Electric power is crucial to the function of other infrastructures, as well as to the stability of the economy and the social order. Disruption of commercial electric power service, even for brief periods of time, can create significant consequences to the function of other sectors, and make living in some environments untenable. This analysis, conducted in 2017 for the United States Department of Energy (DOE) as part of the Grid Modernization Laboratory Consortium (GMLC) Initiative, focuses on describing the function of each of the other infrastructure sectors and subsectors, with an eye towards those elements of these sectors that depend on primary electric power service through the commercial electric power grid. It leverages the experience of Sandia analysts in analyzing historical disruptive events, and from the development of capabilities designed to identify the physical, logical, and geographic connectivity between infrastructures. The analysis goes on to identify alternatives for the provision of primary electric power service, and the redundancy of said alternatives, to provide a picture of the sector’s ability to withstand an extended disruption.
This study developed and tested biologically inspired computational methods to detect anomalous signals in data streams that could indicate a pending outbreak or bio-weapon attack. Current large-scale biosurveillance systems are plagued by two principal deficiencies: (1) timely detection of disease-indicating signals in noisy data and (2) anomaly detection across multiple channels. Anomaly detectors and data fusion components modeled after human immune system processes were tested against a variety of natural and synthetic surveillance datasets. A pilot scale immune-system-based biosurveillance system performed at least as well as traditional statistical anomaly detection data fusion approaches. Machine learning approaches leveraging Deep Learning recurrent neural networks were developed and applied to challenging unstructured and multimodal health surveillance data. Within the limits imposed of data availability, both immune systems and deep learning methods were found to improve anomaly detection and data fusion performance for particularly challenging data subsets.
This document summarizes research performed under the Laboratory Directed Research and Development (LDRD) project titled Developing Fugitive Emissions Sensor Networks: New Optimization Algorithms for Monitoring, Measurement and Verification. The purpose of this project is to develop methods and software to enhance detection programs through optimal design of the sensor network. This project includes both software development and field work. While this project is focused on methane emissions, the sensor placement optimization framework can be applied to a wide range of applications, including the placement of water quality sensors, surveillance cameras, fire and chemical detectors. This research has the potential to improve national security by improving the way sensors are deployed in the field.
Simulation models can improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, then on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.
Crude oil produced on the North Slope of Alaska (NSA) is primarily transported on the Trans-Alaska Pipeline System (TAPS) to in-state refineries and the Valdez Marine Terminal in southern Alaska. From the Terminal, crude oil is loaded onto tankers and is transported to export markets or to three major locations along the U.S. West Coast: Anacortes-Ferndale area (Washington), San Francisco Bay area, and Los Angeles area. North Slope of Alaska production has decreased about 75% since the 1980s, which has reduced utilization of TAPS.
The National Transportation Fuels Model was used to simulate a hypothetical increase in North Slope of Alaska crude oil production. The results show that the magnitude of production utilized depends in part on the ability of crude oil and refined products infrastructure in the contiguous United States to absorb and adjust to the additional supply. Decisions about expanding North Slope production can use the National Transportation Fuels Model take into account the effects on crude oil flows in the contiguous United States.
Improved validation for models of complex systems has been a primary focus over the past year for the Resilience in Complex Systems Research Challenge. This document describes a set of research directions that are the result of distilling those ideas into three categories of research -- epistemic uncertainty, strong tests, and value of information. The content of this document can be used to transmit valuable information to future research activities, update the Resilience in Complex Systems Research Challenge's roadmap, inform the upcoming FY18 Laboratory Directed Research and Development (LDRD) call and research proposals, and facilitate collaborations between Sandia and external organizations. The recommended research directions can provide topics for collaborative research, development of proposals, workshops, and other opportunities.
This report gives introductory guidance on the level of effort required to create a data warehouse for mining data. Numerous tutorials have been provided to demonstrate the process of downloading raw data, processing the raw data, and importing the data into a PostgreSQL database. Additional information and tutorial has been provided on setting up a Hadoop cluster for storing vasts amounts of data. This report has been generated as a deliverable for a New Mexico Small Business Assistance (NMSBA) project.
This report contains the written footprint of a Sandia-hosted workshop held in Albuquerque, New Mexico, June 22-23, 2016 on “Complex Systems Models and Their Applications: Towards a New Science of Verification, Validation and Uncertainty Quantification,” as well as of pre-work that fed into the workshop. The workshop’s intent was to explore and begin articulating research opportunities at the intersection between two important Sandia communities: the complex systems (CS) modeling community, and the verification, validation and uncertainty quantification (VVUQ) community The overarching research opportunity (and challenge) that we ultimately hope to address is: how can we quantify the credibility of knowledge gained from complex systems models, knowledge that is often incomplete and interim, but will nonetheless be used, sometimes in real-time, by decision makers?
Alaska oil fields provide an important, but diminishing, portion of the crude oil processed by Alaska and U.S. West Coast refineries. Production of crude oil in Alaska is being stressed by declining production in mature fields, high costs for developing and producing new fields, increasing competition from tight oil production in the Lower 48 states, and low global oil prices. The National Transportation Fuel Model is a network model of petroleum infrastructure in the Lower 48 states and portions of Canada developed at Sandia National Laboratories. It provides a simulation capability for analysis of system-wide responses to stressing events. Until now, however, this model did not explicitly include the petroleum infrastructure of Alaska and the transport of crude oil by marine shipments from Alaska to West Coast refineries. This paper describes the methods and information requirements for adding Alaska infrastructure to the National Transportation Fuel Model, provides an overview of the new Alaska portion of the model, and presents an example simulation of a closure of a large San Francisco refinery.