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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

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
NASA NTRS2020

Lessons Learned from Sonic Boom Flight Research Projects conducted by NASA Armstrong Flight Research Center

Over the course of four years, a team of aerospace engineers at the National Aeronautics and Space Administration Armstrong Flight Research Center completed four projects, each with the objective to research sonic boom signatures from a ground and building level perspective. The relatively compressed timeline of these projects resulted in the team amassing a large number of lessons learned. With each successive project, these lessons have been more relied upon and referenced. This report serves as a written record of the teams tribal knowledge capturing the relevant lessons learned and their importance for future projects.

Erin R Waggoner↗
NASA NTRS2016

Enabling the Direct Detection of Earth-Sized Exoplanets with the LBTI HOSTS Project: A Progress Report

NASA has funded a project called the Hunt for Observable Signatures of Terrestrial Systems (HOSTS) to survey nearby solar type stars to determine the amount of warm zodiacal dust in their habitable zones. The goal is not only to determine the luminosity distribution function but also to know which individual stars have the least amount of zodiacal dust. It is important to have this information for future missions that directly image exoplanets as this dust is the main source of astrophysical noise for them. The HOSTS project utilizes the Large Binocular Telescope Interferometer (LBTI), which consists of two 8.4-m apertures separated by a 14.4-m baseline on Mt. Graham, Arizona. The LBTI operates in a nulling mode in the mid-infrared spectral window (8-13 micrometers), in which light from the two telescopes is coherently combined with a 180 degree phase shift between them, producing a dark fringe at the location of the target star. In doing so the starlight is greatly reduced, increasing the contrast, analogous to a coronagraph operating at shorter wavelengths. The LBTI is a unique instrument, having only three warm reflections before the starlight reaches cold mirrors, giving it the best photometric sensitivity of any interferometer operating in the mid-infrared. It also has a superb Adaptive Optics (AO) system giving it Strehl ratios greater than 98% at 10 micrometers. In 2014 into early 2015 LBTI was undergoing commissioning. The HOSTS. project team passed its Operational Readiness Review (ORR) in April 2015. The team recently published papers on the target sample, modeling of the nulled disk images, and initial results such as the detection of warm dust around eta Corvi. Recently a paper was published on the data pipeline and on-sky performance. An additional paper is in preparation on Beta Leo. We will discuss the scientific and programmatic context for the LBTI project, and we will report recent progress, new results, and plans for the science verification phase that started in February 2016, and for the survey.

Hunt for Observable Signatures of Terrestrial Syst↗
DOE OSTI2026

Dark Matter-Induced Nuclear De-Excitation at SBND with Ab Initio Nuclear Theory

We explore the sensitivity of the Short-Baseline Near Detector (SBND) experiment to light dark matter using MeV-scale electromagnetic activity. Inelastic scattering of dark matter with argon nuclei can excite nuclear states that subsequently de-excite via the emission of MeV-scale photons, producing localized low-energy "blip" signatures in a liquid argon time projection chamber. We perform state-of-the-art ab initio nuclear calculations, including all relevant argon excited states with energies up to 18 MeV, to provide reliable predictions for these signals. After accounting for relevant backgrounds, we find that SBND can probe previously unexplored regions of parameter space for light dark matter.

Dutta, Bhaskar [Bucharest, IFIN-HH; Texas A-M] (OR↗
NASA NTRS2018

An Overview of Lessons Learned from Sonic-boom Flight Research Projects Conducted by NASA Armstrong Flight Research Center

Over the course of four years, a team of aerospace engineers at the National Aeronautics and Space Administration Armstrong Flight Research Center completed four projects, each with the objective to research sonic-boom signatures from a ground-and building-level perspective. The relatively compressed timeline of these projects resulted in many lessons learned. With each successive project, these lessons have been more relied upon and referenced. This paper provides a high-level overview of the teams relevant lessons learned and the importance of these lessons for future projects.

lessons learned↗
DOE OSTI2022

Discovery of Signatures, Anomalies, and Precursors in Synchrophasor Data with Matrix Profile and Deep Recurrent Neural Networks (Final Project Report)

The widespread deployment of phasor measurement unit (PMU) across the U.S. together with the burgeoning machine learning technology made it possible to develop data-driven PMU data analytics to improve grid security and reliability in a more insightful and effective manner. Although PMU applications have been explored for over a decade, the representative PMU usage is limited to the bulk power system monitoring mainly due to the data integrity issues associated with PMUs (typically missing, fragmented, and wrongly amplified data). To forge a breakthrough on this stalemate and embrace PMUs for power system control and protection as well, we applied various advanced machine learning and big data analysis technology to the power system event detection and classification as the first step toward the power system control and protection pertaining to grid security enhancement.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2015

System-Level Experimental Validations for Supersonic Commercial Transport Aircraft Entering Service in the 2018-2020 Time Period

This report describes the work conducted under NASA funding for the Boeing N+2 Supersonic Experimental Validation project to experimentally validate the conceptual design of a supersonic airliner feasible for entry into service in the 2018 -to 2020 timeframe (NASA N+2 generation). The primary goal of the project was to develop a low-boom configuration optimized for minimum sonic boom signature (65 to 70 PLdB). This was a very aggressive goal that could be achieved only through integrated multidisciplinary optimization tools validated in relevant ground and, later, flight environments. The project was split into two phases. Phase I of the project covered the detailed aerodynamic design of a low boom airliner as well as the wind tunnel tests to validate that design (ref. 1). This report covers Phase II of the project, which continued the design methodology development of Phase I with a focus on the propulsion integration aspects as well as the testing involved to validate those designs. One of the major airplane configuration features of the Boeing N+2 low boom design was the overwing nacelle. The location of the nacelle allowed for a minimal effect on the boom signature, however, it added a level of difficulty to designing an inlet with acceptable performance in the overwing flow field. Using the Phase I work as the starting point, the goals of the Phase 2 project were to design and verify inlet performance while maintaining a low-boom signature. The Phase II project was successful in meeting all contract objectives. New modular nacelles were built for the larger Performance Model along with a propulsion rig with an electrically-actuated mass flow plug. Two new mounting struts were built for the smaller Boom Model, along with new nacelles. Propulsion integration testing was performed using an instrumented fan face and a mass flow plug, while boom signatures were measured using a wall-mounted pressure rail. A side study of testing in different wind tunnels was completed as a precursor to the selection of the facilities used for validation testing. As facility schedules allowed, the propulsion testing was done at the NASA Glenn Research Center (GRC) 8 x 6-Foot wind tunnel, while boom and force testing was done at the NASA Ames Research Center (ARC) 9 x 7-Foot wind tunnel. During boom testing, a live balance was used for gathering force data. This report is broken down into nine sections. The first technical section (Section 2) covers the general scope of the Phase II activities, goals, a description of the design and testing efforts, and the project plan and schedule. Section 3 covers the details of the propulsion system concepts and design evolution. A series of short tests to evaluate the suitability of different wind tunnels for boom, propulsion, and force testing was also performed under the Phase 2 effort, with the results covered in Section 4. The propulsion integration testing is covered in Section 5 and the boom and force testing in Section 6. CFD comparisons and analyses are included in Section 7. Section 8 includes the conclusions and lessons learned.

Magee, Todd E.↗
NASA NTRS1988

The Serendip piggyback SETI project

The Serendip project, an ongoing SETI program of monitoring and processing broadband radio signals acquired by existing radio astronomy observatories, are summarized. Serendip operates in a piggyback mode, making use of whatever observing plan is under way at its host observatory. The Serendip system at NRAO and the signature detection and identification techniques used by the project are described. The method used to reject terrestrial interference is discussed.

Lampton, Michael↗
DOE OSTI2025

First Constraint on Atmospheric Millicharged Particles with the LUX-ZEPLIN Experiment

We report on a search for millicharged particles (mCPs) produced in cosmic ray atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes—meson decay and proton bremsstrahlung—are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers, allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c 2 and fractional charges between 0.001 and 0.02 of the electron charge (𝑒). With an exposure of 60 live days and a 5.5 metric ton fiducial mass, we observed no significant excess over background. This represents the first experimental search for atmospheric mCPs and the first search for mCPs using an underground LXe experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1986

The Red River Valley archeological project

The Red River Valley Archeology Project is a long-term effort involving numerous individuals and institutions engaged in archeological investigations in the Texas and Oklahoma portions of the Red River Valley. To date the focus of the project was on site location. The project acquired both Thermal Infrared Multispectral Scanner (TIMS), TMS, and color infrared photographs over a significant portion of the project area in an effort to define signatures for archeological sites and to assist in the detailed geomorphological mapping of the flood plain. Preliminary analysis of acquired data indicates that both the TIMS and TMS can make a substantial contribution to landform definition, the identification of cultural resources, and to the clarification of site-landform correlations in this riverine environment.

Bennett, Jack↗
NASA NTRS1993

Evolution of miniature detectors and focal plane arrays for infrared sensors

Sensors that are sensitive in the infrared spectral region have been under continuous development since the WW2 era. A quest for the military advantage of 'seeing in the dark' has pushed thermal imaging technology toward high spatial and temporal resolution for night vision equipment, fire control, search track, and seeker 'homing' guidance sensing devices. Similarly, scientific applications have pushed spectral resolution for chemical analysis, remote sensing of earth resources, and astronomical exploration applications. As a result of these developments, focal plane arrays (FPA) are now available with sufficient sensitivity for both high spatial and narrow bandwidth spectral resolution imaging over large fields of view. Such devices combined with emerging opto-electronic developments in integrated FPA data processing techniques can yield miniature sensors capable of imaging reflected sunlight in the near IR and emitted thermal energy in the Mid-wave (MWIR) and longwave (LWIR) IR spectral regions. Robotic space sensors equipped with advanced versions of these FPA's will provide high resolution 'pictures' of their surroundings, perform remote analysis of solid, liquid, and gas matter, or selectively look for 'signatures' of specific objects. Evolutionary trends and projections of future low power micro detector FPA developments for day/night operation or use in adverse viewing conditions are presented in the following test.

Watts, Louis A.↗
NASA NTRS1995

Observational studies of the clearing phase in proto-planetary disk systems

Progress in this study is summarized for its second year. An extensive program of high dispersion UV spectral studies using the IUE (International Ultraviolet Explorer) has resulted in acquisition of 40 Herbig Ae/Be star and related object spectra. We find that accreting, circumstellar gas is detected with velocities consistent with material in free-fall toward the stars in approximately 38% of the sample. With acquisition of optical measurements of the projected stellar rotational velocities, we find that the systems with accreting gas exhibit systematically higher projected rotational velocities than the systems showing signatures of outflowing material only. When combined with polarimetric and photometric data, the IUE spectra for these stars indicate that accretion in intermediate-mass pre-main sequence stars is confined to the plane of the circumstellar dust disk. This is in contrast to lower-mass PMS stars where accretion, persumably along magnetic field lines, is seen at polar latitudes. Our data also support significant clearing of the central regions of these circumstellar disks, as originally suggested. The model which most closely matches the observational data is accretion from a disk envelope, as developed by Calvet et al. (1994).

Grady, Carol A.↗
NASA NTRS2011

The Effect of Sonic Booms on Earthquake Warning Systems

Several aerospace companies are designing quiet supersonic business jets for service over the United States. These aircraft have the potential to increase the occurrence of mild sonic booms across the country. This leads to interest among earthquake warning (EQW) developers and the general seismological community in characterizing the effect of sonic booms on seismic sensors in the field, their potential impact on EQW systems, and means of discriminating their signatures from those of earthquakes. The SonicBREWS project (Sonic Boom Resistant Earthquake Warning Systems) is a collaborative effort between Seismic Warning Systems, Inc. (SWS) and NASA Dryden Flight Research Center. This project aims to evaluate the effects of sonic booms on EQW sensors. The study consists of exposing high-sample-rate (1000 sps) triaxial accelerometers to sonic booms with overpressures ranging from 10 to 600 Pa in the free field and the built environment. The accelerometers record the coupling of the sonic boom to the ground and surrounding structures, while microphones record the acoustic wave above ground near the sensor. Sonic booms are broadband signals with more high-frequency content than earthquakes. Even a 1000 sps accelerometer will produce a significantly aliased record. Thus the observed peak ground velocity is strongly dependent on the sampling rate, and increases as the sampling rate is reduced. At 1000 sps we observe ground velocities that exceed those of P-waves from M L 3 earthquakes at local distances, suggesting that sonic booms are not negligible for EQW applications. We present the results of several experiments conducted under SonicBREWS showing the effects of typical-case low amplitude sonic booms and worst-case high amplitude booms. We show the effects of various sensor placements and sensor array geometries. Finally, we suggest possible avenues for discriminating sonic booms from earthquakes for the purposes of EQW.

Wurman, Gilead↗
DOE OSTI2021

Multi-INT Signature Collection and Exploitation for Security Los Alamos LDRD Report

This project demonstrates the feasibility of a 2-D passive tracking capability for ground-based and airborne moving sources using multi-INT signal processing of acoustic and seismic signals. We leveraged the recent DDSTE-supported investment in rapid deployable sensors (the Los Alamos Rapid-deployable Sensor Array—LARSA), and used uncrewed aerial vehicles (UAV) as a data source. Moving source types are characterized by their variability with time (exemplified but not limited to the Doppler shift of a passing vehicle) and the signatures used for detection should have similar characteristics. We have demonstrated that time-varying sensor signatures (TVS) combined with time-stable sensor signatures provide a more complete understanding of moving source behavior and drive towards path prediction. Importantly, we have collected a new, relevant LANL dataset of moving sources (small UAV flights and vehicle movement), developed new signatures, and demonstrated a capability to identify, track and forecast the position of TVS.

42 ENGINEERING↗
NASA NTRS2011

Component-Level Electronic-Assembly Repair (CLEAR) Spacecraft Circuit Diagnostics by Analog and Complex Signature Analysis

The Component-Level Electronic-Assembly Repair (CLEAR) project at the NASA Glenn Research Center is aimed at developing technologies that will enable space-flight crews to perform in situ component-level repair of electronics on Moon and Mars outposts, where there is no existing infrastructure for logistics spares. These technologies must provide effective repair capabilities yet meet the payload and operational constraints of space facilities. Effective repair depends on a diagnostic capability that is versatile but easy to use by crew members that have limited training in electronics. CLEAR studied two techniques that involve extensive precharacterization of "known good" circuits to produce graphical signatures that provide an easy-to-use comparison method to quickly identify faulty components. Analog Signature Analysis (ASA) allows relatively rapid diagnostics of complex electronics by technicians with limited experience. Because of frequency limits and the growing dependence on broadband technologies, ASA must be augmented with other capabilities. To meet this challenge while preserving ease of use, CLEAR proposed an alternative called Complex Signature Analysis (CSA). Tests of ASA and CSA were used to compare capabilities and to determine if the techniques provided an overlapping or complementary capability. The results showed that the methods are complementary.

Oeftering, Richard C.↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

CWMD Nuclear Forensics Quarterly Report

Analysis and evaluation of nuclear debris is a key component to identify forensic signatures in a post-detonation environment. Currently, methods exist and are practiced in obtaining signatures from post-detonation debris. However, efforts to reduce the timeline in obtaining signatures is continuous within nuclear forensics research and will be forwarded by this research. This education and research project will explore combined material chemistry and separations of radionuclides to provide actionable signatures in less time than standard methods. The research will use the phase distribution of radionuclides in debris to develop targeted and rapid dissolution methods. These dissolution methods will be coupled with microfluidics to achieve samples suitable for analysis within reduced timelines.

and nuclear chemistry↗
NASA NTRS2016

A Psychoacoustic Evaluation of Noise Signatures from Advanced Civil Transport Aircraft

The NASA Environmentally Responsible Aviation project has been successful in developing and demonstrating technologies for integrated aircraft systems that can simultaneously meet aggressive goals for fuel burn, noise and emissions. Some of the resulting systems substantially differ from the familiar tube and wing designs constituting the current civil transport fleet. This study attempts to explore whether or not the effective perceived noise level metric used in the NASA noise goal accurately reflects human subject response across the range of vehicles considered. Further, it seeks to determine, in a quantitative manner, if the sounds associated with the advanced aircraft are more or less preferable to the reference vehicles beyond any differences revealed by the metric. These explorations are made through psychoacoustic tests in a controlled laboratory environment using simulated stimuli developed from auralizations of selected vehicles based on systems noise assessments.

Rizzi, Stephen A.↗