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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 433 records · Page 24

Ideal Integrating Bolometer

We describe a new "ideal integrator" bolometer as a prototype for a new generation of sensitive, flexible far-IR detectors suitable for use in large arrays. The combination of a non-dissipative sensor coupled with a fast heat switch provides breakthrough capabilities in both sensitivity and operation. The bolometer temperature varies linearly with the integrated infrared power incident on the detector, and may be sampled intermittently without loss of information between samples. The sample speed and consequent dynamic range depend only on the heat switch reset cycle and can be selected in software. Between samples, the device acts as an ideal integrator with noise significantly lower than resistive bolometers. Since there is no loss of information between samples, the device is well-suited for large arrays. A single SQUID readout could process an entire column of detectors, greatly reducing the complexity, power requirements, and cost of readout electronics for large pixel arrays.

Kogut, A.↗

Aerospace Power Systems Design and Analysis (APSDA) Tool

The conceptual design of space and/or planetary electrical power systems has required considerable effort. Traditionally, in the early stages of the design cycle (conceptual design), the researchers have had to thoroughly study and analyze tradeoffs between system components, hardware architectures, and operating parameters (such as frequencies) to optimize system mass, efficiency, reliability, and cost. This process could take anywhere from several months to several years (as for the former Space Station Freedom), depending on the scale of the system. Although there are many sophisticated commercial software design tools for personal computers (PC's), none of them can support or provide total system design. To meet this need, researchers at the NASA Lewis Research Center cooperated with Professor George Kusic from the University of Pittsburgh to develop a new tool to help project managers and design engineers choose the best system parameters as quickly as possible in the early design stages (in days instead of months). It is called the Aerospace Power Systems Design and Analysis (APSDA) Tool. By using this tool, users can obtain desirable system design and operating parameters such as system weight, electrical distribution efficiency, bus power, and electrical load schedule. With APSDA, a large-scale specific power system was designed in a matter of days. It is an excellent tool to help designers make tradeoffs between system components, hardware architectures, and operation parameters in the early stages of the design cycle. user interface. It operates on any PC running the MS-DOS (Microsoft Corp.) operating system, version 5.0 or later. A color monitor (EGA or VGA) and two-button mouse are required. The APSDA tool was presented at the 30th Intersociety Energy Conversion Engineering Conference (IECEC) and is being beta tested at several NASA centers. Beta test packages are available for evaluation by contacting the author.

Truong, Long V.↗

Uncertainty Analysis of Historical Hurricane Data

An analysis of variance (ANOVA) study was conducted for historical hurricane data dating back to 1851 that was obtained from the U. S. Department of Commerce National Oceanic and Atmospheric Administration (NOAA). The data set was chosen because it is a large, publicly available collection of information, exhibiting great variability which has made the forecasting of future states, from current and previous states, difficult. The availability of substantial, high-fidelity validation data, however, made for an excellent uncertainty assessment study. Several factors (independent variables) were identified from the data set, which could potentially influence the track and intensity of the storms. The values of these factors, along with the values of responses of interest (dependent variables) were extracted from the data base, and provided to a commercial software package for processing via the ANOVA technique. The primary goal of the study was to document the ANOVA modeling uncertainty and predictive errors in making predictions about hurricane location and intensity 24 to 120 hours beyond known conditions, as reported by the data set. A secondary goal was to expose the ANOVA technique to a broader community within NASA. The independent factors considered to have an influence on the hurricane track included the current and starting longitudes and latitudes (measured in degrees), and current and starting maximum sustained wind speeds (measured in knots), and the storm starting date, its current duration from its first appearance, and the current year fraction of each reading, all measured in years. The year fraction and starting date were included in order to attempt to account for long duration cyclic behaviors, such as seasonal weather patterns, and years in which the sea or atmosphere were unusually warm or cold. The effect of short duration weather patterns and ocean conditions could not be examined with the current data set. The responses analyzed were the storm latitude, longitude and intensity, as recorded in the data set, 24 or 120 hours beyond the current state. Several ANOVA modeling schemes were examined. Two forms of validation were used: 1) comparison with official hurricane prediction performance metrics and 2) cases studies conducted on hurricanes from the 2005 season, which were not included within the model construction and ANOVA assessment. In general, the ANOVA technique did not perform as well as the established official prediction performance metrics published by NOAA; still, the technique did remarkably well in this demonstration with a difficult data set and could probably be made to perform better with more knowledge of hurricane development and dynamics applied to the problem. The technique provides a repeatable prediction process that eliminates the need for judgment in the forecast.

Green, Lawrence L.↗

Planning the Unplanned Experiment: Assessing the Efficacy of Standards for Safety Critical Software

We need well-founded means of determining whether software is t for use in safety-critical applications. While software in industries such as aviation has an excellent safety record, the fact that software aws have contributed to deaths illustrates the need for justi ably high con dence in software. It is often argued that software is t for safety-critical use because it conforms to a standard for software in safety-critical systems. But little is known about whether such standards `work.' Reliance upon a standard without knowing whether it works is an experiment; without collecting data to assess the standard, this experiment is unplanned. This paper reports on a workshop intended to explore how standards could practicably be assessed. Planning the Unplanned Experiment: Assessing the Ecacy of Standards for Safety Critical Software (AESSCS) was held on 13 May 2014 in conjunction with the European Dependable Computing Conference (EDCC). We summarize and elaborate on the workshop's discussion of the topic, including both the presented positions and the dialogue that ensued.

Graydon, Patrick J.↗

Effect of Material Porosity on Residual Stress in an Additive Manufacturing Simulation using the Generalized Method of Cells

The effect of material porosity on final part distortion and residual stresses in a selective laser sintering manufacturing simulation is presented here. A time-dependent thermo-mechanical model is used with the open-source FEA software CalculiX. Effective homogenized material properties for Inconel 625 are precomputed using NASA’s Micromechanics Analysis Code with the Generalized Method of Cells. The evolving porosity of the material is estimated with each pass of the laser beam during simulation runtime. A comparison with a homogenous model and the evolving model shows that the model with evolving porosity predicts larger distortions with greater residual stresses.

Additive Manufactuirng↗

Automation software for a materials testing laboratory

The software environment in use at the NASA-Lewis Research Center's High Temperature Fatigue and Structures Laboratory is reviewed. This software environment is aimed at supporting the tasks involved in performing materials behavior research. The features and capabilities of the approach to specifying a materials test include static and dynamic control mode switching, enabling multimode test control; dynamic alteration of the control waveform based upon events occurring in the response variables; precise control over the nature of both command waveform generation and data acquisition; and the nesting of waveform/data acquisition strategies so that material history dependencies may be explored. To eliminate repetitive tasks in the coventional research process, a communications network software system is established which provides file interchange and remote console capabilities.

Mcgaw, Michael A.↗

Increased ephemeris accuracy using attitude-dependent aerodynamic force coefficients for inertially stabilized spacecraft

The FREEMAC program used to generate the aerodynamic coefficients, as well as associated routines that allow the results to be used in other software is described. These capabilities are applied in two numerical examples to the short-term orbit prediction of the Gamma Ray Observatory (GRO) and Hubble Space Telescope (HST) spacecraft. Predictions using attitude-dependent aerodynamic coefficients were made on a modified version of the PC-based Ephemeris Generation Program (EPHGEN) and were compared to definitive orbit solutions obtained from actual tracking data. The numerical results show improvement in the predicted semi-major axis and along-track positions that would seem to be worth the added computational effort. Finally, other orbit and attitude analysis applications are noted that could profit from using FREEMAC-calculated aerodynamic coefficients, including orbital lifetime studies, orbit determination methods, attitude dynamics simulators, and spacecraft control system component sizing.

Folta, David C.↗

Time Dependence of Collision Probabilities During Satellite Conjunctions

The NASA Conjunction Assessment Risk Analysis (CARA) team has recently implemented updated software to calculate the probability of collision (P (sub c)) for Earth-orbiting satellites. The algorithm can employ complex dynamical models for orbital motion, and account for the effects of non-linear trajectories as well as both position and velocity uncertainties. This “3D P (sub c)” method entails computing a 3-dimensional numerical integral for each estimated probability. Our analysis indicates that the 3D method provides several new insights over the traditional “2D P (sub c)” method, even when approximating the orbital motion using the relatively simple Keplerian two-body dynamical model. First, the formulation provides the means to estimate variations in the time derivative of the collision probability, or the probability rate, R (sub c). For close-proximity satellites, such as those orbiting in formations or clusters, R (sub c) variations can show multiple peaks that repeat or blend with one another, providing insight into the ongoing temporal distribution of risk. For single, isolated conjunctions, R (sub c) analysis provides the means to identify and bound the times of peak collision risk. Additionally, analysis of multiple actual archived conjunctions demonstrates that the commonly used “2D P (sub c)” approximation can occasionally provide inaccurate estimates. These include cases in which the 2D method yields negligibly small probabilities (e.g., P (sub c)) is greater than 10 (sup -10)), but the 3D estimates are sufficiently large to prompt increased monitoring or collision mitigation (e.g., P (sub c) is greater than or equal to 10 (sup -5)). Finally, the archive analysis indicates that a relatively efficient calculation can be used to identify which conjunctions will have negligibly small probabilities. This small-P (sub c) screening test can significantly speed the overall risk analysis computation for large numbers of conjunctions.

Hall, Doyle T.↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

Progress in the Development of a Nozzle Design Methodology for Pulsed Detonation Engines

The Johns Hopkins University Applied Physics Laboratory (JHU/APL), in support of the NASA Glenn Research Center (NASA GRC), is investigating performance methodologies and system integration issues related to Pulsed Detonation Engine (PDE) nozzles. The primary goal of this ongoing effort is to develop design and performance assessment methodologies applicable to PDE exit nozzle(s). APL is currently focusing its efforts on a common plenum chamber design that collects the exhaust products from multiple PDE tubes prior to expansion in a single converging-diverging exit nozzle. To accomplish this goal, a time-dependent, quasi-one-dimensional analysis for determining the flow properties in and through a single plenum and exhaust nozzle is underway. In support of these design activities, parallel modeling efforts using commercial Computational Fluid Dynamics (CFD) software are on-going. These efforts include both two and three-dimensional as well as steady and time-dependent computations to assess the flow in and through these devices. This paper discusses the progress in developing this nozzle design methodology.

Leary, B. A.↗

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

Effects of Electrical Stimulation on hiPSC-CM Responses to Classic Ion Channel Blockers

Abstract Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) hold great potential for personalized cardiac safety prediction, particularly for that of drug-induced proarrhythmia. However, hiPSC-CMs fire spontaneously and the variable beat rates of cardiomyocytes can be a confounding factor that interferes with data interpretation. Controlling beat rates with pacing may reduce batch and assay variations, enable evaluation of rate-dependent drug effects, and facilitate the comparison of results obtained from hiPSC-CMs with those from adult human cardiomyocytes. As electrical stimulation (E-pacing) of hiPSC-CMs has not been validated with high-throughput assays, herein, we compared the responses of hiPSC-CMs exposed with classic cardiac ion channel blockers under spontaneous beating and E-pacing conditions utilizing microelectrode array technology. We found that compared with spontaneously beating hiPSC-CMs, E-pacing: (1) reduced overall assay variabilities, (2) showed limited changes of field potential duration to pacemaker channel block, (3) revealed reverse rate dependence of multiple ion channel blockers on field potential duration, and (4) eliminated the effects of sodium channel block on depolarization spike amplitude and spike slope due to a software error in acquiring depolarization spike at cardiac pacing mode. Microelectrode array optogenetic pacing and current clamp recordings at various stimulation frequencies demonstrated rate-dependent block of sodium channels in hiPSC-CMs as reported in adult cardiomyocytes. In conclusion, pacing enabled more accurate rate- and concentration-dependent drug effect evaluations. Analyzing responses of hiPSC-CMs under both spontaneously beating and rate-controlled conditions may help better assess the effects of test compounds on cardiac electrophysiology and evaluate the value of the hiPSC-CM model.

Wei, Feng↗

RETScreen Plus Software Tutorial

Greater emphasis is being placed on reducing both the carbon footprint and energy cost of buildings. A building's energy usage depends upon many factors one of the most important is the local weather and climate conditions to which it's electrical, heating and air conditioning systems must respond. Incorporating renewable energy systems, including solar systems, to supplement energy supplies and increase energy efficiency is important to saving costs and reducing emissions. Also retrofitting technologies to buildings requires knowledge of building performance in its current state, potential future climate state, projection of potential savings with capital investment, and then monitoring the performance once the improvements are made. RETScreen Plus is a performance analysis software module that supplies the needed functions of monitoring current building performance, targeting projected energy efficiency improvements and verifying improvements once completed. This tutorial defines the functions of RETScreen Plus as well as outlines the general procedure for monitoring and reporting building energy performance.

Ganoe, Rene D.↗