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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Recovery Simulator and Analysis Formulation: Mathematical Framework for Enhanced Resilience and Resource Allocation

This report introduces recovery simulator and analysis (RSA), a framework aimed at enhancing the resilience of electrical grids post-disruption. The RSA model leverages an optimization problem formulation that focuses on maximizing the load served (or optionally customers served) through a coordinated and cooptimized recovery of non-black start generation, transmission lines, feeders and substations subject to labor budget constraints. By integrating advanced linear programming techniques, the simulator selects efficient reocovery pathways, optimizing both short-term and long-term grid recovery strategies. The mathematical framework guides decision-making through a comprehensive evaluation of potential recovery actions, factoring in the trade-offs between labor constraints and load (or optionally customer) restoration efficacy. This enables grid operators to simulate diverse outage scenarios and delineate optimal recovery pathways, thereby prioritizing critical repair tasks and ensuring resource allocation is both economical and effective. The intended use case of RSA is to allow planners to explore many recovery scenarios quickly and determine assets most critical across a wide range of scenarios, and therefore strong candidates for hardening or additional investment. RSA might also be used in an operational setting, following a single event, for exploring efficient recovery pathways.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations

In edge computing, users' service profiles are migrated between edge servers due to user mobility. Reinforcement Learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE's boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. Here, we extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures.

Edge computing↗
DOE OSTI2025

A sequential Attacker-Defender game for distribution systems resilience enhancement against extreme weather events

Improving distribution system resilience against frequent extreme weather events is important for reliable power system operations. Especially when dealing with events such as hurricanes that have short-term predictions, proactive pre-event preparedness plays a vital role in system resilience performance. In this paper, we propose a novel approach to construct pre-event resource allocation plans for system operators to cope with upcoming threats through a sequential attacker-defender game framework. The sequential attacker-defender game is designed to model the interaction between the extreme weather and the system operator. In each round of the game, the attacker and the defender sequentially update their current strategies by accounting for the opponent’s action set. The attacker model is formulated as a bi-level problem to identify the severe outage scenarios, and the defender model is formulated as a two-stage optimization problem to determine the allocation of restoration resources including mobile responsive resources and repair crews. Two scale-reduction strategies are proposed to ensure the scalability of the game scheme. Finally, case studies on the IEEE 33-bus and a 7149-node practical utility system validate the effectiveness of the proposed sequential game and the efficiency of the scale-reduction strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

DISTRI: Distributed Multi-Facility HPC Simulator (DISTRI) v2.1

DISTRI is an advanced network simulator designed for multi-facility computational infrastructures with agentic behavior. It simulates HPC facilities where computational resources act as autonomous agents, making intelligent decisions about job scheduling, load balancing, and resource allocation. The simulator focuses on developing and testing decentralized algorithms that promote resilience and efficiency in multi-facility environments. Key Features: - Agentic Resource Behavior: Processors and DTNs act as autonomous agents with decision-making capabilities - Pheromone-Based Load Balancing: Decentralized load balancing inspired by ant colony optimization - Dual Topology Support: Mesh (normal operations) and Dumbell (network testing) topologies - Comprehensive TCP Simulation: Realistic TCP implementations with multiple congestion control algorithms - Failure Resilience Testing: Processor failure simulation with automatic job reassignment - Extensive Visualization: Detailed performance analysis and metrics collection - Research-Ready: Designed for algorithm development and benchmarking

Bez, Jean Luca [Lawrence Berkeley National Laborat↗
DOE OSTI2025

Integrated Metrics for County-Level Resilience Ranking Using Entropy and TOPSIS

In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.

Bhusal, Narayan [Oak Ridge National Laboratory (OR↗
DOE OSTI2025

Enhancing Security and Resiliency in Operational Technology Environments Through Network Slicing and Federated Learning

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation. The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, security, and architectural implications of introducing network slicing into traditionally static OT infrastructures such as Industrial Control Systems (ICS) and SCADA. Through simulated deployments and case studies, the research demonstrates how slicing enables better isolation between critical and non-critical services, thereby improving response time, throughput, and security in sensitive environments. The second question considers: How to dynamically implement network slicing and take advantage of network resources towards integrating decentralized machine learning? In response, this thesis proposes a framework that combines Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Federated Learning (FL) to enable real-time analytics while maintaining data locality. The proposed approach reduces the burden on centralized infrastructure and minimizes privacy risks by supporting on-site training of models across distributed OT nodes, coordinated through dynamically allocated network slices. The third focus explores: How slicing helps to increase the resiliency of OT networks through the orchestration of a dynamic DMZ? To answer this, the thesis presents a method for creating and managing Dynamic Demilitarized Zones (DMZs) using network slicing. This enables flexible and automated isolation of sensitive subsystems during threat scenarios or high-risk operations. Coupled with intelligent orchestration and containerized security services, the dynamic DMZ significantly enhances the system's ability to respond to cyber incidents without halting production. Ultimately, this thesis contributes a comprehensive architecture that blends network slicing with machine learning, secure segmentation, and automation, paving the way for resilient, adaptive, and intelligent OT environments. Performance evaluations across multiple scenarios show improvements in system reliability, threat response time, model accuracy, and resource utilization, providing a strong foundation for future industrial automation systems.

Rodiles Delgado, Brian G↗
NASA NTRS2021

Resilience Engineering's Potential for Advanced Air Mobility (AAM)

The national airspace (NAS) will rapidly evolve in the next ten to twenty years. Plans for Advanced Air Mobility (AAM) during that period envision highly automated airspace management systems and electrically powered vehicles. AAM concepts also anticipate limited human roles. The goal of limiting the human role is to minimize the potential for misadventures, yet how the human role is limited needs to be carefully considered in order to also preserve the potential for human successes. The field of resilience engineering (RE) focuses on how systems can change in order to seize an opportunity or withstand an unforeseen challenge. RE methods rely on the use of empirical data to optimize the ability of any system to adapt. RE studies have shown how individual and team initiatives ensure resilient system performance by creating safety through flexibility. Benefits of the RE approach include improved awareness of operational circumstances and how system elements depend on each other, and the ability to allocate limited resources and prepare for surprise. RE offers the ability to account for and incorporate the human role as an essential element in order to ensure NAS systems’ resilient performance. Data on the human contribution to safe and resilient system performance, which is termed “work as done,” are available but are not being considered as the NAS evolves. We present an approach that describes how use of RE can enable the evolving NAS to adapt, and perform, in a resilient manner.

Aviation Safety↗
DOE OSTI2024

iFair: Achieving Fairness in the Allocation of Scarce Resources for Senior Health Care

Efficient resource allocation is crucial in many domains, particularly in senior care, where assigning resources to older adults must consider uncertainties associated with vulnerable populations. In collaboration with Senior Health Facilities (SHFs) and domain experts, this paper presents iFair, a novel framework designed to assist decision-makers in equitably allocating scarce resources to older adults. iFair was prototyped in the context of ongoing work on a data exchange platform, CAREDEX, used for enhancing older adults' resilience during disasters. A key novelty of iFair focuses on aligning resident preferences with resources in urgent situations, expediting care, and enhancing task efficiency. We integrate static and dynamic environmental data, including facility layouts and sensor data, with detailed resident profiles to cater to the individual needs and preferences of residents. While our framework primarily focuses on allocation within facilities, it also extends to a regional scale to support the planning and transfer of seniors to mutual aid facilities. Our experiments adapt data from a real SHF to emulate resource allocation in an emergency fire evacuation setting and highlight the delicate balance that decision-makers can achieve between efficiency and fairness.

Kenne, Modeste Mefenya↗
DOE OSTI2025

Evaluating the combined effects of light and water availability on the early growth and physiology of Tamarindus indica : Implications for restoration

Abstract Premise The tamarind tree (Tamarindus indica) is a species of significant cultural, economic, and ecological value, with a pantropical distribution. However, the tamarind is experiencing a decline in wild populations in its native range, but the reasons for its decline remain unknown. Methods We examined the critical early life‐history stages for tamarind establishment to understand how varying levels of light and water availability and watering frequency affect its regeneration. Through three greenhouse experiments, we assessed the impact of these resources on the germination, survival, growth, and physiological responses of tamarind seedlings and saplings. Results Water availability was critical for seed germination, but not light levels or pre‐germination treatments. Light was the primary limiting factor for seedling growth. Tamarinds in high light availability grew taller, had more biomass and larger diameter, but the effect of light was modulated by water availability, indicating that there was an interaction between both resources. Water and light affected specific leaf area and leaf dry matter content but not biomass allocation, root‐to‐shoot ratio, or stomatal conductance. Water availability influenced sapling growth, but watering frequency did not, indicating a resilience of tamarind saplings to changes in rainfall periodicity but a sensitivity to total rainfall amounts. Conclusions Our study underscores the importance of considering both light and water availability in tamarind restoration efforts and contribute to understanding plant responses and trade‐offs under different levels of critical resources. Our findings will inform conservation strategies to support the regeneration and long‐term survival ofTamarindus indicain its native habitats.

Plant Sciences↗
DOE OSTI2024

Reinforcement Learning to Enhance Optimal Operation of Resilient Community Energy Systems

This paper presents a novel model-free multi-agent Reinforcement Learning (RL) control method to enhance the resilience of community energy systems in island mode, which coordinates multiple objectives without the necessity of identifying system models that require expert knowledge. Specifically, a community-level coordinator agent is designed to allocate renewable energy resources among different buildings, and multiple building-level agents are developed to optimize load schedules based on limited energy resources and requirements of building loads and occupants’ comfort. In a two-day evaluation, our RL approach demonstrated a similar performance against MPC without requiring system models and formulation of optimization problems as required in MPC.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
NASA NTRS2023

Regulatory response to a hybrid ancestral nitrogenase in Azotobacter vinelandii

Biological nitrogen fixation, the microbial reduction of atmospheric nitrogen to bioavailable ammonia, represents both a major limitation on biological productivity and a highly desirable engineering target for synthetic biology. However, the engineering of nitrogen fixation requires an integrated understanding of how the gene regulatory dynamics of host diazotrophs respond across sequence-function space of its central catalytic metalloenzyme, nitrogenase. Here, we interrogate this relationship by analyzing the transcriptome of Azotobacter vinelandii engineered with a phylogenetically inferred ancestral nitrogenase protein variant. The engineered strain exhibits reduced cellular nitrogenase activity but recovers wild-type growth rates following an extended lag period. We find that expression of genes within the immediate nitrogen fixation network is resilient to the introduced nitrogenase sequence-level perturbations. Rather the sustained physiological compatibility with the ancestral nitrogenase variant is accompanied by reduced expression of genes that support trace metal and electron resource allocation to nitrogenase. Our results spotlight gene expression changes in cellular processes adjacent to nitrogen fixation as productive engineering considerations to improve compatibility between remodeled nitrogenase proteins and engineered host diazotrophs.

nitrogen fixation↗
DOE OSTI2026

Port Technical Assistance Program: A Proposed Initiative to Support U.S. Ports Through Energy Innovation

This document highlights the crucial importance of the maritime sector within the United States (U.S.) economy, serving as a key node for trade and global goods transportation. U.S. ports are currently grappling with challenges posed by rising shipping demands, the health impacts of diesel fuel reliance, and the technology adoptions of global trade partners due to increasing environmental regulations. This document proposes forming a technical assistance program, led by Pacific Northwest National Laboratory (PNNL), to help U.S. ports transition to sustainable energy solutions that not only improve environmental and community outcomes, but also enhance resilience to unexpected weather events and reduce pressure on local utilities. Coordinated by PNNL, this initiative would offer a web-based platform of consolidated resources and tools for comprehensive strategic planning and cost-benefit analysis. It also seeks to establish a collaborative network between ports and national laboratories to facilitate the sharing of best practices. The document suggests that optimizing resource allocation and providing tailored support could be achieved through strategic categorization of ports within this network, with relevant attributes and examples detailed in the report. The anticipated benefits of such a program could include increased efficiency and cost savings in port operations and the advancement of scientific innovation via alternative energy research. Also, it could enhance economic growth and competitiveness in international trade by enabling ports to meet shipping demands and develop resilient critical infrastructure through informed, long-term strategies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Utility-Scale Shared Energy Storage Deployment: Challenges, Research Gaps, and Opportunities

Although community energy storage (CES) and behind-the-meter (BTM) energy storage systems have been widely used to offer homeowners and communities a variety of localized benefits, their scalability and grid support functionalities are limited. On the other hand, utility-scale shared energy storage (USES) systems may offer a number of benefits for grid integration, scalability, and economic viability. When compared to BTM and CES alternatives, these large-scale systems provide more storage capacity, more efficient operations, and more economically viable options. The deployment of USES presents opportunities for optimizing grid performance, integrating renewable energy resources, and improving energy security at the community level. However, significant research gaps exist in optimizing the integration and operation of these systems, especially to allocate energy for consumer use, grid services, and enhancing energy resilience. This paper reviews the literature in this regard, focusing on the opportunities, research gaps, and challenges associated with USES deployment. Firstly, the paper provides an overview of USES systems and emphasizes their benefits. Secondly, the key challenges are identified, and research gaps associated with the operation and integration of these systems are highlighted. Lastly, some potential solutions and opportunities that can be adopted to facilitate the rapid deployment and management of USES are presented. Technological, economic, regulatory, and environmental aspects are also discussed in this paper, providing an overview of the current state and future prospects of this technology.

Gautam, Mukesh [BATTELLE (PACIFIC NW LAB)] (ORCID:↗
NASA NTRS2018

A Prognostics Framework Development for Swarm Satellite Formations

Prognostics is the science of predicting the failure(s) of a component or a system and understanding how the performance will change in the event of a failure or degradation mechanism. With accurate predictions of possible failures, autonomous mitigative actions can be taken to correct/repair any issues or alert human operators of a failure threshold exceedance requiring condition-based maintenance. Although there is extensive research on failure predictions for a component or a system, there are significantly more opportunities to foray into failure predictions and prognostics for a system of systems such as an airspace consisting of multiple aircraft, a fleet of unmanned aerial vehicles, and a swarm of intelligent satellite systems. Failure prediction and mitigation are particularly important in autonomous systems such as satellite swarm systems that need effective resource management and minimal human interactions. Based on NASA's decadal survey, there is a clear need to prioritize the development of satellite swarm technology for studies of space physics and Earth science. The science community will propose future missions that return in-situ measurements from a 3-D (three-dimensional) volume of space, with relative spacecraft motion and inter-satellite baselines controlled according to the mission objectives. For such multi-spacecraft missions, it is required that ground operations resources do not scale with the number of satellites, thus compromising the swarm or leading to inefficiencies in resource allocation. Swarms of tens or hundreds of small satellites will require autonomy in attitude control, navigation and failure. Although significant research has been conducted in the areas of autonomous formation flying algorithms, less attention has been given to the development of resilient systems robust to failures.The focus of this research paper is the integration of model-based prognostics into the swarm dynamics control and decision-making algorithms. We simulate swarm management strategies for a subsystem failure to demonstrate the importance of failure predictions by comparing two cases: (i) no health information is provided to the system and utilized in the decision-making process and (2) system health information is obtained using prognostics and employed by the control system. One example scenario presented is for the GPS (Global Positioning System) system of an individual satellite to perform off-nominally due to increasing estimated error. In this scenario, the keep-out zone for that satellite would become more conservative, thereby decreasing the risk of collision. This is achieved via tuning the individual artificial repulsive functions assigned to each satellite.This paper is structured as follows. First we provide an overview of current swarm technology development, where we specifically use the term swarm to define multiple satellites flying in formation in similar orbits, with cross-link communication and station-keeping capabilities. Second, we give an introduction to the Swarm Orbital Dynamics Advisor (SODA), a tool that accepts high-level configuration commands and provides the orbital maneuvers required to achieve the prescribed formation configuration. Third, we provide the details of the model-based prognostics algorithm implementation in SODA. Finally, we present different case studies for potential component/subsystem failures and the swarm responses based with and without failure prediction information.

prognostics↗
NASA NTRS2017

Representing Water Scarcity in Future Agricultural Assessments

Globally, irrigated agriculture is both essential for food production and the largest user of water. A major challenge for hydrologic and agricultural research communities is assessing the sustainability of irrigated croplands under climate variability and change. Simulations of irrigated croplands generally lack key interactions between water supply, water distribution, and agricultural water demand. In this article, we explore the critical interface between water resources and agriculture by motivating, developing, and illustrating the application of an integrated modeling framework to advance simulations of irrigated croplands. We motivate the framework by examining historical dynamics of irrigation water withdrawals in the United States and quantitatively reviewing previous modeling studies of irrigated croplands with a focus on representations of water supply, agricultural water demand, and impacts on crop yields when water demand exceeds water supply. We then describe the integrated modeling framework for simulating irrigated croplands, which links trends and scenarios with water supply, water allocation, and agricultural water demand. Finally, we provide examples of efforts that leverage the framework to improve simulations of irrigated croplands as well as identify opportunities for interventions that increase agricultural productivity, resiliency, and sustainability.

Irrigated Agriculture; Crop Model; Hydrologic Mode↗
DOE OSTI2025

IEEE PES GM Poster - Cyber-Informed Engineering Approach to Mitigating BESS Supply Chain Concerns

Battery energy storage systems (BESS) are increasingly important to meet the needs of grid resilience and reliability. BESS provide critical grid services, maintaining stability of the grid with increased variable conditions. However, there are significant geopolitical and security concerns regarding their operation in critical infrastructure, due to lack of a domestic supply chain and prevalence of foreign entity of concern (FEOC) components in BESS and associated inverter-based resources. The supply chain challenge is dually exacerbated by a lack of alternative suppliers who can meet the economic targets for energy delivery and a potentially adversarial supply chain. Solutions are needed to secure components, addressing mixed layers of risk and engineering controls. This paper presents a specific application of Cyber-Informed Engineering (CIE) principles for BESS and recommends an alternative strategy to blocking the supply chain, ensuring that grid modernization targets can be met despite lack of a validated or secure supply chain. This study focuses on the United State (U.S.) use case, but the process can be applied globally to address supply chain security challenges. CIE practices represent the next step in functional assurance and risk mitigation, ensuring optimal resource allocation and enhancing security measures to safeguard the future of energy in the U.S. and beyond.

25 - ENERGY STORAGE↗
NASA NTRS2024

Climate Extremes and Risks: Links Between Climate Science and Decision-Making

The World Climate Research Programme (WCRP) envisions a future where actionable climate information is universally accessible, supporting decision makers in preparing for and responding to climate change. In this perspective, we advocate for enhancing links between climate science and decision-making through a better and more decision-relevant understanding of climate impacts. The proposed framework comprises three pillars: climate science, impact science, and decision-making, focusing on generating seamless climate information from sub-seasonal, seasonal, decadal to century timescales informed by observed climate events and their impacts. The link between climate science and decision-making has strengthened in recent years, partly owing to undeniable impacts arising from disastrous weather extremes. Enhancing decision-relevant understanding involves utilizing lessons from past extreme events and implementing impact-based early warning systems to improve resilience. Integrated risk assessment and management require a comprehensive approach that encompasses good knowledge about possible impacts, hazard identification, monitoring, and communication of risks while acknowledging uncertainties inherent in climate predictions and projections, but not letting the uncertainty lead to decision paralysis. The importance of data accessibility, especially in the Global South, underscores the need for better coordination and resource allocation. Strategic frameworks should aim to enhance impact-related and open-access climate services around the world. Continuous improvements in predictive modeling and observational data are critical, as is ensuring that climate science remains relevant to decision makers locally and globally. Ultimately, fostering stronger collaborations and dedicated investments to process and tailor climate data will enhance societal preparedness, enabling communities to navigate the complexities of a changing climate effectively.

climate extremes↗
DOE OSTI2024

Distributed Energy Resource Interconnection Roadmap

Adopting strategic reforms to DER interconnection can help reduce interconnection delays, fairly allocate costs, improve transparency and equity, and support efficient and strategic investment in distribution and sub-transmission grids that can accommodate a rapidly evolving energy landscape. The Interconnection Innovation e-Xchange envisions this roadmap and its transmission system companion volume as collaborative resources to facilitate continued stakeholder discussions, problem-solving, and innovation as the U.S. works together to enable simpler, faster, and fairer interconnection of clean energy resources all while enhancing the reliability, resiliency, and security of our electric grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗