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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multicube retroreflector and retroreflector-based communication system

A “multicube” retroreflector may include “nested” corner cubes. Such a design combines the ease of fabrication of conventional corner cubes (e.g., stamping from a sheet) and the ability to easily and densely package them with a greater range of angles over which light will be reflected back towards the source. Such multicube retroreflectors may reflect 50% more light than corner cube retroreflectors. A retroreflector, or an array of retroreflectors, may be used as part of a communication system. A modulated retroreflector or an array thereof can form the basis of a unique communication system. For instance, by using a modulated retroreflector, or an array of such retroreflectors, a host system (e.g., an aircraft) can receive data from a remote system (e.g., a drone, an air-dropped sensor package, etc.) in a way that is difficult to intercept, and minimizes the power, computation, and antenna pointing requirements for the remote system.

42 ENGINEERING↗
DOE OSTI2020

Detecting gas leaks using unmanned aerial vehicles

Methods, systems and computer program products for detecting gas leaks using a drone are provided. Aspects include capturing a first set of data regarding a presence of a gas in the geographic area while flying along the initial flight path. Aspects also include creating secondary flight paths through regions in the geographic area in which the presence of the gas exceeds a threshold amount and capturing a second set of data regarding a concentration of the gas in the one or more regions while flying along the secondary flight paths. Aspects further include capturing wind data while flying along the initial and second flight paths and creating a three-dimensional gas plume model for gas leaks identified in the geographic area based on the first set of data, the second set of data and the wind data, wherein the three-dimensional gas plume model identifies a source of the gas leaks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Solar panel field array support system and apparatus and method for construction use

A combined PV panel assembly jig and forklift transport pallet is used to assemble PV panels for transport to a field array from a protected manufacturing environment. The panels are assembled to have adhesively applied rails for transport by a robotic drone on a ground-support rack and are pre-wired. The PV panel assembly jig holds, protects, and aligns the PV panels in an upside down position, opposite to their operational position, for ease of wiring in order to decrease the manual labor required in the field. Once the pallet is transported to the load station at the end of a row of solar panel racks in the field array, a robotic loader lifts the upside down PV panels from the combined PV panel assembly jig and forklift transport pallet in an arcing overhead motion that lifts, tilts, and deposits the PV panels in an upright position at the loading station of a railed rack support as ground-mounted in a solar panel field array.

14 SOLAR ENERGY↗
DOE OSTI2020

Machine Learning for Automated Extraction of Building Geometry

As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.

Touzani, Samir↗
DOE OSTI2021

Intelligent Consequence Control by Aerial Reconnoiter Using Unmanned Systems

In a nuclear accident, or in the aftermath of an improvised nuclear device or radiological dispersal device detonation, radioactive materials may pose a severe health threat to individuals near the site of the incident. The Intelligent Consequence Control by Aerial Reconnoiter Using Unmanned Systems (ICARUS) does the dull, dirty, and dangerous (and, sometimes, deep) work of mapping and characterizing valuable information to enable a safe and appropriate response. ICARUS provides timely answers to the questions, “What is it?” “Where is it?”, and “How far does it extend?” Using commercial, off-the-shelf proven components, the integrated chemical, biological, radiological, nuclear, and explosives detection architecture for our drones provides a solution for collecting radiological, chemical, and optical information on developing complex situations, returning actionable data for determining if it is safe for further response efforts. ICARUS can determine and transmit the location, identity, and intensity of radionuclide contamination to a remote base station. It can deliver on the unexpected.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Multi-sensor agent devices

Apparatus, methods, and systems implementing multi-sensor agent devices are described herein. The agent devices can each include a plurality of sensors for measuring parameters of interest to an entity such as an electric power utility. The sensors can be organized in individually-IP-addressable sensor clusters, with each sensor cluster including an associated microcontroller. The agent devices can be controlled by a control center of the entity to operate in a coordinated manner, such as to gather and transmit data regarding parameters of interest. The agent devices can be transported to desired areas for data collection by unmanned aerial systems such as drones, and the collected data can be stored in a distributed blockchain ledger.

Fuhr, Peter L.↗
DOE OSTI2022

UAS Sensors in Difficult Locations

SDRD Annual Report for FY21 detailing Trainham's project that investigated using unmanned aerial systems (drones) to get into difficult places such as deserts and tunnels to measure VOCs, etc. A complementary summary will be derived from this report.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning: Preprint

We study multiagent reinforcement learning (MARL) with constraints. This setting is gaining importance as MARL algorithms find new applications in real-world systems ranging from power grids to drone swarms. Most constrained MARL (C-MARL) algorithms use a primal-dual approach to enforce constraints through a penalty function added to the reward. In this paper, we study the structural effects of the primal-dual approach on the constraints and value function. First, we show that using the constraint evaluation as the penalty leads to a weak notion of safety, but by making simple modifications to the penalty function, we can enforce meaningful probabilistic safety constraints. Second, we show that the penalty term changes the value function in a way that is easy to model, and demonstrate the consequences of not doing so. We conclude with simulations in a simple constrained multiagent environment to back up the theoretical results.

data-driven control↗
DOE OSTI2023

Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms. Most C-MARL algorithms use a primal-dual approach to enforce constraints through a penalty function added to the reward. In this paper, we study the structural effects of this penalty term on the MARL problem. First, we show that the standard practice of using the constraint function as the penalty leads to a weak notion of safety. However, by making simple modifications to the penalty term, we can enforce meaningful probabilistic (chance and conditional value at risk) constraints. Second, we quantify the effect of the penalty term on the value function, uncovering an improved value estimation procedure. We use these insights to propose a constrained multiagent advantage actor critic (C-MAA2C) algorithm. Simulations in a simple constrained multiagent environment affirm that our reinterpretation of the primal-dual method in terms of probabilistic constraints is effective, and that our proposed value estimate accelerates convergence to a safe joint policy.

chance constraints↗
DOE OSTI2023

SMALL UNMANNED MARINE HYDROKINETIC PLATFORMS FOR POWER GENERATION IN COASTAL AND TIDAL WATERS

The feasibility and optimization of small unmanned mobile marine hydrokinetic (MHK) energy platforms for harvesting marine current energy in coastal and tidal waters are examined. A case study of a platform based on the use of a free-surface waterwheel (FSWW) mounted on an autonomous unmanned surface vehicle (USV) was conducted. Such platforms can serve as recharging stations for aerial drones (UAVs), enabling extension of the UAVs’ autonomous operating time. An unmanned MHK platform potentially meets this need with sustainable power harvested from water currents. For the case study, six different waterwheel configurations were field-tested in the Intracoastal Waterway of South Florida in support of determining the configuration that produced the most power. Required technologies for unmanned operations of the MHK platform were developed and tested. The data from the field-testing were analyzed to develop an empirical relation between the wheel’s theoretical hydrokinetic power produced and the mechanical power harnessed by the MHK platform with various waterwheel configurations during field-testing. The field data was also used to determine the electrical power generated by the FSWW configurations during field-testing. The study has led to the development of standardized testing procedures. The empirical relation is used to examine predicted power production through scaling up different physical aspects of the waterwheel.

McKinney, Adriana↗
DOE OSTI2024

UAS IMPLEMENTATION CONSIDERATIONS FOR NUCLEAR SECURITY

Uncrewed aerial systems (UAS) have been an area of focus for the Office of International Nuclear Security within the US Department of Energy’s National Nuclear Security Administration and other foreign and domestic organizations for several years. This emerging technology provides significant capabilities to the nuclear security realm, but there are many things to consider when implementing them into an established security design or network. The goal of this paper is to discuss some of the benefits, challenges, and lessons learned with using UAS at nuclear facilities and during transport of material. Generic examples of UAS implementation will be used to facilitate a publicly releasable paper and presentation. The paper will start with a summary of the types and capabilities of UAS to provide a better understanding for people unfamiliar with current and new capabilities of these systems. Since there are many different types and sizes of UAS, this paper will focus on drones 55 lb and smaller. Then some of the use cases of UAS for security and challenges of employing them will be covered. Finally, lessons learned and some best practices that Oak Ridge National Laboratory has discovered from research, development, testing, and evaluation will be summarized.

Stockwell, Brandon↗
DOE OSTI2024

Empowering Critical Infrastructure Communication with Secure 5G Private Networks

With the transformational New Radio- Unlicensed (NR-U), 5G network can be operated with unlicensed and shared spectrum. Private 5G networks without any licensed bands, which are both highly expensive and usually available to only large commercial wireless providers, can now be used for a whole range of new applications including smart factories, warehouses, connected cars and drones. 5G’s support of a) massive machine type communication (mMTC) for a large number of connected devices with b) ultra-reliable low latency communication (URLLC) capability when needed, and c) up to 20 times higher data rate with enhanced mobile broadband (eMBB) than previously available, enables new and powerful capabilities in a wireless network. While these capabilities are transformational, necessary security and reliability requirements have to be satisfied when used in critical infrastructure such as factories, power plants, water systems, ports, and other industrial facilities. 5G standards have introduced significant security improvement over 4G/LTE as well as mitigations for new attack surfaces created by changes in the 5G network. This talk will discuss these security improvements and whether they meet the security properties required for mission critical communication over wireless.

5G↗
DOE OSTI2020

Data Exploration of UAV Flights Recorded on Smartphones

The increasing accessibility and technological advancements of unmanned aerial vehicles (UAVs) pose legitimate concerns to nuclear non-proliferation efforts. To deter and combat unwanted or nefarious UAV activity near or around nuclear sensitive sites, the ability to quickly and easily detect drones becomes vital. Our team is exploring the use of ubiquitous smartphones as multi-modal sensors (microphone, accelerometer, gyroscope, etc.) to meet this challenge. In conjunction with Idaho National Lab, two UAV flight tests and one stationary in-air test were conducted and recorded with eight Samsung Galaxy S8 smartphones. This work presents initial characterization and annotation of acoustic signals associated with UAV activity.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

Resilient Communications LDRD Poster

This poster is for the LDRD Poster Session held at INL. The intent is to highlight the intent of the project which was to develop a resilient communication capability for the WSComm waveform. The poster shows notional pictures of communications being inhibited by a low power transmitter such as one found in modern drones. It then highlights interference tolerance performance and notionally shows that replay attacks may impede the signal. By implementing Transmission Security (TRANSEC), one can protect the waveform from such attacks.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Ensuring reliable connectivity to cellular-connected UAVs with up-tilted antennas and interference coordination

To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs.

3GPP↗
DOE OSTI2023

Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

We report high quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is often of limited availability, incomplete, or only accessible for a substantial fee or through a non-disclosure agreement. Recently, remotely sensed data (e.g., satellite imagery, aerial photography) have emerged as a potentially rich source of energy systems information. However, the use of these data is frequently challenged by its sheer volume and complexity, precluding manual analysis. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information from remotely sensed data, facilitating large-scale acquisition of critical energy system variables. Here we present a systematic review of the literature on this emerging topic, providing an in-depth survey and review of papers published within the past two decades. We first taxonomize the existing literature into ten major areas, spanning the energy value chain. Within each research area, we distill and critically discuss major features that are relevant to energy researchers, including, for example, key challenges regarding the accessibility and reliability of the methods. We then synthesize our findings to identify limitations and trends in the literature as a whole, and discuss opportunities for innovation. These include the opportunity to extend the methods beyond electricity to broader energy systems and wider geographic areas; and the ability to expand the use of these methods in research and decision making as satellite data become cheaper and easier to access. We also find that there are persistent challenges: limited standardization and rigor of performance assessments; limited sharing of code, which would improve replicability; and a limited consideration of the ethics and privacy of data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Sensor-Equipped Unmanned Surface Vehicle for High-Resolution Mapping of Water Quality in Low- to Mid-Order Streams

Longitudinal profiling of water quality via the deployment of sensors from watercraft has advanced the understanding of spatial patterns in large rivers and lakes; however, a similar approach in low- to mid-order streams is lacking. To fill this gap, we developed an unmanned surface vehicle (USV)-water quality measurement platform (the “AquaBOT”). The components of the AquaBOT included a nitrate sensor, multiparameter sonde (temperature, conductivity, turbidity, dissolved oxygen, chlorophyll), quantum sensor, and global positioning system (GPS) mounted to a small pontoon-style USV. The AquaBOT was tested in four streams and rivers in Iowa and Tennessee. All measured water quality parameters varied longitudinally, and greater ranges were generally observed along the low-order, agriculturally influenced streams in Iowa. Nitrate, in particular, was spatially heterogeneous. For example, during one run in early June, concentrations ranged from 10.5 to 12.5 mg N L –1 along a 2.3 km reach and hotspots were observed directly downstream of some tile drains. The spatial resolution of AquaBOT data collected in June was 10× higher than grab sampling data, and measurements were collected in less time and at a comparable cost. Here, the AquaBOT can complement existing measurement approaches and will lead to advancements in understanding the processes driving water quality along the stream-to-river continuum.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Dataset: Breaking the barrier of human-annotated training data for machine-learning-aided plant research using aerial imagery

This dataset supports the implementation described in the manuscript "Breaking the Barrier of Human-Annotated Training Data for Machine-Learning-Aided Biological Research Using Aerial Imagery." It comprises UAV aerial imagery used to execute the code available at https://github.com/pixelvar79/GAN-Flowering-Detection-paper. For detailed information on dataset usage and instructions for implementing the code to reproduce the study, please refer to the GitHub repository.

generative and adversarial learning↗