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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Bird’s Eye View: Development of an Operational ARM Unmanned Aerial Capability for Atmospheric Research in Arctic Alaska

Here, we present that unmanned aerial capabilities offer exciting new perspectives on the Arctic atmosphere and the US Department of Energy is working with partners to offer such perspectives to the research community. Thorough understanding of aerosols, clouds, boundary layer structure and radiation is required to improve representation of the Arctic atmosphere in weather forecasting and climate models. To develop such understanding, new perspectives are needed to provide details on the vertical structure and spatial variability of key atmospheric properties, along with information over difficult-to-reach surfaces such as newly-forming sea ice. Over the last three years, the US Department of Energy (DOE) has supported various flight campaigns using unmanned aircraft systems (UAS, also known as UAVs and drones) and tethered balloon systems (TBS) at Oliktok Point, Alaska. These activities have featured in-situ measurements of thermodynamic state, turbulence, radiation, aerosol properties, cloud microphysics and turbulent fluxes to provide a detailed characterization of the lower atmosphere. Alongside a suite of active and passive ground-based sensors and radiosondes deployed by the DOE Atmospheric Radiation Measurement (ARM) program through the third ARM Mobile Facility (AMF-3), these flight activities demonstrate the ability of such platforms to provide critically-needed information. In addition to providing new and unique datasets, lessons learned during initial campaigns have assisted toward the development of an exciting new community resource.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Can we use antipredator behavior theory to predict wildlife responses to high-speed vehicles?

Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle scenario. We addressed this gap by (a) providing an overview of the main hypotheses and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generate quantitative predictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed using a simulation approach wherein we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. The slope of the FID vs. approach speed relationship was then related back to three different behavioral rules animals may rely on to avoid approaching threats: the spatial, temporal, or delayed margin of safety. We used literature on birds for goals (b) and (c). Our review considered the following eight models: the economic escape model, Blumstein’s economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generate quantitative predictions about escape distance with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape behavior. We derive some applied applications of our findings by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high-speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will face in the next decade.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

UAS Edge Computing of Energy Infrastructure Damage Assessment

Energy infrastructure assessments are needed within 72 hours of natural disasters, and previous data collection methods have proven too slow. We demonstrate a scalable end-to-end solution using a prototype unmanned aerial system that performs on-the-edge detection, classification (i.e., damaged or undamaged), and geo-location of utility poles. The prototype is suitable for disaster response because it requires no local communication infrastructure and is capable of autonomous missions. Collections before, during, and after Hurricane Ida in 2021 were used to test the system. The system delivered an F1 score of 0.65 operating with a 2.7 s/frame processing speed with the YOLOv5 large model and an F1 score of 0.55 with a 0.48 s/frame with the YOLOv5 small model. Geo-location uncertainty in the bottom half of the frame was ~8 m, mostly driven by error in camera pointing measurement. With additional training data to improve performance and detect additional types of features, a fleet of similar drones could autonomously collect actionable post-disaster data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

TEAMER: Electrically Engaged Undulation System for Unmanned Underwater Vehicles

This TEAMER RFTS 1 (Request for Technical Support) project supported the flume tank testing of a long range, high endurance unmanned underwater vehicle (UUV) to monitor maritime space. Today, battery-powered remotely operated vehicles (ROVs) lack the duration to make persistent, wide-area data collection possible.The proposed solution, an Electrically Engaged UnduLation (EEL) drone, can sustain missions for longer duration through hydrodynamic energy harvesting. Power is provisioned via the piezoelectric effect, a material-led phenomenon that converts applied stress into electricity. The EEL subsystems include power, propulsion, navigation, ballast, telemetry, and instrumentation. By mimicking the gait of aquatic eels, EEL can counter currents during maneuvering and level-flight. The identified opportunity is in the future capability of extreme endurance UUVs in swarms. The specific goal for the EEL development is to expand the spatio-temporal coverage of the existing ocean observation mission by overcoming significant challenges of autonomous robotics. Some of the challenges presented include novel compliant mechanism for robust actuation, bio-inspired design to emulate efficient locomotion, smart material-based energy harvesting for sustained power, and swarming architecture through enabled autonomy.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Timeseries Photos of a Variably Inundated Stream: Umtanum Creek, Washington, United States

This dataset is associated with a broader study using game camera timeseries photos collected to evaluate stream variable inundation via changes in width (i.e. wet fraction). Four game cameras were deployed along Umtanum Creek (Washington, United States) to track changes in stream inundation over time. Drone imagery was collected at the same location on October 18, 2024 which was used to construct a digital elevation model (DEM) of the streambed topography. The associated paper and data can be found at https://doi.org/10.1016/j.envsoft.2025.106715 (Bao et al., 2025a)) and https://doi.org/10.15485/2589885 (Bao et al., 2025b), respectively. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to this readme, this data package also includes a file-level metadata (FLMD) files that describes each file and a data dictionaries (DD) that describe all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) field protocol; and (5) folders containing game camera photos. Game camera photos are organized into folders for each camera (CDL, CUL, CDR, CUR; see readme for information on camera naming) by the month photos were collected. All files are .csv, .jpg, or .pdf.

AI image segmentation↗
DOE OSTI2024

Utilizing 3D Mechanical Earth Models for Calibration and Validation in a Large-Scale Carbon Capture and Storage Project in North Dakota

Conference paper presented at 17th International Conference on Greenhouse Gas Control Technologies (GHGT-17), Calgary, Alberta, Canada, October 20–24, 2024. Three-dimensional (3D) mechanical earth models (MEMs) are pivotal in assessing geological sites for carbon dioxide (CO 2 ) storage and mitigating potential risks associated with storage and injection. The Energy & Environmental Research Center is exploring innovative carbon storage monitoring techniques at a CO 2 storage site near Beulah, North Dakota. These methods aim to provide lower-impact, faster feedback for commercial carbon capture and storage (CCS) projects. The research includes monitoring CO 2 injection using various approaches: 1) an automated, integrated modular monitoring station; 2) time-lapse electromagnetic (EM) field surveys; 3) drone-based surveillance; 4) time-lapse seismic techniques; and 5) advanced wellbore monitoring.

02 PETROLEUM↗
DOE OSTI2017

Final Technical Report: The Incubation of Next-Generation Radar Technologies to Lower the Cost of Wind Energy

The National Wind Institute (NWI) at Texas Tech University (TTU) has had an impressive and well documented 46-year history of wind related research activities (http://www.depts.ttu.edu/nwi/). In 2011 with funding from the United States Department of Energy (DOE), an NWI team applied radar technologies and techniques to document the complex flows occurring across a wind plant. The resulting efforts yielded measurements that exceeded the capabilities of commercial lidar technologies with respect to maximum range, range resolution and scan speed. The NWI team was also the first to apply dual-Doppler synthesis and objective analysis techniques to resolve the full horizontal wind field (i.e. not just the line-of-sight wind speeds) to successfully define turbine inflow and wake flows across large segments of wind plants. While these successes advanced wind energy interests, the existing research radar platforms were designed to serve a diversity of meteorological applications, not specifically wind energy. Because of this broader focus and the design choices made during their development, the existing radars experienced technical limitations that inhibited their commercial viability and wide spread adoption. This DOE project enabled the development of a new radar prototype specifically designed for the purpose of documenting wind farm complex flows. Relative to other “off the shelf” radar technologies, the specialized transmitter and receiver chains were specifically designed to enhance data availability in non-precipitating atmospheres. The new radar prototype was integrated at TTU using components from various suppliers across the world, and installed at the Reese Technology Center in May 2016. Following installation, functionality and performance testing were completed, and subsequent comparative analysis indicated that the new prototype greatly enhances data availability by a factor of 3.5-50 in almost all atmospheric conditions. The new prototype also provided enhanced signal quality in clear air (i.e. non-precipitating) environments, mitigated atmospheric attenuation, and extended the useful range of data collection to beyond 30km in cooperative atmospheric conditions. Additionally, the new DOE-X prototype appears to benefit from Bragg scattering when the thermal stratification of the atmosphere is strong (i.e. nocturnal hours). This result was not possible in any capacity with the previous technology. Combined, these developments represent the achievement of all project objectives, advance the Technical Readiness Level (TRL) to a level of 7, and open the door for more widespread adoption and usage in the wind energy sector. At the same time, radar induced artifacts from multi-trip echoes and ground targets increased with the new technology, and these required additional attention for some applications. Commercialization activity accelerated in parallel with the DOE funded project, as SmartWind Technologies, L.L.C., was contracted to provide two new radar systems to DONG Energy to monitor the Westermost Rough wind plant off the east coast of the United Kingdom. These new early stage commercial radars systems were closely related to the DOE prototype, but maintain more robust ancillary support systems. The radars were installed during the summer of 2016, and have been operational since that time. Additionally, commercially funded advancements have since been made in processing sophistication to mitigate the previously identified radar artifacts. TTU and SmartWind Technologies stand ready to provide public and private partners focused on wind plant optimization with this new capability. Ancillary interests have also been identified as the initial deployments of the technology have shown the ability to identify and track avian and drone activity, opening up multi-purpose operational opportunities.

17 WIND ENERGY↗
DOE OSTI2017

Proposed Methodology for a Tour-Based Freight Model

This study is part of the US Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility initiative. It is included in the Multi-Modal pillar of SMART Mobility and is titled: Optimization of Intra-City Freight Movement and New Delivery Methods. The primary purpose of this study is to analyze the energy reduction opportunities provided by new freight modes and technologies. This will be done by developing a tour-based freight model for Columbus, Ohio. This model will be used to analyze the energy and emission savings of alternative technologies including drones, central delivery lockers, and the use of electric vehicles for last-mile freight deliveries. The tour-based model will also be used to examine the growth and distribution of freight traffic and its impacts on the local transportation system. Development of this model will be coordinated with the Mid-Ohio Regional Planning Commission (MORPC), the Metropolitan Planning Organization (MPO) for the Columbus, Ohio area.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Polarized Radar for Detection and Automatic Non-Visual Assessment of Unmanned Aerial Systems

This Laboratory Directed Research and Development (LDRD) effort performed fundamental Research and Development (R&D) to develop a robust radar processing algorithm capable of assessing the difference between an Unmanned Aerial System (UAS) and a biological target such as a bird, based on mathematics applied to the polarized radar returns of the target object, alone. The current threats of using such a UAS as a delivery platform for a host of destructive components is a major concern for the protection of various assets. Most recently, on 14th Sept. 2019, dozens of suicide or kamikaze drones (UAV-X) coordinated an attack on two Saudi oil facilities that demonstrated the potential to disrupt global oil supplies. While radar-based UAS detection systems can detect UAS at ranges greater than 1-km, the issues of excessive Nuisance/False Alarm Rates (NAR/FAR) from natural sources (birds in particular) has not been sufficiently addressed. In this effort we describe and utilize the Adaptive Polarization Difference Imaging-based (APDI) algorithms for the detection and automatic non-visual assessment of Unmanned Aerial System applications. Originally developed for optical imaging and sensing of polarization information in nature, the algorithms developed here are modified to serve for the target detection purposes in counter-UAS (cUAS) environments. We exploit the polarization statistics of the observing scene for detection and identification of changes within the scene and assess from these changes for UAS/bird classifications. Several cases are considered from independent data sources, including numerically generated data, anechoic chamber data as well as experimental radar data, to show the applicability of the techniques developed here. The methods developed in this effort are designed to be used in cUAS setups but have shown promise for a multitude of other radar-based classification uses as well.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Cybersecurity for Distance Relay Protection

This project is a DOE follow-up effort on the CREDC workshop held on September 13, 2018 in Cambridge, MA to discuss cybersecurity of distance relays, which considered the benefits, vulnerabilities and risk mitigations for the use of communication systems in power system protection. The objectives of this project are to define the taxonomy of relay protection and associated communications; define use cases describing approaches to reduce the cyber-attack surface on those protective relays; and evaluate the loss of operational functional capability from changes to communication coverage. Mitigating controls will also be evaluated to understand if there are other approaches to reduce attack surfaces while maintaining communications or partial communications. Distance relays are used to protect transmission lines of approximately 10 to 300 miles in length, by detecting short circuits (i.e., faults) on the lines and then tripping circuit breakers in the substation. Such protection systems are a subset of the power system and they incorporate sensing, logic and communication functions. Protection system exposure to cyberattack could be drastically limited by disconnecting relays from all vulnerable communication systems, but this may adversely impact overall power system performance in the absence of cyberattack. This project began with a use case analysis of protection systems with communications, as summarized in this report. It continued with modeling, testing and evaluation in a miniature power system (MPS), located in the Western Area Power Administration (WAPA) Electric Power Training Center (EPTC). The project also incorporated feedback from two industry meetings held in February and September 2019. The suggested next steps account for and complement the work already underway with DOE/CESER funding: 1. Study the performance of LCD and PC vs. PUTT, which is less reliant on communication system performance and GPS timing references. The PUTT scheme could prove to be more resilient to cyberattack or communications-related disruption. It could also be more tolerant of message re-routing with SDN/SDR communication systems. On the other hand, it will be more vulnerable to false tripping during dynamic events or to loss of the voltage signal. The optimum choice of scheme may depend on the specific power system and risk assessment. This study could provide a new template for evaluation based on business functions. 2. Research and develop new methods to detect and monitor distributed physical attacks, possibly using drones, video sensors, thermal sensors, machine learning and other advanced techniques. This will help mitigate the impact of cyberattack on the protection system, and will also help mitigate the impact of wild fires. 3. Implement a scalable PKI for use in electric utility protection systems. This will encourage widespread adoption of secure authentication methods that are already available, but not widely used at present. This will help secure engineering access to the relays. 4. Investigate the use of SDN in combination with SDR to achieve better cybersecurity and electromagnetic security of the network, incorporating path variability. This would help secure both engineering access and peer-to-peer GOOSE messaging. 5. Perform additional testing, with operator evaluation of “red button” scenarios, PUTT vs. LCD, relay mis-operations, and other cyberattacks in the EPTC. This is an important advantage of testing in the EPTC rather than by computer simulation or even hardware-in-the-loop simulation; the EPTC is already dedicated to managing the situational awareness, operator response times and other human impacts. One of the project objectives was to settle on a common nomenclature for this problem space. We have concluded that the OSI layer model, supplemented by ANSI device numbers and other IEEE standards, is already well-accepted by the industry. The IEEE PSRC knowledge base provides a great deal of public information

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Radiation Mapping UAV [Poster]

The NuMI (Neutrinos at the Main Injector) target system delivers a high-energy flux of muon-neutrinos towards a detector more than 800 km away with the goal of better understanding the origin of the universe and its evolution. The target system becomes radioactive because it is irradiated by a sub-Mega-Watt power proton beam. Radiological hazard is one of critical issues for a radiological worker which limits the iractivities near the target system; however, they often need to quantify the radiation level of the target system manually. Thus, the objective of this project is to reduce their exposure to radiation while improving the accuracy of the measurements. A quadcopter UAV fitted with a radiation dosimeter is ideal to traverse the irradiated environment as it can be deployed in any space and requires minimal hardware integration to the test room. Due to the lack of indoor GPS signal, this UAV utilizes a series of ultrasonic beacons that release several varied-frequency pulses simultaneously in order to localize the drone. The flight controller returns a 3D coordinate tied to the radiation data to understand precisely where radiation spikes are being emitted from the horn. The flight controller stores log data that can be processed into an easily interpretable map of radiation intensities. The radiation map serves as one of the primary references for planning the transportation of the NuMI horn.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Counter Unmanned Aircraft System (CUAS) Implementation Storyline

This report is an FY21 Deliverable for NA-21.1, International Nuclear Security, WBS 21.1.1.1.4 Counter Unmanned Aircraft Systems (CUAS), under C.1.4, Develop Suite of Engagement Materials and Tools for Country/Regional Teams. The responsibility for protecting nuclear assets is an immense challenge for governments. Policy changes based on geopolitics, instability of government regimes, and technological advances that facilitate theft of nuclear material all contribute to the enduring task of staying current with these and other threats. One of the recent threats to come along is the use of drones or Unmanned Aircraft Systems (UAS) to circumvent traditional Physical Security Systems. There are significant resources invested on physical protection systems for securing nuclear assets, but many of these systems can now be bypassed by simply flying over these measures intended to guard against ground attacks. In recent years the threat increased to hostile groups to attack or surveil due to advances and availability of UAS platforms. It is now more urgent to implement systems and procedures to counter the UAS threat. The United States (U.S.) federal government granted authority to the Department of Energy (DOE) to implement a Counter UAS to protect nuclear assets. This document provides a very practical and transparent view of the trials, tribulations and challenges we encountered while implementing the first Counter-Unmanned Aircraft System (CUAS) Program in the DOE at Los Alamos National Laboratory (LANL).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Hydrogen Contamination Detector: Protecting the future of zero-emission fuel cell energy [Brief]

Los Alamos National Laboratory developed an electrochemical detector to protect zero-emission hydrogen fuel cells from contaminated fuels. Fuel cells power environmentally clean vehicles, fork lifts, drones, and provide auxiliary power. High-purity hydrogen is essential to avoid poisoning fuel cells. Current analysis methods are costly and cannot detect impurities in realtime. The Hydrogen Contamination Detector measures the two highest impact Department of Energy-identified impurities with a simple, low-cost unit that provides 24/7, point-of-service analysis. Los Alamos partnered with H2Frontiers to conduct field trials at hydrogen refueling stations and with Skyre LLC for commercialization through a DOE Technology Commercialization Fund project.

08 HYDROGEN↗
DOE OSTI2018

GMLC Southeast Regional Consortium: Technology Driven Improvements to Power Grid Resiliency

Recognizing that electric grid challenges can differ by geographical regions across the US, the Department of Energy (DOE) created a pioneer regional partnership to provide technical assistance to stakeholders facing grid modernization challenges. The southeast regional teams will transfer technologies developed under DOE auspices to stakeholders for testing, evaluation, and market validation. Regional members will help define critical challenges and pave the way for transition of technologies to the commercial sector. Using the depth and expertise at Oak Ridge National Laboratory, Savannah River National Laboratory, University of Tennessee’s CURENT, CAPER and the Clemson 20 MVA Electrical Grid Laboratory—along with critical industry partners, such as Duke Energy, TVA, Southern Company, EPB, and Santee Cooper—the Southeast Regional Consortium team will conduct collaborative research to advance the state of the grid in the Southeast region and improve power system resiliency to enhance responsiveness, enable faster restoration of power, and increase distributed energy resource (DER) concentrations. This report provides details of the technical research and demonstration projects accomplished by Oak Ridge National Laboratory (ORNL) as part of the Southeast Regional Consortium. Specifically, ORNL analyzed the application of optical current and voltage measurements impedance relays which would enable step distance protection to be deployed on distribution systems. This would improve the precision of determining the geographic location of faults, increase network topology flexibility, and allow bi-directional power flow. Next, ORNL deployed time sensitive networking technology on the EPB fiberoptic data network to study the impact of increased sensor traffic on existing supervisory control and data acquisition data. The deterministic latency and high bandwidth were demonstrated by remotely inspecting a substation using a drone with full HD streaming video operated at the EPB Control Center. Finally, a new application-based communication and control system developed at ORNL call CSEISMIC was tested for future deployment on the EPB Control Center microgrid using a hardware-in-the-loop testbed that was also developed at ORNL. The controller was able to successfully optimize ancillary service when the microgrid is connected to the distribution network. The seamless islanding, resynchronization, and reconnection of the microgrid was also demonstrated through the testing.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
DOE OSTI2021

Section 110 Documentation of a Douglas AD-2 Skyraider Crash Site, Area 1, Nevada National Security Site, Nye County, Nevada

The National Nuclear Security Administration, Nevada Field Office has undertaken a Section 110 current condition assessment of the unanticipated discovery of an aviation crash site in Nye County, Nevada. Because no undertaking is planned, an area of potential effect (APE) was not defined. A study area of 0.55 acres was established to delineate the general extent of the debris scatter and establish a boundary for the cultural resource documentation of the site by Desert Research Institute (DRI). The Douglas AD-2 Skyraider drone crash site (26NY17011) is recommended eligible for the National Register of Historic Places (NRHP) under Significance Criterion A, as a physical remnant of Project 5.1 “Atomic Weapons Effects on AD Type Aircraft in Flight,” one of the series of military effects studies conducted as part of Shot Simon during Operation Upshot-Knothole in 1953. Furthermore, the site is eligible under Criterion D because closer examination of the debris could address research questions concerning the Type 1 Remote Control Equipment developed by the Naval Air Experimental Station that was onboard to remotely pilot the aircraft.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Adapting Secure MultiParty Computation to Support Machine Learning in Radio Frequency Sensor Networks

In this project we developed and validated algorithms for privacy-preserving linear regression using a new variant of Secure Multiparty Computation (MPC) we call "Hybrid MPC" (hMPC). Our variant is intended to support low-power, unreliable networks of sensors with low-communication, fault-tolerant algorithms. In hMPC we do not share training data, even via secret sharing. Thus, agents are responsible for protecting their own local data. Only the machine learning (ML) model is protected with information-theoretic security guarantees against honest-but-curious agents. There are three primary advantages to this approach: (1) after setup, hMPC supports a communication-efficient matrix multiplication primitive, (2) organizations prevented by policy or technology from sharing any of their data can participate as agents in hMPC, and (3) large numbers of low-power agents can participate in hMPC. We have also created an open-source software library named "Cicada" to support hMPC applications with fault-tolerance. The fault-tolerance is important in our applications because the agents are vulnerable to failure or capture. We have demonstrated this capability at Sandia's Autonomy New Mexico laboratory through a simple machine-learning exercise with Raspberry Pi devices capturing and classifying images while flying on four drones.

42 ENGINEERING↗
DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗