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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Assurance by Design for Cyber Physical Data-Driven Systems

Currently, Cyber Physical Data-Driven Systems (CPDDS) employ machine learning for the classification, data fusion, and control of our nation’s infrastructure, such as the power grid, transportation networks (e.g., fuel distribution, air traffic control), and DoD long-duration collaborative autonomous platforms including unmanned underwater, ground, surface, space, and aerial systems. Many CPDDSs are system-of-systems that should be designed to communicate over disadvantaged networks. It is important to assure that the CPDDSs are resilient against physical and cyber threats by design. Additionally, their design should tolerate misclassification errors resulting from natural and/or adversarial distribution shifts within their data driven components. The all-domain nature of the problem of assuring the design of CPDDSs requires a multi-disciplinary perspective as outlined in this chapter.

Chikkagoudar, Satish↗
DOE OSTI2023

A scalable framework for quantifying field-level agricultural carbon outcomes

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N 2 O and CH 4 ) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study

Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2026

Cross-sectoral impact of emerging technologies on U.S. manufacturing resilience and competitiveness

Introducing new technologies in one energy-intensive industry can affect how other industries operate and stay resilient, yet these cross-sector interactions are often underappreciated in conventional technology roadmaps. In practice, industrial systems do not evolve in isolation. They are linked through shared upstream and downstream dependencies, such as electricity and fuel supply, critical materials, transportation networks, and enabling infrastructure. As a result, large-scale technology deployment in one sector can reshape resource availability, infrastructure demand, and operational risk in others. These interdependencies mean that technology deployment decisions in one sector can create unintended bottlenecks or cascading benefits in others. Here, this article argues that a cross-sector, system-of-systems perspective is essential for evaluating and scaling emerging technologies in energy-intensive industries. By framing industrial transformation as an interconnected systems challenge rather than a set of isolated sectoral decisions, the study highlights how interdependence shapes technology feasibility, adoption pathways, and resilience outcomes. The article illustrates how cross-sector linkages can amplify both risks and benefits, and it emphasizes the importance of integrated planning approaches that account for shared dependencies, cascading impacts, and co-optimization opportunities. Adopting this broader perspective can support more robust technology roadmaps, improve strategic coordination across industries, and strengthen the long-term resilience of the industrial sector as a whole.

Nain, Preeti [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2021

A Markov framework for generalized post-event systems recovery modeling: From single to multihazards

State-dependent models can be used to represent the system recovery process as a series of stochastic transitions from lower to higher functional states. However, the applications of these models have been limited in scope and there is a lack of a generalized recovery modeling framework. A generalized framework would permit a robust forecasting of systems and system-of-systems recovery under multiple hazards, and more broadly, would contribute to community disaster preparedness. This paper develops a generalized post hazard-event recovery modeling framework based on state-dependent Markov-type processes. We then apply the proposed framework to solve a spectrum of problems that range from hind-casting single-system recovery following a single hazard event to forecasting post-event trajectories under multiple hazards and modeling the recovery of a system-of-systems. First, Markov chains are used to hind-cast the observed recovery for a portfolio of buildings affected by the 2014 South Napa, California, earthquake. Next, Markov processes are used to formulate a parametric post hazard-event recovery model, which can be updated using Bayesian statistics when relevant datasets become available. Semi-Markov processes are then used to develop a more general model of single hazard recovery, which accounts for the intensity of the loading and level of damage caused by the event. Semi-Markov processes with non-renewal features are then used to account for multihazard interactions in a post-event recovery model, and applied to a case study that involves a community in Charleston, South Carolina. Lastly, Markov-type processes are combined with Bayesian networks to model the recovery of residential, commercial, educational, and industrial buildings (system-of-systems) following a hazard event. Overall, these applications demonstrate the versatility of the Markov framework towards handling recovery problems with varying levels of complexity.

42 ENGINEERING↗
DOE OSTI2022

A Typology for Characterizing Human Action in MultiSector Dynamics Models

The role of individual and collective human agency is increasingly recognized as a prominent and arguably paramount determinant in shaping the behavior, trajectory, and vulnerability of multisector systems. This human influence operates at multiple scales: from short-term (hourly to daily) to long-term (annually to centennial) timescales, and from the local to the global, pushing systems towards either desirable or undesirable outcomes. However, the effort to represent human systems in multisector models has been fragmented across philosophical, methodological, and disciplinary lines. To cohere insights across diverse modeling approaches, we present a new typology for classifying how human actors are represented in the broad suite of coupled human-natural system models that are applied in MultiSector Dynamics (MSD) research. The typology conceptualizes a “sector” as a system-of-systems that includes a diverse group of human actors, defined across individual to collective social levels, involved in governing, provisioning, and utilizing products, goods, or services towards some human end. We trace the salient features of modeled representations of human systems by organizing the typology around three key questions: 1) Who are the modeled actors in MSD systems? 2) What are their modeled actions? 3) How and for what purpose are these actors and actions operationalized in a computational model? The typology is used to critically examine existing models and chart the frontier of future human systems modeling for MSD research.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A Safety and Management Framework to Enable Automated Mobility Districts in Urban Areas

Automated mobility technology is beginning to emerge as a viable means to create sustainable and effective public mobility systems within denser urban environments. Automated mobility districts (AMDs) describe major urban districts or activity centers in which deployments of multiple automated vehicle (AV) transit and ride-hailing fleets are supported to meet public mobility needs. The authors put forward a framework to enable AMDs and their governing and management jurisdictional authorities to manage safety of AV operations based on lessons learned from the last century of automated guideway transit and roadway intersection traffic control systems. The essential concept is that of operational management and safety-critical control of multiple AV fleets using a “system-of-systems” approach to system safety analysis. The safety analysis would focus on safe passage of the AV fleet vehicles through complex roadway intersections and junctions, especially in the presence of other non-automated modes such as pedestrians and manually operated vehicles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Dysfunctionality Hazard Curve: Risk-Based Tool to Support the Resilient Design of Systems Subjected to Multihazards

While resilience metrics have been proposed and studied given a functionality recovery curve, they have not emphasized enough on accounting for the uncertainties in the multihazard occurrences and intensities. Moreover, these resilience metrics are not risk-based (i.e., they do not express the system's resilience loss as a frequency of exceedance), leading to inconsistencies in system performance description when compared to performance-based engineering frameworks. A risk-based tool termed dysfunctionality hazard curve is proposed to assess the resilience of systems subjected to single hazards or multihazards with inter-event dependencies. Dysfunctionality hazard curve expresses system resilience performance as frequency of exceedance of time to full functionality. In doing so, it characterizes system recovery as a sequence of repair activities and also considers the uncertainties in the multihazard occurrences and intensities. Dysfunctionality hazard curve is demonstrated for a residential building susceptible to earthquake and hurricane hazards. Results indicate that Dysfunctionality hazard curve for earthquakes is greater than that for hurricane winds under single hazards. Under multihazards, considering inter-event dependencies during system recovery leads to larger dysfunctionality hazard curve than ignoring them. Finally, the concept of dysfunctionality hazard curve is also extended to a system-of-systems consisting of residential and commercial buildings.

42 ENGINEERING↗
DOE OSTI2025

Early Fault Detection in Nuclear Systems: A Digital Engineering Approach

Nuclear energy systems present unique challenges in terms of ensuring safety, reliability, and efficiency during their design and operation. Early fault detection is critical for mitigating risks and fostering system resilience. However, current methods often fall short at identifying faults during early stages, potentially leading to costly delays and safety risks. The present work proposes a comprehensive digital engineering approach that leverages digital twins, digital threads, model-based systems engineering, artificial intelligence, and immersive extended reality to support early fault detection in nuclear systems. Through a series of case studies, we highlight specific gaps in the fault detection mechanisms of traditional nuclear design and operation processes, then demonstrate a suite of solutions we are working to implement to address these shortcomings in similar projects. Our findings suggest that a digital engineering approach to design and operation can significantly improve fault detection, ultimately leading to reductions in risk.

42 - ENGINEERING↗
DOE OSTI2026

SoS-SDQN: System of Systems Software-defined Quantum Networking

Quantum networks are needed for quantum domain applications that may run across different network deployments - point-to-point, multi-node networks, and possibly across inter-domain quantum networks, like the quantum internet. Unlike classical computing networks, quantum networks involve heterogeneous systems nodes that currently require local and manual control. This needs a unified control approach to help them integrate, work seamlessly, and have global knowledge. Software-defined Networking (SDN) has been successfully leveraged in classical networking for seamless and software-driven control of network infrastructure and packet switching, but not in management of quantum network applications across heterogeneous systems. In this paper, we review the current state of the art and present early developments of a System-of-Systems Software-defined Quantum Networking architecture (SoS-SDQN), a generic architecture that supports software-driven quantum network experiments across heterogeneous quantum systems. The architecture modifies the generic SDN to address the domain requirements of quantum networks and proposes a multilevel SDQN to provide a global view of network status, running applications, and control of incorporated heterogeneous quantum systems. We also design and implement a SoS SouthBound Quantum Interface (SoS-SBQI), a quantum infrastructure protocol that abstracts automation of applications across the network.

Alnajjar, Anees [ORNL] (ORCID:0000000237101601)↗
DOE OSTI2024

AutoCSM

A template system-of-systems modeling approach for automating the development, deployment, and integration of cooling system models (CSMs) for supercomputing facilities within the ExaDigiT framework. AutoCSM is a Python-based framework to assist in CSM developers in accelerating the creation and deployment of system-level thermal-hydraulic CSMs. The intention is for this tool specifically to help standardize digital twin workflows for ExaDigiT. However, this tool can be used independent of ExaDigiT (and even other systems besides CSMs).

Greenwood, MichaelScott [Oak Ridge National Labora↗
DOE OSTI2024

Cyber100 Compass [SWR 23-64]

Cyber100 Compass ("Compass") is a unique risk assessment framework that will enable grid system planners to understand and mitigate cybersecurity risk for grids transitioning to high levels of renewable generation, including 100%. The idea for Compass was developed by NREL based on past work on high-renewable grids and a series of discussions with DOE. Compass is part of Cyber100, a portfolio of proposed research activities that would greatly expand understanding of cybersecurity for high-renewable grids. Compass is a desktop application designed with a user-friendly interface. The tool gathers information from users, conducts probabilistic backend calculations, and outputs a series of visualizations to help users understand and analyze their cybersecurity risks based on the unique features of their future grid. Compass will take as inputs the values for different conditions and produce a risk score of the resulting grid. By trying different configurations, system planners can compare the resultant risks against their own risk tolerance and decide which system-of-system controls to implement as they transition toward a 100% renewable grid.

Martin, Maurice↗
DOE OSTI2026

Cooperative Automated Cohort Driving on Connected Infrastructure, Arterial Roadways, and Highways: Final Project Demonstration and System-of-Systems Model Correlation

This project seeks to synergize vehicle automated driving and connectivity data to improve mobility and energy efficiency of groups of mixed vehicles operating in close proximity (vehicle cohort) on various infrastructure. A custom cellular communication network links vehicles operating as a cohort with infrastructure to a centralized system-of-systems digital twin with an AI-based optimal behavior planner. The data contained in this set are from final testing and technology demonstrations to U.S. Department of Energy staff at the American Center for Mobility. The data contain single-lane, single-light scenarios; multi-lane, multi-light arterial scenarios; and limited-access highway scenarios. All test cases were derived from simulations and replicated on the test track. The project employed two and four light-duty vehicles with connectivity and drive automation for the testing. The baseline scenario without connectivity was run under the control of the system-of-systems centralized planner but operating each vehicle with an intelligent driver model controlling the velocity, lane utilization, and vehicle gap. This was to ensure the highest compatibility with the simulation in terms of dynamic behavior. The connected cohort case utilized AI optimization to perform coordinated and cooperative control for energy, as well as safe, comfortable behavior for the cohort. The dataset is appropriately named with unconnected and connected designations, with comparisons sharing the same run index number. The included PowerPoint and PDF files describe the test setup and provide an overview of results from the project. ![image](de-EE0009209_March_2023_Data_Arterial_Scenario_Results.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

CCE Phase 2: System-of-System Analysis

During Phase 1, Consequence Prioritization, the CCE Team identified High Consequence Events (HCEs) that can be accomplished through cyber means to impact critical functions, services, and processes. During Phase 2, System-of-Systems Analysis (SoS Analysis), the CCE Team will conduct a systematic review and analysis of information related to the equipment, systems, processes, operations, maintenance, testing, and procurement practices based on the HCEs identified in Phase 1.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Research Roadmap on Grid-Forming Inverters

This research roadmap is intended to fill the knowledge gap by providing a system view of grid-forming inverter-based resource controls and their impact on grid stability, which we believe is central to meeting some of the challenges to operating the future North American electric power system. This includes the roles and requirements of grid-forming inverter-based resources—including solar photovoltaics, wind generators, and energy storage. For this roadmap, we focus on a specific family of grid-forming inverter control approaches that do not rely on an external voltage source (i.e., no phase-locked loop) and that can share load without explicit communications. Although the roadmap is focused narrowly on system challenges for grid-forming controls and power system stability, including interactions with protection, we hope it serves as a foundational element for future system-of-systems roadmapping needed in a broader grid modernization effort with increasing deployments of inverter-based resources. The roadmap first introduces formal definitions for the grid stability topics and then describes the differences between grid-forming and traditional grid-following control approaches for inverter-based resources. The core of the roadmap consists of a review of current research and an outline of research needs related to five grid-forming inverter topics: frequency control, voltage control, system protection, fault ride-through and voltage recovery, and modeling and simulation. The review both delineates contemporary advances and highlights open research questions that must be addressed to enable the widespread adoption of inverter-based resources across the grid. Feedback from industry on these research questions is incorporated, including discussions during the Workshop on Grid-forming Inverters for Low-inertia Power Systems. The workshop included industry presentations and discussion of ongoing research, technology gaps, and piloting needs. This roadmap concludes by offering a multiyear perspective on the gradual field validation of grid-forming inverters. This perspective recognizes that the scale and scope of the types of power systems that inverters will be called on to provide grid-forming services will and should begin modestly. Specifically, this roadmap recognizes that inverter controls today are predominantly grid-following and that future power systems will involve a mix of inverter-based resources with both grid-following and grid-forming control capabilities. Growth over time will depend on how well grid-forming inverters perform and what advantages they bring as penetration levels (instantaneous and average) of inverter-based resources increases. This recognition, in turn, establishes a natural sequence of priorities for the research questions that must be addressed. Following this multiyear perspective, the roadmap offers short descriptions of two specific near-term research priorities: the review of regulatory and technical standards and the development of advanced modeling techniques. These priorities are foundational. We recommend immediate pursuit of them in parallel with and in direct support of the research outlined by our multiyear perspective.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

INTERSECT Architecture Specification: System-of-systems Architecture (Version 0.5)

Oak Ridge National Laboratory (ORNL)’s Self-driven Experiments for Science / Interconnected ScienceEcosystem (INTERSECT) architecture project, titled “An Open Federated Architecture for the Laboratory of the Future”, creates an open federated hardware/software architecture for the laboratory of the future using a novel system of systems (SoS) and microservice architecture approach, connecting scientific instruments, robot-controlled laboratories and edge/center computing/data resources to enable autonomous experiments, “self-driving” laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The architecture project is divided into three focus areas: design patterns; SoS architecture; and microservices architecture. The design patterns area focuses on describing science use cases as design patterns that identify and abstract the involved hardware/software components and their interactions interms of control, work and data flow. The SoS architecture area focuses on an open architecture specification for the federated ecosystem that clarifies terms, architectural elements, the interactions between them and compliance. The microservices architecture describes blueprints for loosely coupled microservices, standardized interfaces, and multi-programming language support. This document is the SoS Architecture specification only, and captures the system of systems architecture design for the INTERSECT Initiative and its components. It is intended to provide a deep analysis and specification of how the INTERSECT platform will be designed, and to link the scientific needs identified across disciplines with the technical needs involved in the support, development, and evolution of a science ecosystem. PLEASE NOTE: This is a working document and reflects current discussions and design activity among the authors. There may be inconsistencies within the document as different parts evolve at a different pace. We invite comments and thoughts from the public on this and following working drafts. The first finished version of this document is scheduled for release in September 2023.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Nuclear System-of-Systems Capabilities Analytic Process

This dissertation discusses the impetus for, development of, and initial demonstration of NuSCAPTM: the Nuclear System-of-Systems Capabilities Analytic Process TM . NuSCAP is an approach executed via a Python® application that enables capabilities-based vulnerability analyses of military systems of systems (SOS) exposed to prompt nuclear weapon effects. The NuSCAP application calls on industry-standard, fast-running nuclear weapon effects tools and the Monte Carlo N-Particle®1 (MCNP®) code to evaluate the impact of nuclear weapon environments on the military capabilities of a complex and networked SOS.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗