 3D rendering. Major model errors, such as incorrect polygon intersections and random face orientations, are detected and fixed automatically. Interactive viewing and editing tools are provided to view the results, to modify and clean the model and to change surface attributes. Our display system provides a simple-to-use user interface for interactive exploration of buildings. Using only the mouse buttons, the user can move inside and outside the building and change floors. Several viewing and rendering options are provided, such as restricting the viewing frustum, avoiding wall collisions, and selecting different rendering algorithms. A plan view of the current floor, with the position of the eye point and viewing direction on it, is displayed at all times. The scene illumination can be manipulated, by interactively controlling intensity values for 5 light sources.
We summarize recent Ulysses observations of the variances over large temporal scales in the interplanetary magnetic field components and their increase as Ulysses approached the South Solar Pole. A model of these fluctuations is shown to provide a very good fit to the observed amplitude and temporal variation of the fluctuations. In addition, the model predicts that the transport of cosmic rays in the heliosphere will be significantly altered by this level of fluctuations. In addition to altering the inward diffusion and drift access of cosmic rays over the solar poles, we find that the magnetic fluctuations also imply a large latitudinal diffusion, caused primarily by the associated field-line random walk.
We study the errors brought by finite volume effects and dilution effects on the practical determination of the count probability distribution function P(sub N)(n,l), which is the probability of having N objects in a cell of volume l cubed for a set of average number density n. Dilution effects are particularly revelant to the so-called sparse sampling strategy. This work is mainly done in the framework of the Bailan & Schaeffer scaling model, which assumes that the Q-body correlation functions obey the scaling relation Xi(sub Q)(lambda r(sub l),....lambda r(sub Q) = lambda(exp -(Q-1)gamma) Xi(sub Q)(r(sub 1),....r(sub Q)). We use three synthetic samples as references to perform our analysis: a fractal generated by a Rayleigh-Levy random walk with approximately 3 x 10(exp 4) objects, a sample dominated by a spherical power-law cluster with approximately 3 x 10(exp 4) objects and a cold dark matter (CDM) universe involving approximately 3 x 10(exp 5) matter particles.
Shot noise models provide a useful mathematical representation for some physical models for the quasi-periodic oscillations recently observed from several Galactic bulge and burst X-ray sources. For such models, correlations between the shot onset times and oscillation phases can have important effects on the expected autocorrelation function and power spectrum, with important consequences for the interpretation of observed power spectra. Making use of analytic and Monte Carlo techniques, two physically plausible correlations of this kind, one corresponding to drift and jitter in the oscillation phase and the other to a random walk in the oscillation phase, are examined.
A cosmological gamma-ray burst model that reproduces the observed gamma-ray spectra was developed. This model, which is an outgrowth of work on synchrotron emission from cosmological sources, creates the observed spectra from a power law spectrum through Compton attenuation in dense molecular clouds. It restricts the burst source to the centers of galaxies, and it is easily tested through comparisons with time dependent burst spectra. Dr. Brainerd continued development of a Monte Carlo code that tracks the random walk of a gamma-ray through a plasma in a strong magnetic field and used this code to show that a two temperature plasma in a strong magnetic field can suppress soft x-ray emission.
A two-component model for the spectrum of interplanetary magnetic fluctuations was proposed on the basis of ISEE observations, and has found an intriguing level of application in other solar wind studies. The model fluctuations consist of a fraction of 'slab' fluctuations, varying only in the direction parallel to the locally uniform mean magnetic field B(0) and a complement of 2D (two-dimensional) fluctuations that vary in the directions transverse to B(0). We have developed an spectral method computational algorithm for computing the magnetic flux surfaces (flux tubes) associated with the composite model, based upon a precise analogy with equations for ideal transport of a passive scalar in planar two dimensional geometry. Visualization of various composite models will be presented, including the 80 percent 2D/ 20 percent slab model with delta B/B(0) approximately equals 1 and a minus 5/3 spectral law, that is thought to approximately represent a snapshot of solar wind turbulence. Characteristically, the visualizations show that flux tubes, even when defined as regular on some plane, shred and disperse rapidly as they are viewed along the parallel direction. This diffusive process, which generalizes the standard picture of field line random walk, will be discussed in detail. Evidently, the traditional picture that flux tubes randomize like strands of spaghetti with a uniform tangle along the axial direction is in need of modification.
A detailed comparison is presented of the distribution of 60 micron IR and 20 cm radio continuum emission within 25 galaxies, mostly disk spirals. Local maxima in the thermal IR and nonthermal radio emission are found to be spatially coincident on scales of less than about 0.4 kpc in the nearest sample galaxies. The IR-red disk in normal spirals appears to be characterized by a shorter scale length than that of the radio continuum disk, suggesting that the IR-to-radio ratio should decrease as a function of radius. A model that successfully accounts for the observations is introduced which is based on the assumptions of steady-state star formation activity within the disk on kpc scales and a tight coupling between the origins of the dust-heating radiation and the radio-emitting cosmic-ray electrons. The underlying source is described as an exponential disk. The results also suggest that a random walk process cannot by itself describe the temporal evolution of cosmic rays.
We postulated that aging is associated with disruption in the fractallike long-range correlations that characterize healthy sinus rhythm cardiac interval dynamics. Ten young (21-34 yr) and 10 elderly (68-81 yr) rigorously screened healthy subjects underwent 120 min of continuous supine resting electrocardiographic recording. We analyzed the interbeat interval time series using standard time and frequency domain statistics and using a fractal measure, detrended fluctuation analysis, to quantify long-range correlation properties. In healthy young subjects, interbeat intervals demonstrated fractal scaling, with scaling exponents (alpha) from the fluctuation analysis close to a value of 1.0. In the group of healthy elderly subjects, the interbeat interval time series had two scaling regions. Over the short range, interbeat interval fluctuations resembled a random walk process (Brownian noise, alpha = 1.5), whereas over the longer range they resembled white noise (alpha = 0.5). Short (alpha s)- and long-range (alpha 1) scaling exponents were significantly different in the elderly subjects compared with young (alpha s = 1.12 +/- 0.19 vs. 0.90 +/- 0.14, respectively, P = 0.009; alpha 1 = 0.75 +/- 0.17 vs. 0.99 +/- 0.10, respectively, P = 0.002). The crossover behavior from one scaling region to another could be modeled as a first-order autoregressive process, which closely fit the data from four elderly subjects. This implies that a single characteristic time scale may be dominating heartbeat control in these subjects. The age-related loss of fractal organization in heartbeat dynamics may reflect the degradation of integrated physiological regulatory systems and may impair an individual's ability to adapt to stress.
Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.
Complex fluctuation of unknown origin appear in the normal gait pattern. These fluctuations might be described as being (1) uncorrelated white noise, (2) short-range correlations, or (3) long-range correlations with power-law scaling. To test these possibilities, the stride interval of 10 healthy young men was measured as they walked for 9 min at their usual rate. From these time series we calculated scaling indexes by using a modified random walk analysis and power spectral analysis. Both indexes indicated the presence of long-range self-similar correlations extending over hundreds of steps; the stride interval at any time depended on the stride interval at remote previous times, and this dependence decayed in a scale-free (fractallike) power-law fashion. These scaling indexes were significantly different from those obtained after random shuffling of the original time series, indicating the importance of the sequential ordering of the stride interval. We demonstrate that conventional models of gait generation fail to reproduce the observed scaling behavior and introduce a new type of central pattern generator model that sucessfully accounts for the experimentally observed long-range correlations.
A theoretical and experimental investigation of single-phase and particle-laden weakly swirling jets was conducted. The jets were injected vertically downward from a 19 mm diameter tube with swirl numbers ranging from 0 to 0.33. The particle-laden jets had a single loading ratio (0.2) with particles having a SMD of 39 microns. Mean and fluctuating properties of both phases were measured using nonintrusive laser based methods while particle mass flux was measured using an isokinetic sampling probe. The continuous phase was analyzed using both a baseline kappa-epsilon turbulence model and an extended version with modifications based on the flux Richardson number to account for effects of streamline curvature. To highlight effects of interphase transport rates and particle/turbulence interactions, effects of the particles were analyzed as follows: (1) locally homogeneous flow (LHF) analysis, where interphase transport rates are assumed to be infinitely fast; (2) deterministic separated flow (DSF) analysis, where finite interphase transport rates are considered but particle/turbulence interactions are ignored; and (3) stochastic separated flow (SSF) analysis, where both effects are considered using random-walk computations.
A theoretical and experimental investigation of single-phase and particle-laden weakly swirling jets was conducted. The jets were injected vertically downward from a 19 mm diameter tube with swirl numbers ranging from 0 to 0.33. The particle-laden jets had a single loading ratio (0.2) with particles having an SMD of 39 micrometers. Mean and fluctuating properties of both phases were measured using nonintrusive laser based methods while particle mass flux was measured using an isokinetic sampling probe. The continuous phase was analyzed using both a baseline k-epsilon turbulence model and an extended version with modifications based on the flux Richardson number to account for effects of streamline curvature. To highlight effects of interphase transport rates and particle/turbulence interactions, effects of the particles were analyzed as follows: (1) locally homogeneous flow (LHF) analysis, where interphase transport rates are assumed to be infinitely fast; (2) deterministic separated flow (DSF) analysis, where finite interphase transport rates are considered but particle/turbulence interactions are ignored; and (3) stochastic separated flow (SSF) analysis, where both effects are considered using random-walk computations.
With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.
The dynamics of root growth was studied in weightlessness. In the absence of the gravitropic reference direction during weightlessness, root movements could be controlled by spontaneous growth processes, without any corrective growth induced by the gravitropic system. If truly random of nature, the bending behavior should follow so-called 'random walk' mathematics during weightlessness. Predictions from this hypothesis were critically tested. In a Spacelab ESA-experiment, denoted RANDOM and carried out during the IML-2 Shuttle flight in July 1994, the growth of garden cress (Lepidium sativum) roots was followed by time lapse photography at 1-h intervals. The growth pattern was recorded for about 20 h. Root growth was significantly smaller in weightlessness as compared to gravity (control) conditions. It was found that the roots performed spontaneous movements in weightlessness. The average direction of deviation of the plants consistently stayed equal to zero, despite these spontaneous movements. The average squared deviation increased linearly with time as predicted theoretically (but only for 8-10 h). Autocorrelation calculations showed that bendings of the roots, as determined from the 1-h photographs, were uncorrelated after about a 2-h interval. It is concluded that random processes play an important role in root growth. Predictions from a random walk hypothesis as to the growth dynamics could explain parts of the growth patterns recorded. This test of the hypothesis required microgravity conditions as provided for in a space experiment.
The morphological evolution of crystals growing from an incongruent vapor phase is studied using a Monte Carlo model, and the full range of growth morphologies is recovered. The diffusion in the bulk nutrient and the anisotropy in the interface kinetics are morphologically destabilizing and stabilizing, respectively. For a given set of simulation parameters and lattice symmetries there is a critical size, which scales linearly with the mean free path in the vapor, beyond which a crystal cannot retain its stable, macroscopically faceted growth shape. Surface diffusion stabilizes faceted growth on the shorter scale of the mean surface diffusion length. In simulations with a uniform drift superimposed on the random walk nutrient transport, crystal faces oriented toward the drift show enhanced morphological stability compared to the purely diffusive situation. Rotational drifts with periodic reversal of direction are morphologically stabilizing for all crystal facets.
Ulysses has collected data between 1 and 5 AU during, and just following solar maximum, when the heliospheric current sheet (HCS) can be thought of as reaching its maximum tilt and being subject to the maximum amount of turbulence in the solar wind. The Ulysses solar wind plasma instrument measures the vector velocity and can be used to estimate the flow speed and direction in turbulent 'eddies' in the solar wind that are a fraction of an astronomical unit in size and last (have either a turnover or dynamical interaction time of) several hours to more than a day. Here, in a simple exercise, these solar wind eddies at the HCS are characterized using Ulysses data. This character is then used to define a model flow field with eddies that is imposed on an ideal HCS to estimate how the HCS will be deformed by the flow. This model inherently results in the complexity of the HCS increasing with heliocentric distance, but the result is a measure of the degree to which the observed change in complexity is a measure of the importance of solar wind flows in deforming the HCS. By comparison with randomly selected intervals not located on the HCS, it appears that eddies on the HCS are similar to those elsewhere at this time during the solar cycle, as is the resultant deformation of the interplanetary magnetic field (IMF). The IMF deformation is analogous to what is often termed the 'random walk' of interplanetary magnetic field lines.