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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 37 records · Page 2

Uncertainty Analysis Framework for the Hospital Resource Supply Model for Covid-19

In March and April of 2020 there was widespread concern about availability of medical resources required to treat Covid-19 patients who become seriously ill. A simulation model of supply management was developed to aid understanding of how to best manage available supplies and channel new production. Forecasted demands for critical therapeutic resources have tremendous uncertainty, largely due to uncertainties about the number and timing of patient arrivals. It is therefore essential to evaluate any process for managing supplies in view of this uncertainty. To support such evaluations, we developed a modeling framework that would allow an integrated assessment in the context of uncertainty quantification. At the time of writing there has been no need to execute this framework because adaptations of the medical system have been able to respond effectively to the outbreak. This report documents the framework and its implemented components should need later arise for its application.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Sandia Wind Energy Program: FY21 Accomplishments

This report summarizes Fiscal Year 2021 accomplishments from Sandia National Laboratories Wind Energy Program. The portfolio consists of funding provided by the DOE EERE Wind Energy Technologies Office (WETO), Advanced Research Projects Agency-Energy (ARPA-E), DOE Small Business Innovation Research (SBIR), and the Sandia Laboratory Directed Research and Development (LDRD) program. These accomplishments were made possible through capabilities investments by WETO, internal Sandia investment, and partnerships between Sandia and other national laboratories, universities, and research institutions around the world.

17 WIND ENERGY↗

Modifications to Sandia's MDT and WNTR tools for ERMA

ERMA is leveraging Sandia’s Microgrid Design Toolkit (MDT) [1] and adding significant new features to it. Development of the MDT was primarily funded by the Department of Energy, Office of Electricity Microgrid Program with some significant support coming from the U.S. Marine Corps. The MDT is a software program that runs on a Microsoft Windows PC. It is an amalgamation of several other software capabilities developed at Sandia and subsequently specialized for the purpose of microgrid design. The software capabilities include the Technology Management Optimization (TMO) application for optimal trade-space exploration, the Microgrid Performance and Reliability Model (PRM) for simulation of microgrid operations, and the Microgrid Sizing Capability (MSC) for preliminary sizing studies of distributed energy resources in a microgrid.

97 MATHEMATICS AND COMPUTING↗

Sandia Wind Energy Program - FY23 Accomplishments

This report summarizes Fiscal Year 2023 accomplishments from Sandia National Laboratories Wind Energy Program. The portfolio consists of funding provided by the DOE EERE Wind Energy Technologies Office (WETO), Advanced Research Projects Agency-Energy (ARPA-E), Advanced Manufacturing Office (AMO), the Sandia Laboratory Directed Research and Development (LDRD) program, and private industry. These accomplishments were made possible through capabilities investments by WETO, internal Sandia investment, and partnerships between Sandia and other national laboratories, universities, and research institutions around the world. Sandia’s Wind Energy Program is primarily built around core capabilities as expressed in the strategic plan thrust areas, with 29 staff members in the Wind Energy Design and Experimentation department and the Wind Energy Computational Sciences department leading and supporting R&D at the time of this report. Staff from other departments at Sandia support the program by leveraging Sandia’s unique capabilities in other disciplines.

17 WIND ENERGY↗

Sandia Wind Energy Program: FY22 Accomplishments

This report summarizes Fiscal Year 2022 accomplishments from Sandia National Laboratories Wind Energy Program. The portfolio consists of funding provided by the DOE EERE Wind Energy Technologies Office (WETO), Advanced Research Projects Agency-Energy (ARPA-E), Advanced Manufacturing Office (AMO), and the Sandia Laboratory Directed Research and Development (LDRD) program. These accomplishments were made possible through capabilities investments by WETO, internal Sandia investment, and partnerships between Sandia and other national laboratories, universities, and research institutions around the world. Sandia’s Wind Energy Program is primarily built around core capabilities as expressed in the strategic plan thrust areas, with 29 staff members in the Wind Energy Design and Experimentation department and the Wind Energy Computational Sciences department leading and supporting R&D at the time of this report. Staff from other departments at Sandia support the program by leveraging Sandia’s unique capabilities in other disciplines.

17 WIND ENERGY↗

Socioeconomically-inspired modeling to justify use of fine-grain mobility data

When designing measures to control infectious disease spread, it is crucial to understand the structure of the population for which interventions are being implemented. Recent work has highlighted the need for models that incorporate demographic heterogeneity not just in age structure but also by socioeconomic status (SES). Appropriately capturing additional sources of population heterogeneity requires considerable data and model development. To understand the potential disagreement between SES-explicit or SES-agnostic disease models, we adapted Sandia’s Adaptive Recovery Model (ARM) model to consider differences in contact structure and mortality by Social Vulnerability Index (SVI) on a theoretical network. We also incorporated an Average network that did not consider SVI. By exploring disparities in vaccine and PPE uptake by SES and comparing to Average networks, as well as analyzing the influence of global vs. local contact, we found that the two model constructions often predicted different outcomes. Whether these differences are truly reflective of incorporating SES, and which model most closely represents reality, merits further investigation.

99 GENERAL AND MISCELLANEOUS↗

Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗