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At least 433 records · Page 24

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Tuning Phase Lock Loop Controller of Grid Following Inverters by Reinforcement Learning to Support Networked Microgrid Operations

The dynamic operation of networked microgrids leads to varying topological configurations and generator commitments and dispatches. These variations correspond to systems with different electrical characteristics. The fixed control gains of high-speed power electronic devices may result in undesirable system performance when the electrical characteristics change significantly. As such, it is necessary to tune the control gains of power electronics devices to adapt to the changing system characteristics. This paper uses observer-based reinforcement learning to automatically tune the proportional-integral (PI) gains of phase lock loop (PLL) controller of grid-following (GFL) inverters to adapt to the changing system strengths, that would be seen in networked microgrid operations. Simulation results using an operational electric distribution system, modeled as networked microgrids, are presented to demonstrate the need and effectiveness of the proposed adaptive controls.

networked microgrids, reinforcement learning, grid↗

DOE OSTI2022

EQC: Ensembled Quantum Computing for Variational Quantum Algorithms

Variational quantum algorithms (VQA), which are comprised of a classical optimizer and a parameterized quantum circuit, emerges as one of the most promising approaches of harvesting quantum power in the noisy-intermediate-scale-quantum (NISQ) era. However, the deployment of VQAs on today's NISQ devices often faces considerable system noise and prohibitively slow training speeds. On the other hand, the expensive supporting sources and infrastructure make quantum computers extremely keen on high utilization. In this paper, we propose a novel way of thinking about a quantum backend: rather than relying on one physical device which tends to introduce platform-specific noise and bias, a quantum ensemble, which distributes quantum tasks across parallel devices, can serve as a virtualized quantum computer for offering reduced noise levels through an adaptive mixture and also provide significantly improved training speeds through parallelization. With this idea, we build a distributive VQA optimization framework called DVQA, serving as the first effort in adopting parallel quantum devices for cooperative VQA training. To further constraint noise and speed-up convergence, we design a model for individual NISQ devices concerning their properties and running conditions, and propose a weighting mechanism for regularizing the returned gradients. Extensive evaluations on 10 IBM-Q quantum devices using the VQE example show that the distributive VQA training framework can substantially boost the training speed by 10.5x on average (up to 86x and at least 5.2x) with improved training accuracy.

Stein, Samuel A.↗

NASA NTRS1998

Impact of resistance exercise during bed rest on skeletal muscle sarcopenia and myosin isoform distribution

Because resistance exercise (REx) and bed-rest unloading (BRU) are associated with opposing adaptations, our purpose was to test the efficacy of REx against the effects of 14 days of BRU on the knee-extensor muscle group. Sixteen healthy men were randomly assigned to no exercise (NoEx; n = 8) or REx (n = 8). REx performed five sets of leg press exercise with 80-85% of one repetition maximum (1 RM) every other day during BRU. Muscle samples were removed from the vastus lateralis muscle by percutaneous needle biopsy. Myofiber distribution was determined immunohistochemically with three monoclonal antibodies against myosin heavy chain (MHC) isoforms (I, IIa, IIx). MHC distribution was further assessed by quantitative gel electrophoresis. Dynamic 1-RM leg press and unilateral maximum voluntary isometric contraction (MVC) were determined. Maximal neural activation (root mean squared electromyogram) and rate of torque development (RTD) were measured during MVC. Reductions (P < 0.05) in type I (15%) and type II (17%) myofiber cross-sectional areas were found in NoEx but not in REx. Electrophoresis revealed no changes in MHC isoform distribution. The percentage of type IIx myofibers decreased (P < 0.05) in REx from 9 to 2% and did not change in NoEx. 1 RM was reduced (P < 0.05) by 9% in NoEx but was unchanged in REx. MVC fell by 15 and 13% in NoEx and REx, respectively. The agonist-to-antagonist root mean squared electromyogram ratio decreased (P < 0.05) 19% in REx. RTD slowed (P < 0.05) by 54% in NoEx only. Results indicate that REx prevented BRU-induced myofiber atrophy and also maintained training-specific strength. Unlike spaceflight, BRU did not induce shifts in myosin phenotype. The reported benefits of REx may prove useful in prescribing exercise for astronauts in microgravity.

NASA Discipline Musculoskeletal↗

DOE OSTI2021

Adaptive Spatially Aware I/O for Multiresolution Particle Data Layouts

Large-scale simulations on nonuniform particle distributions that evolve over time are widely used in cosmology, molecular dynamics, and engineering. Such data are often saved in an unstructured format that neither preserves spatial locality nor provides metadata for accelerating spatial or attribute subset queries, leading to poor performance of visualization tasks. Furthermore, the parallel I/O strategy used typically writes a file per process or a single shared file, neither of which is portable or scalable across different HPC systems. We present a portable technique for scalable, spatially aware adaptive aggregation that preserves spatial locality in the output. We evaluate our approach on two supercomputers, Stampede2 and Summit, and demonstrate that it outperforms prior approaches at scale, achieving up to 2.5× faster writes and reads for nonuniform distributions. Furthermore, the layout written by our method is directly suitable for visual analytics, supporting low-latency reads and attribute-based filtering with little overhead.

Usher, Will↗

DOE OSTI2024

O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process (GP) regression was employed to provide short-term forecasts using data collected by loop detectors (sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

42 ENGINEERING↗

NASA NTRS2009

Distributed Control Architecture for Gas Turbine Engine

The transformation of engine control systems from centralized to distributed architecture is both necessary and enabling for future aeropropulsion applications. The continued growth of adaptive control applications and the trend to smaller, light weight cores is a counter influence on the weight and volume of control system hardware. A distributed engine control system using high temperature electronics and open systems communications will reverse the growing trend of control system weight ratio to total engine weight and also be a major factor in decreasing overall cost of ownership for aeropropulsion systems. The implementation of distributed engine control is not without significant challenges. There are the needs for high temperature electronics, development of simple, robust communications, and power supply for the on-board electronics.

Dennis Culley↗

DOE OSTI2021

Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Machine learning for modern power distribution systems: Progress and perspectives

The application of machine learning (ML) to power and energy systems (PES) is being researched at an astounding rate, resulting in a significant number of recent additions to the literature. As the infrastructure of electric power systems evolves, so does interest in deploying ML techniques to PES. However, despite growing interest, the limited number of reported real-world applications suggests that the gap between research and practice is yet to be fully bridged. To help highlight areas where this gap could be narrowed, this article discusses the challenges and opportunities in developing and adapting ML techniques for modern electric power systems, with a particular focus on power distribution systems. These systems play a crucial role in transforming the electric power sector and accommodating emerging distributed technologies to mitigate the impacts of climate change and accelerate the transition to a sustainable energy future. The objective of this article is not to provide an exhaustive overview of the state-of-the-art in the literature, but rather to make the topic accessible to readers with an engineering or computer science background and an interest in the field of ML for PES, thereby encouraging cross-disciplinary research in this rapidly developing field. To this end, the article discusses the ways in which ML can contribute to addressing the evolving operational challenges facing power distribution systems and identifies relevant application areas that exemplify the potential for ML to make near-term contributions. At the same time, key considerations for the practical implementation of ML in power distribution systems are discussed, along with suggestions for several potential future directions.

Marković, Marija (ORCID:0000000247839837)↗

DOE OSTI2025

Outlook towards deployable continual learning for particle accelerators

Particle accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization and control, anomaly detection and machine protection. With recent advancements, machine learning (ML) holds promise to assist in more advance prognostics, optimization, and control. While ML based solutions have been developed for several applications in particle accelerators, only few have reached deployment and even fewer to long term usage, due to particle accelerator data distribution drifts caused by changes in both measurable and non-measurable parameters. In this paper, we identify some of the key areas within particle accelerators where continual learning can allow maintenance of ML model performance with distribution drifts. Particularly, we first discuss existing applications of ML in particle accelerators, and their limitations due to distribution drift. Next, we review existing continual learning techniques and investigate their potential applications to address data distribution drifts in accelerators. By identifying the opportunities and challenges in applying continual learning, this paper seeks to open up the new field and inspire more research efforts towards deployable continual learning for particle accelerators.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Postglacial migration shaped the genomic diversity and global distribution of the wild ancestor of lager-brewing hybrids

The wild, cold-adapted parent of hybrid lager-brewing yeasts, Saccharomyces eubayanus, has a complex and understudied natural history. The exploration of this diversity can be used both to develop new brewing applications and to enlighten our understanding of the dynamics of yeast evolution in the wild. Here, we integrate whole genome sequence and phenotypic data of 200 S. eubayanus strains, the largest collection known to date. S. eubayanus has a multilayered population structure, consisting of two major populations that are further structured into six subpopulations. Four of these subpopulations are found exclusively in the Patagonian region of South America; one is found predominantly in Patagonia and sparsely in Oceania and North America; and one is specific to the Holarctic ecozone. Plant host associations differed between subpopulations and between S. eubayanus and its sister species, Saccharomyces uvarum. S. eubayanus is most abundant and genetically diverse in northern Patagonia, where some locations harbor more genetic diversity than is found outside of South America, suggesting that northern Patagonia east of the Andes was a glacial refugium for this species. All but one subpopulation shows isolation-by-distance, and gene flow between subpopulations is low. However, there are strong signals of ancient and recent outcrossing, including two admixed lineages, one that is sympatric with and one that is mostly isolated from its parental populations. Using our extensive biogeographical data, we build a robust model that predicts all known and a handful of additional regions of the globe that are climatically suitable for S. eubayanus, including Europe where host accessibility and competitive exclusion by other Saccharomyces species may explain its continued elusiveness. We conclude that this industrially relevant species has rich natural diversity with many factors contributing to its complex distribution and natural history.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

CMIP7 data request: impacts and adaptation priorities and opportunities

The Coupled Model Intercomparison Project Phase 7 (CMIP7) undertook an extensive process to gather community input and refine data requests related to impacts and adaptation applications of Earth System Model (ESM) outputs. The Impacts and Adaptation (I&A) Data Request Team worked with CMIP7 leadership to distribute an open solicitation across many communities that use climate model outputs requesting inputs for new and existing variables, the most applicable temporal characteristics, and groupings of variables that together allow for specific application opportunities. This input was then collated and translated into CMIP7 standard templates for inclusion in the broader data request, leading to 13 I&A data request opportunities, 60 variable groups and 539 unique variables sought by vulnerability, impacts, adaptation, and climate services user communities. Here, we describe these opportunities and variable groups, as well as new insights into how ESM groups can prioritize outputs that set off a chain of further analyses, ultimately informing decisions impacting society and natural systems. These include an emphasis on high-resolution outputs to allow further modeling of climate impacts at regional and local scales, improved representation of extreme weather events, enhanced accuracy of downscaling and bias-adjustment techniques, and support for more detailed assessments for decision-making in adaptation and mitigation strategies. There is also broad interest in more extensive provisioning of two-dimensional variables at the Earth's surface, prioritizing experiments that enhance our understanding of both the recent past and future scenarios, and providing outputs that allow further downscaling and bias adjustment. We emphasize that variable groups are the fundamental level at which to engage with the I&A data request, matching the scale of input and the way output provision enables specific I&A applications. Given resource constraints, we applaud CMIP7 efforts to foster strong engagement and communication between ESM groups and the I&A team to build consensus around prudent compromises in priority variables, temporal resolutions, simulation experiments, time subsets, and ensemble members.

Ruane, Alex C. [NASA Goddard Inst. for Space Studi↗

DOE OSTI2021

Risk-Adaptive Experimental Design for High-Consequence Systems: LDRD Final Report

Constructing accurate statistical models of critical system responses typically requires an enormous amount of data from physical experiments or numerical simulations. Unfortunately, data generation is often expensive and time consuming. To streamline the data generation process, optimal experimental design determines the 'best' allocation of experiments with respect to a criterion that measures the ability to estimate some important aspect of an assumed statistical model. While optimal design has a vast literature, few researchers have developed design paradigms targeting tail statistics, such as quantiles. In this project, we tailored and extended traditional design paradigms to target distribution tails. Our approach included (i) the development of new optimality criteria to shape the distribution of prediction variances, (ii) the development of novel risk-adapted surrogate models that provably overestimate certain statistics including the probability of exceeding a threshold, and (iii) the asymptotic analysis of regression approaches that target tail statistics such as superquantile regression. To accompany our theoretical contributions, we released implementations of our methods for surrogate modeling and design of experiments in two complementary open source software packages, the ROL/OED Toolkit and PyApprox.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Engineering Microgrids Amid the Evolving Electrical Distribution System

Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system’s resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Target Detection via Cognitive Radars Using Change-Point Detection, Learning, and Adaptation

Many radar detection algorithms that assume a stationary environment (clutter) have been proposed and analyzed over the years. However, in practice, changes in the nonstationary environment can perturb the parameters of the clutter distribution, or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which are required to rapidly realize the nonstationarity, accurately learn the new characteristics of the environments, and adaptively update the detector. In this paper, aiming to develop a fully cognitive radar for target detection in nonstationary environments, we propose a unifying framework that integrates (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by applying sparse theory and kernel density estimation methods, and (iii) adaptive target detection by automatically modifying the likelihood-ratio test and corresponding detection threshold. Further, with extensive numerical examples, we demonstrate the achieved improvements in detection performance due to the proposed framework in comparison to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case. Herein, we also use Wilcoxon rank-sum tests to evaluate the statistical significance of the performance improvements

42 ENGINEERING↗

NASA NTRS1995

The Role of Nutrition in the Changes in Bone and Calcium Metabolism During Space Flight

On Earth, the primary purpose of the skeleton is provide structural support for the body. In space, the support function of the skeleton is reduced since, without gravity, structures have only mass and no weight. The adaptation to space flight is manifested by shifts in mineral distribution, altered bone turnover, and regional mineral deficits in weight-bearing bones. The shifts in mineral distribution appear to be related to the cephalic fluid shift. The redistribution of mineral from one bone to another or to and from areas in the same bone in response to alterations in gravitational loads is more likely to affect skeletal function than quantitative whole body losses and gains. The changes in bone turnover appear dependent upon changes in body weight with weight loss tending to increase bone resorption as well as decrease bone formation. During bedrest, the bone response to unloading varies depending upon the routine activity level of the subjects with more active subjects showing a greater suppression of bone formation in the iliac crest with inactivity. Changes in body composition during space flight are predicted by bedrest studies on Earth which show loss of lean body mass and increase tn body fat in adult males after one month. In ambulatory studies on Earth, exercising adult males of the same age, height, g weight, body mass index, and shoe size show significantly higher whole body mineral and lean body mass. than non-exercising subjects. Nutritional preference appears to change with activity level. Diet histories in exercisers and nonexercisers who maintain identical body weights show no differences in nutrients except for slightly higher carbohydrate intake in the exercisers. The absence of differences in dietary calcium in men with higher total body calcium is noteworthy. In this situation, the increased bone mineral content was facilitated by the calcium endocrine system. This regulatory system can be by-passed by raising dietary calcium. Increased calcium intake can increase the calcium content in normally loaded bone. However, bone with a higher calcium content still decreases proportionally to normal bone during unloading. Nutritional requirements in space should be reevaluated with respect to these adaptive changes to loading and physical activity.

Morey-Holton, Emily R.↗

DOE OSTI2021

Overvoltage Prevention and Curtailment Reduction Using Adaptive Droop-Based Supplementary Control in Smart Inverters

Recent developments in the renewable energy sector have seen an unprecedented growth in residential photovoltaic (PV) installations. However, high PV penetration levels often lead to overvoltage problems in low-voltage (LV) distribution feeders. Smart inverter control such as active power curtailment (APC)-based overvoltage control can be implemented to overcome these challenges. The APC technique utilizes a constant droop-based approach which curtails power rigidly, which can lead to significant energy curtailment in the LV distribution feeders. In this paper, different variations of the APC technique with linear, quadratic, and exponential droops have been analyzed from the point-of-view of energy curtailment for a LV distribution network in North America. Further, a combinatorial approach using various droop-based APC methods in conjunction with adaptive dynamic programming (ADP) as a supplementary control scheme has also been proposed. The proposed approach minimizes energy curtailment in the LV distribution network by adjusting the droop gains. Simulation results depict that ADP in conjunction with exponential droop reduces the energy curtailment to approximately 50% compared to using the standard linear droop.

42 ENGINEERING↗

NASA NTRS1973

Analysis of temperature and pressure distribution of containers for nuclear waste material disposal in space

A computer program was adapted from a previous generation program to analyze the temperature and internal pressure response of a radioactive nuclear waste material disposal container following impact on the earth. This program considers component melting, LiH dissociation, temperature dependent properties and pressure and container stress response. Analyses were performed for 21 cases with variations in radioactive power level, container geometry, degree of deformation of the container, degree of burial and soil properties. Results indicated that the integrity of SS-316 containers could be maintained with partial burials of either underformed or deformed containers. Results indicated that completely buried waste containers, with power levels above 5 kW, experienced creep stress rupture failures in 4 to 12 days.

Vanbibber, L. E.↗

NASA NTRS2000

NASA/University JOint VEnture (JOVE) Program. VIXEN(tm): Object-Oriented, Technology-Adaptive, Virtual Information Exchange Environment

Vixen is a collection of enabling technologies for uninhibited distributed object computing. In the Spring of 1995 when Vixen was proposed, it was an innovative idea very much ahead of its time. But today the technologies proposed in Vixen have become standard technologies for Enterprise Computing. Sun Microsystems J2EE/EJB specifications, among others, are independently proposed technologies of the Vixen type. I have brought Vixen completely under the J2EE standard in order to maximize interoperability and compatibility with other computing industry efforts. Vixen and the Enterprise JavaBean (EJB) Server technologies are now practically identical; OIL, another Vixen technology, and the Java Messaging System (JMS) are practically identical; and so on. There is no longer anything novel or patentable in the Vixen work performed under this grant. The above discussion, notwithstanding, my independent development of Vixen has significantly helped me, my university, my students and the local community. The undergraduate students who worked with me in developing Vixen have enhanced their expertise in what has become the cutting edge technology of their industry and are therefore well positioned for lucrative employment opportunities in the industry. My academic department has gained a new course: "Multi-media System Development", which provides a highly desirable expertise to our students for employment in any enterprise today. The many Outreach Programs that I conducted during this grant period have exposed local Middle School students to the contributions that NASA is making in our society as well as awakened desires in many such students for careers in Science and Technology. I have applied Vixen to the development of two software packages: (a) JAS: Joshua Application Server - which allows a user to configure an EJB Server to serve a J2EE compliant application over the world wide web; (b) PCM: Professor Course Manager: a J2EE compliant application for configuring a course for distance learning. These types of applications are, however, generally available in the industry today.

Anyiwo, Joshua C.↗