Search NASA⌕ Search

SEARCH · Search NASA

Results for “Data Telemetry”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Methods and systems for evaluating data transportability in distribution grids

This document describes systems and techniques for evaluating and improving distribution-grid data transportability. These systems and techniques allow engineers to quantify the data transportability of a communication system within or connected to a distribution grid, which represents an ability to transport in real-time telemetry from source locations (e.g., sensors in the distribution grid) to control mechanisms. Distribution engineers can use the sensor readings to perform grid analytics, control operating parameters, and operate protection systems. Distribution engineers can also use the transportability of the communication system to evaluate the observability of the distribution grid, which represents an ability to combine actual measurements and various types of computations (e.g., analytics, estimators, forecasters) from a system model. Distribution engineers can then generate a sensor allocation plan that indicates the number and location of sensors to maximize observability for a fixed sensor cost and/or minimize sensor cost for predetermined observability.

Taft, Jeffrey D.↗
DOE OSTI2013

ACCURATE CHARACTERIZATION OF HIGH-DEGREE MODES USING MDI OBSERVATIONS

We present the first accurate characterization of high-degree modes, derived using the best Michelson Doppler Imager (MDI) full-disk full-resolution data set available. A 90 day long time series of full-disk 2 arcsec pixel{sup -1} resolution Dopplergrams was acquired in 2001, thanks to the high rate telemetry provided by the Deep Space Network. These Dopplergrams were spatially decomposed using our best estimate of the image scale and the known components of MDI's image distortion. A multi-taper power spectrum estimator was used to generate power spectra for all degrees and all azimuthal orders, up to l = 1000. We used a large number of tapers to reduce the realization noise, since at high degrees the individual modes blend into ridges and thus there is no reason to preserve a high spectral resolution. These power spectra were fitted for all degrees and all azimuthal orders, between l = 100 and l = 1000, and for all the orders with substantial amplitude. This fitting generated in excess of 5.2 Multiplication-Sign 10{sup 6} individual estimates of ridge frequencies, line widths, amplitudes, and asymmetries (singlets), corresponding to some 5700 multiplets (l, n). Fitting at high degrees generates ridge characteristics, characteristics that do not correspond to the underlying mode characteristics. We used a sophisticated forward modeling to recover the best possible estimate of the underlying mode characteristics (mode frequencies, as well as line widths, amplitudes, and asymmetries). We describe in detail this modeling and its validation. The modeling has been extensively reviewed and refined, by including an iterative process to improve its input parameters to better match the observations. Also, the contribution of the leakage matrix on the accuracy of the procedure has been carefully assessed. We present the derived set of corrected mode characteristics, which includes not only frequencies, but line widths, asymmetries, and amplitudes. We present and discuss their uncertainties and the precision of the ridge-to-mode correction schemes, through a detailed assessment of the sensitivity of the model to its input set. The precision of the ridge-to-mode correction is indicative of any possible residual systematic biases in the inferred mode characteristics. In our conclusions, we address how to further improve these estimates, and the implications for other data sets, like GONG+ and HMI.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Evaluation of E3SM land model snow simulations over the western United States

Abstract. Seasonal snow has crucial impacts on climate, ecosystems, and humans, but it is vulnerable to global warming. The land component (ELM) of the Energy Exascale Earth System Model (E3SM) mechanistically simulates snow processes from accumulation, canopy interception, compaction, and snow aging to melt. Although high-quality field measurements, remote sensing snow products, and data assimilation products with high spatio-temporal resolution are available, there has been no systematic evaluation of the snow properties and phenology in ELM. This study comprehensively evaluates ELM snow simulations over the western United States at 0.125∘ resolution during 2001–2019 using the Snow Telemetry (SNOTEL) in situ networks, MODIS remote sensing products (i.e., MCD43 surface albedo product), the spatially and temporally complete (STC) snow-covered area and grain size (MODSCAG) and MODIS dust and radiative forcing in snow (MODDRFS) products (STC-MODSCAG/STC-MODDRFS), and the snow property inversion from remote sensing (SPIReS) product and two data assimilation products of snow water equivalent and snow depth – i.e., University of Arizona (UA) and SNOw Data Assimilation System (SNODAS). Overall the ELM simulations are consistent with the benchmarking datasets and reproduce the spatio-temporal patterns, interannual variability, and elevation gradients for different snow properties including snow cover fraction (fsno), surface albedo (αsur) over snow cover regions, snow water equivalent (SWE), and snow depth (Dsno). However, there are large biases of fsno with dense forest cover and αsur in the Rocky Mountains and Sierra Nevada in winter, compared to the MODIS products. There are large discrepancies of snow albedo, snow grain size, and light-absorbing particle-induced snow albedo reduction between ELM and the MODIS products, attributed to uncertainties in the aerosol forcing data, snow aging processes in ELM, and remote sensing retrievals. Against UA and SNODAS, ELM has a mean bias of −20.7 mm (−35.9 %) and −20.4 mm (−35.5 %), respectively, for spring, and −13.8 mm (−27.8 %) and −10.2 mm (−22.2 %), respectively, for winter. ELM shows a relatively high correlation with SNOTEL SWE, with mean correlation coefficients of 0.69 but negative mean biases of −122.7 mm. Compared to the snow phenology of STC-MODSCAG and SPIReS, ELM shows delayed snow accumulation onset dates by 17.3 and 12.4 d, earlier snow end dates by 35.5 and 26.8 d, and shorter snow durations by 52.9 and 39.5 d, respectively. This study underscores the need for diagnosing model biases and improving ELM representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Adaptive Cybersecurity for Distributed Energy Resources (AdCyDER): Online Reinforcement Learning with Stackelberg-Optimized Defenses — Pipeline Architecture, Evaluation Methodology, and Findings from a Synthetic-Data Evaluation

This report documents the design and evaluation of an integrated online-learning pipeline developed within the AdCyDER project for Distributed Energy Resource (DER) cybersecurity. The pipeline couples a Reinforcement Learning (RL) attack classifier — which produces an attack-type probability distribution — with a Stackelberg game-theoretic (GT) defense selector that consumes those distributions alongside SME-encoded priors over (defense, attack) effectiveness pairings and perdefense costs to choose grid-health-preserving defenses. The objective is not attack classification per se but production of distributions that drive effective defense selection through the Stackelberg layer, learned from delayed grid-health feedback rather than labeled attack data. AdCyDER as a whole is broader than the work presented here; this report covers the specific RL/GT loop integration and its evaluation. We present the integrated pipeline (SCADA telemetry with Fronius inverter physics, Suricata IDS, time-windowed aggregation, per-facility LSTM classifier, Stackelberg optimizer, OpenC2 actuators), an experimental campaign of 28 eight-hour iterations across three baseline modes, and a pipeline-ordered diagnostic protocol. The protocol identifies two distinct failure modes within the loop: paired supervised ceilings on the same features establish that the deployed online RL classifier (macro F1 ≈ 0.07) sits at least 4.7× below a same-architecture supervised LSTM (≈ 0.34) and 10–11× below a linear feature-signal ceiling (≈ 0.70–0.79 depending on per-facility isolation), localizing the dominant failure to the training procedure; and the reward signal driving online updates carries weak directional coupling with classifier correctness in the methodology-expected direction (multi-lens convergent: top-decile P(true) records produce more frequent state changes and slightly larger improvements, top-vs-bot Cohen’s 𝑑 ≈ −0.19), but at effect magnitudes too small to drive gradient-based learning at the campaign sample size. The original learning hypothesis is not supported by the data. The primary contributions are the diagnostic methodology — proposed as a transferable falsification protocol for online RL/GT defense pipelines learning from delayed environmental reward — and the open, reproducible experimental infrastructure. We outline reward reformulation as the highest-priority aspirational next step given the underpowered-but-aligned Q6 reading, with hardware-in-the-loop evaluation as the broadest scope-expansion option.

Blakely, Benjamin [Argonne National Laboratory (AN↗
DOE OSTI2022

EJFAT: Towards Intelligent Compute Destination Load Balancing

To handle increased data flow, Jefferson Lab (JLab) is partnering with ESnet for development of an AI/ML directed compute work Load Balancer (LB) of UDP streamed data. The LB is FPGA based featuring dynamically configurable, low latency and high throughput destination address switching. The LB provides integration of edge and core computing to support JLab experimental programs, the Electron-Ion Collider, as well as data centers of the future. In the ESnet/JLab FPGA Accelerated Transport (EJFAT) initiative, the function of the LB Data Plane (DP) is to redirect data streams to selectable (but unknown to sender) destination hosts based on current worload and within that host to destination ports as a function of sub- stream id. This effects hierarchical scaling, first across compute machines for processing over a series of events and second, across ports so different data source sub-streams may be assigned to different processors for further parallelization. The LB Control Plane (CP) programs the DP using compute farm telemetry to direct and balance workloads across a compute cluster as the operating conditions require. While Proportional/Integrative/Derivative (PID) controllers are often seen in similar applications, here we investigate the feasibility of a Reinforcement Learning (RL) based schedule manager running in the CP to provide dynamic updates to the DP scheduling policy.

Lawrence, David↗
DOE OSTI2012

COMMENTS ON THE SEARCH FOR ELECTROSTATIC DISCHARGES ON MARS

Ruf et al. used the Deep Space Network (DSN) to search for the emission of non-thermal radiation by martian dust storms, theoretically predicted by Renno et al. They detected the emission of non-thermal radiation that they were searching for, but were surprised that it contained spectral peaks suggesting modulation at various frequencies and their harmonics. Ruf et al. hypothesized that the emission of non-thermal radiation was caused by electric discharges in a deep convective dust storm, modulated by Schumann resonances (SRs). Anderson et al. used the Allen Telescope Array (ATA) to search for similar emissions. They stated that they found only radio frequency interference (RFI) during their search for non-thermal emission by martian dust storms and implicitly suggested that the signal detected by Ruf et al. was also RFI. However, their search was not conducted during the dust storm season when deep convective storms are most likely to occur. Here, we show that the ubiquitous dust devils and small-scale dust storms that were instead likely present during their observations are too shallow to excite SRs and produce the signals detected by Ruf et al. We also show that the spectral and temporal behavior of the signals detected by Anderson et al. corroborates the idea that they originated from man-made pulse-modulated telecommunication signals rather than martian electric discharges. In contrast, an identical presentation of the signals detected by Ruf et al. demonstrates that they do not resemble man-made signals. The presentation indicates that the DSN signals were consistent with modulation by martian SRs, as originally hypothesized by Ruf et al. We propose that a more comprehensive search for electrostatic discharges be conducted with either the ATA or DSN during a future martian dust storm season to test the hypothesis proposed by Ruf et al.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Ecological interactions between hatchery summer steelhead and wild Oncorhynchus mykiss in the Willamette River basin, 2014

The purpose of this study was to determine the extent to which juvenile hatchery summer steelhead and wild winter steelhead overlap in space and time, to evaluate the extent of residualism among hatchery summer steelhead in the South Santiam River, and to evaluate the potential for negative ecological interactions among hatchery summer steelhead and wild winter steelhead. Because it is not possible to visually discern juvenile winter steelhead from resident rainbow trout, we treated all adipose-intact juvenile O. mykiss as one group that represented juvenile wild winter steelhead. The 2014 study objectives were to 1) estimate the proportion of hatchery summer steelhead that residualized in the South Santiam River in 2014, 2) determine the extent to which hatchery and naturally produced O. mykiss overlapped in space and time in the South Santiam River, and 3) characterize the behavioral interactions between hatchery-origin juvenile summer steelhead and naturally produced O. mykiss. We used a combination of radio telemetry and direct observations (i.e., snorkeling) to determine the potential for negative interactions between hatchery summer and wild winter steelhead juveniles in the South Santiam River. Data collected from these two independent methods indicated that a significant portion of the hatchery summer steelhead released as smolts did not rapidly emigrate from the South Santiam River in 2014. Of the 164 radio-tagged steelhead that volitionally left the hatchery, only 66 (40.2%) were detected outside of the South Santiam River. Forty-four (26.8% of 164) of the radio-tagged hatchery summer steelhead successfully emigrated to Willamette Falls. Thus, the last known location of the majority of the tagged fish (98 of 164 = 59.8%) was in the South Santiam River. Thirty-three of the tagged hatchery steelhead were detected in the South Santiam River during mobile-tracking surveys. Of those, 21 were found to be alive in the South Santiam River over three months after their release, representing a residualization rate of 12.8% (21 of 164). Snorkeling revealed considerable overlap of habitat use (in space and time) by residual hatchery steelhead and naturally produced O. mykiss in the South Santiam River. Results from our study (and others) also indicated that hatchery steelhead juveniles typically dominate interactions with naturally produced O. mykiss juveniles. The overlap in space and time, combined with the competitive advantage that residual hatchery steelhead appear to have over naturally produced O. mykiss, increases the potential for negative ecological interactions that could have population-level effects on the wild winter steelhead population of the South Santiam River.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Final Technical Report Wireless Microsensors System for Monitoring Deep Subsurface Operations

This final technical report describes the main findings of the project Wireless Microsensors System for Monitoring Deep Subsurface Operations (FE0031850). The project was part of the U.S. Department of Energy National Energy Technology Laboratory FOA 1998 program to develop new sensor systems for direct observation of parameters associated with CO2 injection and to provide data collection without being disruptive to operations. The overall DOE program was aimed at developing and validating innovative transformational sensor systems, amenable for integration with autonomous intelligent monitoring systems, that are capable of being deployed within the casing annulus and do not have casing perforation or wires/cables in the annulus for installation, power supply, or data transmission needs. Project accomplishments included 1) design and fabrication of a wireless downhole sensor system to monitor parameters for CO2 storage, 2) field testing of the sensor system in two legacy oil & gas wells, and 3) development of an analysis approach that validates the measurements and demonstrates the application of the technology to depict CO2 movement in the subsurface. The project leveraged new sensor technologies along with specialized wellbore telemetry, deployment, and analysis methods designed to address the challenges and risks related to CO2 storage in the subsurface. Results from field testing were a mixture of successes and challenges. The temperature sensor rings, installation procedures in legacy oil & gas wells, wireless powering demonstration, automated data collection, and material compatibility were successful. The wireless data transfer through cement to the wellhead via the sensor relays was not functional beyond the first relay. Consequently, work in the last year of the project included some additional testing of data transmission through different materials along with modeling and analysis of field data for CO2 monitoring applications. This work suggested there are options like polymer cements and open hole annuli that may allow point-to-point transmission along the borehole. The techno-economic analysis suggests that the sensor system is ~40% less expensive than fiber optic distributed temperature system. Modeling of CO2 storage applications suggests temperature can provide an indicator of CO2 saturation but would be best combined with pressure sensors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Light-Level Geolocation of the LANL Population of Western Bluebirds

Options for tracking and reconstructing animal movement are increasingly accessible due to rapidly decreasing costs, smaller sizes, and a proliferation of analytical inference techniques (Rutz and Hays, 2009; Wikelski et al., 2007). A wide variety of options exists for determining movement patterns of animals—from high-resolution pinpoint ARGOS satellite tags to time-consuming and logistically challenging radio telemetry. One option, light-level geolocation, offers small, affordable devices with long battery lives known as global location sensors (GLS), making them ideal for gathering preliminary migratory data. Geolocation works by inferring patterns of animal movement from light-level transitions between day and night (Hill and Braun, 2001). The recent advances in tracking technologies allow biologists to zoom in on migratory behavior, delineating heretofore-unidentified intraspecific migratory behavior (Delmore et al., 2012). The ubiquity of migratory divides (populations within a species that exhibit different migratory patterns) remains largely unknown, but their existence can lead to favorable demographic metrics (e.g., genetic diversity) in conservation contexts (Møller et al., 2011) and necessitate the development of population-specific conservation plans (Delmore et al., 2012). Delimiting the populations within a species that migrate and the extent of their migrations relative to individuals that remain resident across a species’ range has important evolutionary, ecological, and conservation implications. Within this context, Los Alamos National Laboratory (LANL) biologists have leveraged the local Avian Nestbox Network (ANN; Fair and Myers, 2002) to evaluate the migratory behaviors of a common local bird species, the Western Bluebird (Sialia mexicana).

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Factors affecting powerhouse passage of spring migrant smolts at federally operated hydroelectric dams of the Snake and Columbia rivers

From 2008 to 2018, acoustic telemetry studies were conducted to evaluate dam passage survival of spring migrant Chinook salmon and steelhead smolts at seven of the eight federally operated dams on the lower Snake and Columbia rivers. Data from over 87 000 dam passage events were evaluated using regression modeling to identify the effect of spill operations, environmental conditions, and fish characteristics on powerhouse passage probability. In general, powerhouse passage was positively correlated with discharge, negatively correlated with forebay temperature and fish size, and higher for fish that passed the dam at night and for those that approached from the powerhouse side of the river, suggesting powerhouse passage is largely a function of smolt activity level and swimming ability. As such, spilling large volumes of water to reduce powerhouse passage is likely to be most effective during times of reduced activity and swimming ability (e.g., at night, high flows, and cold temperatures). This information can be used to develop dam- and time-specific spill operations that optimize smolt passage, power generation, and other competing demands, such as adult passage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Hydrologic Model Files (PRMS) for Historical Conditions in the East River Watershed, Colorado between 1987-2019

The data package contains model input and output file for the East River watershed (85 km2) located in the headwaters of the Colorado River. The model is the U.S. Geological Survey (USGS) Precipitation-Modeling Runoff System (PRMS) for the historical, or baseline, condition (1987-2019) to assess the efficiency of summer monsoon rains to generate streamflow. The finite difference grid resolution is 100 m with elevations resampled from the USGS National Elevation Dataset. LANDFIRE raster data is used to derive parameters of dominant cover type, summer and winter cover density, canopy interception characteristics for snow and rain and transmission coefficient for shortwave radiation. Climate forcing uses minimum and maximum daily temperature lapse rates defined by the two Snow Telemetry (SNOTEL) stations located in the vicinity of the East River adjusted for aspect. Schofield SNOTEL snowfall is spatially distributed using LiDAR derived snow depth observations from the Airborne Snow Observatory (ASO) flown 4 April 2016. Snow depths are converted to snow water equivalent based on ground surveys and density modeling. Rainfall is spatially distributed using the monthly Parameter-elevation Relationships on Independent Slopes Model 30-year monthly averages. Simulated solar radiation is calibrated to match weather station observations. Model verification of snow water equivalent (SWE) accumulation and ablation relies on the 2018 and 2019 ASO maps and weather station snow depth. The data package contains model input files in text format, model documentation in pfd and a readme.txt file to help users understand the file structure to run the program.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains

Abstract In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high‐resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station‐based SWE observations are used as the prediction target. All DL models produce higher median Nash‐Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

eCounter: Inline Per-IP Network Monitoring at Millisecond Resolution via eBPF

Scientific data acquisition (SciDAQ) systems are shifting from archive-based workflows to streaming paradigms, where real-time, fine-grained network monitoring becomes essential. While P4-enabled devices offer per-packet in-band observability, they require specialized switches and routers. Host-side tools like Prometheus exporters lack sufficient temporal granularity. To bridge this gap, we present eCounter, a lightweight, hardware-agnostic, inline telemetry agent built on extended Berkeley Packet Filter (eBPF). eCounter captures per-interface ingress and egress traffic, categorized by IP address and protocol, at millisecond to sub-millisecond resolution. In a 100 Gbps environment, it continuously exports up to 3,257 time-series bins per second with only 4% CPU utilization at a 35¿KiB/s data rate. We evaluate eCounter across diverse NIC MTU settings, hook types, CPU architectures and operating systems, and observed negligible impact on concurrent high-throughput streaming applications. Complexity analysis confirms that it can be readily scaled to distributed SciDAQ deployments.

Mei, Xinxin [Computational Sciences and Technology↗
DOE OSTI2016

Comparison of Actual Costs to Integrate Commercial Buildings with the Grid

During the past decade, the technology to automate demand response (DR) in buildings and industrial facilities has advanced significantly. Automation allows rapid, repeatable, reliable operation. This study focuses on costs for DR automation in commercial buildings with some discussion on residential buildings and industrial facilities. DR automation technology relies on numerous components, including communication systems, hardware and software gateways, standards-based messaging protocols, controls and integration platforms, and measurement and telemetry systems. This paper discusses the impact factors that contribute to the costs of automated DR systems, with a focus on OpenADR 1.0 and 2.0 systems. In addition, this report compares cost data from several DR automation programs and pilot projects, evaluates trends in the cost per unit of DR and kilowatts (kW) available from automated systems, and applies a standard naming convention and classification or taxonomy for system elements. In summary, median costs for the 56 installed automated DR systems studied here are about $200/kW. The deviation around this median is large with costs in some cases being an order of magnitude greater or less than median. Costs to automate fast DR systems for ancillary services are not fully analyzed in this report because additional research is needed to determine the total such costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

JSATS Decoder Software Manual

The Juvenile Salmon Acoustic Telemetry System (JSATS) Decoder is a software application that converts a digitized acoustic signal (a waveform stored in the .bwm file format) into a list of potential JSATS Acoustic MicroTransmitter (AMT) tagcodes along with other data about the signal including time of arrival and signal to noise ratios (SNR). This software is capable of decoding single files, directories, and viewing raw acoustic waveforms. When coupled with the JSATS Detector, the Decoder is capable of decoding in ‘real-time’ and can also provide statistical information about acoustic beacons placed within receive range of hydrophones within a JSATS array. This document details the features and functionality of the software. The document begins with software installation instructions (section 2), followed in order by instructions for decoder setup (section 3), decoding process initiation (section 4), then monitoring of beacons (section 5) using real-time decoding features. The last section in the manual describes the beacon, beacon statistics, and the results file formats. This document does not consider the raw binary waveform file format.

Flory, Adam E.↗
DOE OSTI2026

WM26 Paper Multi-Robot Collaboration for Hazardous Environments

Hazardous nuclear and industrial facilities are rarely designed for robots. Work in these domains demand precise manipulation and robust mobility in cluttered, constrained spaces where off-the-shelf platforms struggle and “one-size-fits-all” machines become costly and complex. Idaho National Laboratory (INL) is developing an autonomous, multi-robot inspection system that coordinates task-specific platforms rather than relying on a single omni-tool robot. An electric truck serves as a power and compute hub for a custom manipulator co-developed with Florida International University (FIU), a commercial mini crawler, a pan–tilt–zoom camera, and a Nexxis Argus LiDAR mapping system. Working in concert, these robots generate spatial, radiation, and temperature maps of the pit environments at the Hanford Waste Tank Farms. These systems will capture visual records and environmental telemetry to allow for analysis post inspection. The system architecture uses Robot Operating System 2 (ROS 2) for publish/subscribe integration, NVIDIA Isaac Sim and Unity for simulation and visualization, and algorithms such as NVBlox to fuse data into unified 3D overlays. This robot-agnostic approach reduces operator burden by enabling autonomy across heterogeneous platforms and lets each robot be used where it is strongest. Having autonomous functions means operators don’t have to fully control multiple different components. The ease of use could allow for more widespread adoption of advanced robotics at waste management sites that see continued use. By coordinating simpler, purpose-built mechanisms, the approach lowers design and manufacturing complexity, reduces capital risk in contaminated settings, and improves controllability for complex inspection and manipulation tasks. We present the architecture, early results, and lessons learned from building and deploying this coordinated multi-robot system, with the goal of accelerating safe, cost-effective adoption of advanced robotics at waste-management sites.

42 - ENGINEERING↗
DOE OSTI2025

Measuring Local Turbulence Along the Optical Path: Multi-Beam Optical Seeing Sensor

Deflection of light along the optical path is a major source of image degradation for ground-based telescopes. Methods have been developed to measure upper atmospheric seeing based on models of the turbulence in the atmosphere, but due to boundary conditions, transmission within telescope enclosures is more complex. The Multi-beam Optical Seeing Sensor (MOSS) directly measures the component of the image quality degradation from inhomogeneity of the index of refraction within the telescope dome. MOSS outputs four near-parallel beams of light that travel along the optical path and are imaged by the telescope’s detector, landing like starlight on the telescope’s focal plane. By using a strobed light source, we can ‘freeze’ the instantaneous index variations transverse to the optical path. This system captures both ‘dome’ and ‘mirror’ seeing. Through plotting the standard deviation of differential motion between pairs of beams, MOSS enables characterization of the length scale of turbulence within the dome. The temporal coherence of temperature gradients can be probed with different pulse lengths, and the spatial coherence by comparing pairs at different separations across the aperture of the telescope. Optical path turbulence measurements, alongside other telemetry metrics, will guide thermal and airflow management to optimize image quality. A MOSS prototype was installed in the 1.2[Formula: see text]m Auxiliary Telescope (AuxTel) at the Vera C. Rubin Observatory in Chile, and preliminary data constrain the optical path turbulence with a lower bound of 1.4 arcsec. The optical path turbulence varied throughout the night of observing.

Astronomical seeing↗
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↗