SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Over time, multiple information repositories, access controls, and management practices created disparate information silos. Gaining accurate insights from lab information had become overly burdensome especially for new employees and collaborators
Broad beam Tracking Data Relay Satellite /TDRS/ for manned and automated spacecraft using VHF bands
The system includes an implantable transmitter, external receiver-retransmitter collar, and a microprocessor-controlled demodulator. The size of the implant is suitable for animals with body weights of a few kilograms or more; further size reduction of the implant is possible. The ECG is sensed by electrodes designed for internal telemetry and to reduce movement artifacts. The R-wave characteristics are then specifically selected to trigger a short radio frequency pulse. Temperatures are sensed at desired locations by thermistors and then, based on a heartbeat counter, transmitted intermittently via pulse interval modulation. This modulation scheme includes first and last calibration intervals for a reference by ratios with the temperature intervals to achieve good accuracy even over long periods. Pulse duration and pulse sequencing are used to discriminate between heart rate and temperature pulses as well as RF interference.
Materials performance data from operational spacecraft and spacecraft experiments has slowly accumulated over the years. Much of this data is obtained indirectly by analyzing temperature measurements telemetered to ground. Due to the Space Transportation System (STS) program, materials performance data has also been obtained by flying materials on satellites, or on the Space Shuttle itself, for limited periods of time, and then returning the materials to ground for post-flight examination. During the course of NASA contract NAS8-98213, the authors have extended the analysis of data reported for selected materials flown on a variety of spacecraft over the past 30 years. In this paper we discuss changes in two properties of silver- or aluminum-backed (metallized) FEP Teflon due to exposure to specific environmental factors on-orbit. First, the change in solar absorptance (alpha(sub s)) of metallized FEP Teflon as a function of particulate radiation will be discussed. Data providing the change in solar absorptance of metallized FEP as a function of days on orbit has been published for NTS-2 satellite, several NavStar Global Positioning Satellites (GPS), the SCATHA experiment, and the ML-101 satellite. The unique approach that we have taken is to calculate the absorbed radiation dose within the Ag/FEP for the various satellite orbits, and to plot the change in solar absorptance, alpha(sub s), as a function of the dose; the behavior agrees with the same data from laboratory tests. Second, the variation of the atomic oxygen induced material recession rate of metallized FEP will be discussed. Data from certain Space Shuttle flights, a Lockheed experiment [approximately 105 days in low Earth orbit (LEO)], and the Long Duration Exposure Facility allows the variation of the material recession rate of Ag/FEP to be estimated under conditions that include atomic oxygen exposure.
By rapidly alternating displays of SPOT satellite images acquired on 27 July 1991 and 25 July 1992 we are able to see spatial details of terrain movements along fault breaks associated with the 28 June 1992 Landers, California earthquake that are virtually undetectable by any other means.
Atmospheric aerosols and ozone are designated as observing system priorities in the 2017–2027 Decadal Survey report [1]. Accurate records of stratospheric aerosols are a vital piece of the puzzle regarding climate change. Stratospheric ozone has been the subject of observation and research for decades. Its importance is exhibited in the United States Clean Air Act [2], which mandates that NASA monitor atmospheric ozone. Measurements from satellites provided data on the initial decline of ozone in the late 1970s and early 1980s that supported the adoption of the Montreal Protocol, and current observations hint at a potential recovery. Adequate determination of that recovery requires continuous and, in the case of multiple instruments, overlapping data records. However, most current satellite systems are well beyond their expected lifetimes, and so we look towards the future of satellite observations of stratospheric ozone and aerosols to develop the Stratospheric Aerosol and Gas Experiment (SAGE) IV Pathfinder. Enabled by the NASA Earth Science Technology Office (ESTO)’s Instrument Incubator Program (IIP), the SAGE IV Pathfinder project has developed and validated a prototype demonstration that paves the way for a future SAGE IV spaceflight mission. Utilizing solar occultation imaging, SAGE IV will be capable of measuring ozone, aerosol, and other trace gas species with the same quality as previous SAGE instruments but with greatly improved pointing knowledge. Furthermore, current technological advancements allow SAGE IV to fit within a CubeSat framework and make use of commercial hardware, significantly reducing the size and cost when compared with traditional missions and enabling sustainability of future measurements. SAGE IV will meet the definition of the newly-recommended Venture-Continuity missions by “bringing forward innovative approaches to sustain measurements at lower costs”. The latest results from development of the SAGE IV Pathfinder are presented. [1] “Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space,” National Academies of Sciences, Engineering, and Medicine, 2018. [2] “United State Clean Air Act,” 42 U.S.C. 767 1b(d)(2).
Abstract not provided.
Ecosystem model prediction is important for understanding ecosystem responses to climate change and for management support. Model prediction and quantification of predictive uncertainty of ecosystems have long been investigated. The traditional workflow, which calibrates models to match observations and then uses the calibrated models for predictions, relies heavily on inverse modeling to constrain uncertain parameters in complex forward models. This inversion-based prediction approach is infeasible for complex models with heterogeneous parameter uncertainties and incapable of rapid integration of streaming and multiple sources of data because of the difficulty and computational cost in the model inversion, which is typically ill-posed and can require hundreds of thousands of expensive forward simulations to be performed iteratively. We propose to circumvent inverse modeling by precomputing an ensemble of unconstrained forward simulations and then using machine learning (ML) methods to learn the statistical relationship between simulated observation and prediction quantities. Once the ML model has learned the relationship, it can be used to make predictions of future system behavior with uncertainty quantification based on observations. The proposed learningbased inversion-free model prediction (LIMP) framework is computationally efficient which only requires a few thousands of fully parallelizable forward simulations. Additionally, LIMP can continually update predictions based on streaming observations from multiple locations and sources without necessarily requiring extra model simulations. In this study, we apply LIMP to a regional terrestrial ecosystem model with 47 parameters for testing, refining, and evaluating the approach. We demonstrate that LIMP can be used for efficient model prediction, rapid data assimilation, and cost-effective experimental design for improving robust predictive understanding of ecosystems.
This paper describes a summer internship project undertaken at Sandia National Labs (SNL), both current status and future work. The project was to explore various machine learning approaches for use on turbulent flow data. Specifically, unsupervised classification of turbulent flow data was explored. First, the usage of models in this field is discussed, and several issues in the common usage of the models are identified. Solutions to these issues are then proposed, in the form of a Bayesian filtering approach which probabilistically incorporates multiple sources of data to improve confidence in a result. Several types of sensors are suggested for this method, the incorporation of which range from semi-supervised learning approaches to fully unsupervised. These approaches are then tested on several turbulent flow cases.
Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.
Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.
This manuscript presents the most recent results and findings to identify bond wire lift-offs and surface defects in high-power isolated gate bipolar junction transistor (IGBT) modules. The authors of this manuscript formerly proposed a low-cost, piezoelectric resonator-based measurement unit to detect bond wire related degradation in larger IGBT modules. Since high-power IGBT modules are expensive, it was apparent that evaluating our proposed method by inducing controlled damage to fresh (new) IGBTs may not be cost-effective especially when multiple sets of data need to be captured by inducing damage to different levels. In order to overcome this limitation, IGBT bond wires have been mimicked using a 3D printed enclosure, a PCB, and copper wires with dimensions very closely resembling a real IGBT. Using this method, multiple test devices can be built at the cost of a real IGBT, and the proposed technique could be fine-tuned without damaging expensive, real IGBTs. Our recent findings can be used to determine real IGBT degradation and bond wire lift-offs using only two sensors, as opposed to six transducers used in the first iteration of the setup. In addition to optimizing the sensor count, we have also identified the best possible locations of these sensors by attempting multiple placements inside the IGBT casing.
Disclosed is an apparatus and methodology for structural health monitoring (SHM) in which smart devices interrogate structural components to predict failure, expedite needed repairs, and thus increase the useful life of those components. Piezoelectric wafer active sensors (PWAS) are applied to or integrated with structural components and various data collected there from provide the ability to detect and locate cracking, corrosion, and disbanding through use of pitch-catch, pulse-echo, electro/mechanical impedance, and phased array technology. Stand alone hardware and an associated software program are provided that allow selection of multiple types of SHM investigations as well as multiple types of data analysis to perform a wholesome investigation of a structure.
The Community Coordinated Modeling Center (CCMC) at NASA Goddard Space Flight Center (GSFC) leads heliophysics research by providing open access to numerous models and their outputs. Our resources are available on-demand and continuously updated with real-time data, covering sun-earth interactions across multiple domains. These domains include coronal, heliosphere, inner and global magnetosphere, ionosphere, thermosphere, and lower atmosphere interactions. Operating in a hybrid environment, CCMC utilizes both self-owned hardware and Amazon Web Services (AWS) cloud infrastructure. Managing petabytes of data across multiple locations necessitates robust data engineering solutions. To address this challenge, CCMC has adopted industry-standard and open-source tools. We use Apache Airflow as our primary data engineering platform, Python for scripting and data processing, and GitLab for version control and CI/CD. Additionally, we employ Kubernetes for containerized services, Grafana and Prometheus for metrics and monitoring, and Terraform and Puppet for reproducible infrastructure as code. This presentation will discuss lessons learned from our data engineering experiences, platforms evaluated but found unsuitable for our scientific data requirements, and specific techniques developed to enhance data transfer speed and reliability. By using these technologies effectively, CCMC continues to advance heliophysics research through efficient data management and open-access modeling.
Femtosecond laser electronic excitation tagging (FLEET) velocimetry is an important diagnostic technique for seedless velocimetry measurements particularly in supersonic and hypersonic flows. Typical FLEET measurements feature a single laser line and camera system to achieve one-component velocimetry along a line, although some multiple-spot and multiple-component configurations have been demonstrated. In this work, tomographic imaging is used to track the three-dimensional location of many FLEET spots. A quadscope is used to combine four unique views onto a single high-speed image intensifier and camera. Tomographic reconstructions of the FLEET emission are analyzed for three-component velocimetry from multiple FLEET spots. Glass wedges are used to create many (nine) closely spaced FLEET spots with less than 10% transmission losses. These developments lead to a significant improvement in the dimensionality and spatial coverage of a FLEET instrument with some increases in experimental complexity and data processing. Multiple-point three-component FLEET velocimetry is demonstrated in an underexpanded jet.
NASA Engineering's Orion Script Generator (OSG) is a program designed to run on Exploration Flight Test One Software. The script generator creates a SuperScript file that, when run, accepts the filename for a listing of Compact Unique Identifiers (CUIs). These CUIs will correspond to different variables on the Orion spacecraft, such as the temperature of a component X, the active or inactive status of another component Y, and so on. OSG will use a linked database to retrieve the value for each CUI, such as "100 05," "True," and so on. Finally, OSG writes SuperScript code to display each of these variables before outputting the ssi file that allows recipients to view a graphical representation of Orion Flight Test One's status through these variables. This project's main challenge was creating flexible software that accepts and transfers many types of data, from Boolean (true or false) values to "Unsigned Long Long'' values (any number from 0 to 18,446,744,073,709,551,615). We also needed to allow bit manipulation for each variable, requiring us to program functions that could convert any of the multiple types of data into binary code. Throughout the project, we explored different methods to optimize the speed of working with the CUI database and long binary numbers. For example, the program handled extended binary numbers much more efficiently when we stored them as collections of Boolean values (true or false representing 1 or 0) instead of as collections of character strings or numbers. We also strove to make OSG as user-friendly and accommodating of different needs as possible its default behavior is to display a current CUI's maximum value and minimum value with three to five intermediate values in between, all in descending order. Fortunately, users can also add other input on the same lines as each CUI name to request different high values, low values, display options (ascending, sine, and so on), and interval sizes for generating intermediate values. Developing input validation took up quite a bit of time, but OSG's flexibility in the end was worth it.
RT-Display is a MATLAB-based data acquisition environment designed to use a variety of commercial off-the-shelf (COTS) hardware to digitize analog signals to a standard data format usable by other post-acquisition data analysis tools. This software presents the acquired data in real time using a variety of signal-processing algorithms. The acquired data is stored in a standard Operator Interactive Signal Processing Software (OISPS) data-formatted file. RT-Display is primarily configured to use the Agilent VXI (or equivalent) data acquisition boards used in such systems as MIDDAS (Multi-channel Integrated Dynamic Data Acquisition System). The software is generalized and deployable in almost any testing environment, without limitations or proprietary configuration for a specific test program or project. With the Agilent hardware configured and in place, users can start the program and, in one step, immediately begin digitizing multiple channels of data. Once the acquisition is completed, data is converted into a common binary format that also can be translated to specific formats used by external analysis software, such as OISPS and PC-Signal (product of AI Signal Research Inc.). RT-Display at the time of this reporting was certified on Agilent hardware capable of acquisition up to 196,608 samples per second. Data signals are presented to the user on-screen simultaneously for 16 channels. Each channel can be viewed individually, with a maximum capability of 160 signal channels (depending on hardware configuration). Current signal presentations include: time data, fast Fourier transforms (FFT), and power spectral density plots (PSD). Additional processing algorithms can be easily incorporated into this environment.
An algorithm is developed for calibrating the spatial nonuniformity of image-array (CCD-type) detectors. Like other techniques this approach uses multiple, spatially displaced images. In circumstances where high-precision flat fields are not available by other means (i.e., sky flats) this technique is advantageous as it uses the data frames for gain calibration even when the array images extended, nonuniform, sources. Numerical experiments and direct observations with intrinsically uniform and quite nonuniform detectors show that this algorithm is useful when data frames are crowded with sources - circumstance where 'median filtering' flatfielding techniques often fail. The algorithm described is robust and efficiently uses information from multiple data frames to determine pixel gain variations, using visible and IR array observations of extended sources.