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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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1,740 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric Conditions

Understanding the impact of atmospheric conditions on SARS-CoV2 is critical to model COVID-19 dynamics and sheds a light on the future spread around the world. Furthermore, geographic distri- butions of expected clinical severity of COVID-19 may be closely linked to prior history of respiratory diseases and changes in humidity, tem- perature, and air quality. In this context, we postulate that by tracking topological features of atmospheric conditions over time, we can provide a quanti?able structural distribution of atmospheric changes that are likely to be related to COVID-19 dynamics. As such, we apply the machinery of persistence homology on time series of graphs to extract topological signatures and to follow geographical changes in relative humidity and temperature. We develop an integrative machine learning framework named Topological Lifespan LSTM (TLife-LSTM) and test its predictive capabilities on forecasting the dynamics of SARS-CoV2 cases. We validate our framework using the number of con?rmed cases and hospitalization rates recorded in the states of Washington and California in the USA. Our results demonstrate the predictive potential of TLife-LSTM in forecasting the dynamics of COVID-19 and modeling its complex spatio-temporal spread dynamics.

Gel, Yulia R.↗
NASA NTRSDate not supplied

Extinction of Solid Diffusion Flame in Microgravity: Details of Quenching and Blowoff Processes

Long duration microgravity experiments aboard the International Space Station determine diffusion flame extinction limits of PMMA spheres. Upon ignition from an electrically-heated coil, the 4-cm-diameter samples are exposed to forced flows ranging from 0.2 to 80 cm/s and an oxygen ranging from 13 to 28% in one atmosphere total pressure. Extinction is reached as oxygen concentration gradually decreases by natural depletion. Five extinction tests are presented at different flow velocities and oxygen concentrations. Quenching at low velocity is observed with the flame tip shrinking upstream and blowoff is observed when a hole forms in the flame at the forward flow stagnation point. However, these processes are not quasistatic. The quenching motion involves periodic flame tip pulsating toward downstream and shrinking upstream with a continually decreasing flame size at the end of each cycle. In blowoff, the flame base pulsates between the flame hole and the downstream location, although with far fewer cycles compared to quenching. The pulsations appear to be the result of a premixed flame front spreading into a combustible mixture. Two specific cases are discussed. In the first, at very low flow velocity (~ 0.4 cm/s) and elevated oxygen, self-sustained flame tip cyclic pulsations are observed for a lengthy period (~15 minutes). In the second case with a higher flow velocity (50 cm/s), the diffusion flame is stabilized at the shoulder of the spherical sample after local stagnation point blowoff. With steady decrease in ambient oxygen due to depletion, spinning flamelets are formed. The long-duration microgravity environment makes it possible to observe. these interesting and detailed extinction processes.

Flame quenching↗
DOE OSTI2021

Topological Machine Learning Methods for Power System Responses to Contingencies: Preprint

While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remain untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topologybased system that depends on neural network architecture for impact metrics classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, namely, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates both more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn complex relationships between electrical properties and local topological measurements on the simulated response to contingencies for NREL-SIIP power system.

graph theory↗
DOE OSTI2021

Topological Machine Learning Methods for Power System Responses to Contingencies

While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remains untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topology-based system that depends on a neural network architecture for impact metric classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn more about the complex relationships between electrical properties and local topological measurements on their simulated response to contingencies for the NREL-SIIP power system.

contingency analysis↗