SEARCH · Search NASA

Results for “conditional random field”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2016

Aggregate modeling of fast-acting demand response and control under real-time pricing

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop a more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. The results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.

Aggregate Modeling↗

DOE OSTI2013

APPLICATION OF JITTER RADIATION: GAMMA-RAY BURST PROMPT POLARIZATION

A high degree of polarization of gamma-ray burst (GRB) prompt emission has been confirmed in recent years. In this paper, we apply jitter radiation to study the polarization feature of GRB prompt emission. In our framework, relativistic electrons are accelerated by turbulent acceleration. Random and small-scale magnetic fields are generated by turbulence. We further determine that the polarization property of GRB prompt emission is governed by the configuration of the random and small-scale magnetic fields. A two-dimensional compressed slab, which contains a stochastic magnetic field, is applied in our model. If the jitter condition is satisfied, the electron deflection angle in the magnetic field is very small and the electron trajectory can be treated as a straight line. A high degree of polarization can be achieved when the angle between the line of sight and the slab plane is small. Moreover, micro-emitters with mini-jet structures are considered to be within a bulk GRB jet. The jet 'off-axis' effect is intensely sensitive to the observed polarization degree. We discuss the depolarization effect on GRB prompt emission and afterglow. We also speculate that the rapid variability of GRB prompt polarization may be correlated with the stochastic variability of the turbulent dynamo or the magnetic reconnection of plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Detecting outbreaks using a spatial latent field

In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.

Safta, Cosmin [Sandia National Laboratories (SNL-C↗

DOE OSTI2013

Theory of magnetic field line random walk in noisy reduced magnetohydrodynamic turbulence

When a magnetic field consists of a mean part and fluctuations, the stochastic wandering of its field lines is often treated as a diffusive process. Under suitable conditions, a stable value is found for the mean square transverse displacement per unit parallel displacement relative to the mean field. Here, we compute the associated field line diffusion coefficient for a highly anisotropic 'noisy' reduced magnetohydrodynamic model of the magnetic field, which is useful in describing low frequency turbulence in the presence of a strong applied DC mean magnetic field, as may be found, for example, in the solar corona, or in certain laboratory devices. Our approach is nonperturbative, based on Corrsin's independence hypothesis, and makes use of recent advances in understanding factors that control decorrelation over a range of parameters described by the Kubo number. Both Bohm and quasilinear regimes are identified.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Quantitative Boltzmann–Gibbs Principles via Orthogonal Polynomial Duality

We study fluctuation fields of orthogonal polynomials in the context of particle systems with duality. We thereby obtain a systematic orthogonal decomposition of the fluctuation fields of local functions, where the order of every term can be quantified. This implies a quantitative generalization of the Boltzmann–Gibbs principle. In the context of independent random walkers, we complete this program, including also fluctuation fields in non-stationary context (local equilibrium). For other interacting particle systems with duality such as the symmetric exclusion process, similar results can be obtained, under precise conditions on the n particle dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

Certifying the quantumness of a generalized coherent control scenario

We consider the role of quantum mechanics in a specific coherent control scenario, designing a “coherent control interferometer” as the essential tool that links coherent control to quantum fundamentals. Building upon this allows us to rigorously display the genuinely quantum nature of a generalized weak-field coherent control scenario (utilizing 1 vs. 2 photon excitation) via a Bell-CHSH test. Specifically, we propose an implementation of “quantum delayed-choice” in a bichromatic alkali atom photoionization experiment. The experimenter can choose between two complementary situations, which are characterized by a random photoelectron spin polarization with particle-like behavior on the one hand, and by spin controllability and wave-like nature on the other. Because these two choices are conditioned coherently on states of the driving fields, it becomes physically unknowable, prior to measurement, whether there is control over the spin or not.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models

We use a conditional Karhunen-Lo` eve (KL) model to quantify and reduce uncertainty in a stochastic partial differential equation (SPDE) problem with partially-known space-dependent coefficient, Y (x). We assume that a small number of Y (x) measurements are available and model Y (x) with a KL expansion. We achieve reduction in uncertainty by conditioning the KL expansion coefficients on measurements. We consider two approaches for conditioning the KL expansion: In Approach 1, we condition the KL model first and then truncate it. In Approach 2, we first truncate the KL expansion and then condition it. We employ the conditional KL expansion together with Monte Carlo and sparse grid collocation methods to compute the moments of the solution of the SPDE problem. Uncertainty of the problem is further reduced by adaptively selecting additional observation locations using two active learning methods. Method 1 minimizes the variance of the PDE coefficient, while Method 2 minimizes the variance of the solution of the PDE. We demonstrate that conditioning leads to dimension reduction of the KL representation of Y (x). For a linear diffusion SPDE with uncertain log-normal coefficient, we show that Approach 1 provides a more accurate approximation of the conditional log-normal coefficient and solution of the SPDE than Approach 2 for the same number of random dimensions in a conditional KL expansion. Furthermore, Approach 2 provides a good estimate for the number of terms of the truncated KL expansion of the conditional field of Approach 1. Finally, we demonstrate that active learning based on Method 2 is more efficient for uncertainty reduction in the SPDE’s states (i.e., it leads to a larger reduction of the variance) than active learning using Method 2.

Conditioned Karhunen-Lo` eve expanion, machine lea↗

DOE OSTI2014

The expected anisotropy in solid inflation

Solid inflation is an effective field theory of inflation in which isotropy and homogeneity are accomplished via a specific combination of anisotropic sources (three scalar fields that individually break isotropy). This results in specific observational signatures that are not found in standard models of inflation: a non-trivial angular dependence for the squeezed bispectrum, and a possibly long period of anisotropic inflation (to drive inflation, the ''solid'' must be very insensitive to any deformation, and thus background anisotropies are very slowly erased). In this paper we compute the expected level of statistical anisotropy in the power spectrum of the curvature perturbations of this model. To do so, we account for the classical background values of the three scalar fields that are generated on large (superhorizon) scales during inflation via a random walk sum, as the perturbation modes leave the horizon. Such an anisotropy is unavoidably generated, even starting from perfectly isotropic classical initial conditions. The expected level of anisotropy is related to the duration of inflation and to the amplitude of the squeezed bispectrum. If this amplitude is close to its current observational limit (so that one of the most interesting predictions of the model can be observed in the near future), we find that a level of statistical anisotropy F{sup 2} gives frozen and scale invariant vector perturbations on superhorizon scales.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Application of stochastic Galerkin FEM to the complete electrode model of electrical impedance tomography

The aim of electrical impedance tomography is to determine the internal conductivity distribution of some physical body from boundary measurements of current and voltage. The most accurate forward model for impedance tomography is the complete electrode model, which consists of the conductivity equation coupled with boundary conditions that take into account the electrode shapes and the contact resistances at the corresponding interfaces. If the reconstruction task of impedance tomography is recast as a Bayesian inference problem, it is essential to be able to solve the complete electrode model forward problem with the conductivity and the contact resistances treated as a random field and random variables, respectively. In this work, we apply a stochastic Galerkin finite element method to the ensuing elliptic stochastic boundary value problem and compare the results with Monte Carlo simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Spatiotemporal modeling of node temperatures in supercomputers

Los Alamos National Laboratory (LANL) is home to many large supercomputing clusters. These clusters require an enormous amount of power (~500-2000 kW each), and most of this energy is converted into heat. Thus, cooling the components of the supercomputer becomes a critical and expensive endeavor. Recently a project was initiated to investigate the effect that changes to the cooling system in a machine room had on three large machines that were housed there. Coupled with this goal was the aim to develop a general good-practice for characterizing the effect of cooling changes and monitoring machine node temperatures in this and other machine rooms. This paper focuses on the statistical approach used to quantify the effect that several cooling changes to the room had on the temperatures of the individual nodes of the computers. The largest cluster in the room has 1,600 nodes that run a variety of jobs during general use. Since extremes temperatures are important, a Normal distribution plus generalized Pareto distribution for the upper tail is used to model the marginal distribution, along with a Gaussian process copula to account for spatio-temporal dependence. A Gaussian Markov random field (GMRF) model is used to model the spatial effects on the node temperatures as the cooling changes take place. This model is then used to assess the condition of the node temperatures after each change to the room. The analysis approach was used to uncover the cause of a problematic episode of overheating nodes on one of the supercomputing clusters. Lastly, this same approach can easily be applied to monitor and investigate cooling systems at other data centers, as well.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Interaction of energetic particles with waves in strongly inhomogeneous solar wind plasmas

Observations performed in the solar wind by different satellites show that electron beams accelerated in the low corona during solar flares can propagate up to distances around 1 AU, that Langmuir waves' packets can be clumped into spikes with peak amplitudes three orders of magnitude above the mean, and that the average level of density fluctuations can reach several percents. A Hamiltonian model is built describing the properties of Langmuir waves propagating in a plasma with random density fluctuations by the Zakharov's equations and the beam by means of particles moving self-consistently in the fields of the waves. Numerical simulations, performed using parameters relevant to solar type III conditions at 1 AU, show that when the average level of density fluctuations is sufficiently low, the beam relaxation and the wave excitation processes are very similar to those in a homogeneous plasma and can be described by the quasilinear equations of the weak turbulence theory. On the contrary, when the average level of density fluctuations overcomes some threshold depending on the ratio of the thermal velocity to the beam velocity, the plasma inhomogeneities crucially influence the characteristics of the Langmuir turbulence and the beam-plasma interaction.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

4D ML reconstruction as a tool for volumetric PET-based treatment verification in ion beam radiotherapy

Purpose: An innovative strategy to improve the sensitivity of positron emission tomography (PET)-based treatment verification in ion beam radiotherapy is proposed. Methods: Low counting statistics PET images acquired during or shortly after the treatment (Measured PET) and a Monte Carlo estimate of the same PET images derived from the treatment plan (Expected PET) are considered as two frames of a 4D dataset. A 4D maximum likelihood reconstruction strategy was adapted to iteratively estimate the annihilation events distribution in a reference frame and the deformation motion fields that map it in the Expected PET and Measured PET frames. The outputs generated by the proposed strategy are as follows: (1) an estimate of the Measured PET with an image quality comparable to the Expected PET and (2) an estimate of the motion field mapping Expected PET to Measured PET. The details of the algorithm are presented and the strategy is preliminarily tested on analytically simulated datasets. Results: The algorithm demonstrates (1) robustness against noise, even in the worst conditions where 1.5 × 10{sup 4} true coincidences and a random fraction of 73% are simulated; (2) a proper sensitivity to different kind and grade of mismatches ranging between 1 and 10 mm; (3) robustness against bias due to incorrect washout modeling in the Monte Carlo simulation up to 1/3 of the original signal amplitude; and (4) an ability to describe the mismatch even in presence of complex annihilation distributions such as those induced by two perpendicular superimposed ion fields. Conclusions: The promising results obtained in this work suggest the applicability of the method as a quantification tool for PET-based treatment verification in ion beam radiotherapy. An extensive assessment of the proposed strategy on real treatment verification data is planned.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2014

Dark-matter halo assembly bias: Environmental dependence in the non-Markovian excursion-set theory

In the standard excursion-set model for the growth of structure, the statistical properties of halos are governed by the halo mass and are independent of the larger-scale environment in which the halos reside. Numerical simulations, however, have found the spatial distributions of halos to depend not only on their mass but also on the details of their assembly history and environment. Here we present a theoretical framework for incorporating this 'assembly bias' into the excursion-set model. Our derivations are based on modifications of the path-integral approach of Maggiore and Riotto that models halo formation as a non-Markovian random-walk process. The perturbed density field is assumed to evolve stochastically with the smoothing scale and exhibits correlated walks in the presence of a density barrier. We write down conditional probabilities for multiple barrier crossings and derive from them analytic expressions for descendant and progenitor halo mass functions and halo merger rates as a function of both halo mass and the linear overdensity δ {sub e} of the larger-scale environment of the halo. Our results predict a higher halo merger rate and higher progenitor halo mass function in regions of higher overdensity, consistent with the behavior seen in N-body simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Thermo-Mechanical Phase-Field Modeling of Fracture in High-Burnup UO2 Fuels Under Transient Conditions

This study presents a novel multiphysics phase-field fracture model to analyze high-burnup uranium dioxide (UO2) fuel behavior under transient reactor conditions. Fracture is treated as a stochastic phase transition, which inherently accounts for the random microstructural effects that lead to variations in the value of fracture strength. Moreover, the model takes into consideration the effects of temperature and burnup on thermal conductivity. Therefore, the model is able to predict crack initiation, propagation, and complex morphologies in response to thermal gradients and stress distributions. Several simulations were conducted to investigate the effects of operational and transient conditions on fracture behavior and the resulting cracking patterns. High-burnup fuels exhibit reduced thermal conductivity, elevating temperature gradients and resulting in extensive radial and circumferential cracks. Transient heating rates and temperatures significantly affect fracture patterns, with higher heating rates generating steeper gradients and more irregular crack trajectories. This approach provides critical insights into fuel integrity during accident scenarios and supports the safety evaluation of extended burnup limits.

Chemistry↗

DOE OSTI2017

Local properties of the large-scale peaks of the CMB temperature

In the present work, we study the largest structures of the CMB temperature measured by Planck in terms of the most prominent peaks on the sky, which, in particular, are located in the southern galactic hemisphere. Besides these large-scale features, the well-known Cold Spot anomaly is included in the analysis. All these peaks would contribute significantly to some of the CMB large-scale anomalies, as the parity and hemispherical asymmetries, the dipole modulation, the alignment between the quadrupole and the octopole, or in the case of the Cold Spot, to the non-Gaussianity of the field. The analysis of the peaks is performed by using their multipolar profiles, which characterize the local shape of the peaks in terms of the discrete Fourier transform of the azimuthal angle. In order to quantify the local anisotropy of the peaks, the distribution of the phases of the multipolar profiles is studied by using the Rayleigh random walk methodology. Finally, a direct analysis of the 2-dimensional field around the peaks is performed in order to take into account the effect of the galactic mask. The results of the analysis conclude that, once the peak amplitude and its first and second order derivatives at the centre are conditioned, the rest of the field is compatible with the standard model. In particular, it is observed that the Cold Spot anomaly is caused by the large value of curvature at the centre.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2011

Spherical Harmonic-based Random Fields Based on Real Particle 3D Data: Improved Numerical Algorithm and Quantitative Comparison to Real Particles

Many parameters affect the cyclone efficiency, and these parameters can have different effects in different flow regimes. Therefore the maximum-efficiency cyclone length is a function of the specific geometry and operating conditions in use. In this study, we obtained a relationship describing the minimum particle diameter or maximum cyclone efficiency by using a theoretical approach based on cyclone geometry and fluid properties. We have compared the empirical predictions with corresponding literature data and observed good agreement. The results address the importance of fluid properties. Inlet and vortex finder cross-sections, cone-apex diameter, inlet Reynolds number and surface roughness are found to be the other important parameters affecting cyclone height. The surface friction coefficient, on the other hand, is difficult to employ in the calculations.We developed a theoretical approach to find the maximum-efficiency heights for cyclones with tangential inlet and we suggested a relation for this height as a function of cyclone geometry and operating parameters. In order to generalize use of the relation, two dimensionless parameters, namely for geometric and operational variables, we defined and results were presented in graphical form such that one can calculate and enter the values of these dimensionless parameters and then can find the maximum efficiency height of his own specific cyclone.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Considerations for the temperature stratification in a pre-burn constant-volume combustion chamber

In recent years, the Engine Combustion Network (ECN) has developed as a worldwide reference for understanding and describing engine combustion processes, successfully bringing together experimental and numerical efforts. Since experiments and numerical simulations both target the same boundary conditions, an accurate characterization of the stratified environment that is inevitably present in experimental facilities is required. The difference between the core-, and pressure-derived bulk-temperature of pre-burn combustion vessels has been addressed in various previous publications. Additionally, thermocouple measurements have provided initial data on the boundary layer close to the injector nozzle, showing a transition to reduced ambient temperatures. The conditions at the start of fuel injection influence physicochemical properties of a fuel spray, including near nozzle mixing, heat release computations, and combustion parameters. To address the temperature stratification in more detail, thermocouple measurements at larger distances from the spray axis have been conducted. Both the temperature field prior to the pre-combustion event that preconditions the high-temperature, high-pressure ambient, as well as the stratification at the moment of fuel injection were studied. To reveal the cold boundary layer near the injector with a better spatial resolution, Rayleigh scattering experiments and thermocouple measurements at various distances close to the nozzle have been carried out. The impact of the boundary layers and temperature stratification are illustrated and quantified using numerical simulations at Spray A conditions. Next to a reference simulation with a uniform temperature field, six different stratified temperature distributions have been generated. These distributions were based on the mean experimental temperature superimposed by a randomized variance, again derived from the experiments. The results showed that an asymmetric flame structure arises in the computed results when the temperature stratification input is used. In these predictions, first-stage ignition is advanced by 24 $\mu$s , while second-stage ignition is delayed by 11 $\mu$s. At the same time a lift-off length difference between the top and the bottom of up to 1.1 mm is observed. Furthermore, the lift-off length is less stable over time. Given the shown dependency, the temperature data is made available along with the vessel geometry data as a recommended basis for future numerical simulations.

42 ENGINEERING↗