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A Lab with a View: Findings from NASA's Aircraft Bioaerosol Collector
No full textAirborne microorganisms in the upper troposphere and lower stratosphere remain elusive due to a lack of reliable sample collection systems. To address this problem, we designed, installed, and flight-validated a novel Aircraft Bioaerosol Collector (ABC) for NASA's C-20A that can make collections for microbiological research investigations up to altitudes of 13.7 km. Herein we report results from the first set of science flights-four consecutive missions flown over the United States (US) from 30 October to 2 November, 2017. To ascertain how the concentration of airborne bacteria changed across the tropopause, we collected air during aircraft Ascent/Descent (0.3 to 11 km), as well as sustained Cruise altitudes in the lower stratosphere (~12 km). Bioaerosols were captured on DNA-treated gelatinous filters inside a cascade air sampler, then analyzed with molecular and culture-based characterization. Several viable bacterial isolates were recovered from flight altitudes, including Bacillus sp., Micrococcus sp., Arthrobacter sp., and Staphylococcus sp. from Cruise samples and Brachybacterium sp. from Ascent/Descent samples. Using 16S V4 sequencing methods for a culture-independent analysis of bacteria, the average number of total OTUs was 305 for Cruise samples and 276 for Ascent/Descent samples. Surprisingly, our results revealed a homogeneous distribution of bacteria in the atmosphere up to 12 km. The observation could be due to atmospheric conditions producing similar background aerosols across the western US, as suggested by modeled back trajectories and satellite measurements. However, the influence of aircraft-associated bacterial contaminants could not be fully eliminated and that background signal was reported throughout our dataset. Considering the tremendous engineering challenge of collecting biomass at extreme altitudes where contamination from flight hardware remains an ever-present issue, we note the utility of using the stratosphere as a proving ground for planned life detection missions across the solar system
Application of System Identification to Parachute Modeling
No full textParachute models are used in numerous flight simulation tools to predict a wide range of parachute flight performance characteristics (e.g., parachute inflation loads, parachute stability and dynamics, vehicle touchdown conditions, and, ultimately, the safety and survivability of the system using the parachute). The current state of the art in developing parachute models is to initially estimate the parachute characteristics based on the parachute geometry and historical data and then add increased model fidelity based on data from wind tunnel and/or flight tests. This approach, however, can be deficient in identifying which parachute states (e.g., angle of attack, sideslip, angular rates, flyout angles, descent rate, dynamic pressure, proximity to other parachutes) are responsible for the parachute motion, and the relationship between those states and the forces on the parachute
High-Performance Computing Optimization for Aladyn Adaptive Neural Network Molecular Dynamics Mini-Application
No full textThis report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel Skylake microarchitecture, and on graphic accelerators, such as Nvidia V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance.
Atomistic computer simulations are a fundamental tool in materials research to model
material properties form physics-based first principles. Atomic interaction, governed by
Quantum Mechanics (QM) require sophisticated and highly computationally demanding
mathematical models to calculate [1]. Classical methods use approximate functional forms,
empirically fitted through a set of variable parameters to emulate atomic energies as direct
functions of atomic coordinates [2]. While empirical potentials are computationally much
simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3],
they are substantially less accurate compared to quantum calculations and applicable only
to very specific atomic configurations or predefined crystallographic phases. A recently
suggested approach is to use heuristic machine learning methods [4], such as those based
on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a
sufficiently large database of QM-calculated structures [5,6]. This approach reduces
significantly the computational complexity, allowing for simulations of orders of
magnitude larger systems compared to QM-based methods without compromising
accuracy. Still, compared to classical methods using empirical energy functions, ANN
methods remain two- to three orders of magnitude more computationally demanding.
Hence, the computational cost of simulations, together with the need for extensive training
of ANNs, still makes the practical implementation of ANN-based methods quite
challenging.
The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization
strategies to develop highly scalable parallel algorithms for ANN-based atomistic
simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance
computing (HPC) hardware based on multicore central processing units
(CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal
is to optimize the performance on a single HPC compute node, before implementing
scaling to multi-node parallelization using message passing interface (MPI). At the same
time, the open source code of Aladyn can serve as a training model for students and
professors in academia
Developing an Air Quality Index for Microgravity Indoor Environments/Space Missions
No full textIndoor pollution sources (on Earth) that release gases or particles into the air are the primary cause of air quality problems in indoor environments. The development of an adequate tool to understand pollution levels in a certain location is of high importance. This tool must be able to inform about the levels of pollution in a simple and understandable way but also, used to take a series of predetermined measures to protect the health of the exposed population. One of the most useful and up to date approaches for characterizing air pollution is the Air Quality Index (AQI). It is an easily-calculated powerful data-driven tool, that summarizes a complex phenomenon, such as air pollution, in straightforward indicators. The AQI system has been developed in different countries around the world, mainly for outdoor environments, based on the results of risk assessments, epidemiology studies, and current local air pollution regulations and standards.Air quality in microgravity indoor environments is of fundamental importance to crew health, with concerns encompassing both gaseous contaminants and particulate matter. Although the concentration of gases in the microgravity indoor environment is well studied, aerosols remain one of the major pollutants that affect air quality and has reported adverse health effects and hasn't been reported under these unique conditions. Earth-based AQIs can't be extrapolated to microgravity indoor environments due to different aerosol characteristics and altered lung deposition in low gravity.Concurrent with the aerosol-focused AQI effort, we assess and document how the process would apply for combining particles & gases into a composite index, with the ability to query each AQI independently. All this information can be combined in a spacecraft-specific AQI for future space missions and habitats. The objective of this work are to determine what areas of expertise will contribute, what research and data will be required, and explore the scope of effort needed to formulate a spacecraft AQI in addition to analyzing ISS aerosol sampling data and incorporate results from both aerosol Sampling experiments (the only relevant data available from space)
Space Mission Design at NASA Ames
No full textSTI is for a presentation being given to visitors coming to NASA Ames from the United Arab Emirates
Explore Flight: We're with You When You Fly. NASA Aeronautics Research
No full textOverview NASA Aeronautics activities, and how NASA Ames Research Center contributes to NASA's Aeronautics activities
Active Array Measurements using the Portable Laser Guided Robotic Metrology System
No full textIn this paper, we will discuss the impact of mounting structures on the installed performance of phased arrays. In particular, performance data for the Conformal, Lightweight Antennas for Aeronautical Communications Technology (CLAS-ACT) antenna will be presented. Performance data from a series of mounting configurations will show that null depth and location is particularly susceptible while the main beam steering angle remain relatively stable. In addition, the Portable Laser Guided Robotic antenna range (PLGR) will be discussed as a suitable instrument for measuring antenna patterns in complex or difficult locations that are challenging for traditional ranges. The PLGR antenna range was recently developed at the National Aeronautics and Space Administration's (NASA) Glenn Research Center (GRC) and deployed to measure in situ antenna patterns
12E.5 Forecasting Frost: Using High Resolution WRF Runs to Predict Frost Occurrence in the Tea Growing Regions of Kenya
No full textNo abstract availabl
Surface Turbulent Fluxes in Convection: Using CYGNSS Observations to Improve Sampling of Satellite-Based Flux Estimates in the Tropics
No full textNo abstract availabl
Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables
No full textTo adequately constrain the frequency of energy deposition in active region cores in the solar corona, systematic comparisons between detailed models and observational data are needed. In this paper, we describe a pipeline for forward modeling active region emission using magnetic field extrapolations and field-aligned hydrodynamic models. We use this pipeline to predict time-dependent emission from active region NOAA 1158 for low-, intermediate-, and high-frequency nanoflares. In each pixel of our predicted multi-wavelength, time-dependent images, we compute two commonly used diagnostics: the emission measure slope and the time lag. We find that signatures of the heating frequency persist in both of these diagnostics. In particular, our results show that the distribution of emission measure slopes narrows and the mean decreases with decreasing heating frequency and that the range of emission measure slopes is consistent with past observational and modeling work. Furthermore, we find that the time lag becomes increasingly spatially coherent with decreasing heating frequency while the distribution of time lags across the whole active region becomes more broad with increasing heating frequency. In a follow-up paper, we train a random forest classifier on these predicted diagnostics and use this model to classify real observations of NOAA 1158 in terms of the underlying heating frequency