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DOE OSTI2018

An Analytic Framework for Optimal Milling Parameter Selection

The selection of optimal parameters is an important step in machining process definition. This paper presents a decision analytic framework for completing this task. The approach is demonstrated for titanium milling. An influence diagram showing the decision situation, the corresponding uncertainties, and the value is presented. The optimization objective is the selection of machining parameters that minimize cost while considering uncertainty in tool life and stability. The uncertainties are characterized using a probability distribution taking into account all available information. The cost associated with tool failure and unstable cutting conditions is incorporated in the cost formulation. A process probability tree showing the uncertainties and the corresponding costs is constructed. The optimization results show a 90% reduction in machining cost as compared to tool manufacturer-recommended parameters. The proposed framework is normative and robust and can be applied for optimizing process parameters in conventional (such as milling and turning) and non-conventional (such as electric discharge machining, electro-chemical machining) processes. A discussion section regarding inference, experimental design, and risk aversion is included.

Karandikar, Jaydeep↗

DOE OSTI2019

Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids

Power distribution grids are structurally operated radially, such that energized lines form a collection of trees with a substation at the root of each tree. The operational topology may change from time to time, however tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This work develops a learning framework to reconstruct the radial operational structure of the distribution grid from synchronized voltage measurements. To detect operational lines our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions and Gaussian injections in particular. Furthermore, our algorithm applies to the practical case of unbalanced three-phase power flow. The performance is validated on AC power flow simulations over three phase IEEE distribution grid test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2012

Spectator field dynamics in de Sitter and curvaton initial conditions

We investigate the stochastic behaviour of long wavelength modes of light spectator scalar fields during inflation. When starting from a classical field value, the probability distribution for the spectator both spreads out and moves towards an equilibrium distribution. We study the timescales for a mixed quadratic and quartic potential. The timescale of equilibration depends on the parameters of the model, and can be surprisingly large, more than thousands of e-folds for the majority of light spectators. These results imply that the initial conditions for spectator fields are not automatically erased during inflation. These general results can be used to determine the probability of the value of the zero-mode inside a Universe-sized patch, which is relevant for observations. As an example, we apply the results to the curvaton model to calculate the probability distribution of the curvature perturbation and discuss 'typical' Universes.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

Aerosol optical depth increase in partly cloudy conditions

Remote sensing observations of aerosol from surface and satellite instruments are extensively used for atmospheric and climate research. From passive sensors, the apparent cloud-free atmosphere in the vicinity of clouds often appears to be brighter then further away from the clouds, leading to an enhancement in the retrieved aerosol optical depth. Mechanisms contributing to this enhancement, including contamination by undetected clouds, hygroscopic growth of aerosol particles, and meteorological conditions, have been debated in recent literature, but an extent to which each of these factors influence the observed enhancement is poorly known. Here we used 11 years of daily global observations at 10x10 km2 resolution from the MODIS on the NASA Terra satellite to quantify as a function of cloud fraction (CF). Our analysis reveals that, averaged over the globe, the clear sky is enhanced by ? = 0.05 which corresponds to relative enhancements of 25% in cloudy conditions (CF=0.8-0.9) compared with relatively clear conditions (CF=0.1-0.2). Unlike the absolute enhancement ?, the relative increase in ? is rather consistent in all seasons and is 25-35% in the subtropics and 15-25% at mid and higher latitudes. Using a simple Gaussian probability density function model to connect cloud cover and the distribution of relative humidity, we argue that much of the enhancement is consistent with aerosol hygroscopic growth in the humid environment surrounding clouds. Consideration of these cloud-dependent effects will facilitate understanding aerosol-cloud interactions and reduce the uncertainty in estimates of aerosol radiative forcing by global climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

The influence of surface properties on the plasma dynamics in radio-frequency driven oxygen plasmas: Measurements and simulations

Plasma parameters and dynamics in capacitively coupled oxygen plasmas are investigated for different surface conditions. Metastable species concentration, electronegativity, spatial distribution of particle densities as well as the ionization dynamics are significantly influenced by the surface loss probability of metastable singlet delta oxygen (SDO). Simulated surface conditions are compared to experiments in the plasma-surface interface region using phase resolved optical emission spectroscopy. It is demonstrated how in-situ measurements of excitation features can be used to determine SDO surface loss probabilities for different surface materials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

STATISTICAL DECOUPLING OF A LAGRANGIAN FLUID PARCEL IN NEWTONIAN COSMOLOGY

The Lagrangian dynamics of a single fluid element within a self-gravitational matter field is intrinsically non-local due to the presence of the tidal force. This complicates the theoretical investigation of the nonlinear evolution of various cosmic objects, e.g., dark matter halos, in the context of Lagrangian fluid dynamics, since fluid parcels with given initial density and shape may evolve differently depending on their environments. In this paper, we provide a statistical solution that could decouple this environmental dependence. After deriving the evolution equation for the probability distribution of the matter field, our method produces a set of closed ordinary differential equations whose solution is uniquely determined by the initial condition of the fluid element. Mathematically, it corresponds to the projected characteristic curve of the transport equation of the density-weighted probability density function (ρPDF). Consequently it is guaranteed that the one-point ρPDF would be preserved by evolving these local, yet nonlinear, curves with the same set of initial data as the real system. Physically, these trajectories describe the mean evolution averaged over all environments by substituting the tidal tensor with its conditional average. For Gaussian distributed dynamical variables, this mean tidal tensor is simply proportional to the velocity shear tensor, and the dynamical system would recover the prediction of the Zel’dovich approximation (ZA) with the further assumption of the linearized continuity equation. For a weakly non-Gaussian field, the averaged tidal tensor could be expanded perturbatively as a function of all relevant dynamical variables whose coefficients are determined by the statistics of the field.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Unavoidable multilevel biaxial symmetry breaking in chiral hybrid liquid crystals

Chiral nematic or cholesteric liquid crystals (LCs) are fluid mesophases with long-ranged orientational order featuring a quasilayered periodicity imparted by a helical director configuration but lacking long-range positional order. Doping molecular cholesteric LCs with strongly anisotropic uniaxial colloidal particles adds another level of complexity because of the interplay between weak surface-anchoring boundary conditions and bulk-based elastic distortions near the particle-LC interface. Using cylindrical colloidal disks and rods with different geometric shapes and surface conditions, we demonstrate that these colloidal inclusions generically exhibit biaxial orientational probability distributions which may impart anomalously strong local biaxiality onto the hybrid cholesteric LC structure. Unlike nonchiral hybrid molecular-colloidal LCs, where biaxial order emerges only at critical colloid volume fractions exceeding some uniaxial-biaxial transition value, the orientational probability of the colloidal inclusions immersed in chiral nematic hosts is unambiguously biaxial even at infinite dilution. We demonstrate that the colloids induce local biaxial perturbations within the molecular orientational order of the LC host medium which strongly enhances the weak but native biaxial order of chiral nematic LC induced by the chiral symmetry breaking of the director field. With the help of analytical modeling and computer simulations based on the Landau–de Gennes free energy of the host LC around the colloids, we rationalize the observed multilevel biaxial order and conclude that it is not only unavoidable but also strongly enhanced compared to both achiral hybrid LCs and purely molecular cholesteric LCs.

biaxial liquid crystals↗

DOE OSTI2024

Seasonal Cost-Benefit Analysis of Automated Distribution Feeder Upgrades with Advanced Mitigation Technologies

The increasing deployment of distributed solar photovoltaics (DPV) to meet clean energy goals can trigger adverse grid operation issues, such as voltage excursions and the violation of thermal loading constraints of the power delivery elements (e.g., lines and transformers) on the evolving electricity infrastructure. Such integration issues would require distribution upgrades with associated costs to mitigate them and to maintain reliable and resilient grid operating conditions. Traditional distribution network upgrade approaches use a specific single snapshot analysis that is overly conservative. This study considers a multi-time point analysis to capture both moderate (probable bounds) and extreme grid operating conditions using time points such as minimum load with minimum photovoltaics (PV), maximum load with maximum PV, maximum load with minimum PV, and minimum load with maximum PV. Further, this study investigates seasonal variation impacts and associated distribution upgrade costs for a spring season case (March, representing a low load and high PV scenario) and a summer case (July, representing a high load and high PV scenario). Such seasonal analysis will allow system operators to characterize upgrade requirements and associated costs across various periods. Because the spatial distribution of DPV can impact upgrade and associated costs, this study investigates three common DPV deployment scenarios - randomly deployed, close to the substation, and far from the substation - at different penetration levels. Apart from spatial distribution impacts, this project evaluates the techno-economic impacts of the nodal photovoltaic penetration factor (NPPF) for generating the various DPV deployment scenarios at increasing penetration levels. This project investigates the impact of varying nodal PV-to-load ratios using conservative and extreme NPPF values of 3 and 10, respectively. This study investigates the deployment of traditional infrastructure upgrade strategies, such as installing new voltage regulating equipment, transformers and lines replacements, and the activation of advanced inverter functionality (e.g., autonomous volt/VAR) in expanding PV hosting capacity. Existing DPV systems are assumed to operate with the legacy unity power factor, and we considered the possibility of retrofitting such systems with the activation of volt/VAR control as integration standards and regulations continue to evolve to allow such functions. The cost-benefit analysis metrics used in study include distribution upgrade costs, average cost per watt of the upgrade cost, average marginal cost per watt of the upgrade cost, and power losses.

14 SOLAR ENERGY↗

DOE OSTI2013

Long-Term Probability Distribution of Wind Turbine Planetary Bearing Loads (Poster)

Among the various causes of bearing damage and failure, metal fatigue of the rolling contact surface is the dominant failure mechanism. The fatigue life is associated with the load conditions under which wind turbines operate in the field. Therefore, it is important to understand the long-term distribution of the bearing loads under various environmental conditions. The National Renewable Energy Laboratory's 750-kW Gearbox Reliability Collaborative wind turbine is studied in this work. A decoupled analysis using several computer codes is carried out. The global aero-elastic simulations are performed using HAWC2. The time series of the drivetrain loads and motions from the global dynamic analysis are fed to a drivetrain model in SIMPACK. The time-varying internal pressure distribution along the raceway is obtained analytically. A series of probability distribution functions are then used to fit the long-term statistical distribution at different locations along raceways. The long-term distribution of the bearing raceway loads are estimated under different environmental conditions. Finally, the bearing fatigue lives are calculated.

17 WIND ENERGY↗

DOE OSTI2013

Inverse Modeling of Hydrologic Parameters Using Surface Flux and Runoff Observations in the Community Land Model

This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Two inversion strategies, the deterministic least-square fitting and stochastic Markov-Chain Monte-Carlo (MCMC) - Bayesian inversion approaches, are evaluated by applying them to CLM4 at selected sites. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the least-square fitting provides little improvements in the model simulations but the sampling-based stochastic inversion approaches are consistent - as more information comes in, the predictive intervals of the calibrated parameters become narrower and the misfits between the calculated and observed responses decrease. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to the different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

inverse, modeling, hydrologic, parameters, flux, r↗

DOE OSTI2014

Numerical simulations of quiet sun magnetism: On the contribution from a small-scale dynamo

We present a series of radiative MHD simulations addressing the origin and distribution of the mixed polarity magnetic field in the solar photosphere. To this end, we consider numerical simulations that cover the uppermost 2-6 Mm of the solar convection zone and we explore scales ranging from 2 km to 25 Mm. We study how the strength and distribution of the magnetic field in the photosphere and subsurface layers depend on resolution, domain size, and boundary conditions. We find that 50% of the magnetic energy at the τ = 1 level comes from fields with the less than 500 G strength and that 50% of the energy resides on scales smaller than about 100 km. While the probability distribution functions are essentially independent of resolution, properly describing the spectral energy distribution requires grid spacings of 8 km or smaller. The formation of flux concentrations in the photosphere exceeding 1 kG requires a mean vertical field strength greater than 30-40 G at τ = 1. The filling factor of kG flux concentrations increases with overall domain size as the magnetic field becomes organized by larger, longer-lived flow structures. A solution with a mean vertical field strength of around 85 G at τ = 1 requires a subsurface rms field strength increasing with depth at the same rate as the equipartition field strength. We consider this an upper limit for the quiet Sun field strength, which implies that most of the convection zone is magnetized close to the equipartition. We discuss these findings in view of recent high-resolution spectropolarimetric observations of quiet Sun magnetism.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

ACCRETION RATES OF MOONLETS EMBEDDED IN CIRCUMPLANETARY PARTICLE DISKS

We examine the gravitational capture probability of colliding particles in circumplanetary particle disks and accretion rates of small particles onto an embedded moonlet, using analytic calculation, three-body orbital integrations, and N-body simulations. Expanding our previous work, we take into account the Rayleigh distribution of particles' orbital eccentricities and inclinations in our analytic calculation and orbital integration and confirm agreement between them when the particle velocity dispersion is comparable to or larger than their mutual escape velocity and the ratio of the sum of the physical radii of colliding particles to their mutual Hill radius (r-tilde{sub p}) is much smaller than unity. As shown by our previous work, the capture probability decreases significantly when the velocity dispersion is larger than the escape velocity and/or r-tilde{sub p}{approx}>0.7. Rough surfaces of particles can enhance the capture probability. We compare the results of three-body calculations with N-body simulations for accretion of small particles by an embedded moonlet and find agreement at the initial stage of accretion. However, when particles forming an aggregate on the moonlet surface nearly fill the Hill sphere, the aggregate reaches a quasi-steady state with a nearly constant number of particles covering the moonlet, and the accretion rate is significantly reduced compared to the three-body results.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Exploring two-field inflation in the Wess-Zumino model

We explore inflation via the effective potential of the minimal Wess-Zumino model, considering both the real and imaginary components of the complex field. Using transport techniques, we calculate the full allowed range of n{sub s}, r and f{sub NL} for different choices of the single free parameter, v, and present the probability distribution of these signatures given a simple choice for the prior distribution of initial conditions. Our work provides a case study of multi-field inflation in a simple but realistic setting, with important lessons that are likely to apply more generally. For example, we find that there are initial conditions consistent with observations of n{sub s} and r for values of v that would be excluded if only evolutions in the real field direction were to be considered, and that these may yield enhanced values of f{sub NL}. Moreover, we find that initial conditions fixed at high energy density, where the potential is close to quartic in form, can still lead to evolutions in a concave region of the potential during the observable number of e-folds, as preferred by present data. The Wess-Zumino model therefore provides an illustration that multi-field dynamics must be taken into account when seeking to understand fully the phenomenology of such models of inflation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Dispersal of Amur tiger from spatial distribution and genetics within the eastern Changbai mountain of China

Abstract Population dispersal and migration often indicate an expanded habitat and reduced inbreeding probability, and to some extend reflects improvement in the condition of the population. The Amur tiger population in the northern region of the Changbai mountain in China mostly distributes along the Sino–Russian border, next to the population in southwest Primorye in Russia. The successful dispersal westward and transboundary movement are crucial for the persistence of the Amur tiger in this area. This study explored the spatial dispersal of the population, transboundary migration, and the genetic condition of the Amur tiger population within the northern Changbai mountain in China, using occurrence data and fecal samples. Our results from 2003 to 2016 showed that the Amur tiger population in this area was spreading westward at a speed of 12.83 ± 4.41 km every three years. Genetic diversity of the Amur tiger populations in southwest Primorye was slightly different than the population in our study area, and the potential individual migration rate between these two populations was shown to be about 13.04%. Furthermore, the relationships between genetic distances and spatial distances indicated the existence of serious limitations to the dispersal of the Amur tiger in China. This study provided important information about spatial dispersal, transboundary migration, and the genetic diversity of Amur tigers in China, showed the urgent need for Amur tiger habitat restoration, and suggested some important conservation measures, such as corridor construction to eliminate dispersal barriers and joint international conservation to promote trans‐boundary movement.

Ning, Yao↗

DOE OSTI2022

A Conditional Autoencoder for Galaxy Photometric Parameter Estimation

Astronomical photometric surveys routinely image billions of galaxies, and traditionally infer the parameters of a parametric model for each galaxy. This approach has served us well, but the computational expense of deriving a full posterior probability distribution function is a challenge for increasingly ambitious surveys. In this paper, we use deep learning methods to characterize galaxy images, training a conditional autoencoder on mock data. The autoencoder can reconstruct and denoise galaxy images via a latent space engineered to include semantically meaningful parameters, such as brightness, location, size, and shape. Our model recovers galaxy fluxes and shapes on mock data with a lower variance than the Hyper Suprime-Cam photometry pipeline, and returns reasonable answers even for inputs outside the range of its training data. When applied to data in the training range, the regression errors on all extracted parameters are nearly unbiased with a variance near the Cramr-Rao bound.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Estimating the Contributions to Human Error Probability from the Convolution of the Distribution of Time Available and Time Required

As part of their duties, Human Reliability Analysis must often evaluate if crews in nuclear power plants (NPPs) can complete tasks associated with a human-failure event within time limits. For example, the time required in NPP scenarios is determined by systematic and structured walkthroughs, feasibility studies, recorded times from training exercises, and interviews with experienced operators and experts. Typically, a point estimate is derived for the estimate (mean, maximum, or 95th percentile of time required). Using point-estimate values can mask the risk associated with variability among crews, plant conditions and set-up, environmental conditions, and other impact factors under which these actions are executed. While point estimates for time required and time available have served the industry well, without considering the uncertainty they could lead to biased understanding about the risk. The Integrated Human Event Analysis System - General Methodology (IDHEAS-G) model (developed by the US Nuclear Regulatory Commission, NRC) for human error probability calculates human error probability by summing two probabilities: insufficient time and cognitive error. As such, the model takes a more holistic approach by considering the full distributions for time required and time available to calculate the human error probability because the time available to complete the task is insufficient. In this study, we expand on the work of the NRC and discuss methods for estimating these time considerations. For example, for the time required, the impact of Performance Influencing Factors (PIFs) on the distribution was divided into impacts that are aleatory in nature, such as crew-to-crew variability, and those that are epistemic (i.e., the PIFs). Starting with the factors that introduce aleatory uncertainty, a first-order distribution was developed from a large set of time required (i.e., NPP task completion times) data for the range of operator actions that occur in the NPP control room under simulated accident conditions. The first-order distribution can then be adjusted to account for epistemic uncertainty using research associated with the impact of applicable PIFs on the time required. We also develop guidance for analysts to address the probability distributions for the time available. The guidance we developed on how to estimate time required and time available distributions is based on the identification of pertinent research and data, data analyses, and expert knowledge elicitation.

human error probability, human performance, time e↗

DOE OSTI2026

GPM IMERG V07B and V06B: Evaluation Using Ground-Based Radar Observations and Application in Global Mesoscale Convective System Tracking

This study evaluates the latest Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG V07B) against its predecessor V06B, for studying mesoscale convective systems (MCSs). Both versions are compared using ground-based radar and rain gauge data from five meteorologically diverse regions: the contiguous United States (including eastern coastlines), Amazon rainforest, central Argentina mountains, equatorial Indian Ocean, and northern Australia across multiple temporal (0.5–6 hours) and spatial scales (0.1°–0.25°). An updated global MCS tracking dataset is developed by integrating satellite-observed infrared brightness temperature with IMERG V07B. Comparation of IMERG against radar observations reveals that IMERG demonstrates better performance in capturing the probability distribution and quantitative contributions of rainfall (from no-rain to intense-rain conditions) over tropical oceans than over land, with marked improvements in IMERG V07B for heavy-to-intense rain (> 10 mm h-1). Over land, systematic biases persist: IMERG tends to overestimate light-to-moderate rain (1–10 mm h-1) while underestimating heavy-to-intense rain. Additionally, aggregating IMERG to coarser resolutions (3-hourly or 0.25°) improves consistency with radar observations, outperforming the 1-hourly/0.1° resolution. The new IMERG V07B-based global MCS dataset exhibits consistent statistical characteristics with the V06B-based dataset, despite lower mean rain rates and reduced heavy precipitation contributions. These findings offer valuable insights for utilizing IMERG V07B in global precipitation studies, MCS characterization, and model evaluation.

Zhang, Sihan↗

DOE OSTI2022

Kinetic theory of particle-in-cell simulation plasma and the ensemble averaging technique

Abstract We derive the kinetic theory of fluctuations in physically and numerically stable particle-in-cell (PIC) simulations of electrostatic plasmas. The starting point is the single-time correlation at the start of the simulation between the statistical fluctuations of the weighted densities of macroparticle centers in the plasma particle phase-space. The fluctuations are associated with different initial conditions, typically due to the random initial conditions (in velocity space) of the macroparticles/simulation plasma, assigned according to their initial distribution of probability. The single-time correlations at all time steps and in each spatial grid cell are then determined from the Laplace–Fourier transforms of the discretized Klimontovich-like equation for the macroparticles and Maxwell’s equations for the fields, as computed by modern PIC codes. We recover the expressions for the electrostatic field and the plasma particle density fluctuation autocorrelation spectra as well as the kinetic equations describing the average evolution of PIC-simulated plasma particles, first derived by Langdon (1970b Proc. 4th Conf. Numerical Simulation of Plasmas ) using a test macroparticle approach perturbing a discretized Vlasovian plasma and then averaging the obtained physical quantity over the initial macroparticle velocity distribution. We generalize and extend these results to the modern algorithms in PIC codes using arbitrary macroparticle weights. Analytical estimates of statistical fluctuation amplitudes are derived as a function of the plasma simulation parameters, using the central limit theorem in the limit of a large number of macroparticles per cell. The theory is then used to analyze the ensemble averaging technique of PIC simulations where statistical averages are performed over ensembles of PIC simulations, modeling the same plasma physics problem but using different statistical realizations of the initial distribution functions of the macroparticles. This method is illustrated by linear Landau damping uncovering (from noise, which is usually considered numerical) the physical fluctuations driven by a single small amplitude electrostatic wave perturbing a PIC simulation plasma in equilibrium.

fluctuations correlations↗