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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Resilient distribution system leveraging distributed generation and microgrids: a review

With the aging of electricity transmission and distribution infrastructures and increasing intensity of extreme weather events, the aggravated vulnerability of electric distribution systems to extreme weather events has motivated the study of resilient distribution systems. This study presents a review of the state-of-the-art research on distribution grid resilience. First, the definition and quantifying metrics of resilience in the electrical distribution system are summarised. Second, the long-term and short-term measures to enhance the distribution system resilience are discussed. In particular, the recent studies on distributed generation and microgrid-assisted resilience enhancements are reviewed. Finally, recommendations for future research are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Properties of La 0.7 Ca 0.3 MnO 3 under extreme tensile strain

The complex phase diagram of manganites with simultaneously active spin, charge, orbital, and lattice degrees of freedom continues providing surprises. In a recent groundbreaking experiment, membranes of the perovskite manganite La 0.7 Ca 0.3 MnO 3 (LCMO) deposited on a flexible polymer layer were strained up to 8% [S. S. Hong et al., Science 368, 71 (2020)], much more than achieved by regular strain induced by a rigid substrate. By increasing this strain, a metal-insulator transition was reported. Here we reproduce the results of the experiments using Monte Carlo simulations of the two-orbital double-exchange model including Jahn-Teller distortions at hole density x=1/3. The full phase diagram with varying temperature and Jahn-Teller coupling λ is presented. When the bandwidth W of mobile electrons is reduced, thus when the effective Jahn-Teller coupling λ/W is increased, a metal-insulator transition is found in our simulations, between a ferromagnetic metallic state with uniform charge distribution and an insulator with diagonal charge stripes that retains its ferromagnetic character. In between the hole-rich diagonals, staggered orbital order occurs. We also report resistivity and magnetization measurements alongside with spin correlations and charge structure factors. Our overall conclusions are in agreement with the recent experimental and density functional theory results by Hong et al., and we confirm much earlier ground state predictions of striped ferromagnetic order using energy optimization techniques by T. Hotta et al. [Phys. Rev. Lett. 86, 4922 (2001)]. The experimental observation of one of the states predicted by theory suggests that diagonal stripes could be achieved at other hole densities as well, such as x=1/4, if LCMO membranes with that hole doping were subject to similar strains.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Modified Eigen-Decomposition-based Interval Analysis (MEDIA) for Power System Dynamic State Estimation

The Bayesian approach has been used for the dynamic state estimation (DSE) of a power system. However, due to the complexity of noise resources, it is difficult to quantify measurement and process noise using probability density functions (PDFs). To overcome the difficulty, the authors of this paper propose a modified eigen-decomposition-based interval analysis (MEDIA) method, which employs bounds instead of PDFs to quantify the noise, and uses the eigen decomposition method to reduce the negative impact of the overestimation problem. Using the simulation data generated from IEEE 16-machine and IEEE 10-machine systems, it is shown that the proposed MEDIA method can estimate the hard boundaries of dynamic states in real time. Furthermore, comparison with the forward-backward propagation method and the extended set-membership filter also shows that the proposed MEDIA method performs better by providing narrower boundaries in the DSE.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A General Decentralized Dynamic State Estimation With Synchronous Generator Magnetic Saturation

We report the saturation of the nonlinear magnetic circuit of synchronous generators is often neglected when performing Kalman filter-based dynamic state estimation (DSE), yielding significant estimation bias. This letter addresses this problem and proposes a generalized DSE framework to handle magnetic saturation. Moreover, this letter derives a state initialization procedure that improves the Kalman filter tracking speed. The framework is flexible in dealing with different saturation functions and generator models. Numerical results on the Texas 2000-bus system verify the effectiveness of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition

As electric grids experience high penetration levels of renewable generation, fundamental changes are required to address real-time situational awareness. Here, we utilize unique traits of tensors to devise a model-free situational awareness and energy forecasting framework for distribution networks. This work formulates the state of the network at multiple time instants as a three-way tensor; hence, recovering full state information of the network is tantamount to estimating all the values of the tensor. Given measurements received from µphasor measurement units and/or smart meters, the recovery of unobserved quantities is carried out using the low-rank canonical polyadic decomposition of the state tensor—that is, the state estimation task is posed as a tensor imputation problem utilizing observed patterns in measured (sampled) quantities. Two structured sampling schemes are considered, namely, asynchronous slab and fiber sampling. For both schemes, we present sufficient conditions on the number of sampled slabs and fibers that guarantee identifiability of the factors of the state tensor. Numerical results demonstrate the ability of the proposed framework to achieve high estimation accuracy in multiple sampling scenarios.

42 ENGINEERING↗
DOE OSTI2020

How fast can one resize a distributed file system?

Efficient resource utilization becomes a major concern as large-scale distributed computing infrastructures keep growing in size. Malleability, the possibility for resource managers to dynamically increase or decrease the amount of resources allocated to a job, is a promising way to save energy and costs. However, state-of-the-art parallel and distributed storage systems have not been designed with malleability in mind. The reason is mainly the supposedly high cost of data transfers required by resizing operations. Nevertheless, as network and storage technologies evolve, old assumptions about potential bottlenecks can be revisited. In this study, we evaluate the viability of malleability as a design principle for a distributed storage system. We specifically model the minimal duration of the commission and decommission operations. To show how our models can be used in practice, we evaluate the performance of these operations in HDFS, a relevant state-of-the-art distributed file system. We show that the existing decommission mechanism of HDFS is good when the network is the bottleneck, but can be accelerated by up to a factor 3 when storage is the limiting factor. We also show that the commission in HDFS can be substantially accelerated. With the highlights provided by our model, we suggest improvements to speed both operations in HDFS. We discuss how the proposed models can be generalized for distributed file systems with different assumptions and what perspectives are open for the design of efficient malleable distributed file systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

New approaches to an old problem: addressing spatial gaps in the World Stress Map

Abstract A well-recognized characteristic of the World Stress Map (WSM) database is the continued presence of large spatial gaps in the distribution of the data records despite the more than 40-year development history of the database. The current release has more than 30 000 high-quality (A–C) data records (often referred to as ‘stress indicators’), but while some continental areas (such as Australia) have seen a significant increase in spatial converge with the latest release, other continental regions (Africa, central Asia, most of South America) remain markedly sparse. In this contribution we (1) review the current state of the spatial distribution of stress indicators in the continental regions (above sea-level); (2) quantify the clustering of the stress indicators in the latest WSM release using the Hopkins statistic as a way to explore the current spatial distribution of the indicators and assess future WSM releases; and (3) present three approaches (joint inversion, seismic anisotropy and InSAR) that provide a way to fill in the gaps (both in the S Hmax orientation and principal stress magnitudes) in regions that lack active seismicity and where borehole drilling is cost prohibitive. These three approaches have the potential to guide procedures for improving a priori estimates of the ambient stress field in the Earth's crust and reduce the uncertainty in predicting both the magnitude and orientation of the principal tectonic stresses.

58 GEOSCIENCES↗
DOE OSTI2024

Ventenata dubia projected to expand in the western United States despite future novel conditions

Abstract Distributions of both native and invasive species are expected to shift under future climate. Species distribution models (SDMs) are often used to explore future habitats, but sources of uncertainty including novel climate conditions may reduce the reliability of future projections. We explore the potential spread of the invasive annual grass ventenata ( Ventenata dubia ) in the western United States under both current and future climate scenarios using boosted regression tree models and 30 global climate models (GCMs). We quantify novel climate conditions, prediction variability arising from both the SDMs and GCMs, and the agreement among GCMs. Results demonstrate that currently suitable habitat is concentrated inside the invaded range of the northwest, but substantial habitat exists outside the invaded range in the Southern Rockies and southwestern US mountains. Future suitability projections vary greatly among GCMs, but GCMs commonly projected decreased suitability in the invaded range and increased suitability along higher elevations of interior mountainous areas. Climate novelty did not appear to undermine the prediction reliability in many cases where the climate–species relationship was fully represented by the occurrence data. GCM‐derived variability resulting from variation in future cool season precipitation and temperature seasonality was greatest in the Rocky Mountains. SDM‐derived variability was higher in currently suitable habitat, and few GCMs projections agreed that these areas would contain future suitable habitat. However, while prediction variability was high, many GCM projections agreed that parts of the Rocky, Wasatch, and Uinta Mountains would contain highly suitable habitat in the future. As disturbances in the interior mountains occur in coming decades, reducing some natural barriers to invasion, land managers, and conservationists will need to monitor for ventenata in post‐disturbance environments. Changes to invasion potential may not play out for several decades, but results related to current potential may have applications for early detection and rapid response planning.

Environmental Sciences & Ecology↗
DOE OSTI2020

A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems

With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Application of the locally self-consistent embedding approach to the Anderson model with non-uniform random distributions

Highlights: • Typical Medium Theory (TMT) for the Anderson Localization. • Locally Self-Consistent Multiple Scattering Method (LSMS) for Random Disordered Systems. • Linear Scaling Computational Method for Random Disordered Systems. We apply the recently developed embedding scheme for the locally self-consistent method to random disorder electrons systems. The method is based on the locally self-consistent multiple scattering theory and the typical medium theory. The locally self-consistent multiple scattering theory divides a system into many small designated local interaction zones. The subsystem within each local interaction zone is embedded in a self-consistent field from the typical medium theory. This approximation allows the study of random systems with large numbers of sites. We present results for the three dimensional Anderson model with different random disorder potential distributions. Using the typical density of states as an indicator of Anderson localization, we find that the method can capture the localization for commonly studied disorder potentials. These include the uniform distribution, the Gaussian distribution, and even the unbounded Cauchy distribution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Distributed Solar Adoption in Orlando: A Household-Level Model for Distribution Resource Planning

Potential for rooftop solar in Florida is massive (47% of retail sales, 3rd overall nationally), yet adoption lags (12th nationally). A 2018 Florida Public Service Commission ruling authorizing solar third-party ownership (leasing) has substantially increased attention on distributed solar in the state. The city of Orlando has committed to a 100% clean-energy target by 2050 and deployment of solar and storage are expected to contribute significantly to reaching the goal. Deployment of customer-adopted solar, unlike utility-procured solar, is uncertain, but known to be spatially correlated with demographic factors and existing adoption. We develop a new method to adapt NREL's dGen model in order to represent building-level agents in adoption forecasts for the Orlando Utility Commission (OUC) service territory. Using the agent-based model we develop projections of solar adoption, subject to scenarios varying future solar costs and valuation, and aggregate adoption predictions by OUC distribution feeder. We find substantial spatial heterogeneity in the projected level of adoption by OUC distribution feeder. For instance, 25% of all projected adoption through 2050 would be concentrated on just 5% of feeders and 88% of projected adoption on 50% of feeders. Because of the uncertainty in adoption, bottoms-up solar adoption forecasting methods at the household-level are integral to long-term resource planning by anticipating system needs as customers increasingly adopt distributed solar, storage, electric vehicles, and other distributed energy resources.

14 SOLAR ENERGY↗
DOE OSTI2020

Elucidating plasma dynamics in Hasegawa–Wakatani turbulence by information geometry

In this work, the impact of adiabatic electrons on drift-wave turbulence, modeled by the Hasegawa–Wakatani equations, is studied using information length. Information length is a novel theoretical method for measuring distances between statistical states represented by different probability distribution functions (PDFs) along the path of a system and represents the total number of statistically different states that a system evolves through in time. Specifically, the time-dependent PDFs of turbulent fluctuations for a given adiabatic index $A$ are computed. The changes in fluctuation statistics are then quantified in time by using information length. The numerical results provide time traces exhibiting intermittent plasma dynamics, and such behavior is identified by a rapid change in the information length. The effects of $A$ are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Ensemble variational Fokker-Planck methods for data assimilation

Particle flow filters solve Bayesian inference problems by smoothly transforming a set of particles into samples from the posterior distribution. Particles move in state space under the flow of an McKean-Vlasov-Itˆo process. This work introduces the Variational Fokker-Planck (VFP) framework for data assimilation, a general approach that includes previously known particle flow filters as special cases. The McKean-Vlasov-Itˆo process that transforms particles is defined via an optimal drift that depends on the selected diffusion term. It is established that the underlying probability density - sampled by the ensemble of particles - converges to the Bayesian posterior probability density. For a finite number of particles the optimal drift contains a regularization term that nudges particles toward becoming independent random variables. Based on this analysis, we derive computationally-feasible approximate regularization approaches that penalize the mutual information between pairs of particles, and avoid particle collapse. Moreover, the diffusion plays a role akin to a particle rejuvenation approach that aims to alleviate particle collapse. The VFP framework is very flexible. Different assumptions on prior and intermediate probability distributions can be used to implement the optimal drift, and localization and covariance shrinkage can be applied to alleviate the curse of dimensionality. A robust implicit-explicit method is discussed for the efficient integration of stiff McKean- Vlasov-Itˆo processes. Here, the effectiveness of the VFP framework is demonstrated on three progressively more challenging test problems, namely the Lorenz ’63, Lorenz ’96 and the quasi-geostrophic equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions

This paper proposes an approach to select a maintenance strategy from a predefined set of choices considering the decision maker’s behavioral risk profile. It is assumed that the damage state is characterized by a continuous state parameter probabilistically inferred from observable sensor data. This work applies an engineering application of consequence-based decision-making incorporating the acceptable risk intensity of the decision-maker, i.e., the decision-maker’s (individual or an organization) valuation of the outcome of a decision, using a risk profile model. The utility of a decision-maker is subjective, and this paper considers the fact that different decision-makers mentally assign a different importance factor (the utility) to the seriousness or urgency to take necessary actions with the increasing intensity of structural damage. The approach herein incorporates a layer of human psychology on selecting appropriate maintenance strategies that not only depend on the posterior distribution of unmeasurable damage state but also consider the behavioral risk profile of the decision-maker. Further, the collective decision-making of an organization consisting of many individuals is also investigated. The approach is exemplified in a case study involving life cycle monitoring of a miter gate, part of a lock system enabling navigation of inland waterways.

42 ENGINEERING↗
DOE OSTI2021

Distributed electric field sensing using fibre optics in borehole environments

In the past decade, rapid advances in distributed optical fibre sensing technologies have made it possible to record various geophysical data (e.g. strain, temperature and pressure) continuously in both time and space along the fibre, providing an unprecedented quantity and spatial density of data compared to traditional geophysical measurements as well as reducing data acquisition cost. To date, no distributed fibre-based electromagnetic field sensing system has been implemented although electromagnetic sensing could have a broad range of applications to geophysical imaging and monitoring in borehole environments. The goal of this paper is to provide a theoretical feasibility study regarding the design and use of an electromagnetic sensing optical fibre for geophysical applications. First, we present the sensitivity analysis of a ‘hypothetical’ optical fibre coated with polyvinylidene fluoride, a polymer that provides relatively high piezoelectric properties, yet unlike ceramics, is flexible. Next, using a two-dimensional electromagnetic modelling algorithm, we simulate the earth electric-field-to-fibre-strain transfer function and estimate the theoretical sensitivity of the optical fibre to electric fields. Given the state-of-the-art distributed acoustic sensing strain sensitivities in the picometres strain range, our numerical modelling analysis suggests that a perfectly coupled polyvinylidene fluoride–coated optical fibre can measure electric field values in the mV/m to V/m amplitude range. We then apply a cylindrically symmetric modelling algorithm to simulate numerical models demonstrating the applicability of such a fibre in an oilfield environment. Scenarios investigated employ an electric field source and suggest that the measurements can be used to distinguish the oil versus water ratio with a fibre mounted inside a producing steel cased oil well as well as distinguishing between brine and hydrocarbon filled reservoir zones with a fibre located outside of the casing.

58 GEOSCIENCES↗
DOE OSTI2024

Code Benchmark of Depressurized Conduction Cooldown Transient in the High Temperature Test Facility

This paper presents results from modeling of a depressurized conduction cooldown (DCC) transient at the High Temperature Test Facility (HTTF) as part of the OECD-NEA Thermal Hydraulics Code Validation Benchmark for High-Temperature Gas-Cooled Reactors using HTTF Data . This paper briefly describes the benchmark and the models being used. It then presents a comparison of steady state and transient results based on the Problem 2 Exercise 1A and 1B definitions. We compare block and helium temperature distributions, mass flow distribution, and energy balance in steady state. All models show comparable mass flow distributions and energy balances. The temperatures within the core and outer regions are comparable in all models too, but inner reflector temperatures can vary significantly. Despite that, we find that the models are in good agreement for the full-power steady state. In the DCC, we look at block temperature at the core midplane and RCCS water exit temperature. The INL and ANL models are found to be in excellent agreement with one another on block temperature over time, while the agreement when the KAERI and NRG models are added into consideration is good. Differences in the transient heat removal from the RCCS cause the differences in block temperature over time in these models. The CNL models show similar trends to the INL, ANL, KAERI, and NRG models, but the temperatures are high because the volumes used in calculating the average temperature include the heater rods in the CNL models only. The HUN-REN model shows results that suggest significantly lower heat removal in the RCCS which merit further investigation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Integrating Public Health Surveillance and Environmental Data to Model Presence of Histoplasma in the United States

In the United States, the true geographic distribution of the environmental fungus Histoplasma capsulatum remains poorly understood but appears to have changed since it was first characterized. Histoplasmosis is caused by inhalation of the fungus and can range in severity from asymptomatic to life-threatening. Due to limited public health surveillance and under detection of infections, it is challenging to directly use reported case data to characterize spatial risk. Using monthly and yearly county-level public health surveillance data and various environmental and socioeconomic characteristics, we use a spatio-temporal occupancy model to estimate latent, or unobserved, presence of H. capsulatum , accounting for imperfect detection of histoplasmosis cases. We estimate areas with higher probabilities of the presence of H. capsulatum in the East North Central states around the Great Lakes, reflecting a shift of the endemic region to the north from previous estimates. The presence of H. capsulatum was strongly associated with higher soil nitrogen levels. In this investigation, we were able to mitigate challenges related to reporting and illustrate a shift in the endemic region from historical estimates. This work aims to help inform future surveillance needs, clinical awareness, and testing decisions for histoplasmosis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Constraining level densities through quantitative correlations with cross-section data

The adopted level densities (LD) for the nuclei produced through different reaction mechanisms significantly impact the accurate calculation of cross sections for the different reaction channels. Many common LD models make simplified assumptions regarding the overall behavior of the total LD and the intrinsic spin and parity distributions of the excited states. However, very few experimental constraints are taken into account in these models: LD at neutron separation energy coming from average spacings of s- and p-wave resonances ( D 0 and D 1 , respectively) whenever they have been previously measured, and the sometimes subjective extrapolation of discrete levels. These, however, constrain the LD only in very specific regions of excitation energy, and for specific spins and parities. This work aims to establish additional experimental constraints on LD through quantitative correlations between cross sections and LD. This allows for fitting and the determination of detailed structures in LD. For this we use the microscopic Hartree-Fock-Bogoliubov (HFB) LD as a starting point as the HFB LD provide a more realistic spin and parity distributions than phenomenological models such as Gilbert-Cameron (GC). We then associate variations predicted by the HFB model with the structure observed in double-differential cross sections at low outgoing neutron energy, a region that is dominated by the LD input. We also use (n, p) on 56 Fe , as an example case where angle-integrated cross sections are extremely sensitive to LD. For comparison purposes we also perform calculations with the GC model. With this approach we are able to perform fits of the LD based on actual experimental data, constraining the model and ensuring its consistency. This approach can be particularly useful in extrapolating the LD to nuclei for which high-excited discrete levels and/or values of D 0 or D 1 are unknown. Finally, it also predicts neutron-induced inelastic γ cross sections that in some cases can differ significantly from more standard phenomenological LD models such as GC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗