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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Rolling Root Mean Square Based Multimodal Anomaly Detection for Real Time Monitoring of Smart Grid

Reliable real-time monitoring is valuable for maintaining the operational integrity of modern electrical smart grids. Deployment of heterogeneous sensing technologies in substations has enabled high-resolution, multichannel waveform monitoring, but also introduces challenges for anomaly detection due to noise, baseline drift, and modality-dependent signal characteristics. In this work, we present a computationally efficient unsupervised method for multimodal event detection based on Rolling Root Mean Square based Event Detection (RRMSED). The method is developed using in-house, field deployed sensors collecting data at a utility substation. The sensing system comprises voltage and current sensors, triaxial accelerometers, and magnetometers, collectively capturing electrical, vibrational, and magnetic waveform measurements at high temporal resolution. RRMSED operates by extracting rolling RMS energy features and their first-order temporal differences from consecutive waveform segments for each channel and then applying channel-specific statistical thresholds learned from historical data. A persistence-based exceedance logic is employed to robustly identify transient events while suppressing impulsive noise, and to provide precise temporal localization with high resolution. The framework is designed for continuous server-side operation and can be deployed in real time without requiring complex models. Experiments on simulated waveform data with known ground truth demonstrate low false positive (FP) and false negative (FN) rates. Application to real substation data shows RRMSED to identify events that are not captured by conventional monitoring indicators including fast transient detection algorithm currently deployed in the system. These results indicate that rolling RMS based features provide an effective and practical basis for real-time multimodal event detection in smart-grid substations.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2024

Study of a High-speed Outer Rotor Burst Containing Enclosure

The increasing demand for high-speed electric machines in many applications pushes the development of high-power density electric motors. Outer rotor motors, which can be designed with a larger airgap diameter than inner rotor motors for the same overall diameter and, therefore, provide higher torque, distinguish themselves in space-constrained but high torque applications. However, increasing the motor speed and diameter results in significant centrifugal loads for the outer rotor motor due to its higher airgap diameter. Additional attention must be paid at the design stage to consider the extreme situation of possible mechanical failure, such as the sudden burst of the high-speed rotating parts in the motor, and measures must be taken to prevent their damage to other system components in advance. Through the finite element dynamic impact analysis, we studied the damage caused by broken parts on the inner wall of the electric motor housing when the motor rotates at 20,000 RPM. The proposed method is expected to provide reasonable recommendations of the enclosure material and its thickness required to protect other components in the same powertrain system when the high-power density motor fails unexpectedly.

Lin, Lianshan↗
DOE OSTI2020

Methods for In Situ Electroluminescence Imaging of Photovoltaic Modules Under Varying Environmental Conditions

Electroluminescence (EL) imaging is a powerful tool used to identify defects in photovoltaic solar cells. Typically, this type of characterization is performed in the dark using a current injection that equals short-circuit current measured at standard test conditions (STC). Restricting imaging to such a temperature range limits the information obtained about the module and cells. However, it is not trivial to develop a tool that would allow for EL imaging to be performed under a wider range of temperatures. Here we demonstrate an in situ EL imaging capability developed within an environmental chamber that allows for control of sample temperatures between –40 and 90 °C. Additionally, we demonstrate EL imaging of 4-cell mini-modules (MiMo) under front-side mechanical loading. A Raspberry Pi-connected camera with short-pass filter removed is used for EL imaging. The camera is low-cost with a small form-factor, appropriate for use in a harsh, enclosed environment. The camera is installed within a thermally isolating housing mounted within the environmental chamber. Three example cases are given for MiMos that exhibit various forms of degradation including solder fatigue and cell cracking. It is shown that by measuring at conditions above and below STC, different behaviors may be identified. In some cases, restricting characterization to STC may lead to a failure to detect damage existing in the sample.

14 SOLAR ENERGY↗
DOE OSTI2022

Electroluminescence Analysis and Grading of Hail Damaged Solar Panels

We analyzed more than 4000 electroluminescence images of hail damaged solar panels from a cluster of houses in Texas. We enhanced the images for ease of analysis and classified the defects within each solar cell into categories of glass breakage, installer damage, inactive substrings, crack severity, interconnect wire problems, and whether the damage was likely caused by hail. From these statistics, we quantified each panel into five levels of hail damage for insurance claims, and four levels of overall quality for potential resale pricing. Here, we share here some statistics regarding the defects with the hope that the data is useful to others attempting to predict the invisible damage to systems based just on the easily observable glass breakage statistics.

14 SOLAR ENERGY↗
DOE OSTI2025

Intraday Outdoor Efficiency Changes in Metal-Halide Perovskite Photovoltaic Modules

Here, we present outdoor observations of metal-halide perovskite modules deployed in the Photovoltaic Accelerator for Commercializing Technologies center, which houses one of the world's broadest efforts to test metal-halide perovskite photovoltaic modules outdoors. As of January 2025, outdoor testing has encompassed over 150 modules from 14 different partners. Our findings illustrate how daily changes in efficiency, driven by exposure to light, affect field performance in real-world conditions. These effects cannot be explained by existing outdoor performance models and frustrate the notion of a traditional temperature coefficient.

14 SOLAR ENERGY↗
DOE OSTI2025

Design and Analysis of a Mutual Inductance Level Sensor for Liquid Metals

Here, this article describes the design and analysis of an electromagnetic level sensor for use in high-temperature liquid metal systems. The mutual inductance level sensor (MILS) described in this work was fabricated using two single-conductor mineral insulated cables wrapped in a bifilar fashion around a stainless steel tube core and was housed in an isolating thimble that preserved the pressure boundary of the test vessel. Two sensor variations were fabricated that differ only in active length, 1016 and 1778 mm. Experimental data were collected using the 1016-mm sensor (MILS-MKII-040) that demonstrated a sensitivity of 9.2 μ V/mm in a room temperature testing stand that used solid aluminum as a surrogate for liquid metal. Experimental data were collected using the 1778-mm sensor (MILS-MKII-070) that demonstrated a sensitivity of 6.9 μ V/mm in the high-temperature (300 ° C) liquid sodium environment at the mechanisms engineering test loop (METL) of Argonne National Laboratory. The sensor performance was found to be repeatable over the course of several months, with roughly ±1% deviation from nominal output. Finite element models were developed in COMSOL Multiphysics that fully describe each test setup, and the models were validated using experimental data. The validated COMSOL models were used to perform an array of analyses that examined the performance of the sensor in differing environments. Maximizing the coil diameter inside the isolating thimble was found to maximize the signal and sensitivity of the sensor. An optimal operating frequency was found near 1000 Hz using both experimental data and COMSOL. The influence of a metallic thimble surrounding the sensor and a metallic sensor core was quantified and found to be negligible at the optimal operating frequency. The sensitivity of the sensor was quantified when monitoring the level of additional liquid metals. These include lead, lead-bismuth eutectic (LBE), sodium-potassium alloy (NaK), and lithium (in addition to sodium). The sensitivities were quantified using liquid metal properties at 350 ° C and 650 ° C. The geometry of the test stand model, all material properties used in the model, and the results are presented in a manner that allows the reader can replicate the model and perform additional analyses.

COMSOL↗
DOE OSTI2023

Monolithically Integrated $\varepsilon$-Ge/In x Ga 1-x As Quantum Well Laser Design: Experimental and Theoretical Investigation

Here, we have analyzed the electrical and optical phenomenon occurring in a $\varepsilon$-Ge/In x Ga 1-x -Ge/In x Ga 1-x As quantum well (QW) laser through self-consistent physical solvers calibrated using in-house experimental results. A separate confinement heterostructure QW design is proposed to enable lasing from tensile strained germanium ($\varepsilon$-Ge/In x Ga 1-x -Ge) in the range of 1.55 um to 4 um wavelengths as a function of QW thickness and indium (In) composition. Different recombination mechanisms were analyzed as a function of tensile strain in $\varepsilon$-Ge/In x Ga 1-x -Ge QW. Minority carrier lifetime and band alignment are key attributes of a QW laser, which were measured using microwave photoconductive decay and x-ray photoelectron spectroscopy (as a function of In composition), respectively. The transition point of Ge to a direct bandgap material is re-affirmed to be at $\varepsilon$-Ge/In x Ga 1-x = 1.6% (In ~24%) and the transition from type I to type II for $\varepsilon$-Ge/In x Ga 1-x -Ge/In x Ga 1-x As QW is found to be at In ~55%. Also, the transition to a TM mode dominant laser is identified at In ~15%. Using a tunable waveguide design to optimize confinement as a function of In composition, strain, wavelength, QW thickness, refractive index, and geometry, the $\varepsilon$-Ge/In x Ga 1-x -Ge QW laser design provided a net material gain of ~2000 cm -1 and a threshold current density of ~5 kA/cm 2 , which is an improvement over existing Ge based lasers. In conclusion, the impact of In composition and QW thickness on the band structure, polarized gain spectra, and various lasing metrics were analyzed to show $\varepsilon$-Ge/In x Ga 1-x -Ge/InGaAs QW lasers as promising for integrated photonics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Implementing Online Oscillation Monitoring and Forced Oscillation Source Locating at Peak Reliability

This paper introduces a framework of online oscillation monitoring systems and forced oscillation detection & source location tools that Peak Reliability ('PEAK') has implemented for the Reliability Coordinator (RC) function of the Western Interconnection. The framework consists of four main components: (1) Montana Tech's Modal Analysis Software (MAS) engine; (2) Washington State University's Oscillation Monitor System (OMS) software; (3) Forced Oscillation Detection and Source Location Algorithms (FODSL); and (4) PEAK in-house visualization tool and alarming logic built in PI Processbook. The framework has been validated and applied for real system oscillation studies successfully. Implementation experience and lessons learned will be discussed in the paper.

14 SOLAR ENERGY↗
DOE OSTI2024

Historical Power Outages of the United States and the Social Vulnerability Index

Several works have been documented in the literature to study the societal effect of power outages and to analyze their correlation with the Social Vulnerability Index (SVI). Because the SVI is calculated based on the summed rank of multiple vulnerability factors for environmental hazards, it can include factors irrelevant to power outages caused by extreme events. This work performs a detailed correlation analysis for social vulnerability and power outages by considering different SVI themes (e.g., socioeconomic status, household composition, racial and ethnic minority status, and housing and transportation) and power outages with and without a threshold for extreme weather events. Although there is some relation between specific themes and aspects of power outages and the SVI in the results, there is no strong distinction between power outage durations and low vs. high SVI values. These results point to the need for further research that grounds the specific factors and methods used to develop SVI and related indices to energy services and power systems disruptions.

Bhusal, Narayan↗
DOE OSTI2023

Findings from Design and Operation of Connected Neighborhoods

Distributed energy resources and load management are an important emerging part of smart grids. Concurrent management of homeowner preferences and utility objectives requires an intelligent negotiation strategy supported by physical infrastructure that learns, optimizes, and controls the system. While there is a wide body of research related to modelling and simulation of distributed resources, relatively little is known about their practical feasibility. One of the first attempts to address this knowledge gap was through a 62-house connected neighborhood in Alabama. This study makes one more step in advancing this knowledge through a 46-townhome demonstration neighborhood located in Atlanta, GA. It reports hardware design, system architecture, and the results from the summer experimental work. The findings are discussed in the context of earlier experience, and analysis is expanded to areas which were not the focus of earlier research.

Tsybina, Eve↗
DOE OSTI2024

Integrating ORNL’s HPC and Neutron Facilities with a Performance-Portable CPU/GPU Ecosystem

We explore the development of a performance-portable CPU/GPU ecosystem to integrate two of the US Department of Energy’s (DOE’s) largest scientific instruments, the Oak Ridge Leadership Computing facility and the Spallation Neutron Source (SNS), both of which are housed at Oak Ridge National Laboratory. We select a relevant data reduction workflow use-case to obtain the differential scattering cross-section from data collected by SNS’s CORELLI and TOPAZ instruments. We compare the current CPU-only production implementation using the Garnet Python multiprocess package based on the Mantid C++ framework against our proposed CPU/GPU implementation that uses the LLVM-based, just-in-time Julia scientific language and the JACC.jl performance-portable package. Two proxy apps were developed: (i) an app for extracting relevant Mantid kernels (MDNorm) in C++ and (ii) the Julia MiniVATES.jl miniapp. We present performance results for NVIDIA A100 and AMD MI100 GPUs and AMD EPYC 7513 and 7662 CPUs. The results provide insights for future generations of data reduction software that can embrace performance portability for an integrated research infrastructure across DOE’s experimental and computational facilities.

Hahn, Steven↗
DOE OSTI2020

Conceptual Design of DUNE Near Detector Superconducting Magnet System

The Deep Underground Neutrino Experiment (DUNE) has formed a near detector design group (NDDG) tasked with delivering a CDR by the end of 2019. The DUNE Near Detectors will be housed in an underground hall on the Fermilab site. The two main detector systems are a liquid argon detector and a high-pressure gas time projection chamber (HPgTPC). The HPgTPC requires a magnet that generates a 0.5 T solenoidal magnetic field in a large volume of 6 m diameter, and 5 m length. Herein, we present a superconducting magnet system design. We investigated: an open air core magnet with three coils, and a five coils system having two active fringe field shielding coils. Coils positions were optimized to obtain the specified field homogeneity in the magnet good field region, while minimizing the Lorentz forces and the superconductor volume. We discuss the magnetic, mechanical, and thermal conceptual designs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Design of Stand-Alone Cryomodule based on Superconducting Quarter-Wave Resonator

Present superconducting accelerators require a central refrigeration system and piping to distribute liquid helium and nitrogen. Stand-alone cryocooler-based systems are of significant interest both to scientific and industrial customers, as they allow applications at facilities where no cryogenic equipment and expertise exists, where it is undesirable to develop additional cryogenic systems, or where a mobile system that can be transported between beamlines or facilities is required. The technical challenge of this approach is to minimize the heat losses to the liquid helium so that commercially available, self-contained cryocoolers are able to handle the thermal load. This paper presents an improved design for a stand-alone quarter-wave resonator (QWR) cryomodule a based on commercially available cryocoolers. This cryomodule has a particular application in the ATLAS intensity upgrade project where it will house a 72.75 MHz QWR for beam energy adjustments to experimental areas, where no helium cryogenic systems exist. The described cryomodule may also be the critical part of a turnkey system with application to material processing, semiconductor manufacturing, food irradiation and homeland security. The development of low heat loss peripheral components reduces cost and widens applicability of the system. We will discuss the cryomodule and RF coupler designs, including numerical thermal and structural analyses.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Advancing Reel-to-Reel Inspection Techniques for Long HTS Conductors: Comparison and Innovations

The continuous advancement of high-temperature superconductor (HTS) technologies has greatly accelerated the development and deployment of HTS applications. Among the critical tools supporting these advancements are reel-to-reel (R2R) critical current (I c ) measurement techniques, which are extensively used by both manufacturers and end users to characterize long-length REBCO conductors. These techniques play a vital role in quality assurance and quality control (QA/QC), ensuring the reliability and performance of HTS conductors and applications throughout the production cycle. We have developed a range of in-house devices for R2R measurements at the University of Houston and Princeton Plasma Physics Laboratory. These include one-dimensional (1D) scan using a magnetic circuit (MC) and two-dimensional (2D) magnetic field mapping systems based on scanning probe array microscope (SPAM) or scanning probe microscopy (SPM). Each technique offers distinct advantages: the MC system provides ultra-fast scanning speeds, ideal for rapid inspection in large-scale industrial production; the high-resolution SPM delivers detailed insights for conductor research and development; and the SPAM, with its simpler mechanical setup, can be upgraded for higher field and lower temperature conditions with a balanced 2D resolution. Here we compared the magnetization and detection capabilities of these techniques through experiments on rare-earth barium copper oxide (REBCO) coated conductor samples, with data analysis supported by numerical simulations. Based on our comprehensive comparative studies, we propose enhancements for each measurement system and provide guidelines for selecting the optimal technique combinations to meet specific application requirements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Non-Intrusive Appliance Identification with Appliance-Specific Networks

The problem of noninstrusive load monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and do not generalize very well. In this article, we propose a novel NILM method that leverages advances in statistical learning that have not been properly applied in this domain before. The proposed method consists of three stages: first, a Bayesian nonparametric learning-based approach for appliance state extraction; second, synthetic minority oversampling technique for data augmentation and mitigating the heavy imbalance in switching events; and third, appliance-specific lightweight long short-term memory networks for status classification for each appliance. Here, we adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multiappliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples.

42 ENGINEERING↗
DOE OSTI2023

A Framework to Design Consumer-Centric Operational Strategies for Resilience Enhancement

Extreme temperature-related events like heat waves or cold snaps can significantly stress the power distribution grid as electricity demand spikes leading to brownouts or blackouts if not managed properly. In addition, such extreme events can exacerbate inequity, with vulnerable populations (for example, houses with poor insulation, located in non-critical zones, and lack of local resources) at greater risk. There is a growing deployment of distribution systems automation technologies such as advanced metering infrastructure, sensors, and automated control systems to enhance the visibility of the entire distribution grid while meeting resilience objectives. Here, this paper proposes a framework for investigating how automation can improve the distribution system's resilience and ensure customers' health and safety during such extremes. The proposed framework's efficacy will be demonstrated for the Electricity Reliability Council of Texas (ERCOT) during winter storm Uri with varying levels of distribution automation technologies such as feeder isolation using smart switches, remote outage signals using advanced meters, and comfort-aware outage using advanced analytics and communication. Simulation results show that an outage strategy involving advanced grid technology and communication can reduce the occupant exposure to severe cold by 93% and, simultaneously, reduce expected energy not served by 73.2% when compared against feeder isolation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Pressure-Driven Fiber-Optic Sensor for Online Corrosion Monitoring

For many industrial applications, corrosion is a life-limiting phenomenon and therefore requires careful consideration and rigorous experimental testing before structural materials can be reliably deployed, particularly in harsh chemical environments. The traditional approach to measuring corrosion requires exposing many samples to the intended environment and then extracting them individually at discrete intervals for postexposure characterization. This approach does not provide a high degree of temporal resolution, nor does it provide any real-time information regarding dynamic changes in corrosion rates. Additionally, developing an online corrosion monitor capable of surviving harsh chemical environments could provide valuable information regarding the structural health of components and changing process conditions that could accelerate corrosion. To this end, a corrosion sensor was developed based on a pressure-driven Fabry-Pérot cavity (FPC). This sensor uses a pressure control system to internally pressurize the FPC formed between the sensor’s housing and a metal-embedded singlemode optical fiber. Simultaneous measurement of the change in FPC length using low-coherence interferometry and the applied pressure enables the calculation of the relative changes in the sensor’s diaphragm thickness due to corrosion on its outer surface. Measurements were made in situ while actively corroding the sensor and were validated against surveillance specimens that were corroded simultaneously. The uncertainties in the measured corrosion were analyzed using error propagation of the uncertainties in the measured pressures and displacements and were found to be <1% of the initial diaphragm thickness.

42 ENGINEERING↗
DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗