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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
DOE OSTI2023

Anticipating gelation and vitrification with medium amplitude parallel superposition (MAPS) rheology and artificial neural networks

Abstract Anticipating qualitative changes in the rheological response of complex fluids (e.g., a gelation or vitrification transition) is an important capability for processing operations that utilize such materials in real-world environments. One class of complex fluids that exhibits distinct rheological states are soft glassy materials such as colloidal gels and clay dispersions, which can be well characterized by the soft glassy rheology (SGR) model. We first solve the model equations for the time-dependent, weakly nonlinear response of the SGR model. With this analytical solution, we show that the weak nonlinearities measured via medium amplitude parallel superposition (MAPS) rheology can be used to anticipate the rheological aging transitions in the linear response of soft glassy materials. This is a rheological version of a technique called structural health monitoring used widely in civil and aerospace engineering. We design and train artificial neural networks (ANNs) that are capable of quickly inferring the parameters of the SGR model from the results of sequential MAPS experiments. The combination of these data-rich experiments and machine learning tools to provide a surrogate for computationally expensive viscoelastic constitutive equations allows for rapid experimental characterization of the rheological state of soft glassy materials. We apply this technique to an aging dispersion of Laponite ® clay particles approaching the gel point and demonstrate that a trained ANN can provide real-time detection of transitions in the nonlinear response well in advance of incipient changes in the linear viscoelastic response of the system.

Lennon, Kyle R. (ORCID:0000000212515461)↗
DOE OSTI2020

Automated pipeline framework for processing of large-scale building energy time series data

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for “virtual” energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings’ time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building’s daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

36 MATERIALS SCIENCE↗
NASA NTRS1976

A reliable dual-redundant sensor FDI system for the NASA F8C-DFBW aircraft

In this paper we outline the structure of a sensor failure detection and identification (FDI) system designed for the NASA F8C DFBW aircraft. The system is for use in a dual-redundant environment, and it takes maximal advantage of all functional and kinematic relationships among the sensed variables. The technique is reliable in that we have designed the system to account for a variety of unmodeled effects and have introduced a new concept - the quality sequential probability ratio (QSPR) - that provides a useful measure of our confidence in each test. The methodology used for determining detector parameters and the 'outer loop logic' for combining the various sources of information are described.

Deckert, J. C.↗
NASA NTRS2012

Approaches for Achieving Broadband Achromatic Phase Shifts for Visible Nulling Coronagraphy

Visible nulling coronagraphy is one of the few approaches to the direct detection and characterization of Jovian and Terrestrial exoplanets that works with segmented aperture telescopes. Jovian and Terrestrial planets require at least 10(exp -9) and 10(exp -10) image plane contrasts, respectively, within the spectral bandpass and thus require a nearly achromatic pi-phase difference between the arms of the interferometer. An achromatic pi-phase shift can be achieved by several techniques, including sequential angled thick glass plates of varying dispersive materials, distributed thin-film multilayer coatings, and techniques that leverage the polarization-dependent phase shift of total-internal reflections. Herein we describe two such techniques: sequential thick glass plates and Fresnel rhomb prisms. A viable technique must achieve the achromatic phase shift while simultaneously minimizing the intensity difference, chromatic beam spread and polarization variation between each arm. In this paper we describe the above techniques and report on efforts to design, model, fabricate, align the trades associated with each technique that will lead to an implementations of the most promising one in Goddard's Visible Nulling Coronagraph (VNC).

Bolcar, Matthew R.↗
NASA NTRS2016

Orion GN&C Fault Management System Verification: Scope And Methodology

In order to ensure long-term ability to meet mission goals and to provide for the safety of the public, ground personnel, and any crew members, nearly all spacecraft include a fault management (FM) system. For a manned vehicle such as Orion, the safety of the crew is of paramount importance. The goal of the Orion Guidance, Navigation and Control (GN&C) fault management system is to detect, isolate, and respond to faults before they can result in harm to the human crew or loss of the spacecraft. Verification of fault management/fault protection capability is challenging due to the large number of possible faults in a complex spacecraft, the inherent unpredictability of faults, the complexity of interactions among the various spacecraft components, and the inability to easily quantify human reactions to failure scenarios. The Orion GN&C Fault Detection, Isolation, and Recovery (FDIR) team has developed a methodology for bounding the scope of FM system verification while ensuring sufficient coverage of the failure space and providing high confidence that the fault management system meets all safety requirements. The methodology utilizes a swarm search algorithm to identify failure cases that can result in catastrophic loss of the crew or the vehicle and rare event sequential Monte Carlo to verify safety and FDIR performance requirements.

Brown, Denise↗
NASA NTRS1981

Poster error probability in the Mu-11 Sequential Ranging System

An expression is derived for the posterior error probability in the Mu-2 Sequential Ranging System. An algorithm is developed which closely bounds the exact answer and can be implemented in the machine software. A computer simulation is provided to illustrate the improved level of confidence in a ranging acquisition using this figure of merit as compared to that using only the prior probabilities. In a simulation of 20,000 acquisitions with an experimentally determined threshold setting, the algorithm detected 90% of the actual errors and made false indication of errors on 0.2% of the acquisitions.

Coyle, C. W.↗
DOE OSTI2023

Plasma-based method for delayering of circuits

The present invention relates to methods of delayering a semiconductor integrated circuit die or wafer. In at least one aspect, the method includes exposing a die or wafer to plasma of an etching gas and detecting exposure of one or more metal layers within the die. In one aspect of the invention, the plasma of the etching gas is non-selective and removes all materials in a layer at about the same rate. In another aspect of the invention, two different plasmas of corresponding etching gases are employed with each plasma of the etching gas being selective, thus necessitating the sequential use of both plasmas of corresponding etching gases to remove all materials in a layer.

Shul, Randy J.↗
NASA NTRS1996

Scanning Mode Sensor for Detection of Flow Inhomogeneities

A scanning mode sensor and method is provided for detection of flow inhomogeneities such as shock. The field of use of this invention is ground test control and engine control during supersonic flight. Prior art measuring techniques include interferometry, Schlieren, and shadowgraph techniques. These techniques, however, have problems with light dissipation. The present method and sensor utilizes a pencil beam of energy which is passed through a transparent aperture in a flow inlet in a time-sequential manner so as to alter the energy beam. The altered beam or its effects are processed and can be studied to reveal information about flow through the inlet which can in turn be used for engine control.

Adamovsky, Grigory↗
NASA NTRS1998

Scanning Mode Sensor for Detection of Flow Inhomogeneities

A scanning mode sensor and method is provided for detection of flow inhomogeneities such as shock. The field of use of this invention is ground test control and engine control during supersonic flight. Prior art measuring techniques include interferometry. Schlieren, and shadowgraph techniques. These techniques. however, have problems with light dissipation. The present method and sensor utilizes a pencil beam of energy which is passed through a transparent aperture in a flow inlet in a time-sequential manner so as to alter the energy beam. The altered beam or its effects are processed and can be studied to reveal information about flow through the inlet which can in turn be used for engine control.

Adamovsky, Grigory↗
NASA NTRS2022

Influence of Large-Scale Interplanetary Structures on the Propagation of Solar Energetic Particles: the Multi-Spacecraft Event on 2021 October 9

An intense solar energetic particle (SEP) event was observed on 2021 October 9 by multiple spacecraft distributed near the ecliptic plane at heliocentric radial distances R ≲ 1 au and within a narrow range of heliolongitudes. A stream interaction region (SIR), sequentially observed by Parker Solar Probe (PSP) at R = 0.76 au and 48° east from Earth (ϕ = E48°), STEREO-A (at R = 0.96 au, ϕ = E39°), Solar Orbiter (SolO; at R = 0.68 au, ϕ = E15°), BepiColombo (at R = 0.33 au, ϕ = W02°), and near-Earth spacecraft, regulated the observed intensity-time profiles and the anisotropic character of the SEP event. PSP, STEREO-A, and SolO detected strong anisotropies at the onset of the SEP event, which resulted from the fact that PSP and STEREO-A were in the declining-speed region of the solar wind stream responsible for the SIR and from the passage of a steady magnetic field structure by SolO during the onset of the event. By contrast, the intensity-time profiles observed near Earth displayed a delayed onset at proton energies ≳13 MeV and an accumulation of ≲5 MeV protons between the SIR and the shock driven by the parent coronal mass ejection (CME). Even though BepiColombo, STEREO-A, and SolO were nominally connected to the same region of the Sun, the intensity-time profiles at BepiColombo resemble those observed near Earth, with the bulk of low-energy ions also confined between the SIR and the CME-driven shock. This event exemplifies the impact that intervening large-scale interplanetary structures, such as corotating SIRs, have in shaping the properties of SEP events.

D. Lario↗
DOE OSTI2022

Narrow-Band Least-Squares Infrasound Array Processing

Infrasound data from arrays can be used to detect, locate, and quantify a variety of natural and anthropogenic sources from local to remote distances. However, many array processing methods use a single broad frequency range to process the data, which can lead to signals of interest being missed due to the choice of frequency limits or simultaneous clutter sources. In this work, we introduce a new open-source Python code that processes infrasound array data in multiple sequential narrow frequency bands using the least-squares approach. We test our algorithm on a few examples of natural sources (volcanic eruptions, mass movements, and bolides) for a variety of array configurations. Our method reduces the need to choose frequency limits for processing, which may result in missed signals, and it is parallelized to decrease the computational burden. Improvements of our narrow-band least-squares algorithm over broad-band least-squares processing include the ability to distinguish between multiple simultaneous sources if distinct in their frequency content (e.g., microbarom or surf vs. volcanic eruption), the ability to track changes in frequency content of a signal through time, and a decreased need to fine-tune frequency limits for processing. We incorporate a measure of planarity of the wavefield across the array (sigma tau, στ) as well as the ability to utilize the robust least trimmed squares algorithm to improve signal processing and insight into array performance. Our implementation allows for more detailed characterization of infrasound signals recorded at arrays that can improve monitoring and enhance research capabilities.

58 GEOSCIENCES↗
DOE OSTI2021

Swelling-Assisted Sequential Infiltration Synthesis of Nanoporous ZnO Films with Highly Accessible Pores and Their Sensing Potential for Ethanol

Here, we report a swelling-assisted sequential infiltration synthesis (SIS) approach for the design of highly porous zinc oxide (ZnO) films by infiltration of block copolymer templates such as polystyrene-block- polyvinyl pyridine with inorganic precursors followed by UV ozone-assisted removal of the polymer template. We show that porous ZnO coatings with the thickness in the range between 140 and 420 nm can be obtained using only five cycles of SIS. The pores in ZnO fabricated via swelling-assisted SIS are highly accessible, and up to 98% of pores are available for solvent penetration. The XPS data indicate that the surface of nanoporous ZnO films is terminated with -OH groups. Density functional theory calculations show a lower energy barrier for ethanol-induced release of the oxygen restricted depletion layer in the case of the presence of -OH groups at the ZnO surface, and hence, it can lead to higher sensitivity in sensing of ethanol. We monitored the response of ZnO porous coatings with different thicknesses and porosities to ethanol vapors using combined mass-based and chemiresistive approaches at room temperature and 90 °C. The porous ZnO conformal coatings reveal a promising sensitivity toward detection of ethanol at low temperatures. In conclusion our results suggest the excellent potential of the SIS approach for the design of conformal ZnO coatings with controlled porosity, thickness, and composition that can be adapted for sensing applications.

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

X-ray and γ-ray beam interstellar communication and implications for SETI

The possibility of detecting artificial signals transmitted by alien civilizations via collimated X-ray or gamma-ray beams is investigated. The prospect of using such beams for human communication within the solar system and beyond is also discussed. Detector responses were simulated for input signals and analyzed using relative entropy. For simplicity, all signals were assumed to use on-off keying (OOK) modulation. “Real” signals were generated by taking digital files and sequentially feeding their raw binary data to the detector simulator, the resulting normalized information content of the detector signals was plotted and compared to random noise signals. Since jpeg files contain compressed information, these served as a proxy for artificial alien signals. This showed that there is a clear difference in measured information content between natural and artificial signals, even with relatively poor time resolution in the detector causing the signals to be smeared (dead-time/rise-time intervals many times longer than the duration between signal pulses). It was found that so long as the signal lasts for at least several rise-time/dead-time intervals, the distinction between random and artificial signals is obvious. A space-telescope with high time resolution for searching for such signals is briefly described and its basic requirements are outlined.

43 PARTICLE ACCELERATORS↗
NASA NTRS2007

Robotic Vision-Based Localization in an Urban Environment

A system of electronic hardware and software, now undergoing development, automatically estimates the location of a robotic land vehicle in an urban environment using a somewhat imprecise map, which has been generated in advance from aerial imagery. This system does not utilize the Global Positioning System and does not include any odometry, inertial measurement units, or any other sensors except a stereoscopic pair of black-and-white digital video cameras mounted on the vehicle. Of course, the system also includes a computer running software that processes the video image data. The software consists mostly of three components corresponding to the three major image-data-processing functions: Visual Odometry This component automatically tracks point features in the imagery and computes the relative motion of the cameras between sequential image frames. This component incorporates a modified version of a visual-odometry algorithm originally published in 1989. The algorithm selects point features, performs multiresolution area-correlation computations to match the features in stereoscopic images, tracks the features through the sequence of images, and uses the tracking results to estimate the six-degree-of-freedom motion of the camera between consecutive stereoscopic pairs of images (see figure). Urban Feature Detection and Ranging Using the same data as those processed by the visual-odometry component, this component strives to determine the three-dimensional (3D) coordinates of vertical and horizontal lines that are likely to be parts of, or close to, the exterior surfaces of buildings. The basic sequence of processes performed by this component is the following: 1. An edge-detection algorithm is applied, yielding a set of linked lists of edge pixels, a horizontal-gradient image, and a vertical-gradient image. 2. Straight-line segments of edges are extracted from the linked lists generated in step 1. Any straight-line segments longer than an arbitrary threshold (e.g., 30 pixels) are assumed to belong to buildings or other artificial objects. 3. A gradient-filter algorithm is used to test straight-line segments longer than the threshold to determine whether they represent edges of natural or artificial objects. In somewhat oversimplified terms, the test is based on the assumption that the gradient of image intensity varies little along a segment that represents the edge of an artificial object.

Mchenry, Michael↗
DOE OSTI2023

Particle size influences decay rates of environmental DNA in aquatic systems

Abstract Environmental DNA (eDNA) analysis is a powerful tool for remote detection of target organisms. However, obtaining quantitative and longitudinal information from eDNA data is challenging, requiring a deep understanding of eDNA ecology. Notably, if the various size components of eDNA decay at different rates, and we can separate them within a sample, their changing proportions could be used to obtain longitudinal dynamics information on targets. To test this possibility, we conducted an aquatic mesocosm experiment in which we separated fish‐derived eDNA components using sequential filtration to evaluate the decay rate and changing proportion of various eDNA particle sizes over time. We then fit four alternative mathematical decay models to the data, building towards a predictive framework to interpret eDNA data from various particle sizes. We found that medium‐sized particles (1–10 μm) decayed more slowly than other size classes (i.e., <1 and > 10 μm), and thus made up an increasing proportion of eDNA particles over time. We also observed distinct eDNA particle size distribution (PSD) between our Common carp and Rainbow trout samples, suggesting that target‐specific assays are required to determine starting eDNA PSDs. Additionally, we found evidence that different sizes of eDNA particles do not decay independently, with particle size conversion replenishing smaller particles over time. Nonetheless, a parsimonious mathematical model where particle sizes decay independently best explained the data. Given these results, we suggest a framework to discern target distance and abundance with eDNA data by applying sequential filtration, which theoretically has both metabarcoding and single‐target applications.

Brandão‐Dias, Pedro F. P.↗