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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Classification of Ion Mobility Data Using the Neural Network Approach

Determination of atmospheric and surface elemental and molecular composition of various solar system bodies is essential to the development of a firm understanding of the origin and evolution of the solar system. Furthermore, such data is needed to address the intriguing question of whether or not life exists or once existed elsewhere in the Solar System. As such, these measurements are among the primary scientific goals of NASA s current and future planetary missions. In recent years, significant progress toward both miniaturization and field portability of in situ analytical separation and detection devices have been made with future planetary explorations in mind. However, despite all these advances, accurate in situ identification of atmospheric and surface compounds remains a big challenge. In response to that we are developing various hardware and software tools which would enable us to uniquely identify species of interest in a complex chemical environment.

Duong, T. A.↗
NASA NTRS2019

NASA GES DISC Giovanni: Current and Future

Giovanni (Geospatial Interactive Online Visualization and Analysis Infrastructure), developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), has established a reputation among NASA users for easy access, analysis, and visualization of NASA Earth science data. Currently, Giovanni supports over 1900 variables in eight disciplinary areas. Like any other enterprise application, Giovanni faces big data challenges, such as servicing increasingly large data volumes and more complex data types, while at the same time addressing the demands of a more diverse user community, e.g., placing requests for long-term time series from multiple spatially and temporally dense data records. I will present how Giovanni has been evolving from an on-premises, monolithic software application towards a cloud-enabled implementation to address these challenges.

Analytics↗
NASA NTRS2003

Combining Multiple Gyroscope Outputs for Increased Accuracy

A proposed method of processing the outputs of multiple gyroscopes to increase the accuracy of rate (that is, angular-velocity) readings has been developed theoretically and demonstrated by computer simulation. Although the method is applicable, in principle, to any gyroscopes, it is intended especially for application to gyroscopes that are parts of microelectromechanical systems (MEMS). The method is based on the concept that the collective performance of multiple, relatively inexpensive, nominally identical devices can be better than that of one of the devices considered by itself. The method would make it possible to synthesize the readings of a single, more accurate gyroscope (a virtual gyroscope) from the outputs of a large number of microscopic gyroscopes fabricated together on a single MEMS chip. The big advantage would be that the combination of the MEMS gyroscope array and the processing circuitry needed to implement the method would be smaller, lighter in weight, and less power-hungry, relative to a conventional gyroscope of equal accuracy. The method (see figure) is one of combining and filtering the digitized outputs of multiple gyroscopes to obtain minimum-variance estimates of rate. In the combining-and-filtering operations, measurement data from the gyroscopes would be weighted and smoothed with respect to each other according to the gain matrix of a minimum- variance filter. According to Kalman-filter theory, the gain matrix of the minimum-variance filter is uniquely specified by the filter covariance, which propagates according to a matrix Riccati equation. The present method incorporates an exact analytical solution of this equation.

Bayard, David S.↗
NASA NTRS2023

Machine Intelligence for Radiation Science: Summary of the Radiation Research Society 67th Annual Meeting Symposium

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67 th Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.

radiation↗
NASA NTRS2007

Multi Sensor Approach to Address Sustainable Development

The main objectives of Earth Science research are many folds: to understand how does this planet operates, can we model her operation and eventually develop the capability to predict such changes. However, the underlying goals of this work are to eventually serve the humanity in providing societal benefits. This requires continuous, and detailed observations from many sources in situ, airborne and space. By and large, the space observations are the way to comprehend the global phenomena across continental boundaries and provide credible boundary conditions for the mesoscale studies. This requires a multiple sensors, look angles and measurements over the same spot in accurately solving many problems that may be related to air quality, multi hazard disasters, public health, hydrology and more. Therefore, there are many ways to address these issues and develop joint implementation, data sharing and operating strategies for the benefit of the world community. This is because for large geographical areas or regions and a diverse population, some sound observations, scientific facts and analytical models must support the decision making. This is crucial for the sustainability of vital resources of the world and at the same time to protect the inhabitants, endangered species and the ecology. Needless to say, there is no single sensor, which can answer all such questions effectively. Due to multi sensor approach, it puts a tremendous burden on any single implementing entity in terms of information, knowledge, budget, technology readiness and computational power. And, more importantly, the health of planet Earth and its ability to sustain life is not governed by a single country, but in reality, is everyone's business on this planet. Therefore, with this notion, it is becoming an impractical problem by any single organization/country to bear this colossal responsibility. So far, each developed country within their means has proceeded along satisfactorily in implementing their Earth observing needs but it has left a big void in the developing world who have very limited resources to invest in the space measurements. This paper gives some serious thoughts in what options are there in undertaking this tremendous challenge. The problem is multi-dimensional in terms of budget, technology availability, environmental legislations, public awareness, and communication limitations. Some of these issues are introduced, discussed and possible implementation strategies are provided in this paper to move out of this predicament. A strong emphasis is placed on international cooperation and collaboration to see a collective benefit for this effort

Habib, Shahid↗
NASA NTRS2004

EVA Performance Prediction

Astronaut physical performance capabilities in micro gravity EV A or on planetary surfaces when encumbered by a life support suit and debilitated by a long exposure to micro gravity will be less than unencumbered pre flight capabilities. The big question addressed by human factors engineers is: what can the astronaut be expected to do on EVA or when we arrive at a planetary surface? A second question is: what aids to performance will be needed to enhance the human physical capability? These questions are important for a number of reasons. First it is necessary to carry out accurate planning of human physical demands to ensure that time and energy critical tasks can be carried out with confidence. Second it is important that the crew members (and their ground or planetary base monitors) have a realistic picture of their own capabilities, as excessive fatigue can lead to catastrophic failure. Third it is important to design appropriate equipment to enhance human sensory capabilities, locomotion, materials handling and manipulation. The evidence from physiological research points to musculoskeletal, cardiovascular and neurovestibular degradation during long duration exposure to micro gravity . The evidence from the biomechanics laboratory (and the Neutral Buoyancy Laboratory) points to a reduction in range of motion, strength and stamina when encumbered by a pressurized suit. The evidence from a long history of EVAs is that crewmembers are indeed restricted in their physical capabilities. There is a wealth of evidence in the literature on the causes and effects of degraded human performance in the laboratory, in sports and athletics, in industry and in other physically demanding jobs. One approach to this challenge is through biomechanical and performance modeling. Such models must be based on thorough task analysis, reliable human performance data from controlled studies, and functional extrapolations validated in analog contexts. The task analyses currently carried out for EVA activities are based more on extensive domain experience than any formal analytic structure. Conversely, physical task analysis for industrial and structured evidence from training and EV A contexts. Again on earth there is considerable evidence of human performance degradation due to encumbrance and fatigue. These industrial models generally take the form of a discounting equation. The development of performance estimates for space operations, such as timeline predictions for EVA is generally based on specific input from training activity, for example in the NBL or KC135. uniformed services tasks on earth are much more formalized. Human performance data in the space context has two sources: first there is the micro analysis of performance in structured tasks by the space physiology community and second there is the less structured evidence from training and EV A contexts.

Peacock, Brian↗