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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Discovery of a Fast Iron Low-ionization Outflow in the Early Evolution of the Nearby Tidal Disruption Event AT 2019qiz

We report the results of ultraviolet (UV) and optical photometric and spectroscopic analysis of the tidal disruption event (TDE) AT 2019qiz. Our follow-up observations started <10 days after the source began to brighten in the optical and lasted for a period of six months. Our late-time host-dominated spectrum indicates that the host galaxy likely harbors a weak active galactic nucleus. The initial Hubble Space Telescope (HST) spectrum of AT 2019qiz exhibits an iron and low-ionization broad absorption line (FeLoBAL) system that is seen for the first time in a TDE. This spectrum also bears a striking resemblance to that of Gaia16apd, a superluminous supernova. Our observations provide insights into the outflow properties in TDEs and show evidence for a connection between TDEs and engine-powered supernovae at early phases, as originally suggested by Metzger & Stone (2016). In a time frame of 50 days, the UV spectra of AT 2019qiz started to resemble those of previous TDEs with only high-ionization BALs. The change in UV spectral signatures is accompanied by a decrease in the outflow velocity, which began at 15,000 km/s and decelerated to ~ 10,000 km/s. A similar evolution in the Ha emission-line width further supports the speculation that the broad Balmer emission lines are formed in TDE outflows. In addition, we detect narrow absorption features on top of the FeLoBAL signatures in the early HST UV spectrum of AT 2019qiz. The measured H I column density corresponds to a Lyman-limit system, whereas the metal absorption lines (such as N V, C IV, Fe II, and Mg II) are likely probing the circumnuclear gas and interstellar medium in the host galaxy.

Tiara Hung↗
NASA NTRS2020

Automatic Feature Tracking on Small Bodies for Autonomous Approach

Abstract—The autonomous approach of a spacecraft to an asteroid or comet (a small body) relies heavily on visual feature tracking to aid in estimating relative trajectories and the properties of the small body. Feature tracking for small bodies brings several challenges, including changing lighting, poor visual texture, and a concentration of features in a small part of an image. Six existing, open-source algorithms for feature tracking were tested on a simulated dataset and compared to the ground truth in the path of features. The main finding is that none of the algorithms provide all of the desired characteristics of long feature tracks with low errors and few outliers. Instead, there is a trade-off between long feature tracks and low error. The feature-matching algorithms SIFT, and BRISK provide good error characteristics, but short feature tracks, whereas the optical flow algorithm KLT provides long feature tracks, but with many features of large error. Given the challenges in feature tracking, it is recommended to focus development on each component of a feature tracking system: detection, description, and outlier rejection.

Morrell, Benjamin J↗
NASA NTRS2023

Thermal Emission from the Earth-sized Exoplanet TRAPPIST-1 b using JWST

The TRAPPIST-1 system is remarkable for its seven planets that are similar in size, mass, density, and stellar heating to the rocky planets Venus, Earth, and Mars in our own Solar System [1]. All TRAPPIST-1 planets have been observed with the transmission spectroscopy technique using the Hubble or Spitzer Space Telescopes, but no atmospheric features have been detected or strongly constrained [2–5]. TRAPPIST-1 b is the closest planet to the system’s M dwarf star, and it receives 4 times as much irradiation as Earth receives from the Sun. This relatively large amount of stellar heating suggests that its thermal emission may be measurable. Here we present photometric secondary eclipse observations of the Earth-sized TRAPPIST1 b exoplanet using the F1500W filter of the MIRI instrument on JWST. We detect the secondary eclipse in each of five separate observations with 8.7-sigma confidence when all data are combined. These measurements are most consistent with re-radiation of the TRAPPIST-1 star’s incident flux from only the dayside hemisphere of the planet. The most straightforward interpretation is that there is little or no planetary atmosphere redistributing radiation from the host star and also no detectable atmospheric absorption from carbon dioxide (CO 2 ) or other species.

Thermal Emission↗
NASA NTRS2023

Relationship Between the Sub-Micron Fraction (SMF) and Fine-Mode Fraction (FMF) in the Context of AERONET Retrievals

The sub-micron (SM) aerosol optical depth (AOD) is an optical separation based on the fraction of particles below a specified cutoff radius of the particle size distribution (PSD) at a given particle radius. It is fundamentally different from spectrally separated FM (fine-mode) AOD. We present a simple (AOD-normalized) SM fraction versus FM fraction (SMF vs. FMF) linear equation that explains the well-recognized empirical result of SMF generally being greater than the FMF. The AERONET inversion (AERinv) products (combined inputs of spectral AOD and sky radiance) and the spectral deconvolution algorithm (SDA) products (input of AOD spectra) enable, respectively, an empirical SMF vs. FMF comparison at similar (columnar) remote sensing scales across a variety of aerosol types. SMF (AERinv-derived) vs. FMF (SDA-derived) behavior is primarily dependent on the relative truncated portion (ε c ) of the coarse-mode (CM) AOD associated with the cutoff portion of the CM PSD and, to a second order, the cutoff FM PSD and FM AOD (ε f ). The SMF vs. FMF equation largely explains the SMF vs. FMF behavior of the AERinv vs. SDA products as a function of PSD cutoff radius (“inflection point”) across an ensemble of AERONET sites and aerosol types (urban-industrial, biomass burning, dust, maritime and a mixed class of Arctic aerosols). The overarching dynamic was that the linear SMF vs. FMF relation pivots clockwise about the approximate (SMF, FMF) singularity of (1, 1) in a “linearly inverse” fashion (slope and intercept of approximately 1−ε c and ε c ) with increasing cutoff radius. SMF vs. FMF slopes and intercepts derived from AERinv and SDA retrievals confirmed the general domination of ε c over ε f in controlling that dynamic. A more general conclusion is the apparent confirmation that the optical impact of truncating modal (whole) PSD features can be detected by an SMF vs. FMF analysis.

Norman T. O'Neill↗
NASA NTRS2023

Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products

Coastal upwelling regions are one of the most dynamic areas of the world’s oceans. The California and Baja California Coasts are impacted by both coastal upwelling and the California Current, leading to frontal activity that is captured by gradients in both Sea Surface Temperature (SST) and Sea Surface Salinity (SSS). Satellite data are a great source of spatial data to study fronts. However, biases near coastal areas and coarse resolutions can impair its usefulness in upwelling areas. In this work gradients in SST from NASA Multi-Scale Ultra-High Resolution (MUR) and in two SSS products derived from the Soil Moisture Active Passive (SMAP) NASA mission are compared directly with gradients derived from the Saildrone uncrewed vehicles to validate the gradients as well as to assess their ability to detect known frontal features. The three remotely sensed data sets (MURSST/JPL, SMAP SSS/RSS, SMAP SSS) were co-located with the Saildrone data prior to the calculation of the gradients. Wavelet analysis is used to determine how well the satellite derived SST and SSS products are reproducing the Saildrone derived gradients. Overall results indicate the remote sensing products are reproducing features of known areas of coastal upwelling. Differences between the SST and SSS gradients are mainly associated with the limitations of the microwave derived SSS coverage near land and its reduced spatial resolution. The results are promising for using remote sensing data sets to monitor frontal structure along the California Coast and the application to long term changes in coastal upwelling and dynamics.

Sea Surface Salinity↗
DOE OSTI2024

DETERMINATION OF STRUT QUALITY FACTORS IN ADDITIVELY MANUFACTURED LATTICES USING IN-SITU COMPRESSION TESTING µ-CT

In response to the need for an automated, commercial method to qualify additively manufactured (AM) lattice components, an experiment was conducted to evaluate the effects of defective lattice struts on the structural compression strength. Lattice samples with known defective or missing struts were compressed using a Deben CT5000RT and imaged using xray µ-CT. The compressive force and x-ray computed tomography results were compared to defect free standards to evaluate the impact of each defect type on the overall structure’s compressive strength. This analysis will allow for simplifications to Finite Element Analysis (FEA) on AM parts without sacrificing model fidelity. Understanding the contribution of each defect type and severity will also better inform non-destructive evaluation (NDE) personnel of the inspection parameters necessary to detect the smallest feature of importance.

Dinova, Vincent A.↗
NASA NTRS1972

On the possible detection of H2 in Titan's atmosphere.

Review of the weak absorption features in Titan's spectrum detected at the wavelengths of the 3-0S(1)H2 quadrupole line and, with less certainty, the S(0) line of the same band. The probable reality of these features is discussed in terms of a statistical analysis of the photometric data, and evidence is presented that they are not scattered from Saturn by the sky or the telescope. Based on the assumption that this absorption arises from H2, implied abundances are estimated, and the implications this has for escape and outgassing rates are discussed.

Trafton, L.↗
DOE OSTI2021

Learning the Low Frequency Earthquake Activity on the Central San Andreas Fault

Abstract Low frequency earthquakes (LFEs) originating below the central San Andreas Fault are associated with slow‐slip beneath the seismogenic zone within the more ductile portion of the crust. Monitoring efforts over 15 years detected >1 million LFEs. We train a gradient boosted tree model using statistical features describing the seismic waveforms to estimate the hourly LFE event count. The burst‐like LFE behavior is reproduced, while lower amplitudes are predicted during the most active periods. The hourly event counts are up to 18% greater than the catalog. The ability to continuously monitor LFE activity provides insight to when geodetic measurements of slow slip are possible, without the need for developing a computational‐intensive template‐matching catalog. Similar waveform statistical features are found between detecting LFEs and tremors, which provides additional evidence tremors are composed of LFEs. The approach extracts information contained in continuous seismic waveforms that might benefit detecting precursory signals.

58 GEOSCIENCES↗
NASA NTRS2005

Discovery of Fast X-ray Oscillations During the 1998 Giant Flare from SGR 1900+14

We report the discovery of complex high frequency variability during the August 27, 1998 giant flare from SGR 1900+14 using the Rossi X-ray Timing Explorer (RXTE). We detect an approx. equals 84 Hz oscillation (QPO) during a 1 s interval beginning approximately 1 min after the initial hard spike. The amplitude is energy dependent, reaching a maximum of 26% (rms) for photons above 30 keV, and is not detected below 11 keV, with a 90% confidence upper limit of 14% (rms). Remarkably, additional QPOs are detected in the average power spectrum of data segments centered on the rotational phase at which the 84 Hz signal was detected. Two signals, at 53.5 and 155.1 Hz, are strongly detected, while a third feature at 28 Hz is found with lower significance. These QPOs are not detected at other rotational phases. The phenomenology seen in the SGR 1900+14 flare is similar to that of QPOs recently reported by Israel et al. (2005) from the December 27, 2004 flare from SGR 1806-20, suggesting they may have a common origin, perhaps torsional vibrations of the neutron star crust. Indeed, an association of the four frequencies (in increasing order) found in SGR 1900+14 with l = 2, 4, 7, and 13 toroidal modes appears plausible. We discuss our findings in the context of this model and show that if the stars have similar masses then the magnetic field in SGR 1806-20 must be about twice as large as in SGR 1900+14, broadly consistent with estimates from pulse timing.

Strohmayer, T.↗
DOE OSTI2023

Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Feature Engineering and Ensemble Methods for Imbalanced ICS Intrusion Detection: Pipeline Audit and Constrained Evaluation

Industries are becoming increasingly connected and are more vulnerable to cyberattacks due to the widened attack surface. Industrial Control Systems (ICS) are among the most critical sectors that malicious actors can target, as such attacks can cause significant operational disruption and physical damage. It is imperative to detect such attacks as early as possible. This paper evaluates constraint-conditioned optimistic performance estimates for traditional ML models in ICS intrusion detection (i.e., estimates obtained under contiguous, non-shuffled temporal evaluation without test-set alteration, but with pre-split feature engineering that may introduce temporal leakage, due to dataset constraints). Our findings are threefold. First, we quantify how iterative feature engineering affects tree-based ensemble performance and examine how pipeline decisions (split strategy, sampling scope, and cleaning policy) can inflate or reduce reported IDS results under constraint-bound evaluation. Second, we compare intrinsic class-imbalance handling across ensemble models. Third, under our current pipeline constraints (including pre-split feature engineering), CatBoost achieves the best performance on Water Storage Tank (accuracy: 0.9831, class-1 F1: 0.9682), while Light- GBM achieves the best performance on Gas Pipeline (accuracy: 0.9618, class-1 F1: 0.9086).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings

In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features

The internetworking of grid-connected power electronics converters (PECs) in photovoltaic (PV) farms has inevitably expanded the cyber-attack surfaces. Here this paper presents a comprehensive study on cyber-attack detection and diagnosis for PEC-enabled PV farms via single waveform sensor to distinguish between normal conditions, open-circuit faults, short-circuit faults, and cyber-attacks. To our knowledge, this has not been attempted before. Firstly, we propose frequency-domain magnitude-based residuals to identify short-circuit faults and a time-domain mean current vector-based feature to distinguish open-circuit faults from other threats. These features can fully reflect the specific physical characteristics of PV farms during threat duration. Secondly, unlike micro phasor measurement units (µPMU) and raw electric waveform-based methods, the proposed innovative features can address novel cyber-attacks that are excluded from the training process. Thirdly, an online hardware-in-the-loop (HIL) testbed using the OPAL-RT real-time digital simulator has verified the effectiveness. The monitoring system runs in real-time while using HIL as an operational solar farm and a National Instruments (NI) data acquisition card as the electric waveform sensor at the point of coupling.

42 ENGINEERING↗
NASA NTRS1989

The H2 4-0 S(0, 1, and 2) quadrupole features in Jupiter

In the wake of the detection of Jupiter's H2 S4(2) feature, low-temperature laboratory measurements of NH3 are used in a detailed analysis to ascertain an improved equivalent width for the H2 S4(0) line. Good agreement is obtained between equivalent widths computed from published models and the equivalent widths observed for this line. It is noted that the approximately 150 K effective formation line temperature at Jupiter's averaged disk center, which has been determined from the nominal S4(2)/S4(0) ratio, is in keeping with an H2 ortho-para ratio for Jovian equilibrium tropospheric H2.

Smith, Wm. Hayden↗
DOE OSTI2023

Middleware for a Heterogeneous CAV Fleet

This paper introduces CAN to ROS, a model-based code generation tool used in development, testing, and deployment of a heterogeneous fleet of vehicles with robotic sensing in ROS. Code generation supports two main features: (1) self-configuration for deployment in a heterogeneous vehicle fleet, and (2) quick iteration for testing and development of reading vehicle sensors and robotic control. This tool features the ability to detect the vehicle it is in and regenerate and rebuild itself at runtime to provide the proper two-way bridge between ROS and the sensed on-board vehicle sensor network. Code generation relies on a per-model defined JSON to map a CAN database (DBC) to the desired ROS topic names and message types. The live ROS publishing of CAN messages allows for instant feedback, and the code regeneration allows for adjustments in DBC or vehicle JSON to iteratively hone in on new vehicle signals. Generated ROS nodes are written in C++ for runtime use in lightweight embedded computers. This has been tested in vehicles from three different Original Equipment Manufacturers (OEMs), and can be extended to support a wide array of vehicles. By using a unifying ROS specification, a heterogeneous set of vehicles can be unified into a fleet with abstracted model-specific details; this opens the door for developing cross-model software applications for vehicle control, connected vehicle applications, or fleet monitoring systems.

42 ENGINEERING↗
NASA NTRS2000

A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays

Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.

Non-NASA Center↗
NASA NTRS2005

Automated Extraction of Secondary Flow Features

The use of Computational Fluid Dynamics (CFD) has become standard practice in the design and development of the major components used for air and space propulsion. To aid in the post-processing and analysis phase of CFD many researchers now use automated feature extraction utilities. These tools can be used to detect the existence of such features as shocks, vortex cores and separation and re-attachment lines. The existence of secondary flow is another feature of significant importance to CFD engineers. Although the concept of secondary flow is relatively understood there is no commonly accepted mathematical definition for secondary flow. This paper will present a definition for secondary flow and one approach for automatically detecting and visualizing secondary flow.

Dorney, Suzanne M.↗
NASA NTRS2020

Exocomets from a Solar System Perspective

Exocomets are small bodies releasing gas and dust which orbit stars other than the Sun. Their existence was first inferred from the detection of variable absorption features in stellar spectra in the late 1980s using spectroscopy. More recently, they have been detected through photometric transits from space, and through far-IR/mm gas emission within debris disks. As (exo)comets are considered to contain the most pristine material accessible in stellar systems, they hold the potential to give us information about early stage formation and evolution conditions of extra solar systems. In the solar system, comets carry the physical and chemical memory of the protoplanetary disk environment where they formed, providing relevant information on processes in the primordial solar nebula. The aim of this paper is to compare essential compositional properties between solar system comets and exocomets to allow for the development of new observational methods and techniques. The paper aims to highlight commonalities and to discuss differences which may aid the communication between the involved research communities and perhaps also avoid misconceptions. The compositional properties of solar system comets and exocomets are summarized before providing an observational comparison between them. Exocomets likely vary in their composition depending on their formation environment like solar system comets do, and since exocomets are not resolved spatially, they pose a challenge when comparing them to high fidelity observations.

comets↗