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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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DOE OSTI2020

Federal Energy Management Program’s Technical Resilience Navigator: Communications Plan

The Federal Energy Management Program is working to develop a standard, agency-neutral energy and water technical resilience navigator (TRN) to assist federal agencies with their resilience planning efforts, building on agency-expressed needs. The TRN Communications Plan defines how the project team and FEMP will generate awareness of the TRN, recruit validators, encourage its use across federal agencies, increase awareness among various audiences, and solicit user feedback on its usefulness including suggestions for improvement. In support of this purpose, the communications plan characterizes the key audiences, messages, communications channels, and activities needed to help ensure success. The role of communications is to define strategy and ensure leadership and consistency in messaging, writing, outreach, media relations, editing, and design related to the TRN.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

A high‐order discontinuous Galerkin approach for physics‐based thermospheric modeling

Abstract The accurate prediction of aerodynamic drag on satellites orbiting in the upper atmosphere is critical to the operational success of modern space technologies, such as satellite‐based communication or navigation systems, which have become increasingly popular in the last few years due to the deployment of constellations of satellites in low‐Earth orbit. As a result, physics‐based models of the ionosphere and thermosphere have emerged as a necessary tool for the prediction of atmospheric outputs under highly variable space weather conditions. This paper proposes a high‐fidelity approach for physics‐based space weather modeling based on the solution of the Navier–Stokes equations using a high‐order discontinuous Galerkin method, combined with a matrix‐free strategy suitable for high‐performance computing on GPU architectures. The approach consists of a thermospheric model that describes a chemically frozen neutral atmosphere in nonhydrostatic equilibrium driven by the external excitation of the Sun. A novel set of variables is considered to treat the low densities present in the upper atmosphere and to accommodate the wide range of scales present in the problem. At the same time, and unlike most existing approaches, radial and angular directions are treated in a nonsegregated approach. The study presents a set of numerical examples that demonstrate the accuracy of the approximation and validate the current approach against observational data along a satellite orbit, including estimates of established empirical and physics‐based models of the ionosphere‐thermosphere system. Finally, a one‐dimensional radial derivation of the physics‐based model is presented and utilized for conducting a parametric study of the main thermal quantities under various solar conditions.

Engineering↗

DOE OSTI2023

Operational Uncrewed Aircraft Systems Development for Meteorology and Atmospheric Physics Field Campaign Report

The overarching goal of the project is to develop integrated small, unmanned aircraft systems (SUAS) capabilities for enhanced atmospheric physics measurements. This team included atmospheric scientists, meteorologists, engineers, computer scientists, geographers, and chemists necessary to evaluate the needs and develop the advanced sensing and imaging, robust autonomous navigation, enhanced data communication, and data management capabilities required to use SUAS in atmospheric physics. The flight campaigns at the Southern Great Plains (SGP) U.S. Department of Energy Atmospheric Radiation Measurement (ARM) observatory were an integrated evaluation of the systems in coordinated field tests. This enabled sensor testing, and validation of SUAS technology and integration of unmanned aircraft into the airspace. The set of experiments enabled evaluation and progression of remote UAS deployment operations with observing atmospheric sensors. Each flight test endeavor was notable for the significant lessons learned during every deployment event. Team size and roles, equipment use, aircraft instruments, meteorological instruments, and deployment logistics, were all evaluated and improved at every flight-testing day. The details of these are further discussed in the Results section.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Dedicated short range communications used as fail-safe in autonomous navigation

Here in this paper, we have analyzed scenarios for leader-follower vehicle convoys that have the potential to be unsafe, hazardous, or even fatal in order to provide insight on dangerous driving conditions for autonomous vehicle platoons. These scenarios were created in a simulation software called Prescan, and are made to reflect the behavior of the vehicle dynamics and sensor characteristics of a real-world vehicle convoy that is currently being tested at the American Center for Mobility (ACM) and sponsored by the Department of Energy (DOE), National Renewable Energy Laboratory (NREL), the U.S. Army Combat Capabilities Development Command - Ground Vehicle Systems Center (U.S. Army CC-DEVCOM) and the Michigan Department of Transportation (MDOT). The follower vehicle in this study is tested under multiple scenarios, equipped with a Dedicated Short Range vehicle-to-vehicle communication system (DSRC), a radar-based adaptive cruise control system (ACC) and a precision global positioning system (GPS).

42 ENGINEERING↗

DOE OSTI2024

300_underground robot (final research report)

Recent advancements in mobile robotics have displayed impressive capabilities in traversing and accessing areas that are inaccessible to humans either due to the characteristics of the environment or potential hazards. Furthermore, these advancements within the field of mobile robotics, more specifically uncrewed ground vehicles (UGVs), give the ability to potentially survey, observe, and map these inaccessible areas for humans. However, one of the most challenging areas to implement this technology is underground environments. The main challenge with implementing this technology in underground environments is the dependence on either GPS or RF communication for UGVs to navigate properly. Therefore, in order to properly demonstrate the mapping capabilities of the UGV this challenge must be resolved. The overall goal of this study is to demonstrate the mapping capabilities of a UGV while addressing this challenge and documenting the implementation and testing phase of the robot. The proposed solution to this challenge is to implement a SLAM algorithm onto the main computational device of the UGV utilizing the Robot Operating System (ROS). The algorithm is the open-source software package Slam Toolbox developed by Steve Macenski. Furthermore, the sllidar_ros2 package from Slamtec will be used to gather the lidar data from an A3M1 2D lidar. A separate program will be created to gather odometry information for our UGV robot. All of these software packages will run together in a Docker environment. Through working on this project I have developed a better understanding of the world of robotics/autonomous systems, especially with applications such as navigation and mapping. Furthermore, through this project, I have been given exposure to how research is conducted within a DOE lab setting. As robotics/autonomous systems become more advanced it's important to pursue more avenues of research such as this project as it will ensure the development of our capabilities.

42 ENGINEERING↗

DOE OSTI2024

Characterizing and communicating uncertainty: lessons from NASA’s Carbon Monitoring System

Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines, to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Multiple Hypothesis Tracking for Opportunistic Transmitter Identification

The exploitation of radio frequency (RF) signals from Low Earth Orbit (LEO) communication satellites for the purposes of navigation is an active field of research. In this alternative navigation system, navigation observables are passively extracted from existing RF signals. Because these signals are not designed for navigation purposes and often have proprietary signal structures that are unknown to the receiver, they lack much of the information required for navigation systems or such information is unavailable to the receiver.

42 ENGINEERING↗

DOE OSTI2020

Multi-agent motion planning with sporadic communications for collision avoidance

Here, a novel multi-vehicle motion planning and collision avoidance algorithm is proposed and analyzed. The algorithm aims to reduce the amount of onboard calculations and inter-agent communications needed for each vehicle to successfully navigate through an environment with static obstacles and reach their goals. To this end, each agent first calculates a path to the goal by means of an asymptotically optimal rapidly-exploring random tree (RRT*) with respect to the static obstacles. Then, other agents are treated as dynamic obstacles and potential collisions are determined by means of collision cones. Collision cones depend on the position and velocity from other agents and are grown conservatively between inter-agent communications. Based on the available information, each agent determines if a deconfliction maneuver is needed, if it can continue along its current path, or if communication is needed to make a decision about a conflict. With probability one, our algorithm guarantees that the agents keep from colliding with each other. Under an assumption on the existence of a solution for a vehicle to its goal, this algorithm also solves the planning problem with probability one. Simulations illustrate a group of agents successfully reaching their goal configurations and examine how the uncertainty affects the communication frequency of the multi-agent system.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Grid Communications Supply Chain & Emerging Regulation Challenges Session 1

The TADA Grid Communications Workshops are designed to strengthen cybersecurity and digital assurance across the energy sector by focusing on secure deployment and management of grid communications technologies. These workshops bring together state energy offices, utilities, and technology suppliers to explore the intersection of communications infrastructure, supply chain risks, and emerging regulatory requirements. Participants will apply Cyber-Informed Engineering (CIE) principles to reduce risks in communications systems, engage with INL’s procurement guidance, and explore future tools. Through scenario-based exercises and peer exchange, attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their grid communications projects. The workshops also help participants navigate evolving regulatory frameworks such as FEOC rules in the OBBB, NERC CIP-013, and NDAA 2024, while identifying compliance gaps in mixed-technology environments. A key outcome is the formation of a practitioner network with ongoing access to INL expertise and resources, fostering long-term resilience in the digital energy ecosystem.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2024

Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms

The quantum approximate optimization algorithm (QAOA) has enjoyed increasing attention in noisy, intermediate-scale quantum computing with its application to combinatorial optimization problems. QAOA has the potential to demonstrate a quantum advantage for NP-hard combinatorial optimization problems. As a hybrid quantum-classical algorithm, the classical component of QAOA resembles a simulation optimization problem in which the simulation outcomes are attainable only through a quantum computer. The simulation that derives from QAOA exhibits two unique features that can have a substantial impact on the optimization process: (i) the variance of the stochastic objective values typically decreases in proportion to the optimality gap, and (ii) querying samples from a quantum computer introduces an additional latency overhead. In this paper, we introduce a novel stochastic trust-region method derived from a derivative-free, adaptive sampling trust-region optimization method intended to efficiently solve the classical optimization problem in QAOA by explicitly taking into account the two mentioned characteristics. The key idea behind the proposed algorithm involves constructing two separate local models in each iteration: a model of the objective function and a model of the variance of the objective function. Exploiting the variance model allows us to restrict the number of communications with the quantum computer and also helps navigate the nonconvex objective landscapes typical in QAOA optimization problems. In conclusion, we numerically demonstrate the superiority of our proposed algorithm using the SimOpt library and Qiskit when we consider a metric of computational burden that explicitly accounts for communication costs.

Derivative-free Optimization↗

DOE OSTI2025

Grid Communications: Digital Assurance and Supply Chain Challenges and Emerging Regulation Session Two

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2025

Grid Communications: Cybersecurity and Supply Chain Challenges and Emerging Regulation Session Three

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2023

Vehicle Lateral Offset Estimation Using Infrastructure Information for Reduced Compute Load

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor. IIS reduce the need for camera imaging, image processing, and LIDAR use and point cloud processing. We show that IIS, when combined with traditional sensors, results in more accurate perception and localization outcomes and a reduced AV compute load.

Sharma, Sachin↗

DOE OSTI2022

Photonic circuits for laser stabilization with integrated ultra-high Q and Brillouin laser resonators

The integration of stabilized lasers, sources that generate spectrally pure light, will provide compact, low-cost solutions for applications including quantum information sciences, precision navigation and timing, metrology, and high-capacity fiber communications. We report a significant advancement in this field, demonstrating stabilization of an integrated waveguide Brillouin laser to an integrated waveguide reference cavity, where both resonators are fabricated using the same CMOS-compatible integration platform. We demonstrate reduction of the free running Brillouin laser linewidth to a 292 Hz integral linewidth and carrier stabilization to a 4.9 × 10 –13 fractional frequency at 8 ms reaching the cavity-intrinsic thermorefractive noise limit for frequencies down to 80 Hz. We achieve this level of performance using a pair of 56.4 x 10 6 quality factor Si 3 N 4 waveguide ring-resonators that reduce the high-frequency noise by the nonlinear Brillouin process and the low-frequency noise by Pound–Drever–Hall locking to the ultra-low loss resonator. These results represent an important step toward integrated stabilized lasers with reduced sensitivity to environmental disturbances for atomic, molecular, and optical physics (AMO), quantum information processing and sensing, and other precision scientific, sensing, and communications applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

AviSense: A Real-time System for Detection, Classification, and Analysis of Aviation Signals

Wireless systems are an integral part of aviation. Apart from their apparent use in air-to-ground communication, wireless systems play a crucial role in avionic functions including navigation and landing. An interference-free wireless environment is therefore critical for the uninterrupted operation and safety of an aircraft. Hence, there is an urgency for airport facilities to acquire the capability to continuously monitor aviation frequency bands for real-time detection of interference and anomalies. To meet this critical need, we design and build AviSense, an SDR-based real-time, versatile system for monitoring aviation bands. AviSense detects and characterizes signal activities to enable practical and effective anomaly detection. We identify and tackle the challenges posed by a diverse set of critical aviation bands and technologies. We evaluate our methodology with real-world aviation signal measurements and two custom datasets of anomalous signals. We find that our signal classification capability achieves a true positive rate of ~99%, with few exceptions, and a false positive rate of less than 4%. We also demonstrate that AviSense can effectively distinguish between different types of anomalies. We build and evaluate a prototype implementation of AviSense that supports distributed monitoring.

42 ENGINEERING↗

DOE OSTI2026

Resources for Homeowners: Home Efficiency Upgrades

A fact sheet to support small business building contractors in communicating with homeowners about potential benefits of home efficiency strategies and suggestions for navigating home upgrades. This work was developed under the Building Business Network (B-Biz) program, which aims to improve adoption of high-performance building technology solutions by collaborating with organizations that support small business contractors that provide and service high-performance building technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Navigating Uncertainty: Challenges in Visualizing Ensemble Data and Surrogate Models for Decision Systems

Uncertainty visualization plays a critical role in transforming ensemble simulation data into actionable insights by effectively communicating various dimensions of uncertainty within a system. The emergence of artificial intelligence-driven surrogate models trained on multirun ensemble data offers a transformative opportunity to replace computationally intensive simulations with fast estimates, enabling users to explore data spaces with unprecedented depth and interactivity. However, integrating ensemble data and surrogate models into decision-making workflows and tools introduces novel challenges for uncertainty visualization. These include reconciling and clearly communicating the unique uncertainties associated with ensembles and their surrogate model estimates, and leveraging these approximations to inform actionable decisions. This work explores these challenges in the context of high-dimensional data visualization, bridging discrete datasets with their continuous representations and addressing the complexities of systems that support iterative navigation between input and output spaces. We evaluate the role of uncertainty visualization in fostering intuitive, actionable interactions and identify critical hurdles in advancing this frontier of computational simulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Optimizing Non-Terrestrial Hybrid RF/FSO Links With Reinforcement Learning: Navigating Through Clouds

In the pursuit of ubiquitous broadband connectivity, there has been a significant shift towards the vertical expansion of communication networks into space, particularly through the exploitation of low Earth orbit (LEO) satellite constellations, which are favored for their relatively low latency. However, this approach faces many challenges that need to be addressed, including atmospheric turbulence, high path loss, and dynamic cloud formations. High-altitude pseudo-satellites (HAPS) have emerged as promising relaying layers between LEO satellites and ground stations, enhancing coverage, latency, and direct terrestrial user connectivity. While radio frequency (RF) bands suffer from congestion and limited bandwidth, free space optical (FSO) communications offer higher data rates, but are susceptible to misalignment and weather-induced signal degradation. To address these challenges, a hybrid RF/FSO approach has been proposed to take advantage of both technologies by dynamic switching between RF and FSO based on propagation channel conditions. This paper introduces a reinforcement learning-based algorithm designed to optimize the trajectory of HAPS, maneuver around cloudy areas, and seamlessly switch between the RF and FSO communication modes to maximize the achievable capacity. The proposed approach aims to maximize system performance by intelligently adapting to environmental conditions and offering a promising solution for next-generation space communication networks.

actor-critic algorithm↗