Stochastic Modeling of Length of Day
We propose a general stochastic model for the UT1-LOD system and derive the corresponding Kalman filter model.
stochastic model autoregressive moving average tra↗
SEARCH · Engineering Papers
Results for “stochastic transition”
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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Modeling of transition and surface roughness effects in boundary-layer flows
Experiments were carried out to examine the influence of three-dimensional, stochastic roughness on the growth of incompressible turbulent boundary layers, as well as the effect of streamwise pressure gradients and freestream turbulence intensity on smooth-wall boundary-layer transition. The modeling of these effects in a two-dimensional boundary-layer computation program was examined with the help of the experiments. A model for surface roughness was developed that relates directly measurable statistical parameters quantifying the roughness geometry to the aerodynamic effects. This model should be valid for a limited class of surfaces found on turbomachinery blading and in other engineering applications. Commonly used criteria for the transition onset performed poorly and presumably need to be modified to account for other factors influencing the process.
Feiereisen, W. J.↗
Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction
The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.
Parker, B. Eugene, Jr.↗
Discrete Deterministic and Stochastic Petri Nets
Petri nets augmented with timing specifications gained a wide acceptance in the area of performance and reliability evaluation of complex systems exhibiting concurrency, synchronization, and conflicts. The state space of time-extended Petri nets is mapped onto its basic underlying stochastic process, which can be shown to be Markovian under the assumption of exponentially distributed firing times. The integration of exponentially and non-exponentially distributed timing is still one of the major problems for the analysis and was first attacked for continuous time Petri nets at the cost of structural or analytical restrictions. We propose a discrete deterministic and stochastic Petri net (DDSPN) formalism with no imposed structural or analytical restrictions where transitions can fire either in zero time or according to arbitrary firing times that can be represented as the time to absorption in a finite absorbing discrete time Markov chain (DTMC). Exponentially distributed firing times are then approximated arbitrarily well by geometric distributions. Deterministic firing times are a special case of the geometric distribution. The underlying stochastic process of a DDSPN is then also a DTMC, from which the transient and stationary solution can be obtained by standard techniques. A comprehensive algorithm and some state space reduction techniques for the analysis of DDSPNs are presented comprising the automatic detection of conflicts and confusions, which removes a major obstacle for the analysis of discrete time models.
MATHEMATICAL MODELS↗
Theory of mean E × B shear in a stochastic magnetic field: ambipolarity breaking and radial current
The mean E $ \times $ B shear in a stochastic magnetic field is calculated, using the radial force balance relation and transport equations. This analysis is relevant to the L → H transition with resonant magnetic perturbations, and special focus is placed upon the physics of non-ambipolar transport and radial current. The key physical process is the flow of fluctuating currents along wandering magnetic fields. The increments in poloidal and toroidal rotation, density and ion pressure are calculated. The radial envelope of the magnetic perturbations inside the plasma defines a new scale ${\ell _{{\text{env}}}}$, which is the characteristic scale of the magnetic fluctuation intensity profile. Here, the net particle outflow due to stochastic magnetic fields is calculated and is determined by the net radial current through the separatrix. Implications for the L → H transition are discussed.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Path Properties of Atmospheric Transitions: Illustration with a Low-Order Sudden Stratospheric Warming Model
Many rare weather events, including hurricanes, droughts, and floods, dramatically impact human life. To accurately forecast these events and characterize their climatology requires specialized mathematical techniques to fully leverage the limited data that are available. Here we describe transition path theory (TPT), a framework originally developed for molecular simulation, and argue that it is a useful paradigm for developing mechanistic understanding of rare climate events. TPT provides a method to calculate statistical properties of the paths into the event. As an initial demonstration of the utility of TPT, we analyze a low-order model of sudden stratospheric warming (SSW), a dramatic disturbance to the polar vortex that can induce extreme cold spells at the surface in the midlatitudes. SSW events pose a major challenge for seasonal weather prediction because of their rapid, complex onset and development. Climate models struggle to capture the long-term statistics of SSW, owing to their diversity and intermittent nature. We use a stochastically forced Holton–Mass-type model with two stable states, corresponding to radiative equilibrium and a vacillating SSW-like regime. In this stochastic bistable setting, from certain probabilistic forecasts TPT facilitates estimation of dominant transition pathways and return times of transitions. These “dynamical statistics” are obtained by solving partial differential equations in the model’s phase space. With future application to more complex models, TPT and its constituent quantities promise to improve the predictability of extreme weather events through both generation and principled evaluation of forecasts.
54 ENVIRONMENTAL SCIENCES↗
Visual Performance Challenges to Low-Frequency Perturbations After Long-Duration Space Flight, and Countermeasure Development
Astronauts experience sensorimotor disturbances after long-duration space flight. After a water landing, crewmembers may need to egress the vehicle within a few minutes for safety and operational reasons in various sea state conditions. Exposure to even low-frequency motions induced by sea conditions surrounding a vessel can cause significant motor control problems affecting critical functions. The first objective of this study was to document human visual performance during simulated wave motion below 2.0 Hz. We examined the changes in accuracy and reaction time when subjects performed a visual target acquisition task in which the location of the target was offset vertically during horizontal rotation at an oscillating frequency of 0.8 Hz. The main finding was that both accuracy and reaction time varied as a function of target location, with greater performance decrements occurring when vertical targets were acquired at perturbing frequencies of 0.8 Hz in the horizontal plane. A second objective was to develop a countermeasure, base d on stochastic resonance (SR), to enhance sensorimotor capabilities with the aim of facilitating rapid adaptation to gravitational transitions after long-duration space flight. SR is a mechanism by which noise can enhance the response of neural systems to relevant sensory signals. Recent studies have shown that applying imperceptible stochastic electrical stimulation to the vestibular system (SVS) significantly improved balance and oculomotor responses. This study examined the effectiveness of SVS on improving balance performance. Subjects performed a standard balance task while bipolar SVS was applied to the vestibular system using constant current stimulation through electrodes placed over the mastoid process. The main finding of this study was that balance performance with the application of SR showed significant improvement in the range of 10%-25%. Ultimately an SR-based countermeasure might be fielded either as preflight training to enhance adaptability, or as a miniature patch-type stimulator worn post flight.
Mulavara, Ajitkumar↗
Stochasticity of comet P/Slaughter-Burnham
Three comets are now known to be at or near the 1/1 resonance with Jupiter: P/Slaughter-Burnham, P/Boethin and the newly discovered P/Ge-Wang. Although details of the individual orbits differ, the three comets have very similar dynamical behavior: their orbits show many transitions between the different types of resonant motion (satellite libration, anti-satellite libration, and circulating motion). The stochastic character of such cometary orbits, mainly due to encounters with Jupiter is investigated using Lyapunov Characteristic Indicators. For each comet of the group, we study the influences on the stochasticity of initial eccentricity, inclination, longitude of node, and l-l(sub J) (mean longitude of comet minus mean longitude of Jupiter). We present here our first results for P/Slaughter-Burnham.
Benest, Daniel↗
Structure Identification Within a Transitioning Swept-Wing Boundary Layer
Extensive measurements are made in a transitioning swept-wing boundary layer using hot-film, hot-wire and cross-wire anemometry. The crossflow-dominated flow contains stationary vortices that breakdown near mid-chord. The most amplified vortex wavelength is forced by the use of artificial roughness elements near the leading edge. Two-component velocity and spanwise surface shear-stress correlation measurements are made at two constant chord locations, before and after transition. Streamwise surface shear stresses are also measured through the entire transition region. Correlation techniques are used to identify stationary structures in the laminar regime and coherent structures in the turbulent regime. Basic techniques include observation of the spatial correlations and the spatially distributed auto-spectra. The primary and secondary instability mechanisms are identified in the spectra in all measured fields. The primary mechanism is seen to grow, cause transition and produce large-scale turbulence. The secondary mechanism grows through the entire transition region and produces the small-scale turbulence. Advanced techniques use Linear Stochastic Estimation (LSE) and Proper Orthogonal Decomposition (POD) to identify the spatio-temporal evolutions of structures in the boundary layer. LSE is used to estimate the instantaneous velocity fields using temporal data from just two spatial locations and the spatial correlations. Reference locations are selected using maximum RMS values to provide the best available estimates. POD is used to objectively determine modes characteristic of the measured flow based on energy. The stationary vortices are identified in the first laminar modes of each velocity component and shear component. Experimental evidence suggests that neighboring vortices interact and produce large coherent structures with spanwise periodicity at double the stationary vortex wavelength. An objective transition region detection method is developed using streamwise spatial POD solutions which isolate the growth of the primary and secondary instability mechanisms in the first and second modes, respectively. Temporal evolutions of dominant POD modes in all measured fields are calculated. These scalar POD coefficients contain the integrated characteristics of the entire field, greatly reducing the amount of data to characterize the instantaneous field. These modes may then be used to train future flow control algorithms based on neural networks.
Chapman, Keith↗
A quantitative model of charge injection by ruthenium chromophores connecting femtosecond to continuous irradiance conditions
A kinetic framework for the ultrafast photophysics of tris(2,2-bipyridine)ruthenium(II) phosphonated and methyl-phosphonated derivatives is used as a basis for modeling charge injection by ruthenium dyes into a semiconductor substrate. By including the effects of light scattering, dye diffusion, and adsorption kinetics during sample preparation and the optical response of oxidized dyes, quantitative agreement with multiple transient absorption datasets is achieved on timescales spanning femtoseconds to nanoseconds. In particular, quantitative agreement with important spectroscopic handles—the decay of an excited state absorption signal component associated with charge injection in the UV region of the spectrum and the dynamical redshift of a ~500 nm isosbestic point—validates our kinetic model. Pseudo-first-order rate coefficients for charge injection are estimated in this work, with an order of magnitude ranging from 10 11 to 10 12 s –1 . The model makes the minimalist assumption that all excited states of a particular dye have the same charge injection coefficient, an assumption that would benefit from additional theoretical and experimental exploration. Here, we have adapted this kinetic model to predict charge injection under continuous solar irradiation and find that as many as 68 electron transfer events per dye per second take place, significantly more than prior estimates in the literature.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Tuning nonequilibrium phase transitions with inertia
In striking contrast to equilibrium systems, inertia can profoundly alter the structure of active systems. Here, we demonstrate that driven systems can exhibit effective equilibrium-like states with increasing particle inertia, despite rigorously violating the fluctuation–dissipation theorem. Increasing inertia progressively eliminates motility-induced phase separation and restores equilibrium crystallization for active Brownian spheres. This effect appears to be general for a wide class of active systems, including those driven by deterministic time-dependent external fields, whose nonequilibrium patterns ultimately disappear with increasing inertia. The path to this effective equilibrium limit can be complex, with finite inertia sometimes acting to accentuate nonequilibrium transitions. The restoration of near equilibrium statistics can be understood through the conversion of active momentum sources to passive-like stresses. Unlike truly equilibrium systems, the effective temperature is now density dependent, the only remnant of the nonequilibrium dynamics. Further, this density-dependent temperature can in principle introduce departures from equilibrium expectations, particularly in response to strong gradients. Our results provide additional insight into the effective temperature ansatz while revealing a mechanism to tune nonequilibrium phase transitions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Soliton and strong Langmuir turbulence in solar flare processes
The occurrence of modulational instability in the current sheet of a solar flare is investigated. Special attention is given to the plasma microinstability in this sheet and its relation to the flare process. It is found that solitons or strong Langmuir turbulence are likely to occur in the diffusion region under solar flare conditions in which the electric resistivity could be enhanced by several orders of magnitude in the region, resulting in significant heating and stochastic acceleration of particles. A numerical example is used to demonstrate the transition of the magnetic field velocity and plasma density from the outer MHD region into the diffusive region and then back out again with the completion of the energy conversion process. This is all made possible by an increase in resistivity of four to five orders of magnitude over the classical value.
Song, M. T.↗
Online updating of a Markovian solar forecast representation
Not Available
Sward, Jeffrey A. (ORCID:0000000217438944)↗
On the Stochastic Stability of Deep Markov Models
Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. DMMs using deep neural networks to parametrize the transition of Markov probability distributions have recently been shown to provide more expressiveness in modeling sequential data and dynamical system responses. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a rigorous analytical method to prove the necessary and sufficient conditions of DMM's stochastic stability. This task is achieved by spectral analysis of the efficiently computed Jacobians of probabilistic maps modeled by deep neural networks. We make theoretical connections between the eigenvalues of neural network's weights and the different activation function types used on the stability and overall dynamic behavior of DMMs with Gaussian distributions. We empirically substantiate our theoretical results on stochastic stability and eigenvalue spectra via several numerical experiments. Formal stability guarantees of DMMs can substantially improve their robustness and trustworthiness, necessary for reliable use in safety-critical real-world applications.
Drgona, Jan↗
Assignment of structural transitions during mechanical unwrapping of nucleosomes and their disassembly products
Nucleosome DNA unwrapping and its disassembly into hexasomes and tetrasomes is necessary for genomic access and plays an important role in transcription regulation. Previous single-molecule mechanical nucleosome unwrapping revealed a low- and a high-force transitions, and force-FRET pulling experiments showed that DNA unwrapping is asymmetric, occurring always first from one side before the other. However, the assignment of DNA segments involved in these transitions remains controversial. Here, using high-resolution optical tweezers with simultaneous single-molecule FRET detection, we show that the low-force transition corresponds to the undoing of the outer wrap of one side of the nucleosome (∼27 bp), a process that can occur either cooperatively or noncooperatively, whereas the high-force transition corresponds to the simultaneous unwrapping of ∼76 bp from both sides. This process may give rise stochastically to the disassembly of nucleosomes into hexasomes and tetrasomes whose unwrapping/rewrapping trajectories we establish. In contrast, nucleosome rewrapping does not exhibit asymmetry. To rationalize all previous nucleosome unwrapping experiments, it is necessary to invoke that mechanical unwrapping involves two nucleosome reorientations: one that contributes to the change in extension at the low-force transition and another that coincides but does not contribute to the high-force transition.
59 BASIC BIOLOGICAL SCIENCES↗
Detecting cosmic strings with lensed fast radio bursts
Correlated red noise recently reported from pulsar timing observations may be an indication of stochastic gravitational waves emitted by cosmic strings that formed during a primordial phase transition near the grand unification energy scale. Unfortunately, known probes of cosmic strings, namely the cosmic microwave background anisotropies and string lensing of extragalactic galaxies, are not sensitive enough for low dimensionless string tensions of Gμc -2 = 10 -10 – 10 -7 (where the tension μ is the string energy per unit length) that are needed to explain this putative signal. We show that strong gravitational lensing of fast radio bursts (FRBs) by cosmic strings is a potentially unambiguous avenue to probe that range of string tension values. The image pair of string lensing are expected to have identical magnification factor and parity, and have a typical time delay of ~10 2 (Gμc -2 /10 -8 ) 2 s. Here, the unique spectral fingerprint of each FRB, as well as the possibility to detect correlations in the time series of the electric field of the radio waves, will enable verification of the string lensing interpretation. Very-long-baseline interferometry observations can spatially resolve the image pair and provide a lower bound on the string tension based on the image separation. We calculate the FRB lensing rate as a function of the FRB detection number for several different models of the FRB redshift distribution. We find that a survey detecting ~10 5 FRBs, in line with estimates for the detection rate of the forthcoming survey CHORD, can uncover a strong lensing event for a string tension of Gμc -2 ≃ 10 -7 . Larger FRB surveys, such as Phase 2 of the Square Kilometre Array, have the potential to significantly improve the sensitivity on the string tension to Gμc -2 ≃ 10 -9 .
79 ASTRONOMY AND ASTROPHYSICS↗
Advanced data assimilation in strongly nonlinear dynamical systems
Advanced data assimilation methods are applied to simple but highly nonlinear problems. The dynamical systems studied here are the stochastically forced double well and the Lorenz model. In both systems, linear approximation of the dynamics about the critical points near which regime transitions occur is not always sufficient to track their occurrence or nonoccurrence. Straightforward application of the extended Kalman filter yields mixed results. The ability of the extended Kalman filter to track transitions of the double-well system from one stable critical point to the other depends on the frequency and accuracy of the observations relative to the mean-square amplitude of the stochastic forcing. The ability of the filter to track the chaotic trajectories of the Lorenz model is limited to short times, as is the ability of strong-constraint variational methods. Examples are given to illustrate the difficulties involved, and qualitative explanations for these difficulties are provided. Three generalizations of the extended Kalman filter are described. The first is based on inspection of the innovation sequence, that is, the successive differences between observations and forecasts; it works very well for the double-well problem. The second, an extension to fourth-order moments, yields excellent results for the Lorenz model but will be unwieldy when applied to models with high-dimensional state spaces. A third, more practical method--based on an empirical statistical model derived from a Monte Carlo simulation--is formulated, and shown to work very well. Weak-constraint methods can be made to perform satisfactorily in the context of these simple models, but such methods do not seem to generalize easily to practical models of the atmosphere and ocean. In particular, it is shown that the equations derived in the weak variational formulation are difficult to solve conveniently for large systems.
Miller, Robert N.↗
Demand Driven Cycamore Archetypes
Future nuclear fuel cycle options may present advantages over today’s once-through fuel cycle. Nuclear fuel cycle simulation tools assess the performance of those fuel cycles as well as the dynamics of long-term technology transitions. In many nuclear fuel cycle simulation tools, it has historically been the responsibility of the user to manually define facility deployment schemes and all facility parameters. While this is straightforward in simple fuel cycles, transitions from one fuel cycle to another can be more complex. In particular, deployment schemes for supportive fuel cycle facilities beyond the reactor become complex if the analyst desires to avoid gaps in the nuclear fuel and power supply chain during those transition scenarios. As nuclear fuel cycle analysis approaches questions regarding the feasibility and performance of the deployment schemes and technology choices during technology transition, automation of this historically manual process is necessary. The main objective of this work was to develop and demonstrate Cyclus automation capabilities toward key nuclear fuel cycle transition scenarios. While deploying reactors to meet power demand is trivial, and existed in the earliest versions of CYCLUS, automated, predictive deployment and decommissioning of other facilities is more complex. These include mining, milling, enrichment, fuel fabrication, reprocessing, and others. For example, a balanced closed fuel cycle may require ensuring that there is enough fast reactor fuel for their operation and may drive deployment of a fleet of light water reactors. This concern comprises the main challenge that drove the project effort. The Demand-Driven Cycamore Archetype project (NEUP-FY16-10512) aimed to develop CYCAMORE demand-driven deployment capabilities and thereby automate transition scenario definition. The developed software package, d3ploy, in the form of a CYCLUS Institution agent, deploys Facilities to meet the front-end and back-end demands of the fuel cycle. The University of South Carolina and the University of Illinois applied multiple algorithmic approaches to this challenge. This project developed an in situ demand-driven development schedule calculation through non-optimizing, deterministic-optimizing, and stochastic-optimizing algorithms as CYCLUS archetypes and demonstrated these new archetypes in program-supporting fuel cycle transition scenarios. Both objectives were achieved. This report documents the results and deliverables obtained toward these achievements in detail.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗