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At least 523 records · Page 29

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DOE OSTI2020

A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid

A dynamic neural network (NN) based multi-class classifier is proposed for improving online prediction of coherent generator groups (CGGs), following the occurrences of various contingencies in the power grid. This is motivated by the increasing availability of the measurements from phasor measurement units (PMUs) and the number of grouping schemes is limited. The proposed method consists of three steps. First, by performing offline simulations, a library of system dynamic responses characterized by post-contingency rotor angles and speeds of individual generators is obtained. To generate sufficient data, up to N-2 contingencies and the uncertain parameters associated with the power grid including type and location of disturbance and fault clearing times are modeled. Secondly, the training data-set is produced by generating labels for individual contingencies using a hierarchical clustering method based on rotor angle and speed data. Lastly, the dynamic NN models are trained for online applications such as emergency controls and controlled islanding. The proposed method is tested on the standard 16-generator 68-bus system to demonstrate its performance. Furthermore, the impact of the sample data lengths on the CGG numbers is evaluated. It is interesting to observe that the time domain stability behaviors can be determined by examining the changes in the CGG numbers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Identifying schools at high-risk for elevated lead in drinking water using only publicly available data

Estimating the risk of lead contamination of schools' drinking water at the State level is a complex, important, and unexplored challenge. Variable water quality among water systems and changes in water chemistry during distribution affect lead dissolution rates from pipes and fittings. In addition, the locations of lead-bearing plumbing materials are uncertain. We tested the capability of six machine learning models to predict the likelihood of lead contamination of drinking water at the schools' taps using only publicly available datasets. The predictive features used in the models correspond to those with a proven correlation to the dominant, but commonly unavailable, factors that govern lead leaching: the presence of lead-bearing plumbing materials and water quality conducive to lead corrosion. By combining water chemistry data from public reports, socioeconomic information from the US census, and spatial features using Geographic Information Systems, we trained and tested models to estimate the likelihood of lead contaminated tap water in over 8,000 schools across California and Massachusetts. Our best-performing model was a Random Forest, with a 10-fold cross validation score of 0.88 for Massachusetts and 0.78 for California using the average Area Under the Receiver Operating Characteristic Curve (ROC AUC) metric. The model was then used to assign a lead leaching risk category to half of the schools across California (the other half was used for training). There was good agreement between the modeled risk categories and the actual lead leaching outcomes for every school; however, the model overestimated the lead leaching risk in up to 17% of the schools. This model is the first of its kind to offer a tool to predict the risk of lead leaching in schools at the State level. Further use of this model can help deploy limited resources more effectively to prevent childhood lead exposure from school drinking water.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

NASA NTRS2010

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Smelyanskiy, Vadim N.↗

NASA NTRSDate not supplied

Coupling of Dust Mineralogy to Radiation in NASA GISS ModelE: Impact Upon Dust Optical Properties and Direct Radiative Effect

Estimates of dust direct radiative effect (DRE) are still largely uncertain mainly due to the poor constraining of regional variations of dust absorption, whose known connection with mineral composition is still largely neglected by most Earth System Models. The inaccurate knowledge of soil mineralogy and insufficient measurements of dust physical and optical properties still present the main challenges for a proper representation of the dust-radiation interaction in climate models. We recently upgraded the coupling of dust minerals to shortwave radiation in the NASA GISS ModelE. In particular, we extended the interaction to online-calculated minerals and implemented two different mixing configurations: i) external mixing of three main mineral components and ii) internal mixing of all minerals, allowing a dynamic fraction of iron oxides. We used a semi-empirical approach for the optical calculations, mostly based on aerosol chamber measurements from natural soils, aiming to reduce the influence of uncertain assumptions on the optical properties and mixing state of individual minerals. In this contribution, we present the results of our first calculations of dust optical properties and DREs based on different mixing configurations of interactive minerals. We show the changes to dust optical depth and single scattering albedo, with respect to the control case using homogeneous dust composition, and the consequent global and regional variations in dust DREs at the top of atmosphere and at surface. We also compare the model optical properties with AERONET data, filtered to identify dust scenes. We find that accounting for the diverse properties of dust minerals modifies the pattern of regional dust absorption and therefore its impact upon climate. We also show that the mineralogy effect is sensitive to the assumed mixing state of minerals.

dust direct radiative effect↗

DOE OSTI2023

Historical and future trends in South Asian monsoon low pressure systems in a high-resolution model ensemble

Historical trends in monsoon low pressure systems (LPS), the dominant rain-bearing weather system of South Asia, have been difficult to assess due to changes in the observing network. Future projections have also remained uncertain because prior studies concluded that many coarse-resolution climate models do not accurately simulate LPS. Here, we examine changes in South Asian monsoon LPS simulated by an ensemble of global models, including some with high spatial resolution, that we show skillfully represent LPS. In the ensemble mean, the number of strong LPS (monsoon depressions) decreased over the last 65 years (1950–2014) by about 15% while no trend was detected for weaker LPS (monsoon lows). The reduction in depression counts then moderated, yielding no trend in the periods 1980–2050 or 2015–2050. The ensemble mean projects a shift in genesis from ocean to land and an increase in LPS precipitation of at least 7% K −1 , which together contribute to a projected increase in seasonal mean and extreme precipitation over central India.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Compensation for Long-Duration Energy Storage

Rapidly changing power system conditions, driven by decarbonization goals, are leading to significant growth in renewable energy sources, which can be both variable and uncertain. This has been accompanied with increased reliance on and rapid growth in deployment of energy storage technologies. Currently, approximately 90% of installed, utility-scale energy storage capacity in the United States comes from pumped storage hydropower (PSH). However, development of new PSH has been limited and all recent growth in energy storage has come from batteries, , especially as technology costs have decreased over the years. Most of the current deployment still remains in the form of short-duration (<6 hours) energy storage technologies; the average duration of new storage was 3.7 hours for projects deployed in the first half of 2021 (Wood Mackenzie and Energy Storage Association 2021).

25 ENERGY STORAGE↗

DOE OSTI2026

A robust aerosol impact on clouds along the subtropical to tropical transition

Marine clouds undergo a transition from subtropical stratocumulus (Sc) to shallow cumulus (Cu) and eventually to deep convective (DC) systems as air masses advect from the subtropics towards the deep tropics. How aerosols modulate this Lagrangian cloud evolution remains largely uncertain. Here we use both 5 years of satellite observations mapped along 8 d Lagrangian trajectories and complementary large-eddy simulations from 9 initiation locations across the Northeast Pacific, Southeast Pacific, and Southeast Atlantic. This Lagrangian framework allows us to quantify the aerosol effect and its co-variability with meteorological conditions on cloud microphysics, macrophysics, and top-of-atmosphere radiation through the full Sc-Cu-DC transition. We show that increasing aerosol concentrations leads to deeper and more reflective clouds throughout this cloud transition. Examining the thermodynamic evolution along the trajectory indicates a well-known trend: enhanced moistening near the boundary-layer top and lower free troposphere under polluted conditions, suggesting that part of the co-variability between aerosol and meteorological conditions may be internally driven. The agreement between model simulations and satellite data alongside the multi-basin coherence of the results indicates that aerosols systematically amplify cloud depth and reflectivity during the subtropical–to–tropical cloud transition.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1988

Probabilistic methods for structural response analysis

This paper addresses current work to develop probabilistic structural analysis methods for integration with a specially developed probabilistic finite element code. The goal is to establish distribution functions for the structural responses of stochastic structures under uncertain loadings. Several probabilistic analysis methods are proposed covering efficient structural probabilistic analysis methods, correlated random variables, and response of linear system under stationary random loading.

Wu, Y.-T.↗

NASA NTRS1989

Robustness analysis for real parametric uncertainty

Some key results in the literature in the area of robustness analysis for linear feedback systems with structured model uncertainty are reviewed. Some new results are given. Model uncertainty is described as a combination of real uncertain parameters and norm bounded unmodeled dynamics. Here the focus is on the case of parametric uncertainty. An elementary and unified derivation of the celebrated theorem of Kharitonov and the Edge Theorem is presented. Next, an algorithmic approach for robustness analysis in the cases of multilinear and polynomic parametric uncertainty (i.e., the closed loop characteristic polynomial depends multilinearly and polynomially respectively on the parameters) is given. The latter cases are most important from practical considerations. Some novel modifications in this algorithm which result in a procedure of polynomial time behavior in the number of uncertain parameters is outlined. Finally, it is shown how the more general problem of robustness analysis for combined parametric and dynamic (i.e., unmodeled dynamics) uncertainty can be reduced to the case of polynomic parametric uncertainty, and thus be solved by means of the algorithm.

Sideris, Athanasios↗

NASA NTRS1998

Managing Multiple Tasks in Complex, Dynamic Environments

Sketchy planners are designed to achieve goals in realistically complex, time-pressured, and uncertain task environments. However, the ability to manage multiple, potentially interacting tasks in such environments requires extensions to the functionality these systems typically provide. This paper identifies a number of factors affecting how interacting tasks should be prioritized, interrupted, and resumed, and then describes a sketchy planner called APEX that takes account of these factors when managing multiple tasks.

Freed, Michael↗

DOE OSTI2024

Advanced Computing is at the Forefront of a New “Moonshot” Revolutionizing the North American Power Grid

In the 50+ years since the first humans landed on the moon, computing has grown at breakneck speed. We are faced with another challenge that is just as daunting, and just as important to overcome-modernizing the North American electric power grid-and high-performance computing (HPC) systems with specialized software will be an important element in rising to this challenge. We describe at a high level how software developed in the ExaSGD project addresses this "moonshot" goal by utilizing exascale computing and a novel high performance solver software stack to support the mission of decarbonizing power grid operations in an environment of uncertain weather and climate. To reach the exascale benchmark the team has made a number of first-of-their-kind innovations, including novel method for stochastic optimization, fine grained parallel methods for modeling power systems, and GPU resident sparse numerical linear solvers.

17 WIND ENERGY↗

NASA NTRS2012

Validation of Safety-Critical Systems for Aircraft Loss-of-Control Prevention and Recovery

Validation of technologies developed for loss of control (LOC) prevention and recovery poses significant challenges. Aircraft LOC can result from a wide spectrum of hazards, often occurring in combination, which cannot be fully replicated during evaluation. Technologies developed for LOC prevention and recovery must therefore be effective under a wide variety of hazardous and uncertain conditions, and the validation framework must provide some measure of assurance that the new vehicle safety technologies do no harm (i.e., that they themselves do not introduce new safety risks). This paper summarizes a proposed validation framework for safety-critical systems, provides an overview of validation methods and tools developed by NASA to date within the Vehicle Systems Safety Project, and develops a preliminary set of test scenarios for the validation of technologies for LOC prevention and recovery

Belcastro, Christine M.↗

DOE OSTI2020

Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms

We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system. Three of the most common uncertainty quantification methods - Bayesian Neural Networks (BNN), Concrete Dropout (CD), and Deep Ensembles (DE) - are compared to the standard analytic error propagation. We discuss this comparison in terms endemic to both machine learning ("epistemic" and "aleatoric") and the physical sciences ("statistical" and "systematic"). The comparisons are presented in terms of simulated experimental measurements of a single pendulum - a prototypical physical system for studying measurement and analysis techniques. Our results highlight some pitfalls that may occur when using these UQ methods. For example, when the variation of noise in the training set is small, all methods predicted the same relative uncertainty independently of the inputs. This issue is particularly hard to avoid in BNN. On the other hand, when the test set contains samples far from the training distribution, we found that no methods sufficiently increased the uncertainties associated to their predictions. This problem was particularly clear for CD. In light of these results, we make some recommendations for usage and interpretation of UQ methods.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2005

Performance of a Checkerboard Liner With Uncertain Impedances

The current fleet of large commercial aircraft has successfully achieved FAA noise certifications because of, in part, the successful application of uniform passive duct liner treatments to control engine system noise. One goal of NASA's engine system noise reduction program is to develop technologies to improve the sound absorbing properties of duct liner treatments so that they remain effective in modern turbo fan engines. One such technology being studied is checkerboard or periodic axially and circumferentially segmented liners. A preliminary assessment of the potential of this technology was conducted by applying uncertainties associated with manufacturing, installation, source structure, and tonal frequency to a liner developed using deterministic design methods and generating a measure of improvement with respect to a uniform liner subjected to the same uncertainties. Deterministic design and analysis of the candidate checkerboard liner showed that it obtains a 1.5 dB per duct aspect ratio improvement in liner attenuation over a similarly designed uniform liner. When uncertainties in liner impedances, source structure, and frequency are considered, the performance of the checkerboard liner drops off dramatically. The final results of this paper show that the candidate checkerboard liner has a less than 25 percent chance of outperforming the uniform liner when moderate levels of uncertainty are considered. It is important to note that this study did not include the effects of mean flow on liner performance and, more important to note, that as a gradient based optimization process was used to design the checkerboard liner, it is almost certain that a global optimal design was not found for the candidate checkerboard liner. Had it been possible to find a better deterministically performing checkerboard liner, the probability that this candidate liner would outperform the uniform liner would certainly have been higher.

Robinson, J. H.↗

DOE OSTI2024

Multi-agent voltage control in distribution systems using GAN-DRL-based approach

Active distribution grids can experience voltage fluctuations and violations due to the high penetration of variable distributed energy resources (DERs). These problems might occur because of the uncertain and variable generation natures of these resources, especially solar photovoltaic resources, during panel shadowing scenarios. Volt-VAR control (VVC) is an efficient method that controls the reactive power set-points of the inverters to regulate the voltage of distribution grids. Although several VVC approaches have been proposed recently, the performance of these approaches degrades significantly if behind-the-meter solar generation data are unobservable/missing. Therefore, it is necessary to impute missing/unobservable PV data accurately to be utilized in VVC approaches. Further, this paper proposes a model-free, data-driven, centrally trained, and decentrally executed multi-agent deep reinforcement learning-based VVC architecture to regulate the voltage of distribution networks. A generative adversarial network (GAN) is incorporated to impute the unobservable PV data accurately, which improves the performance of the proposed control architecture. The proposed multi-agent-soft-actor–critic algorithm (MASAC)-based VVC technique utilizes the actual PV dataset as well as the imputed dataset from the GAN framework to learn the optimal coordinated control policy for controlling the optimal reactive power set-points of PV inverters. The effectiveness of the proposed approach is analyzed on a modified IEEE 34-bus test case with added PV inverters. The results are compared and analyzed with a base case model with no VVC and VVC with a local droop control approach, genetic algorithm optimization, and a centralized soft actor–critic-based approach. Moreover, the performance of the proposed approach is compared with that of a multi-agent VVC framework without using the PV generation data and load information as the system state. The results illustrate that the proposed method with more state input improves the voltage profile and reduces the power loss of the network across various loading and PV generation scenarios.

14 SOLAR ENERGY↗

DOE OSTI2023

The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2)

Abstract Earth System Models (ESMs) are increasingly representing agriculture due to its impact on biogeochemical cycles, local and regional climate, and fundamental importance for human society. Realistic large scale simulations may require spatially varying crop parameters that capture crop growth at various scales and among different cultivars, as well as common crop management practices, but their importance is uncertain, and they are often not represented in ESMs. In this study, we examine the impact of using constant versus spatially varying crop parameters using a novel, realistic crop rotation scenario in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2). We implemented crop rotation by using ELMv2's dynamic land unit capability, and then calibrated and validated the model against observations collected at three AmeriFlux sites in the US Midwest with corn soybean rotation. The calibrated model closely captured the magnitude and observed seasonality of carbon and energy fluxes across crops and sites. We performed regional simulations for the US Midwest using the calibrated model and found that spatially varying only a few crop parameters across the region, as opposed to using constant parameters, had a large impact, with the carbon fluxes and energy fluxes both varying by up to 40%. These results imply that large scale ESM simulations using spatially invariant crop parameters may result in biased energy and carbon fluxes estimation from agricultural land, and underline the importance of improving human‐earth systems interactions in ESMs.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1984

Tradeoff methods in multiobjective insensitive design of airplane control systems

The latest results of an ongoing study of computer-aided design of airplane control systems are given. Constrained minimization algorithms are used, with the design objectives in the constraint vector. The concept of Pareto optimiality is briefly reviewed. It is shown how an experienced designer can use it to find designs which are well-balanced in all objectives. Then the problem of finding designs which are insensitive to uncertainty in system parameters are discussed, introducing a probabilistic vector definition of sensitivity which is consistent with the deterministic Pareto optimal problem. Insensitivity is important in any practical design, but it is particularly important in the design of feedback control systems, since it is considered to be the most important distinctive property of feedback control. Methods of tradeoff between deterministic and stochastic-insensitive (SI) design are described, and tradeoff design results are presented for the example of the a Shuttle lateral stability augmentation system. This example is used because careful studies have been made of the uncertainty in Shuttle aerodynamics. Finally, since accurate statistics of uncertain parameters are usually not available, the effects of crude statistical models on SI designs are examined.

Schy, A. A.↗

NASA NTRS1989

Local gravity disturbance estimation from multiple-high-single-low satellite-to-satellite tracking

The idea of satellite-to-satellite tracking in the high-low mode has received renewed attention in light of the uncertain future of NASA's proposed low-low mission, Geopotential Research Mission (GRM). The principal disadvantage with a high-low system is the increased time interval required to obtain global coverage since the intersatellite visibility is often obscured by Earth. The U.S. Air Force has begun to investigate high-low satellite-to-satellite tracking between the Global Positioning System (GPS) of satellites (high component) and NASA's Space Transportation System (STS), the shuttle (low component). Because the GPS satellites form, or will form, a constellation enabling continuous three-dimensional tracking of a low-altitude orbiter, there will be no data gaps due to lack of intervisibility. Furthermore, all three components of the gravitation vector are estimable at altitude, a given grid of which gives a stronger estimate of gravity on Earth's surface than a similar grid of line-of-sight gravitation components. The proposed Air Force mission is STAGE (Shuttle-GPS Tracking for Anomalous Gravitation Estimation) and is designed for local gravity field determinations since the shuttle will likely not achieve polar orbits. The motivation for STAGE was the feasibility to obtain reasonable accuracies with absolutely minimal cost. Instead of simulating drag-free orbits, STAGE uses direct measurements of the nongravitational forces obtained by an inertial package onboard the shuttle. The sort of accuracies that would be achievable from STAGE vis-a-vis other satellite tracking missions such as GRM and European Space Agency's POPSAT-GRM are analyzed.

Jekeli, Christopher↗