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This technology can be applied to most optical frequency domain reflectometry (OFDR) fiber optic strain sensing systems. It is particularly well suited to Armstrong's FOSS technology, which uses efficient algorithms to determine from strain data in real time a variety of critical parameters, including twist and other structural shape deformations, temperature, pressure, liquid level, and operational loads. How It Works This technology enables smart-sensing techniques that adjust parameters as needed in real time so that only the necessary amount of data is acquired—no more, no less. Traditional signal processing in fiber optic strain sensing systems is based on fast Fourier transform (FFT), which has two key limitations. First, FFT requires having analysis sections that are equal in length along the whole fiber. Second, if high resolution is required along one portion of the fiber, FFT processes the whole fiber at that resolution. Armstrong's adaptive spatial resolution innovation makes it possible to efficiently break up the length of the fiber into analysis sections that vary in length. It also allows the user to measure data from only a portion of the fiber. If high resolution is required along one section of fiber, only that portion is processed at high resolution, and the rest of the fiber can be processed at the lower resolution. Why It Is Better To quantify this innovation's advantages, this new adaptive method requires only a small fraction of the calculations needed to provide additional resolution compared to FFT (i.e., thousands versus millions of additional calculations). This innovation provides faster signal processing and precision measurement only where it is needed, saving time and resources. The technology also lends itself well to long-term bandwidth-limited monitoring systems that experience few variations but could be vulnerable as anomalies occur. More importantly, Armstrong's adaptive algorithm enhances safety, because it automatically adjusts the resolution of sensing based on real-time data. For example, when strain on a wing increases during flight, the software automatically increases the resolution on the strained part of the fiber. Similarly, as bridges and wind turbine blades undergo stress during big storms, this algorithm could automatically adjust the spatial resolution to collect more data and quickly identify potentially catastrophic failures. This innovation greatly improves the flexibility of fiber optic strain sensing systems, which provide valuable time and cost savings to a range of applications. For more information about the full portfolio of FOSS technologies, see DRC-TOPS-37 or visit https://technology-afrc.ndc.nasa.gov/featurestory/fiber-optic-sensing

Reaction spheres technology operate on a physics similar to reaction wheels, which by the conservation of angular momentum uses a rotating flywheel to spin a body in the opposite direction. Sphere systems that utilize magnetic torqueing rather than mechanical are also smaller, are more reliable, have low friction losses, and have improved lifetime performance. The proposed reaction sphere provides improved performance over traditional wheels and satisfies the push for component miniaturization, increased pointing accuracy, and power efficiency on CubeSats. Primary aims are to develop a low-friction method to contain a sphere in spaceflight and determine the feasibility of on-orbit momentum storage to supplement battery power. With appropriate placement of permanent magnets, the sphere systems can generate relatively equal value of momentum and torques for any spin axis. This sphere at any speed, produces more momentum than the wheels, resulting in faster attitude stability.

NASAs iodine vapor feed system is based on a mechanism that holds and maintains the solid iodine is contact with a heated surface, in this case the walls of the propellant tank. The mechanism provides a robust and reliable steady-state delivery of sublimated iodine vapor to the ion propulsion system by ensuring good thermal contact between the solid iodine and the tank walls. To date, the technology development effort includes extensive thermal, mechanical and flow modelling together with testing of components and subsystems required to feed iodine propellant to a 200-W Hall thruster. The feed system has been designed to use materials that are resistant to the highly-reactive nature of iodine propellant. Dynamic modeling indicates that the feed system tubing can be built is such a way as to reduce vibrationally-induced stresses that occur during launch. Thermal modeling has been performed to demonstrate that the feed system heater power levels are sufficient to heat the tank and propellant lines to operating temperatures, and sublime the iodine in the storage tank to supply propellant for reliable and long-term operation.

Storage and transfer of fluid commodities such as oxygen, hydrogen, natural gas, nitrogen, argon, etc. is an absolute necessity in virtually every industry on Earth. These fluids are typically contained in one of two ways; as low pressure, cryogenic liquids, or as a high pressure gases. Energy storage is not useful unless the energy can be practically obtained ("un-stored") as needed. Here the goal is to store as many fluid molecules as possible in the smallest, lightest weight volume possible; and to supply ("un-store") those molecules on demand as needed in the end-use application. The CFC concept addresses this dual storage/usage problem with an elegant charging/discharging design approach. The CFC's packaging is ingeniously designed, tightly packing aerogel composite materials within a container allows for a greater amount of storage media to be packed densely and strategically. An integrated conductive membrane also acts as a highly effective heat exchanger that easily distributes heat through the entire container to discharge the CFC quickly, it can also be interfaced to a cooling source for convenient system charging; this feature also allows the fluid to easily saturate the container for fast charging. Additionally, the unit can be charged either with cryogenic liquid or from an ambient temperature gas supply, depending on the desired manner of refrigeration. Finally, the heater integration system offers two promising methods, both of which have been fabricated and tested, to evenly distribute heat throughout the entire core, both axially and radially.

Some of the current challenges faced by research in artificial intelligence and autonomous control systems include providing self control, resilience, adaptability, and stability for intelligent systems, especially over a long period of time, in changing environments. The Evolvable Neural Software System (ENSS), Formulation for Emotion Embedding in Logic Systems (FEELS), Stability Algorithm for Neural Entities (SANE), and the Logic Expansion for Autonomously Reconfigurable Neural Systems (LEARNS) are foundations for tackling some of these challenges, by providing the basic algorithms evolvable systems could use to manage its own behavior. These algorithms would allow networks to self regulate, noticing unusual behavior and the circumstances that may have caused that behavior, and then correcting to behave more predictably when similar circumstances are encountered. The process is similar to how psychology in organisms evolved iteratively, eventually finding and keeping better responses to given stimuli.

NASA Goddard Space Flight Center now offers a new capability for meeting this Big Data challenge: MERRA Analytic Services (MERRA/AS). MERRA/AS combines the power of high-performance computing, storage-side analytics, and web APIs to dramatically improve customer access to MERRA data. It represents NASAs first effort to provide Climate Analytics-as-a-Service. Retrospective analyses (or reanalyses) such as MERRA have long been important to scientists doing climate change research. MERRA is produced by NASAs Global Modeling and Assimilation Office (GMAO), which is a component of the Earth Sciences Division in Goddards Sciences and Exploration Directorate. GMAOs research and development activities aim to maximize the impact of satellite observations in climate, weather, atmospheric, and land prediction using global models and data assimilation. These products are becoming increasingly important to application areas beyond traditional climate science. MERRA/AS provides a new cloud-based approach to storing and accessing the MERRA dataset. By combining high-performance computing, MapReduce analytics, and NASAs Climate Data Services API (CDS API), MERRA/AS moves much of the work traditionally done on the client side to the server side, close to the data and close to large compute power. This reduces the need for large data transfers and provides a platform to support complex server-side data analysesit enables Climate Analytics-as-a-Service. MERRA/AS currently implements a set of commonly used operations (such as avg, min, and max) over all the MERRA variables. Of particular interest to many applications is a core collection of about two dozen MERRA land variables (such as humidity, precipitation, evaporation, and temperature). Using the RESTful services of the Climate Data Services API, it is now easy to extract basic historical climatology information about places and time spans of interest anywhere in the world. Since the CDS API is extensible, the community can participate in MERRA/ASs development by contributing new and more complex analytics to the MERRA/AS service. MERRA/AS demonstrates the power of CAaaS and advances NASAs ability to connect data, science, computational resources, and expertise to the many customers and applications it serves.

The present innovation is an engineering tool known as the HHT Data Processing System (HHTDPS). The HHTDPS allows applying the Transform, or 'T,' to a data vector in a fashion similar to the heritage FFT. It is a generic, low cost, high performance personal computer (PC) based system that implements the HHT computational algorithms in a user friendly, file driven environment. Unlike other signal processing techniques such as the Fast Fourier Transform (FFT1 and FFT2) that assume signal linearity and stationarity, the Hilbert-Huang Transform (HHT) utilizes relationships between arbitrary signals and local extrema to find the signal instantaneous spectral representation. Using the Empirical Mode Decomposition (EMD) followed by the Hilbert Transform of the empirical decomposition data, the HHT allows spectrum analysis of nonlinear and nonstationary data by using an engineering a-posteriori data processing, based on the EMD algorithm. This results in a non-constrained decomposition of a source real value data vector into a finite set of Intrinsic Mode Functions (IMF) that can be further analyzed for spectrum interpretation by the classical Hilbert Transform. The HHTDPS has a large variety of applications and has been used in several NASA science missions. NASA cosmology science missions, such as Joint Dark Energy Mission (JDEM/WFIRST), carry instruments with multiple focal planes populated with many large sensor detector arrays with sensor readout electronics circuitry that must perform at extremely low noise levels. A new methodology and implementation platform using the HHTDPS for readout noise reduction in large IR/CMOS hybrid sensors was developed at NASA Goddard Space Flight Center (GSFC). Scientists at NASA GSFC have also used the algorithm to produce the first known Hilbert-Transform based wide-field broadband data cube constructed from actual interferometric data. Furthermore, HHT has been used to improve signal reception capability in radio frequency (RF) communications. This NASA technology is currently available to the medical community to help in the diagnosis and prediction of syndromes that affect the brain, such as stroke, dementia, and traumatic brain injury. The HHTDPS is available for non-exclusive and partial field of use licenses.

This dual band infrared imaging system is capable of spatial resolution of 60 m from orbit and earth observing expected NEDT less than 0.2o C. It is designed to fit within the top two-thirds of a 3U CubeSat envelope, installed on the International Space Station, or deployed on other orbiting or airborne platforms. This infrared imaging system will utilize a newly conceived strained-layer superlattice GaSb/InAs broadband detector array cooled to 60 K by a miniature mechanical cryocooler. The camera is controlled by a sensor chip assembly consisting of a newly developed 25 m pitch, 640 x 512 pixel.

The invention uses the superconducting "proximity effect" and/or the "inverse proximity effect" to form a spatially varying order parameter. When designed to expel magnetic flux from a region of space, the proximity effect(s) are used in concert to make the superconducting order parameter strongly superconducting in the center and more weakly superconducting toward the perimeter. The shield is then passively cooled through the superconducting transition temperature. The superconductivity first nucleates in the center of the shielding body and expels the field from that small central region by the Meissner effect. As the sample is further cooled the region of superconducting order grows, and as it grows it sweeps the magnetic flux lines outward.