Encrypted Decentralized Multi-Agent Optimization for Privacy Preservation in Cyber-Physical Systems
Not provided.
Automation & Control Systems↗
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Results for “Computer Science - Operating Systems”
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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An Interactive and Adaptive Learning Cyber Physical Human System for Manufacturing With a Case Study in Worker Machine Interactions
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Automation & Control Systems↗
Data-Driven Decentralized Online System Identification-Based Integral Model-Predictive Voltage and Frequency Control in Microgrids
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Automation & Control Systems↗
Imperceptible Attacks on Fault Detection and Diagnosis Systems in Smart Buildings
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Automation & Control Systems↗
A Rainbow Coverage Path Planning for a Patrolling Mobile Robot With Circular Sensing Range
In this paper, we proposed a coverage path (CP) planning approach for a mobile robot moving in a dynamically changing environment. Our family of algorithms begins by finding the first path around the center of interior obstacles/disks of a given target region (TR) through the Graham scan algorithm. The first path is then used as a foundational path to generate a collision free, first order differentiable, and observable CP through a complete set of input and output transformation algorithms which together form the rainbow CP planning approach. The last algorithm of the rainbow CP planning approach finds a sufficient number of observation points on the CP needed to observe the TR. The novelty of our rainbow CP planning approach is that it partitions the TR into different shapes with different properties needed to obtain complete coverage while achieving first order path differentiability. The main technical contributions of the proposed approach is to provide a holistic solution that segments any TR, uses triangulation to determine the line of sights and observation points, and computes the collision-free CP within a quadratic runtime. The proposed method can be readily generalized to address problems of higher dimensions, and it is scalable with respect to the size of TR and the number of robots. Computer simulations are used to illustrate the effectiveness and correctness of the proposed approach.
54 ENVIRONMENTAL SCIENCES↗
Blockchain-Based Decentralized Replay Attack Detection for Large-Scale Power Systems
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Automation & Control Systems↗
KIVA-hpFE. Predictive turbulent reactive and multiphase flow in engines - An Overview
Research and development of KIVA-hpFE for turbulent reactive and multiphase flow particularly as related to engine modeling program has relevance to National energy security and climate change. Climate change is a source problem, and energy national security is consumption of petroleum products problem. Accurately predicting engine processes leads to, lower greenhouse gas (GHG) emission, where engines in the transportation sector currently account for 26% of the U.S. GHG emissions. Less dependence on petroleum products leads to greater energy security. By Environmental Protection Agency standards, some vehicles are now reaching 42 to the 50 mpg mark. These are conventional gasoline engines. Continued investment and research into new technical innovations, the potential exists to save more than 4 million barrels of oil per day or approximately $200 to $400 million per day. This would be a significant decrease in emission and use of petroleum and a very large economic stimulus too! It is estimated with further advancements in combustion, the current emissions can be reduced up to 40%. Enabling better understanding of fuel injection and fuel-air mixing, thermodynamic combustion losses, and combustion/emission formation processes enhances our ability to help solve both problems. To provide adequate capability for accurately simulating these processes, minimize time and labor for development of engine technology, are the goals of our KIVA development program.
33 ADVANCED PROPULSION SYSTEMS↗
Equation-free and variable free modeling for complex/multiscale systems. Coarse-grained computation in science and engineering using fine-grained models
The work explored the linking of modern developing machine learning techniques (manifold learning and in particular diffusion maps) with traditional PDE modeling/discretization/scientific computation techniques via the equation-free methodology developed by the PI. The result (in addition to several PhD degrees, two of them by CSGF Fellows) was a sequence of strong developments - in part on the algorithmic side, linking data mining with scientific computing, and in part on applications, ranging from PDE discretizations to molecular dynamics and complex network dynamics.
97 MATHEMATICS AND COMPUTING↗
Institute for Sustained Performance, Energy, and Resilience (SUPER): University of Utah Final Report
Over the course of this project, computational scientists working on behalf of the Department of Energy's Office of Science (DOE SC) were exploiting a new generation of petascale computing resources to make previously inaccessible discoveries in a broad range of disciplines including physics, chemistry and material science. The computational systems underpinning this work increased in performance potential from tens to hundreds of Pflop/s, and evolved significantly from those in use at the beginning of the project in terms of available concurrency; wider use of accelerators such as graphics processing units(GPUs); and changes to the memory hierarchy including the incorporation of a new generation of persistent devices (e.g., phase change memory). To ensure that DOE's computational scientists can successfully exploit this emerging generation of high performance computing (HPC) systems, the University of Southern California (USC) assembled a broad team of computer scientists with the expertise to address the most pressing challenges of DOE's computing systems: (a) end-to-end performance optimization, including single-node performance, interprocessor communication, load balancing and I/O; (b) performance portability for heterogeneous systems, including accelerators; (c) minimizing energy consumption; (d) resilient computation; and, (e) exploiting new memory technology.
97 MATHEMATICS AND COMPUTING↗
Supporting Co-Design of Extreme-Scale Systems with In Situ Visual Analysis of Event-Driven Simulations (Final Report)
Extreme-scale systems for compute- and data-centric science are pushing the boundaries of system architectures in order to achieve ambitious goals in terms of computing capability, resilience, and power efficiency. Future systems necessitate emerging designs that incorporate new technologies, system organizations, and computer science techniques, and these changes are not yet well understood. Discrete-event-driven simulation of computing system architectures and subsystems has emerged as a productive and cost-effective means to evaluating potential designs, along with capabilities for executing these simulations of extreme-scale systems. However, techniques for analyzing the behavior of these simulations have not kept pace with our ability to perform the simulations, preventing us from extracting the most value from this promising approach. The objective of this project is to support the codesign of extreme-scale system architectures for compute and data-centric science through research and development of novel methods for analysis and visualization of large-scale event-driven simulations.
97 MATHEMATICS AND COMPUTING↗
Uniformity Is Key in Defining Structure–Function Relationships for Atomically Dispersed Metal Catalysts: The Case of Pt/CeO 2
Catalysts consisting of atomically dispersed Pt (Pt iso ) species on CeO 2 supports have received recent interest due to their potential for efficient metal utilization in catalytic convertors. However, discrepancies exist between the behavior (reducibility, interaction strength with adsorbates) of high surface area Pt iso /CeO 2 systems and of well-defined surface science and computational model systems, suggesting differences in Pt local coordination in the two classes of materials. Here, we reconcile these differences by demonstrating that high surface area Pt iso /CeO 2 synthesized at low Pt loadings (<0.1% weight) exhibit resistance to reduction and sintering up to 500 °C in 0.05 bar H 2 and minimal interactions with CO—properties previously seen only for model system studies. Alternatively, Pt loadings >0.1 weight % produce a distribution of sub-nanometer Pt structures, which are difficult to distinguish using common characterization techniques, and exhibit strong interactions with CO and weak resistance to sintering, even in 0.05 bar H 2 at 50 °C—properties previously seen for high surface area materials. This work demonstrates that low metal loadings can be used to selectively populate the most thermodynamically stable adsorption sites on high surface area supports with atomically dispersed metals. Further, the site uniformity afforded by this synthetic approach is critical for the development of relationships between atomic scale local coordination and functional properties. Comparisons to recent studies of Pt iso /TiO 2 suggest a general compromise between the stability of atomically dispersed metal catalysts and their ability to interact with and activate molecular species.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Computational Science at the National Laboratory of the Rockies
Computational methods underpin advancing the science and engineering of energy technologies and developing a knowledge base to optimize energy systems. NLR's Computational Science Center (CSC) proudly focuses on providing the service of computing, advancing the science of computing, and enabling DOE missions.
97 MATHEMATICS AND COMPUTING↗
Virtual Framework for Science Federations with Instruments Access and Control
Experimental science workflows require federations of geographically dispersed science instruments and computing systems connected over a wide-area network. We develop a Virtual Infrastructure Twin (VIT) framework, which is a digital twin of the physical infrastructure that utilizes network virtualization and containerization technologies to support the development and testing of science workflow codes. Using VIT, we illustrate the access to instruments via EPICS system and the orchestration of containerized computations across federation computing systems. We also present a machine learning method to convert VIT throughput measurements to closely match the corresponding physical testbed measurements.
Rao, Nageswara↗
Grand Challenges for Biological and Environmental Research: Progress and Future Vision. A Report from the Biological and Environmental Research Advisory Committee
The Biological and Environmental Research (BER) program within the U.S. Department of Energy (DOE) Office of Science supports research focusing on the interconnections between energy production and the living environment. This fundamental research, conducted at universities, DOE national laboratories, and research institutions across the country, explores organisms and ecosystems that can influence the U.S. energy system and advances understanding of the relationships between energy and environment from local to global scales. Research within BER can be categorized into biological systems and Earth and environmental systems. Biological systems research seeks to characterize and predictively understand microbial and plant systems using genomic science, computational analyses, and experimental approaches. Foundational knowledge of the structure and function of these systems underpins the ability to leverage natural processes for energy production, including the sustainable development of biofuels and other bioproducts. Earth and environmental systems research seeks to characterize and predictively understand feedbacks between Earth and energy systems and includes studies on atmospheric physics and chemistry, ecosystem ecology and biogeochemistry, and development and validation of Earth system models extending from regional to global scales. To promote world-class research in these areas, BER supports user facilities that enable observation and measurement of atmospheric, biological, and biogeochemical processes using the latest technologies.
54 ENVIRONMENTAL SCIENCES↗
Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future
Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.
59 BASIC BIOLOGICAL SCIENCES↗
Quantum Networks for Open Science (QNOS) Workshop
Quantum computing systems currently being developed will have extraordinary capabilities to effectively solve complex problems in computational sciences, communication networks, artificial intelligence, and data processing, and will provide a powerful capability for researchers in almost every scientific discipline. Harnessing the full potential of quantum computing will require an ecosystem with a broad spectrum of quantum technologies. Quantum networks are one of the critical and highly anticipated components of this ecosystem. The combination of quantum computing and quantum networks are crucial to the US Department of Energy’s (DOE) mission to provide scientists with the state-of-the-art computational capabilities.
97 MATHEMATICS AND COMPUTING↗
Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework
Driven by major scientific advances in analytical methods, biomonitoring, and computational exposure assessment, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the computationally enabled “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. The AEP framework offers an intuitive approach to successful organization of exposure science data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathway and adverse outcome pathways, completing the source to outcome continuum and setting the stage for more efficient integration of exposure science and toxicity testing information. Together these frameworks form and inform a decision making framework with the flexibility for risk-based, hazard-based or exposure-based decisions.
Teeguarden, Justin G.↗
Science Federation Emulation Testbed: Demonstration of VFSIE Functionalities
The Virtual Federated Science Instrument Environment (VFSIE) is a digital twin of a federated infrastructure of multiple sites with science instruments and computing systems that are geographically dispersed. It emulates the federation using containers and virtual hosts (vhosts) connected over local and wide-area networks for the main purpose of supporting the development and testing of science workflows and software stacks, which will subsequently transition to the physical infrastructure. In this demo, we present VFSIE of a four sites scenario and illustrate the following use cases: (i) execution of federation stack components that expose resources to enable a science user to execute workflows, (ii) execution of remote control commands of beam-line instruments to collect measurements and position the sensor, and (iii) remote execution and access of Jupyter Notebook for tomographic image computations
Al Najjar, Anees↗