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RADIANCE Cybersecurity Plan: Generic Version

Johnson, Jay; Eddy, John P.; Mccarty, Michael V.; Mix, Scott R.; Knight, Mark R.

Under its Grid Modernization Initiative, the U.S. Department of Energy(DOE),in collaboration with energy industry stakeholders developed a multi-year research plan to support modernizing the electric grid. One of the foundational projects for accelerating modernization efforts is information and communications technology interoperability. A key element of this project has been the development of a methodology for engaging ecosystems related to grid integration to create roadmaps that advance the ease of integration of related smart technology. This document is the product of activities undertaken in 2017 through 2019.It provides a Cybersecurity Plan describing the technology to be adopted in the project with details as per the GMLC Call document.

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TYPE Other Report YEAR 2019
DOIOSTI

On-line Generation and Error Handling for Surrogate Models within Multifidelity Uncertainty Quantification

Blonigan, Patrick J.; Geraci, Gianluca; Rizzi, Francesco; Eldred, Michael; Carlberg, Kevin

Uncertainty quantification is recognized as a fundamental task to obtain predictive numerical simulations. However, many realistic engineering applications require complex and computationally expensive high-fidelity numerical simulations for the accurate characterization of the system responses. Moreover, complex physical models and extreme operative conditions can easily lead to hundreds of uncertain parameters that need to be propagated through high-fidelity codes. Under these circumstances, a single fidelity approach, i.e. a workflow that only uses high-fidelity simulations to perform the uncertainty quantification task, is unfeasible due to the prohibitive overall computational cost. In recent years, multifidelity strategies have been introduced to overcome this issue. The core idea of this family of methods is to combine simulations with varying levels of fidelity/accuracy in order to obtain the multifidelity estimators or surrogates with the same accuracy of their single fidelity counterparts at a much lower computational cost. This goal is usually accomplished by defining a prioria sequence of discretization levels or physical modeling assumptions that can be used to decrease the complexity of a numerical realization and thus its computational cost. However ,less attention has been dedicated to low-fidelity models that can be built directly from the small number of high-fidelity simulations available. In this work we focus our attention on Reduced-Order Models that can be considered a particular class of data-driven approaches. Our main goal is to explore the combination of multifidelity uncertainty quantification and reduced-order models to obtain an efficient framework for propagating uncertainties through expensive numerical codes.

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TYPE Other Report YEAR 2019
DOIOSTI

Hybridizing Classifiers and Collection Systems to Maximize Intelligence and Minimize Uncertainty in National Security Data Analytics Applications

Staid, Andrea; Valicka, Christopher G.

There are numerous applications that combine data collected from sensors with machine-learning based classification models to predict the type of event or objects observed. Both the collection of the data itself and the classification models can be tuned for optimal performance, but we hypothesize that additional gains can be realized by jointly assessing both factors together. Through this research, we used a seismic event dataset and two neural network classification models that issued probabilistic predictions on each event to determine whether it was an earthquake or a quarry blast. Real world applications will have constraints on data collection, perhaps in terms of a budget for the number of sensors or on where, when, or how data can be collected. We mimicked such constraints by creating subnetworks of sensors with both size and locational constraints. We compare different methods of determining the set of sensors in each subnetwork in terms of their predictive accuracy and the number of events that they observe overall. Additionally, we take the classifiers into account, treating them both as black-box models and testing out various ways of combining predictions among models and among the set of sensors that observe any given event. We find that comparable overall performance can be seen with less than half the number of sensors in the full network. Additionally, a voting scheme that uses the average confidence across the sensors for a given event shows improved predictive accuracy across nearly all subnetworks. Lastly, locational constraints matter, but sometimes in unintuitive ways, as better-performing sensors may be chosen instead of the ones excluded based on location. This being a short-term research effort, we offer a lengthy discussion on interesting next-steps and ties to other ongoing research efforts that we did not have time to pursue. These include a detailed analysis of the subnetwork performance broken down by event type, specific location, and model confidence. This project also included a Campus Executive research partnership with Texas A&M University. Through this, we worked with a professor and student to study information gain for UAV routing. This was an alternative way of looking at the similar problem space that includes sensor operation for data collection and the resulting benefit to be gained from it. This work is described in an Appendix.

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TYPE SAND Report YEAR 2019
DOIOSTI

Investigation of R-Curve Behavior in Glass Ceramic Materials

Grutzik, S.J.; Strong, Kevin T.; Dai, Steve X.

We demonstrate the ability to measure R-curves of brittle materials using a method adapted from Theo Fett et al. The method is validated with a NIST standard reference material and demonstrated using Si3N4 of two different microstructures; glass-ceramic, and PZT. As expected, each material's R-curve is seen to be slightly different with glass-ceramics showing the most pronounced R-curve effects. Plans for future applications and experimental efforts are discussed.

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TYPE SAND Report YEAR 2019
DOIOSTI

LDRD Ending Project Review: Polymer-Spray Coating Interfaces (Project 215984) [Slides]

Vackel, Andrew; Treadwell, Larico J.; Redline, Erica; Siska, Samantha

The ability to surface engineer structures or components using coatings made by the thermal spray processes is very common practice and offers great design flexibility with traditional structure metallic substrates (e.g., Al, Steel, Ti). However, the joining of high melting temperature materials to a polymeric substrate presents a problem due to the melt deposition coating formation mechanism locally subjecting the polymer substrate to temperatures exceeding the limits of the polymer. Thus, it was desired to modify the surface of a polymer so that a thin metallic film could be robustly bonded to the polymer and act as a heat sink for impinging molten droplets from a thermal spray process and allow a thick film coating to be built upon the polymer.

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TYPE Other Report YEAR 2019
DOIOSTI
Results 21301–21400 of 99,299
Results 21301–21400 of 99,299