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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Effects of Ice Particle Size Vertical Inhomogeneity on the Passive Remote Sensing of Ice Clouds

The solar reflectance bi-spectral (SRBS) and infrared split-window (IRSpW) methods are two of the most popular techniques for passive ice cloud property retrievals from multispectral imagers. Ice clouds are usually assumed to be vertically homogeneous in global operational algorithms based on these methods, although significant vertical variations of ice particle size are typically observed in ice clouds. In this Study we investigate uncertainties in retrieved optical thickness, effective particle size, and ice water path introduced by a homogeneous cloud assumption in both the SRBS and IRSpW methods, and focus on whether the assumption can lead to significant discrepancies between the two methods. The study simulates the upwelling spectral radiance associated with vertically structured clouds and passes the results through representative SRBS and IRSpW retrieval algorithms. Cloud optical thickness is limited to values for which IRSpW retrievals are possible (optical thickness less than about 7). When the ice cloud is optically thin and yet has a significant ice particle size vertical variation, it is found that both methods tend to underestimate the effective radius and ice water path. The reason for the underestimation is the nonlinear dependence of ice particle scattering properties (extinction and single scattering albedo) on the effective radius. Because the nonlinearity effect is stronger in the IRSpW than the SRBS method, the IRSpW-based IWP tends to be smaller than the SRBS counterpart. When the ice cloud is moderately optically thick, the IRSpW method is relatively insensitive to cloud vertical structure and effective radius retrieval is weighted toward smaller ice particle size, while the weighting function makes the SRBS method more sensitive to the ice particle size in the upper portion of the cloud. As a result, when ice particle size increases monotonically toward cloud base, the two methods are in qualitative agreement; in the event that ice particle size decreases toward cloud base, the effective radius and ice water path retrievals based on the SRBS method are substantially larger than those from the IRSpW. The main findings of this Study Suggest that the homogenous cloud assumption can affect the SRBS and IRSpW methods to different extents and, consequently, can lead to significantly different retrievals. Therefore caution should be taken when comparing and combining the ice cloud property retrievals from these two methods.

Zhang, Zhibo↗
NASA NTRS1994

Evaluating the design of an earth radiation budget instrument with system simulations. Part 2: Minimization of instantaneous sampling errors for CERES-I

Much of the new record of broadband earth radiation budget satellite measurements to be obtained during the late 1990s and early twenty-first century will come from the dual-radiometer Clouds and Earth's Radiant Energy System Instrument (CERES-I) flown aboard sun-synchronous polar orbiters. Simulation studies conducted in this work for an early afternoon satellite orbit indicate that spatial root-mean-square (rms) sampling errors of instantaneous CERES-I shortwave flux estimates will range from about 8.5 to 14.0 W/m on a 2.5 deg latitude and longitude grid resolution. Rms errors in longwave flux estimates are only about 20% as large and range from 1.5 to 3.5 W/sq m. These results are based on an optimal cross-track scanner design that includes 50% footprint overlap to eliminate gaps in the top-of-the-atmosphere coverage, and a 'smallest' footprint size to increase the ratio in the number of observations lying within to the number of observations lying on grid area boundaries. Total instantaneous measurement error also depends on the variability of anisotropic reflectance and emission patterns and on retrieval methods used to generate target area fluxes. Three retrieval procedures from both CERES-I scanners (cross-track and rotating azimuth plane) are used. (1) The baseline Earth Radiaton Budget Experiment (ERBE) procedure, which assumes that errors due to the use of mean angular dependence models (ADMs) in the radiance-to-flux inversion process nearly cancel when averaged over grid areas. (2) To estimate N, instantaneous ADMs are estimated from the multiangular, collocated observations of the two scanners. These observed models replace the mean models in computation of satellite flux estimates. (3) The scene flux approach, conducts separate target-area retrievals for each ERBE scene category and combines their results using area weighting by scene type. The ERBE retrieval performs best when the simulated radiance field departs from the ERBE mean models by less than 10%. For larger perturbations, both the scene flux and collocation methods produce less error than the ERBE retrieval. The scene flux technique is preferable, however, because it involves fewer restrictive assumptions.

Stowe, Larry↗
DOE OSTI2026

Quantifying System Strength From Grid-Forming Resources Using Frequency Scan Approach: Preprint

Current industry practices for quantifying the system strength contribution from grid-forming (GFM) resources to ensure stability of power systems dominated by inverter-based resources (IBRs) are primarily based on iterative electromagnetic transient (EMT) time-domain simulation studies. While feasible, these approaches are resource-intensive, lack scalability and intuition, and might not evaluate the system strength contribution over the entire frequency range of interest. This paper introduces a novel, frequency-domain approach to quantify system strength support provided by a GFM resource using frequency scans. The proposed method uses transfer functions from the grid voltage magnitude (V) and phase (?), respectively, to the reactive (Q) and active power (P) output of a GFM resource for quantifying its contribution to system strength. These transfer functions provide a direct measure of the ability of a GFM resource to behave as a stiff voltage source behind a reactance over a specified frequency range, enabling robust quantification of its system strength contribution. The key innovation of this work is the development of a frequency domain system strength metric called the dynamic short-circuit ratio (dSCR) that is suitable for IBR-dominated power systems and is directly related with the familiar short circuit ratio (SCR) metric. The new metric, dSCR, enables the assessment of system strength contributions from both synchronous machines and converter-based GFM resources using a unified benchmark, which is not possible with the traditional SCR metric. The paper also demonstrates how impedance scans could identify if an unstable condition observed during weak grid conditions is a result of the lack active or reactive power support or both. By leveraging the proposed frequency-domain dSCR metric for quantifying system strength contribution from GFM IBRs, the paper demonstrates targeted mitigation strategies for weak grid instabilities without resorting to repeated, time-consuming time-domain simulations. The result is a scalable and efficient approach to remediate stability challenges in power systems with high shares of IBRs and accelerating the integration of GFM technologies for system strength support in power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

DSO+T: Integrated System Simulation (DSO+T Study: Volume 2)

This report summarizes an integrated co-simulation model used by the Distribution System Operator with Transactive (DSO+T) study to represent an electrical generation, delivery, and end-load systems for the purposes of assessing the viability and value proposition of transactive energy coordination of flexible assets versus a business-as-usual case. The integrated co-simulation model includes the bulk generation and transmission system, including the day-ahead and real-time scheduling and dispatch of thermal generators. Forty distribution system operators were modelled in detail, including tens of thousands of residential and commercial buildings and their flexible end-loads. These included HVAC systems, residential water heaters, electric vehicles, and stationary, behind-the-meter, batteries. Both wholesale market and end-load results for the business-as-usual case are presented and compared to actual ERCOT system data to assess the accuracy and representativeness of the resulting model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2014

Open Boundary Particle-in-Cell Simulation of Dipolarization Front Propagation

First results are presented from an ongoing open boundary 2-1/2D particle-in-cell simulation study of dipolarization front (DF) propagation in Earth's magnetotail. At this stage, this study is focused on the compression, or pileup, region preceding the DF current sheet. We find that the earthward acceleration of the plasma in this region is in general agreement with a recent DF force balance model. A gyrophase bunched reflected ion population at the leading edge of the pileup region is reflected by a normal electric field in the pileup region itself, rather than through an interaction with the current sheet. We discuss plasma wave activity at the leading edge of the pileup region that may be driven by gradients, or by reflected ions, or both; the mode has not been identified. The waves oscillate near but above the ion cyclotron frequency with wavelength several ion inertial lengths. We show that the waves oscillate primarily in the perpendicular magnetic field components, do not propagate along the background magnetic field, are right handed elliptically (close to circularly) polarized, exist in a region of high electron and ion beta, and are stationary in the plasma frame moving earthward. We discuss the possibility that the waves are present in plasma sheet data, but have not, thus far, been discovered.

dipolarization front↗
NASA NTRS1994

Communication Research in Aviation and Space Operations: Symptoms and Strategies of Crew Coordination

The day-to-day operators of today's aerospace systems work under increasing pressures to accomplish more with less. They work in operational systems which are complex, technology-based, and high-risk; in which incidents and accidents have far-reaching and costly consequences. For these and other reasons, there is concern that the safety net formerly built upon redundant systems and abundant resources may become overburdened. Although we know that human ingenuity can overcome incredible odds, human nature can also fail in unpredictable ways. Over the last 20 years, a large percentage of aviation accidents and incidents have been attributed to human errors rather than hardware or environmental factors alone. A class of errors have been identified which are not due to a lack of individual, technical competencies. Rather, they are due to the failure of teams to utilize readily available resources or information in a timely fashion. These insights began a training revolution in the aviation industry called Cockpit Resource Management, which later became known as Crew Resource Management (CRM) as its concepts and applications extended to teams beyond the flightdeck. Then, as now, communication has been a cornerstone in CRM training since crew coordination and resource management largely resides within information transfer processes--both within flightcrews, and between flightcrews and the ground operations teams that support them. The research I will describe takes its roots in CRM history as we began to study communication processes in order to discover symptoms of crew coordination problems, as well as strategies of effective crew management. On the one hand, communication is often the means or the tool by which team members manage their resources, solve problems, maintain situational awareness and procedural discipline. Conversely, it is the lack of planning and resource management, loss of vigilance and situational awareness, and non-standard communications that are implicated in accidents and incidents. NASA/Ames Crew Factors researchers have been developing a model of effective crew coordination in order to understand the sources of performance breakdowns, and to develop effective solutions and interventions. Because communication is a primary mechanism by which information is received and transmitted, and because it is observable behavior, we focus on these group processes in order to identify patterns of communication that distinguish effective from less effective crew performance. Since a prime objective is to develop training recommendations for enhancing communication skills, we interpret our findings in the context of relevant task and environmental conditions, role and procedural constraints, and the normal real-time parameters of flight operations. Another research objective is to consider how communication and coordination can be enhanced through design. For example, flight deck and hardware design as well as procedural and software design may greatly influence the efficiency with which crews communicate and coordinate their work. In addition, teams and tasks may be designed, organized, and trained so that team interactions with each other are based upon appropriately shared knowledge, procedures and situation awareness. In short, we are interested in enhancing communication practices through (1) the training of specific communication skills, and (2) the design of equipment, tasks, procedures, and teams that optimize smooth, unambiguous communication processes. Two examples of communication research will be described; one in aviation and one in space operations. The first example is a high-fidelity full mission simulation study which investigates the affect of flightdeck automation on crew coordination and communication (contrasting crew performance in the DC-9 vs. MD88). Additional information is contained in the original extended abstract.

Kanki, Barbara G.↗
DOE OSTI2017

Characterization of the Chlorination Products of Zirconium Alloys used for Nuclear Fuel Cladding

The United States produces over 2000 metric tons of used nuclear fuel material per year, of which 25% is irradiated fuel cladding, and it is expected to increase for the next 50 years. Previous simulation studies have shown that in the irradiated cladding, the major activation products found are {sup 125}Sb, {sup 60}Co, and {sup 99m}Nb, and the major fission product found is {sup 137}Cs. These radioisotopes make it impossible to treat the cladding as low level waste. A chlorination process has been proposed for zirconium reprocessing as it has been proven more efficient than previous mechanical processes, redox processes, and other chemical methods (e.g. iodination). In the chlorination process, zirconium alloy cladding reacts with Cl{sub 2} at 350- 380 deg. C to produce mainly ZrCl{sub 4}. The zirconium tetrachloride has a relatively low sublimation point (331 deg. C) that allows its separation from other products, and so it can be treated as low level waste for disposal or recycling. However, previous work has observed element impurities (iron, tin, chromium, cesium, antimony, and niobium) in the recovered ZrCl{sub 4}, meaning that additional steps are necessary in a chlorination process previous to the reduction of ZrCl, to metallic Zr. The emphasis of this research summary is on the progress that has been made to isolate and characterize the impurities produced after chlorination of three previously characterized non-irradiated zirconium alloys (Zircaloy-2, Zircaloy-4, and a commercial Zr-Nb alloy) widely used as cladding materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2016

UAS Air Traffic Controller Acceptability Study-2: Effects of Communications Delays and Winds in Simulation

This study evaluated the effects of Communications Delays and Winds on Air Traffic Controller ratings of acceptability of horizontal miss distances (HMDs) for encounters between UAS and manned aircraft in a simulation of the Dallas-Ft. Worth East-side airspace. Fourteen encounters per hour were staged in the presence of moderate background traffic. Seven recently retired controllers with experience at DFW served as subjects. Guidance provided to the UAS pilots for maintaining a given HMD was provided by information from self-separation algorithms displayed on the Multi-Aircraft Simulation System. Winds tested did not affect the acceptability ratings. Communications delays tested included 0, 400, 1200, and 1800 msec. For longer communications delays, there were changes in strategy and communications flow that were observed and reported by the controllers. The aim of this work is to provide useful information for guiding future rules and regulations applicable to flying UAS in the NAS.

Comstock, James R., Jr.↗
DOE OSTI2020

Extremum seeking for optimal control problems with unknown time-varying systems and unknown objective functions

We consider the problem of optimal feedback control of an unknown, noisy, time-varying, dynamic system that is initialized repeatedly. Examples include a robotic manipulator which must perform the same motion, such as assisting a human, repeatedly and accelerating cavities in particle accelerators which are turned on for a fraction of a second with given initial conditions and vary slowly due to temperature fluctuations. In this paper, we present an approach that applies to systems of practical interest. The method presented here is model independent; does not require knowledge of the objective function; is robust to measurement noise; is applicable for any set of initial conditions; is applicable to simultaneously controlling an arbitrary number of parameters; and may be implemented with a broad range of continuous or discontinuous functions such as sine or square waves. For systems with convex cost functions we prove that our algorithm will produce controllers that approach the minimal cost. For linear systems we reproduce the cost minimizing linear quadratic regulator optimal controller that could have been designed analytically had the system and cost function been known. We demonstrate the effectiveness of the algorithm with simulation studies of noisy and time-varying systems.

42 ENGINEERING↗
DOE OSTI2018

Ultrafine Pt Nanoparticle-Decorated Pyrite-Type CoS 2 Nanosheet Arrays Coated on Carbon Cloth as a Bifunctional Electrode for Overall Water Splitting

To improve the utilization efficiency of precious metals, metal-supported materials provide a direction for fabricating highly active and stable heterogeneous catalysts. Here, carbon cloth (CC)-supported Earth-abundant CoS 2 nanosheet arrays (CoS 2 /CC) are presented as ideal substrates for ultrafine Pt deposition (Pt-CoS 2 /CC) to achieve remarkable performance toward the hydrogen and oxygen evolution reactions (HER/OER) in alkaline solutions. Notably, the Pt-CoS 2 /CC hybrid delivers an overpotential of 24 mV at 10 mA cm –2 and a mass activity of 3.89 A Ptmg –1 , which is 4.7 times higher than that of commercial Pt/C, at an overpotential of 130 mV for catalyzing the HER. An alkali-electrolyzer using Pt-CoS 2 /CC as a bifunctional electrode enables a water-splitting current density of 10 mA cm –2 at a low voltage of 1.55 V and can sustain for more than 20 h, which is superior to that of the state-of-the-art Pt/C+RuO 2 catalyst. Further experimental and theoretical simulation studies demonstrate that strong electronic interaction between Pt and CoS 2 synergistically optimize hydrogen adsorption/desorption behaviors and facilitate the in situ generation of OER active species, enhancing the overall water-splitting performance. This research highlights the regulation of interfacial and electronic synergy in pursuit of highly efficient and durable supported catalysts for hydrogen and oxygen electrocatalytic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials

Herein, this article presents generalized semiparametric regression models for conditional cumulative incidence functions with competing risks data when covariates are missing by sampling design or happenstance. A doubly robust augmented inverse probability weighted (AIPW) complete-case approach to estimation and inference is investigated. This approach modifies IPW complete-case estimating equations by exploiting the key features in the relationship between the missing covariates and the phase-one data to improve efficiency. An iterative numerical procedure is derived to solve the nonlinear estimating equations. The asymptotic properties of the proposed estimators are established. A simulation study examining the finite-sample performances of the proposed estimators shows that the AIPW estimators are more efficient than the IPW estimators. The developed method is applied to the RV144 HIV-1 vaccine efficacy trial to investigate vaccine-induced IgG binding antibodies to HIV-1 as correlates of acquisition of HIV-1 infection while taking account of whether the HIV-1 sequences are near or far from the HIV-1 sequences represented in the vaccine construct.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Joint Modeling of Wind Speed and Wind Direction Through a Conditional Approach

Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to climate change research. It is therefore crucial to accurately estimate the joint probability distribution of wind speed and direction. In this work, we develop a conditional approach to model these two variables, where the joint distribution is decomposed into the product of the marginal distribution of wind direction and the conditional distribution of wind speed given wind direction. To accommodate the circular nature of wind direction, a von Mises mixture model is used; the conditional wind speed distribution is modeled as a directional dependent Weibull distribution via a two-stage estimation procedure, consisting of a directional binned Weibull parameter estimation, followed by a harmonic regression to estimate the dependence of the Weibull parameters on wind direction. A Monte Carlo simulation study indicates that our method outperforms two other approaches in estimation efficiency: one that utilizes periodic spline quantile regression and another that generates data from the commonly used Abe-Ley distribution for cylindrical data. We illustrate our method by using the output from a regional climate model to investigate how the joint distribution of wind speed and direction may change under some future climate scenarios. Our method indicates significant changes in the variation of wind speed with respect to some directions.

17 WIND ENERGY↗
DOE OSTI2021

Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series

Abstract In this article we propose an extension of singular spectrum analysis for interval‐valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set‐valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.

de Carvalho, Miguel↗
DOE OSTI2018

Failure time distributions for complex equipment

The exponential distribution is inadequate as a failure time model for most components; however, under certain conditions (in particular, that component failure rates are small and mutually independent, and failed components are immediately replaced or perfectly repaired), it is applicable to complex repairable systems with large numbers of components in series, regardless of component distributions, as shown by Drenick in 1960. This result implies that system behavior may become simpler as more components are added. We review necessary conditions for the result and present some simulation studies to assess how well it holds in systems with finite numbers of components. Lastly, we also note that Drenick's result is analogous to similar results in other systems disciplines, again resulting in simpler behavior as the number of entities in the system increases.

42 ENGINEERING↗
DOE OSTI2024

Monitoring covariance in multivariate time series: Comparing machine learning and statistical approaches

Abstract In complex systems with multiple variables monitored at high‐frequency, variables are not only temporally autocorrelated, but they may also be nonlinearly related or exhibit nonstationarity as the inputs or operation changes. One approach to handling such variables is to detrend them prior to monitoring and then apply control charts that assume independence and stationarity to the residuals. Monitoring controlled systems is even more challenging because the control strategy seeks to maintain variables at prespecified mean levels, and to compensate, correlations among variables may change, making monitoring the covariance essential. In this paper, a vector autoregressive model (VAR) is compared with a multivariate random forest (MRF) and a neural network (NN) for detrending multivariate time series prior to monitoring the covariance of the residuals using a multivariate exponentially weighted moving average (MEWMA) control chart. Machine learning models have an advantage when the data's structure is unknown or may change. We design a novel simulation study with nonlinear, nonstationary, and autocorrelated data to compare the different detrending models and subsequent covariance monitoring. The machine learning models have superior performance for nonlinear and strongly autocorrelated data and similar performance for linear data. An illustration with data from a reverse osmosis process is given.

Weix, Derek↗
DOE OSTI2025

Bayesian D‐Optimal Designs for Gaussian Process Surrogate Models

Computer experiments often employ space-filling strategies to create surrogate models with strong predictive performance. The impact of model parameter estimation for Gaussian process surrogates, however, is often overlooked. Obtaining a better initial estimate of the covariance lengthscale parameter, θ, can greatly improve the resulting Gaussian process fit through more effective sequential acquisitions during active learning. In this work, we propose a novel initial design maximizing the Bayesian D-optimality criterion of the Gaussian process lengthscale parameter. Previously published results have shown the emphasis on lengthscale estimation to be promising, but relied on an empirically driven design creation process. Our Bayesian D-optimal designs are rooted in information theory and lead to more informative sequential acquisitions by improving lengthscale estimation. In many cases, these gains eventually result in better surrogates than those seeded with space-filling initial designs. Furthermore, Bayesian D-optimal designs can be tailored to either isotropic or anisotropic covariance structures, and the Bayesian framework enables the inclusion of prior knowledge in the design process, offering greater flexibility and adaptability. Through several simulation studies, we demonstrate the advantages of Bayesian D-optimal designs in terms of both lengthscale estimation accuracy and predictive performance during active learning.

Bayesian experimental design↗
DOE OSTI2019

A sparse Gaussian sigmoid basis function approximation of hyperspectral data for detection of solids

We define a new characterization of emissivity and reflectance curves for compositional exploitation of hyperspectral data. Here, our method decomposes each spectrum into a sparse set of Gaussian sigmoid components using penalized regression. Detection is based on the combination of Gaussian sigmoid components unique to a target material. Focusing on the presence of spectral upslopes and downslopes rather than spectral correlations makes detection more robust to both target variation and spectral variability from atmosphere and background encountered during the collection process. We present simulation studies that demonstrate the potential to reduce false positive rates without compromising sensitivity. Characterization of long–wave infrared (LWIR) experimental data validates our method using minerals of different particle sizes, measurement angles, and collection conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗