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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Measurement-informed Dynamic Aggregation of Distribution Systems

This paper proposes a measurement-informed dynamic aggregation methodology in order to create equivalent representations of distribution systems that are compatible with large-scale transmission analysis. By optimizing an equivalent feeder parameters using time-series measurements of active power, reactive power, and voltage at the Point of Interconnection (POI), the approach yields simplified yet dynamically accurate equivalents. Implemented in PSCAD with models of photovoltaic–battery systems, three-phase motors, and static loads, the method employs hybrid differential evolution and bounded least-squares optimization laying the foundation for for real-time state estimation and optimized sensor placement in distribution networks.

Ahmed, Kazi Ishrak [University of Tennessee, Knoxv↗
DOE OSTI2026

Imputation of urban environmental sensor data using gated attention bidirectional long short-term memory (GA-BiLSTM): methods, performance, and implications

Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.

Data imputation↗
DOE OSTI2017

Chemical Sensing Strategies for Real-Time Monitoring of Transformer Oil: A Review

Power transformers are a central component in the field of energy distribution and transmission. The early recognition of incipient faults in operating transformers is substantially cost effective by lessening impromptu blackouts. A standout amongst the most responsive and dependable strategies utilized for assessing the health of oil filled electrical equipment is dissolved gas analysis (DGA). Nowadays, there is an expanding requirement for better nonintrusive diagnostic and online monitoring tools to survey the internal state of the transformers. Chemical sensors are viewed as a key innovation for condition monitoring of transformer health, coordinating the non-invasiveness with typical sensor features, such as cost, usability, portability, and the integration with the data networks. Low-cost chemical sensors-based DGA techniques are expected to drastically augment the diagnostic abilities empowering the deployment on a broader range of oil filled power assets. The recent development involves both specific sensors designed to detect individual dissolved gas in transformer oil and non-specific sensors, operated in near ambient conditions, with the potential to be applied in a DGA system. In this paper, general background and operating guidelines of DGA are presented to address the origin of the gas formation, methods for their detection and the interpretation of the results by data analytics. The recent significant interest and advancements in chemical sensors to DGA applications are reviewed. Lastly, future research perspectives and challenges for the development of novel DGA chemical sensors are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

GraMeR: Gra ph Me ta R einforcement learning for multi-objective influence maximization

Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Integration of a DER Management System in Riverside. Final report

The tasks in this project covered various aspects, including algorithm development, algorithm integration into a commercial Active Network Management (ANM) platform, hardware-in-the-loop (HIL) testing in an industry-standard testing platform, pilot demonstration in Riverside, California, and also cost and benefit analysis. The DERMS platform in this project can host different algorithms developed on different platforms (e.g., MATLAB and Python) and it can interact with different hardware devices (e.g., different PV inverters, battery inverters, and different sensors). The DER control solution are based on an advanced model-free, layered, and clustered DER control paradigm. At the core of the DER control algorithms was the concept of Extremum Seeking (ES), which is a model-free probing-based control technique. The ES-based control algorithms were tested on major real-world inverters; both individually and in a cluster. It was shown that even legacy equipment (or when paired with a few additional advanced equipment) can support such advanced control. The monitoring algorithms utilize a heterogeneous set of legacy and advanced sensor measurements, such as behind-the-meter DER sensors, distribution-level Phase Measurement Units, distribution-substation Supervisory Control and Data Acquisition (SCADA), and line current sensors, with their limited availability; in order to infer practical network conditions. Sensor data are utilized to achieve resource forecasting, phase identification, and distribution system state estimation. The technology that was developed and demonstrated in this project could be transformational to utilities, including the smaller municipal utilities such as in Riverside, which may not have the resources to deploy advanced distribution system and DERMS solutions in order to support high penetration of solar power integration. This project created a real-world prototype to provide utilities with an assessment of smart grid monitoring and control technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Heuristic based analytics for gas leak source identification

Heuristic-based techniques for gas leak source identification are provided. In one aspect, a method for identifying a location of a gas leak source includes: obtaining gas sensor data and wind data synchronously from a gas leak detection system having a network of interconnected motes comprising gas sensors and wind sensors, with the gas sensors arranged around possible gas leak sources in a given area of interest; identifying the location of the gas leak source using the gas sensor data and wind data; and determining a magnitude of gas leak from the gas leak source using the location of the gas leak source and a distance d between the location of the gas leak source and a select one of the gas sensors from which the gas sensor data was obtained. A gas leak detection system is also provided.

03 NATURAL GAS↗
DOE OSTI2018

Regression for Radioactive Source Detection

Detection of radioactive sources is an important capability that led to the deployment of networks for detecting and localizing low-level, hazardous radiation sources. It is generally expected that such networks outperform the individual detectors by intelligently fusing information from several dispersed sensors. In this paper, we develop a network detection method that uses the slope of a linear regression fit as a test statistic for detecting a point radioactive source within a field of detectors. In our regression model, we compute a least-squared linear fit between the average radiation counts at the detectors and the inverse-squared distances of known detector locations to an estimated source location. We show that the slope of this regression fit is an estimate of the source intensity and can be used as a threshold for source detection purposes. We compare performance of our proposed detection method with that of a fusion-based Sequential Probability Ratio Test (SPRT) method. For performance analyses, two datasets from the Domestic Nuclear Detection Ofces Intelligence Radiation Sensors Systems (IRSS) outdoor tests are used. Each of these tests consists of several runs of a single radioactive source moving in and out of a detector network. We present receiver operating characteristic (ROC) curves and optimal threshold values for the performance each detection method, and determine that our detection method using linear regression fit has slightly better overall performance than SPRT method.

Cordone, G.↗
DOE OSTI2025

Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning

Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the sensor was performed using a Vector Network Analyzer (VNA) and a pair of interrogation antennas to capture resonance behavior under varying thermal and spatial conditions with sensitivities ranging from 0.052 to 0.20 $\frac{𝑀𝐻𝑧}{°C}$. Sensor calibration was conducted using a Long Short-Term Memory (LSTM) model, which leveraged temporal patterns to account for hysteresis effects. The calibration method demonstrated improved performance when combined with an LSTM model, achieving up to a 76% improvement in temperature estimation error when compared with Linear Regression (LR). The experiments highlighted an innovative solution for patch antenna-based non-contact temperature measurement, which addresses limitations with conventional methods such as RFID-based systems, infrared, and thermocouples.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2019

Thread/OpenThread: A Compromise in Low-Power Wireless Multihop Network Architecture for the Internet of Things

Extending an Internet subnet by connecting resource-constrained nodes (e.g., embedded sensors and actuators) over multiple wireless hops is necessary to support the future Internet of Things (IoT). RPL, the IPv6 routing standard for low-power and lossy networks, tried to achieve this goal but has not seen wide adoption in practice. As an alternative, Thread is a recently standardized low-power network protocol for IoT, driven by the Thread group, an industry consortium led by Google/Nest. Here, we provide a comparative analysis of the technical aspects of RPL and Thread based on their specifications, explaining why using Thread, as opposed to RPL, may make sense for the future Internet. Specifically, the fundamental differences between RPL and Thread are their respective scopes and multihop network architectures, which result in Thread's unique design and advantages over RPL. Lastly, we evaluate Thread in an indoor multihop wireless testbed using OpenThread, an official open source implementation of Thread. This work serves as the first analysis of the Thread protocol in academia.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Intelligent Electrochemical Sensors for Precise Identification of Volatile Organic Compounds Enabled by Neural Network Analysis

The volatile organic compounds (VOCs) in a wide spectrum of categories were identified as biomarkers in aquatic environments, playing an important role in marine and freshwater ecology and global atmospheric chemistry. VOCs released from biofuel have also attracted increasing attention. Although the importance has been recognized, the portable detection and analysis methods of VOC in aquatic systems have not yet been well developed and understood. Here, in this work, we innovatively proposed an intelligent electrochemical sensing approach to classify and quantify VOCs in solution. Utilizing the cyclic voltammetry (CV) method with an ionic liquid (IL)-based electrolyte, we analyzed 50 μL samples of various VOC analytes, including acetic acid (AC), acetone, dimethylformamide (DMF), dimethyl sulfoxide (DMSO), ethanol, formaldehyde, formic acid, methanol, methyl formate (MF), toluene, and a formaldehyde-methanol mixture, along with deionized water (DI water). The generated voltammograms were subsequently analyzed using our uniquely designed and optimized 1-D convolutional neural network (1D-CNN). This deep-learning algorithm achieved a 99.09% accuracy in VOC classification validated through fivefold cross-validation and demonstrated an impressive 94.4% test accuracy for methanol detection within a 10 μL error range. For quantification, the system accurately categorized methanol volumes ranging from 0 to 50 μL in 10 μL increments, achieving a 98.18% accuracy. A notable linear correlation (R2 = 95.56%) was found between max current density at the oxidation peak and methanol volume, with the limit of detection (LOD) at 9.3 μL. Such a sensing method exhibits potential for portability, high accuracy, and generalization in the classification and quantification, ultimately reshaping the realm of VOC analysis in solution.

42 ENGINEERING↗
DOE OSTI2016

Optimizing Intermittent Water Supply in Urban Pipe Distribution Networks

In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. Here, we develop a computational model of transition, transient pipe flow in a network, accounting for a wide variety of realistic boundary conditions. We validate the model against several published data sets and demonstrate its use on a real pipe network. The model is extended to consider several optimization problems motivated by realistic scenarios. Here, we demonstrate how to infer water flow in a small pipe network from a single pressure sensor, and show how to control water inflow to minimize damaging pressure transients.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.

R. Kulkarni, Shruti↗
DOE OSTI2023

A Comprehensive Analysis of Real-World Accelerometer Data Quality in a Global Smartphone-based Seismic Network

The proliferation of low-cost sensors in smartphones has facilitated numerous applications; however, large-scale deployments often encounter performance issues. Sensing heterogeneity, which refers to varying data quality due to factors such as device differences and user behaviors, presents a significant challenge. In this research, we perform an extensive analysis of 3-axis accelerometer data from the MyShake system, a global seismic network utilizing smartphones. We systematically evaluate the quality of approximately 22 million 3-axis acceleration waveforms from over 81 thousand smartphone devices worldwide, using metrics that represent sampling rate and noise level. We explore a broad range of factors influencing accelerometer data quality, including smartphone and accelerometer manufacturers, phone specifications (release year, RAM, battery), geolocation, and time. Our findings indicate that multiple factors affect data quality, with accelerometer model and smartphone specifications being the most critical. In addition, we examine the influence of data quality on earthquake parameter estimation and show that removing low-quality accelerometer data enhances the accuracy of earthquake magnitude estimation.

58 GEOSCIENCES↗
DOE OSTI2018

DIRECT SIMULTANEOUS MEASUREMENT OF PARTICULATE MATTER AND AMMONIA STORAGE ON COMBINED SELECTIVE CATALYTIC REDUCTION FILTER SYSTEMS USING RADIO FREQUENCY SENSORS

Tightening global emissions regulations are motivating interest in the development and implementation of Selective Catalytic Reduction + Filtration (SCRF) systems, which are designed to reduce the concentration of tailpipe particulate matter (PM) and NO x emissions. These systems allow designers to combine the NO x reduction capability of an SCR with the filtration capability of a particulate filter on a single unit. Practical implementation of these systems requires reliable measurement and diagnosis of their state – both with respect to trapped particulate matter as well as adsorbed ammonia. Currently, these systems rely on a variety of gas sensors, mounted upstream or downstream of the system, that only provide an indirect inference of the operation state. In this study, a single radio frequency (RF) sensor was used to perform simultaneous measurements of soot loading and ammonia inventory on an SCRF. Several SCRF core samples were tested at varying soot and ash loads in a catalyst reactor bench. Soot levels were measured by monitoring changes in the bulk dielectric properties within the catalyst using the sensor, while ammonia levels were determined by feeding selected regions of the RF spectrum into a pretrained generalized regression neural network model. Results show the RF sensor is able to directly measure the instantaneous ammonia inventory, while simultaneously providing soot loading measurements within 0.5 g/L. These results confirm that simultaneous measurements of both the PM and ammonia loading state of an SCRF are possible using a single RF sensor via analysis of specific features in the full RF spectrum. The results indicate significant potential to remove the control barriers typically associated with the implementation of advanced SCRF systems.

Ragaller, Paul↗
DOE OSTI2018

Resilient Distributed Estimation: Sensor Attacks

Here, this paper studies multi-agent distributed estimation under sensor attacks. Individual agents make sensor measurements of an unknown parameter belonging to a compact set, and, at every time step, a fraction of the agents’ sensor measurements may fall under attack and take arbitrary values. We present the Saturated Innovation Update (SIU) algorithm for distributed estimation resilient to sensor attacks. Under the iterative SIU algorithm, if less than one half of the agent sensors fall under attack, then, all of the agents’ estimates converge at a polynomial rate (with respect to the number of iterations) to the true parameter. The resilience of SIU to sensor attacks does not depend on the topology of the inter-agent communication network, as long as it remains connected. We demonstrate the performance of SIU with numerical examples.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Medium-/Low-Voltage Joint State Estimator Through Linear Uncertainty Propagation

Traditionally, distribution system state estimations (DSSE) are challenged by the lack of measurements at both primary and secondary sides of the system. The widely available cable television (CATV) voltage sensors installed in low-voltage (LV) networks bring opportunities to achieve higher quality DSSE covering a broader area of the distribution network. This study proposes a medium-/low-voltage (MV/LV) joint distribution system state estimation approach using the untapped CATV measurements. It aims at addressing the need for system situational awareness at the grid edge while improving the estimation accuracy at both the primary and secondary sides compared to its disjointed counterpart. Linearized measurement functions and boundary condition uncertainty propagation rules are derived to ensure the computational efficiency and accuracy of the joint state estimator. Numerical experiments are conducted on an IEEE test feeder to demonstrate the efficacy of the proposed method and the value of CATV measurements.

joint state estimation↗
DOE OSTI2024

Test and Validate Distributed Coaxial Cable Sensors for in situ Condition Monitoring of Coal-Fired Boiler Tubes

This project aims to test, validate, and advance the technology readiness level (from TRL5 to TRL7) of a novel low-cost distributed stainless-steel/ceramic coaxial cable sensing (SSC-CCS) technology for in situ monitoring of the boiler tube temperature in existing coal-fired power plants. The novel SSC-CCS sensing technology and associated condition-based monitoring (CBM) software to be demonstrated in this project will lead to an improved understanding of the boiler tube failure mechanisms and a prognostic system to improve the overall performance, reliability, and flexibility of the nation’s coal-fired power plant fleet. A boiler tube monitoring system with distributed coaxial cable temperature sensors and a sensor acquisition system was constructed. The high-temperature coaxial cable sensor with a length of 1.3m was made by using a quartz tube (1mm inner diameter (ID) and 6mm outer diameter (OD)) to concentrically separate a 304 stainless-steel (SS) rod (1mm OD) and SS tube (7.94mm OD and 6.16mm ID). The sensor acquisition system includes a vector network analyzer (VNA), a radio frequency (RF) power amplifier, multiple switches and a USB hub. The distributed stainless-steel quartz coaxial cable sensor (SSQ-CCS) had a linear response to temperature with a resolution uncertainty of σ = 0.77℃. To withstand the harsh conditions of 3,300 steam pressures and 800℃ high temperatures, the sensor was shielded by a protective tube made of the same material as the boiler tube. The protection tube had an OD of 1.5 inches and a thickness of 0.25 inches. In the laboratory tests, the sensor showed good sensitivity and fast response. The drift was bounded between +0.33% and -0.67% during a test at 600℃ for 350 hours, indicating good stability of the sensor. A field test was conducted where four sensors were welded on four superheat tubes (SH-Ts) at a coal-fired power station over 400 days. Conventional thermocouples were welded to the superheater tubes alongside the coaxial cable sensors for the purpose of comparison. Two sensors were capable of distributed sensing, with three multiplexed sensing sections. The other two sensors were single section. During the 400-day test period, the power plant experienced startups and shutdowns. At the steady state operations, the temperature of the boiler tube is about 600℃ (1112°F). The sensors recorded the entire coal-firing processes (start-up, steady state, and shut-down) and the glitch event. A GSM modem and a Watchdog were added to the system to ensure reliable data recording. The GSM modem sent daily messages to plant managers and Clemson team to inform the status of the sensor system. If the system was not normally working, the Watchdog would reboot the system automatically. The new coaxial cable based distributed sensing technology has been proven to be successful in both laboratory and field tests. A comprehensive four-stage multi-physics computational framework has been developed to assist the design, optimization, installation, and operation of SSQ-CCS. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. A conditional-based monitoring system is implemented as well. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations.

01 COAL, LIGNITE, AND PEAT↗