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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Use of Integrated Instrumentation to Detect and Classify Targets in Shallow Water

The detection and classification of targets in shallow water environments (e.g., depth < 20 m) is of increasing interest, both in security applications and more general environmental studies. Here, we report the ability of a cabled integrated instrumentation system to detect and classify targets in a shallow (15 m deep), narrow (150 m wide) channel influenced by moderate tidal currents (peak currents < 2 m/s). The instrumentation system incorporates a passive acoustic array, multibeam sonar, acoustic camera, stereo-optical camera, and acoustic Doppler current profiler. The system effectiveness is benchmarked using autonomous “cooperative” targets (e.g., drifters with known position and acoustic characteristics), divers, and opportunistic observations of fish and marine mammals. By fusing data streams from multiple sensors, detection and classification rates are improved, particularly for smaller targets. Approaches to data management (data rates exceed 80 MB/s), real-time classification, and de-noising of acoustic imagery are also discussed.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

VOLTTRON/volttron-pnnl-aems

The Autonomous Energy Management Software (AEMS) system will continuously optimize the operations of the distributed energy resources in the small and medium size commercial building by minimizing energy consumption and cost, while providing a solution for maximizing decarbonization benefits from electrification of buildings. Initially, AEMS system will manage rooftop air conditioners and heat pumps but it can be extended in the future to manage, hot water heaters, storage (battery and thermal), electric vehicle charging and monitoring solar photovoltaic. AEMS support both energy efficiency and grid service features.

Bleeker, Amelia [Pacific Northwest National Labora↗
DOE OSTI2026

IPC-Fusion (Infrastructure Perception and Control (IPC): Multisensor Data Fusion Software) [SWR-25-153]

As part of the National Laboratory of the Rockies' (NLR’s) Infrastructure Perception and Control Laboratory, the IPC-Fusion toolkit provides a probabilistic, scalable, multi-sensor fusion framework that integrates (late-stage fusion) heterogeneous object detection data from traffic sensors to enable robust, real-time tracking of roadway occupants. The algorithmic design of the toolkit is motivated by the need for creating a digital twin of traffic at the edge in a scalable and affordable manner. The software operates by combining object-level measurements (such as position and velocity) from a suite of sensors (such as radar, lidar, camera) using Kalman filtering and probabilistic data association techniques to overcome individual sensor limitations and achieve superior tracking performance in complex traffic zones. The framework addresses key challenges including heterogeneous measurement uncertainties, asynchronous data streams, varying spatiotemporal data resolutions, robust data association, and adaptive object lifecycle management. Validated on real-world traffic intersection data including vehicles and pedestrians, IPC-Fusion demonstrates enhanced tracking reliability across scenarios involving occlusions, sensor failures, and varying traffic densities, supporting the broader IPC initiative's goal of transforming transportation infrastructure through advanced perception capabilities for intelligent transportation systems, traffic safety applications, and autonomous vehicle support.

Sandhu, Rimple [National Laboratory of the Rockies↗
DOE OSTI2021

Stopping criteria for ending autonomous, single detector radiological source searches

While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-based navigation to autonomous, single-detector, mobile systems. However, these approaches lacked the capacity to terminate a surveying/search task without outside influence of an operator or perfect knowledge of source location (defeating the purpose of such a system). Two stopping criteria are investigated in this work for a machine learning navigated system: one based upon Bayesian and maximum likelihood estimation (MLE) strategies commonly used in source localization, and a second providing the navigational machine learning network with a “stop search” action. A convolutional neural network was trained via reinforcement learning in a 10 m × 10 m simulated environment to navigate a randomly placed detector-agent to a randomly placed source of varied strength (stopping with perfect knowledge during training). The network agent could move in one of four directions (up, down, left, right) after taking a 1 s count measurement at the current location. During testing, the stopping criteria for this navigational algorithm was based upon a Bayesian likelihood estimation technique of source presence, updating this likelihood after each step, and terminating once the confidence of the source being in a single location exceeded 0.9. A second network was trained and tested with similar architecture as the previous but which contained a fifth action: for self-stopping. The accuracy and speed of localization with set detector and source initializations were compared over 50 trials of MLE-Bayesian approach and 1000 trials of the CNN with self-stopping. The statistical stopping condition yielded a median localization error of ~1.41 m and median localization speed of 12 steps. The machine learning stopping condition yielded a median localization error of 0 m and median localization speed of 17 steps. This work demonstrated two stopping criteria available to a machine learning guided, source localization system.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

TEAMER: MADWEC Techno-Economic Analysis

The objective of this project was for the facility to conduct a techno-economic assessment (TEA) of the Maximal Asymmetric Drag Wave Energy Converter (MADWEC), developed by the University of Massachusetts Dartmouth (UMass Dartmouth). MADWEC is used for powering remote monitoring and Autonomous Underwater Vehicle (AUV) charging systems compared to other existing power supply options. The assessment estimates capital expenditures (CapEx), operational expenditures (OpEx), and power performance for 18 scenarios with the purpose of identifying key cost drivers, comparing total system cost, and comparing the power performance of the power supply options in terms of required installed capacity and estimated theoretical annual energy performance. The 18 assessed scenarios include two end-uses: 1) AUV charging and 2) offshore remote monitoring); three power sources: 1) MADWEC), 2) photovoltaic (PV) solar buoy, 3) and traditional battery swapping); and three locations; 1) nearshore, 2) far-offshore, and 3) high-latitude). In addition, other project goals included developing high level installation, operation, and maintenance plans for each scenario. The techno-economic model, created in Microsoft Excel, estimates CapEx, OpEx, and the power performance of each power supply source. The model has a dynamic format that allows custom inputs to accommodate future changes to the systems being assessed. This is a TEA for the MADWEC project, TEAMER RFTS 7 (request for technical support) program.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

Rapid, autonomous analysis of He spectra I: Overview of the RadID program, user experience, and structure

RadID is a new gamma-ray spectrum analysis program for rapid screening of HPGe gamma-ray data to reveal the presence of radionuclide signatures. It is an autonomous, rule-based heuristic system that can identify well over 200 radioactive sources with particular interest in uranium and plutonium characteristics. It executes in about one second. RadID does not require knowledge of the detector efficiency, the source-to-detector distance, or the geometry of the inspected radiation source—including any shielding. In this first of a three-document series we sketch the RadID program’s origin, its minimal requirements, the user experience, and the program operation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Rapid, autonomous analysis of HPGe gamma-ray spectra III: Plutonium identification and characterization

RadID is a gamma-ray spectrum analysis program originally written to assist in the detection of the illicit movement of nuclear material. It is specific to the rapid analysis of HPGe gamma-ray data to reveal the radionuclide signatures of interest that may be present in the spectra. It is an autonomous, rule-based heuristic system that can identify well over 200 radioactive sources in about one second. RadID does not require knowledge of the detector efficiency, the source-todetector distance, or the geometry of the inspected radiation source—including any shielding. In this third of a three-document series we discuss how RadID detects the presence of plutonium isotopes and determines a number of its characteristics, most notably: the spectral characteristics of weapons-grade plutonium, reactor-grade plutonium, and heat-source plutonium used in radioisotope thermoelectric generators.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

The GABLE Report: Garbled Autonomous Bots Leveraging Ethereum

Simple but mission-critical internet-based applications that require extremely high reliability and availability could potentially benefit from running on robust public programmable blockchain platforms such as Ethereum. Unfortunately, program code running on such blockchains is ordinarily publicly viewable, rendering these platforms unsuitable for applications requiring strict privacy of application code, data, and results. However, might it be possible to encode an application's business logic and data for these platforms in such a way that it becomes impossible for unauthorized parties to infer any meaningful information whatsoever about the semantics of the data, and the operations being performed on that data? In this report, we describe GABLE (Garbled Autonomous Bots Leveraging Ethereum), a system concept developed at Sandia that achieves this security goal in a limited, but still useful range of circumstances. GABLE, uses simple but effective algorithms to permit secure private execution of garbled state machines (and more efficient garbled circuits) on public computing resources. We give an example working implementation for garbled state machines, written using the Python and Solidity programming languages, and outline how our methods can be extended to support a more powerful garbled universal circuit model of computation. The capability embodied by the GABLE, system has significant potential applications, a few of which we discuss in this report.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Zeteotech, LLC TRGR Project Final Report

Reliable detection of aerosolized pathogens is difficult due to need to distinguish between the benign bioaerosols such as dander and pollen and the thousands of pathogens capable of infecting people. Accurate identification of airborne pathogens of concern in the past has required the collection of aerosol samples in a filter, periodic collection of the samples, and processing and identification in the laboratory. This process is labor intensive and expensive. Additionally, this method necessarily has a time to detection window of hours to days depending on the collection frequency. Biological pathogens have an incubation period before the onset of symptoms and severe health effects and/or mortality, people will typically be exposed to them without realizing it. This has resulted in a detect to treat strategy for protection against bio releases. While prophylactic measures can still be effective over these time scales, reducing the time to detection will significantly improve the effectiveness of these measures and subsequently reduce the consequences of a release. Various attempts to reduce the time to detection and identification have been plagued by highly undesirable false-positives which degrade confidence in the system. Zeteotech, LLC has developed a mass spectrometer based bioaerosol sensing system which is capable of autonomously identifying airborne pathogens of concern and alert authorities within minutes instead of hours to days. They have deployed these instruments to protect high-risk facilities by alerting authorities of public health events and intentional bioterrorism events in near real time. This makes it possible to more accurately identify the time and location of the release and minimize the number of people that are exposed through prompt quarantining of affected areas.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Does the Hood Canal Bridge traffic noise affect migrating steelhead smolts?

Recent research conducted by NOAA’s Manchester Research Laboratory has provided strong evidence of a substantial migration interference and increased mortality risk associated with the Hood Canal Bridge , which drives the need to investigate the sound and vibration generated by the vehicles crossing the bridge. A month-long monitoring of in-water sound pressure and the vibration of the bridge pontoons was performed at the Hood Canal Bridge by three sets of autonomous sound and vibration recording systems deployed along the bridge. At each deployment site, sound pressure in water was recorded by a hydrophone submerged three meters under water, and the vibration of the bridge pontoon was recorded by an array of three orthogonally mounted single-axis accelerometers. Both sound pressure and acceleration data were analyzed and compared to the threshold levels suggested by previous studies. Results show that the observed sound pressure levels and particle accelerations are strongly correlated with traffic volume across the bridge, and particle accelerations occasionally exceeded the threshold levels that could elicit the avoidance behavior of juvenile salmon. It is concluded that the Hood Canal Bridge traffic noise can potentially affect migrating salmonids due to the intense particle accelerations caused by the bridge pontoons in water.

Zang, Xiaoqin↗
DOE OSTI2025

Design, Build, and Initial Testing of a Portable Methane Measurement Platform

The quantification of methane concentrations in air is essential for the quantification of methane emissions, which in turn is necessary to determine absolute emissions and the efficacy of emission mitigation strategies. These are essential if countries are to meet climate goals. Large-scale deployment of methane analyzers across millions of emission sites is prohibitively expensive, and lower-cost instrumentation has been recently developed as an alternative. Currently, it is unclear how cheaper instrumentation will affect measurement resolution or accuracy. To test this, the Wireless Autonomous Transportable Methane Emission Reporting System (WATCH 4 ERS) has been developed, comprising four commercially available sensing technologies: metal oxide (MOx,), Non-dispersion Infrared (NDIR), integrated infrared (INIR), and tunable diode laser absorption spectrometer (TDLAS). WATCHERS is the accumulated knowledge of several long-term methane measurement projects at Colorado State University’s Methane Emission Technology Evaluation Center (METEC), and this study describes the integration of these sensors into a single unit and reports initial instrument response to calibration procedures and controlled release experiments. Specifically, this paper aims to describe the development of the WATCH 4 ERS unit, report initial sensor responses, and describe future research goals. Meanwhile, future work will use data gathered by multiple WATCH 4 ERS units to 1. better understand the cost–benefit balance of methane sensors, and 2. identify how decreasing instrumentation costs could increase deployment coverage and therefore inform large-scale methane monitoring strategies. Both calibration and response experiments indicate the INIR has little practical use for measuring methane concentrations less than 500 ppm. The MOx sensor is shown to have a logarithmic response to methane concentration change between background and 600 ppm but it is strongly suggested that passively sampling MOx sensors cannot respond fast enough to report concentrations that change in a sub-minute time frame. The NDIR sensor reported a linear change to methane concentration between background and 600 ppm, although there was a noticeable lag in reporting changing concentration, especially at higher values, and individual peaks could be observed throughout the experiment even when the plumes were released 5 s apart. The TDLAS sensor reported all changes in concentration but remains prohibitively expensive. Our findings suggest that each sensor technology could be optimized by either operational design or deployment location to quantify methane emissions. The WATCH4ERS units will be deployed in real-world environments to investigate the utility of each in the future.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Need for research and training reactors for advanced reactor designs

Full text of publication follows. Research and training reactors have served a valuable role in helping train workforce for currently operating fleet of light water reactors. These research and training reactors have been used in reactor laboratory classes to familiarize the students with such vital concepts as approach to criticality, reactor period, neutron moderation, reactivity, flux distribution and leakage, etc. As the industry moves toward advanced non-light-water reactor designs, it is critical that research and training reactors be developed and deployed at university campuses to help train the new generation of nuclear and non-nuclear engineers who are likely to design, build, and operate these advanced reactors. Among the designs currently being pursued for nuclear power generation include molten salt, sodium cooled, and gas cooled designs, with options for various fuel forms. Thus, industry and DOE in collaboration with academic institutions should devise plans on how to familiarize the next generation of nuclear workforce with hands-on experience necessary for such designs. These research and training reactors will play a vital role in familiarizing the future workforce with hands-on experience with concepts associated with fast spectrum reactors, gas cooled reactors, and other features not associated with light water reactors. In addition to classical nuclear engineering concepts, these advanced research and training reactors can also be used for hands-on training as well as for research on features being considered in the design of GEN-IV reactors: cyber security for digital control room operations, hybrid energy system, hydrogen generation, district heating, autonomous control... (author)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗
DOE OSTI2015

Factors Affecting Route Selection and Survival of Steelhead Kelts at Snake River Dams in 2012 and 2013

In 2012 and 2013, Pacific Northwest National Laboratory (PNNL) conducted a study that summarized the passage route proportions and route-specific survival rates of steelhead kelts that passed through Federal Columbia River Power System (FCRPS) dams. To accomplish this, a total of 811 steelhead kelts were tagged with Juvenile Salmon Acoustic Telemetry System (JSATS) transmitters. Acoustic receivers, both autonomous and cabled, were deployed throughout the FCRPS to monitor the downstream movements of tagged kelts. Kelts were also tagged with passive integrated transponder tags to monitor passage through juvenile bypass systems (JBS) and detect returning fish. The current study evaluated data collected in 2012 and 2013 to identify environmental, temporal, operational, individual, and behavioral variables that were related to forebay residence time, route of passage, and survival of steelhead kelts at FCRPS dams on the Snake River. Multiple approaches, including 3-D tracking, bivariate and multivariable regression modeling, and decision tree analyses were used to identify the environmental, temporal, operational, individual, and behavioral variables that had the greatest effect on forebay residence time, route of passage, and route-specific and overall dam passage survival probabilities for tagged kelts at Lower Granite (LGR), Little Goose (LGS), and Lower Monumental (LMN) dams. In general, kelt behavior and discharge appeared to work independently to affect forebay residence times. Kelt behavior, primarily approach location, migration depth, and “searching” activities in the forebay, was found to have the greatest influence on their route of passage. The condition of kelts was the single most important factor affecting their survival. The information gathered in this study may be used by dam operators and fisheries managers to identify potential management actions to improve in-river survival of kelts or collection methods for kelt reconditioning programs to aid the recovery of Snake River steelhead populations.

13 HYDRO ENERGY↗
DOE OSTI2014

Factors Affecting Route Selection and Survival of Steelhead Kelts at Snake River Dams in 2012 and 2013

In 2012 and 2013, Pacific Northwest National Laboratory conducted a study that summarized the passage proportions and route-specific survival rates of steelhead kelts that passed through Federal Columbia River Power System (FCRPS) dams. To accomplish this, a total of 811 steelhead kelts were tagged with Juvenile Salmon Acoustic Telemetry System (JSATS) transmitters. Acoustic receivers, both autonomous and cabled, were deployed throughout the FCRPS to monitor the downstream movements of tagged-kelts. Kelts were also tagged with Passive Integrated Transponder tags to monitor passage through juvenile bypass systems and detect returning fish. The current study evaluated data collected in 2012 and 2013 to identify individual, behavioral, environmental and dam operation variables that were related to passage and survival of steelhead kelts that passed through FCRPS dams. Bayesian model averaging of multivariable logistic regression models was used to identify the environmental, temporal, operational, individual, and behavioral variables that had the highest probability of influencing the route of passage and the route-specific survival probabilities for kelts that passed Lower Granite (LGR), Little Goose (LGS), and Lower Monumental (LMN) dams in 2012 and 2013. The posterior probabilities of the best models for predicting route of passage ranged from 0.106 for traditional spill at LMN to 0.720 for turbine passage at LGS. Generally, the behavior (depth and near-dam searching activity) of kelts in the forebay appeared to have the greatest influence on their route of passage. Shallower-migrating kelts had a higher probability of passing via the weir and deeper-migrating kelts had a higher probability of passing via the JBS and turbines than other routes. Kelts that displayed a higher level of near-dam searching activity had a higher probability of passing via the spillway weir and those that did less near-dam searching had a higher probability of passing via the JBS and turbines. The side of the river in which kelts approached the dam and dam operations also affected route of passage. Dam operations and the size and condition of kelts were found to have the greatest effect on route-specific survival probabilities for fish that passed via the spillway at LGS. That is, longer kelts and those in fair condition had a lower probability of survival for fish that passed via the spillway weir. The survival of spillway weir- and deep-spill passed kelts was positively correlated with the percent of the total discharge that passed through turbine unit 4. Too few kelts passed through the traditional spill, JBS, and turbine units to evaluate survival through these routes. The information gathered in this study describes Snake River steelhead kelt passage behavior, rates, and distributions through the FCRPS as well as provide information to biologists and engineers about the dam operations and abiotic conditions that are related to passage and survival of steelhead kelts.

13 HYDRO ENERGY↗
DOE OSTI2012

The Evolution of the Bacterial Luciferase Gene Cassette (lux) as a Real-Time Bioreporter

The bacterial luciferase gene cassette (lux) is unique among bioluminescent bioreporter systems due to its ability to synthesize and/or scavenge all of the substrate compounds required for its production of light. As a result, the lux system has the unique ability to autonomously produce a luminescent signal, either continuously or in response to the presence of a specific trigger, across a wide array of organismal hosts. While originally employed extensively as a bacterial bioreporter system for the detection of specific chemical signals in environmental samples, the use of lux as a bioreporter technology has continuously expanded over the last 30 years to include expression in eukaryotic cells such as Saccharomyces cerevisiae and even human cell lines as well. Under these conditions, the lux system has been developed for use as a biomedical detection tool for toxicity screening and visualization of tumors in small animal models. As the technologies for lux signal detection continue to improve, it is poised to become one of the first fully implantable detection systems for intra-organismal optical detection through direct marriage to an implantable photon-detecting digital chip. This review presents the basic biochemical background that allows the lux system to continuously autobioluminesce and highlights the important milestones in the use of lux-based bioreporters as they have evolved from chemical detection platforms in prokaryotic bacteria to rodent-based tumorigenesis study targets. In addition, the future of lux imaging using integrated circuit microluminometry to image directly within a living host in real-time will be introduced and its role in the development of dose/response therapeutic systems will be highlighted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Vehicle Automation Benefits and Challenges for Passenger Transport System Beyond Automated Driving

The National Renewable Energy Laboratory has been researching the implementation of fully automated passenger transport systems to be operated within dense urban settings, referred to as Automated Mobility Districts, based on roadway vehicle automation as opposed to track or train-based automation. This research, now in its third phase, is presently addressing the full spectrum of benefits and challenges of full automation of passenger transport systems with respect to fleet electrification and the associated multimodal, large fleet operational management, benefits beyond simply automating the driving tasks. The focal points summarized in the paper and presentation address the benefit-analysis of fleet automation to address added complexities imposed on the multi-fleet operational management when there is a simultaneous implementation of an on-demand service mode that connects with and optimizes the effectiveness of legacy transit systems and new sub-regional autonomous vehicle fleets, with specific emphasis on enhanced ability to better meet peak ridership demand. The research also begins to address challenges in operations arising from lack of personnel present to handle unexpected customer and system needs. Combined, this research articulates vehicle automation benefits and challenges beyond simply automating the driving tasks, addressing additional operational benefits automation provides to address the added complexities imposed by electrification and on-demand modes of operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Integrated Dispatching and Charging Management of an Autonomous Electric Vehicle Ride-Hailing Fleet

Electrification and autonomous driving are two important trends in transportation systems. The convergence of these two technologies will introduce opportunities to improve transportation systems' operation and energy efficiency. One potential application is the commercial ride-hailing fleet with autonomous electric vehicles (AEVs). In order to harvest promising benefits from introducing AEVs into ride-hailing fleets, some unavoidable challenges will need to be resolved to ensure the fleets operates functionally and efficiently. This paper discusses the challenges of dispatching AEVs and their interactions with charging infrastructure. An integrated decision making framework for dispatching and charging has been designed using a system optimization approach to study the AEVs' management within the period when they drop off passengers and pick up the next passengers. Its potential fleet-wide benefits have been illustrated by comparing operations under a heuristic approach. A simulation platform has been designed to test different decision making strategies for the ride-hailing AEV fleets operational performance. Using this platform, detailed case studies have been performed with different fleet sizes, dispatching strategies, and charging infrastructure network settings. Comprehensive analyses from various aspects have been conducted to understand the AEVs' fleet operation performance, (e.g., zero occupancy vehicle miles traveled, successfully served ratio of ride-hailing requests, fleet vehicle charging downtime, and charging infrastructure utilization). Results have provided a deep understandings on operation's dynamics under various fleet system configurations and also have demonstrated advantages of the optimization-based approach for the AEV fleet management. Studies in this paper inform better designs on the future of sophisticated management strategies and charging infrastructure to support ride-hailing AEV fleet operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗