- API data.nasa.gov | Last Updated 2020-01-29T04:19:14.000Z
The goal of this project is to mature the technology readiness of a radiation tolerant smallsat computer system for a subsequent orbital flight demonstration. The system is implemented with commercial off-the-shelf (COTS) field-programmable gate arrays (FPGAs) to provide space-computing performance that improves upon existing radiation hardened processors. Commercial FPGAs are now yielding acceptable levels of total ionizing dose immunity due to the thinning of gate oxides and relative deepening of isolation trenches. If a single event effect mitigation strategy can be implemented on COTS FPGAs, then reliable, high performance space computing can be accomplished at a fraction of the cost of existing radiation hardened processors.
Lightweight and Compace Multifunction Computer-Controlled Strength and Aerobic Training Device, Phase Idata.nasa.gov | Last Updated 2020-01-29T04:57:18.000Z
TDA Research proposes to develop a computer-controlled lightweight and compact device for aerobic and resistive training (DART) to counteract muscular atrophy and bone loss and to improve the overall wellness of astronauts operating in microgravity. The DART will be able to provide resistive loads up to 350 lbf and will accurately simulate the load profile of a mass in a 1-g environment. It will also be capable of applying custom load profiles such as eccentric overloading. In aerobic training mode, the DART will simulate the loads of a rowing machine with loads up to 175. The system will computer-controlled and can automatically calibrate to a user's range of motion. The total weight of the device will be less than 20 lbs and have a compact form factor to enable integration into a small crew module. By using a regenerative energy recovery system, the average power consumption of the DART will be less than 100 W during an exercise session. TDA is able to build on previous experience building exercise equipment for NASA and develop the DART in a short timeframe. TDA will prove the feasibility of providing effective aerobic and resistive training with a single device that is lightweight and compact in Phase I. At the end of Phase I a prototype will be delivered to NASA for evaluation. In Phase II we will advance the technology and provide the second generation prototype to NASA for testing on the International Space Station.
- API data.nasa.gov | Last Updated 2020-01-29T02:13:09.000Z
Fault to Failure Progression (FFP) signature modeling and processing is a new method for applying condition-based signal data to detect degradation, to identify fault modes, and to produce system estimates for State of Health (SoH) and Remaining Useful Life (RUL). The base technology has been applied for prognostic purposes for various government-sponsored programs, but FFP signature modeling and processing has not been applied for the area of Fault Management, nor does it include such features as fault dictionaries, lookup tables, and management algorithms. The technology includes Ridgetop-designed and developed algorithms to do the following: (1) perform Kalman Filtering to reduce noise; (2) transform sensor signal data to reveal underlying (hidden) FFP signatures; (3) normalize units-of-measure dependent signal data into dimensionless FFP signatures to facilitate re-use and reduce the time to characterize and define new FFP signatures; (4) define and use model definitions that reduce memory requirements and support fast and accurate processing and calculations; (5) two forms of trajectory curve characterization, both straight-line and curvilinear; (6) a fast yet accurate, graphics-based mathematical routine to adapt an FFP model to received data; (7) amplitude and time updates similar to Extended Kalman Filtering to estimate how long it will take an adapted FFP model to reach a defined failure threshold; and (8) produce SoH and RUL estimates that rapidly converge to the estimated time-to-failure (TTF) solution. The FFP signature modeling and processing will include additional innovation to support FM to minimize application-specific programming, those include algorithms to simplify fault identification and isolation.
- API data.nasa.gov | Last Updated 2020-01-29T04:07:32.000Z
Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reli- able than the system they are observing. This is in part due to the sensors’ operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electromechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.
- API data.nasa.gov | Last Updated 2020-01-29T02:09:18.000Z
<p>Lithium Ion (Li-Ion) cells are being developed for high-power batteries in space; especially there is a strong need to miniaturize Li-Ion batteries for CubeSat and SmallSat. For this reason, we propose a process to design and implement Goddard Space Flight Center (GSFC) in-house Li-Ion battery pack which provides us control over testing to design a high quality battery pack with low cost, risk reduction, and being able to adapt interface and mechanical form factor.</p> <p>Goddard scientists and engineers are developing SmallSat for the Center’s and NASA’s mission needs. The Goddard in-house Smallsat Li-Ion design address capabilities for NASA’s missions in science, exploration and space operation. This technology development includes two processes: Characterize Li-Ion cells and design battery pack as following:</p><ol style="list-style-type: lower-alpha;"><li>Characterize Li-Ion cells: Contact Li-Ion cells from vendors authorized distributors to procure Li-Ion cells with two common sizes 18650 (18 mm x 65 mm) and 16340 (16 mm x 34 mm). We simply order sufficient quantity of commercial-of-the-shelf (COTS) cells. We will test for safety and performance at cell level. Cell level testing includes studies of the cell physical design, rate performance, cycle lifetime, self-discharge, thermal properties, Lot Acceptance Tests (LAT) for electrical properties, capacity verification, degradation, impedance matching, and mission profile. The test results will be used as our own Li-Ion cells database which in turn will be used to design a flight battery pack for CubeSat or SmallSat.</li><li>Design a battery: As part of this task we will investigate and trade the protection features to either already built-in within the cells or included in the overall battery pack. The protection features to consider include short circuit, over-charging, over-discharging, and to maintain battery temperature.</li></ol>
- API data.nasa.gov | Last Updated 2020-01-29T04:11:51.000Z
In this proposal, we describe a program to develop a high-performance, cost-effective and robust microwave receiver prototype for multi-purpose Non-Destructive Evaluation (NDE). Currently, NDE of space transportation vehicles is primarily carried out on the ground, between missions. For future space missions, as duration and frequency increases, more inspection will need to be performed in space in order to monitor the aging process of the structure and to insure its integrity. For this purpose, NDE equipment that is compact, lightweight, easily operated by human with limited mobility or robot, and that exhibits low power consumption is required. Furthermore, in order to minimize the quantity of embarked equipment, the inspection equipment must be able to perform as many different inspection tasks as possible. Our innovative receiver is based on the integration of a microwave interferometer coupled with a pulsed laser to generate the ultrasound. . Based on the results obtained during Phase 1, we strongly think that we will be able to overcome the limitation generally associated with classical optical receiver: 1) Inability to work in factory environment where thermal, mechanical and optical propagation (fumes, water drops,..) perturbations are present; 2) Reduction in sensitivity caused by the speckle nature of the light reflected from rough surfaces; 3) High system cost due the price of the probe lasers, optics and engineering to develop an optical system working in a harsh environment (fumes, water drops, strong mechanical vibration) and 4) high maintenance cost (Lasers and optics need to be checked and re-aligned frequently). Our proposed approach will lead to a cost-effective prototype with good sensitivity and performances in industrial environment.
- API data.nasa.gov | Last Updated 2020-01-29T04:18:50.000Z
The objective of this project is to demonstrate intelligent health and maintenance status determination and predictive fault diagnosis techniques for NASA rocket engines under online and offline conditions from either on-board or maintenance, test and analytic data. AGNC proposes a Health and Maintenance Status Determination and Predictive Fault Diagnosis System (HMSD/PFDS). The fuzzy qualitative model for model-based residual generation and the rule-based evaluation of residuals using neural-fuzzy combination are defined. Intelligent data fusion strategies for health and maintenance determination and predictive fault diagnosis are developed for rocket engine systems/subsystems. The goal is to ensure safety, cost reduction, graceful degradation and re-optimization in the case of failures, malfunctions and damages. Kalman filter based and rule based evaluation of residuals using neural-fuzzy combination are developed. The use of fuzzy qualitative models takes into account the uncertainties associated with behavior descriptions and incorporates available expert failure symptom knowledge to recognize the particular failure features. Actual or simulated rocket engine sensed or derived data are utilized to evaluate the effectiveness of the health and maintenance determination and fault prognosis approaches for NASA platforms. Phase I is devoted to the HMSD/PFDS design and simulation. Phase II will result in development of a functional prototype.
- API data.nasa.gov | Last Updated 2020-01-29T03:55:42.000Z
The main goal of this work is to design, implement, and demonstrate a guidance and mission planning toolbox for air-launched, unmanned systems, such as guided dropsondes, sonobuoys, or surveillance aircraft, with the primary goal of enabling users to more effectively achieve mission goals by enabling multi-agent interaction and cooperation. Typical missions that will benefit from the MAMS include those where multiple unmanned vehicles are launched from one or more mother aircraft: for example atmospheric research missions making use of many guided dropsondes, missions distributing a fleet of sonobuoys, or surveillance missions requiring multiple UAVs to patrol a given area. As new vehicles are introduced to the environment (launched from the mother aircraft), or as new areas of interest arise, the MAMS will utilize a distributed network method for adjusting the fleet vehicles' trajectories to maximize the mission effectiveness.
- API data.nasa.gov | Last Updated 2020-01-29T02:03:18.000Z
Galileo Orbiter Magnetometer (MAG) calibrated high-resolution data from the Earth-1 flyby in spacecraft, GSE, and GSM coordinates. These data cover the interval 1990-11-05 to 1990-12-31.
- API data.nasa.gov | Last Updated 2020-01-29T03:53:56.000Z
Accurate predictive modeling of certain atmospheric chemical phenomena (i.e. volcano plumes, smog, gas clouds, wildfire smoke, etc.) suffers from a dearth of information, largely due to the fact that the dynamic qualities of the phenomenon evade accurate data collection. In situ measurements are currently made through the use of ground sensors and dropsondes. ?Ground sensors, such as seismometers, tiltmeters, in-ground gas monitors and near-field remote sensings instruments[,]? have limited measurement density and provide only information about atmospheric boundary conditions. Dropsondes can provide measurements over the entire vertical profile, but are limited to sampling over a small time period. In situ measurements can be augmented with satellite-based remote sensing systems, such as ASTER, MODIS, AIRS and OMI, however, satellite-based data suffers from its relatively small spatial density and limited frequency of measurement. A need exists for additional targeted in situ data from volcanic ash clouds, particularly to assess ...particle size distribution, ash cloud height, and ash cloud thickness including spatial (horizontal and vertical) and temporal variability of ash concentration. The proposed innovation, the SuperSwift XT, will meet NASA's need to enhance [the] performance and utility of NASA's airborne science fleet by providing a durable, terrain-following UAS that will be adapted for use in harsh environments containing environmental phenomena that impacts societal activity (i.e. volcanic emissions impacting the safety of passenger aviation). The sUAS will provide targeted, in situ observations from previously inaccessible regions that can significantly advance NASA?s goal of safe, efficient growth in global aviation by aiding in the collection of scientific data from which predictive Volcanic Ash Transport and Dispersion models (VATD) used to inform air traffic management systems.