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PAssive Thermal Coating Observatory Operating in Low Earth Orbit (PATCOOL)
Get PDFThe PATCOOL is a NASA sponsored, University of Florida developed 3U CubeSat meant to investigate the feasibility of using a cryogenic selective surface coating as a new, more efficient way of passively cooling components in space. Initial tests on the ground demonstrate that this coating should provide a much higher reflectance of the Sun's irradiant power than any existing coating, while still providing far-infrared power emission. The ultimate validation of this technology requires on-orbit testing. PATCOOL hosts a 4-sample housing, with the samples shaped as thin cylinders (coin-like). Two samples are coated with state-of-the-art material, while the other pair uses the new coating to be evaluated. The temperatures of all samples during the mission (minimum 72 hours of data collection) are measured via thermistors. The samples are connected via thin Kevlar strings to the housing, to minimize heat transfer. The housing is designed to shield the samples from Earth's thermal radiation, and the CubeSat is attitude stabilized and controlled via a gravity gradient boom, magnetorquers and a reaction wheel set. Thermal Desktop simulations show PATCOOL's ability to thermally isolate the samples from heat exchanges other than with Sun and deep space, thanks to its thermal design and the chosen attitude profil
TESS Data Release Notes: Sector 18 DR25
Get PDFThis release note discusses the science data products produced by the Science Processing Operations Center at Ames Research Center from Sector 18 observations made with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics
Medics: Medical Decision Support System for Long-Duration Space Exploration
Get PDFThe Autonomous Medical Operations (AMO) group at NASA Ames is developing a medical decision support system to enable astronauts on long-duration exploration missions to operate autonomously. The system will support clinical actions by providing medical interpretation advice and procedural recommendations during emergent care and clinical work performed by crew. The current state of development of the system, called MedICS (Medical Interpretation Classification and Segmentation) includes two separate aspects: a set of machine learning diagnostic models trained to analyze organ images and patient health records, and an interface to ultrasound diagnostic hardware and to medical repositories. Three sets of images of different organs and medical records were utilized for training machine learning models for various analyses, as follows: 1. Pneumothorax condition (collapsed lung). The trained model provides a positive or negative diagnosis of the condition. 2. Carotid artery occlusion. The trained model produces a diagnosis of 5 different occlusion levels (including normal). 3. Ocular retinal images. The model extracts optic disc pixels (image segmentation). This is a precursor step for advanced autonomous fundus clinical evaluation algorithms to be implemented in FY20. 4. Medical health records. The model produces a differential diagnosis for any particular individual, based on symptoms and other health and demographic information. A probability is calculated for each of 25 most common conditions. The same model provides the likelihood of survival. All results are provided with a confidence level. Item 1 images were provided by the US Army and were part of a data set for the clinical treatment of injured battlefield soldiers. This condition is relevant to possible space mishaps, due to pressure management issues. Item 2 images were provided by Houston Methodist Hospital, and item 3 health records were acquired from the MIT laboratory of computational physiology. The machine learning technology utilized is deep multilayer networks (Deep Learning), and new models will continue to be produced, as relevant data is made available and specific health needs of astronaut crews are identified. The interfacing aspects of the system include a GUI for running the different models, and retrieving and storing data, as well as support for integration with an augmented reality (AR) system deployed at JSC by Tietronix Software Inc. (HoloLens). The AR system provides guidance for the placement of an ultrasound transducer that captures images to be sent to the MedICS system for diagnosis. The image captured and the associated diagnosis appear in the technicians AR visual display
Medical Data Architecture Prototype Development - Summary of Recent Work and Proposed Ideas for Upcoming Work
Get PDFThe Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions, and in ExMC Gap Med 10: We do not have the capability to provide computed medical decision support during exploration missions. These gaps recognize the need for a comprehensive medical data management system and the accompanying computational support to provide autonomous medical care during long duration exploration missions. As the MDA maturesincluding the capability to comprehensively process and discover medically-relevant information to support medical operations during exploration missionsproject focus will shift to maturing and extending the MDA platform to enable clinical decision support and real-time guidance. To date, the MDA foundational architecture has recommended exploration medical system Level of Care IV requirements through a series of test bed prototype developments and analog demonstrations. The next stage in the development will focus on more autonomous clinical decision making necessary to address challenges in executing a self-contained medical system that enables health care both with and without assistance from ground support. A thorough understanding of current state of medical decision support systems, advanced machine learning algorithms and vast and varied data sources is required. The development of a clinical decision support for exploration missions (Level of Care V) roadmap is needed: one that assesses of current state of the art of clinical decision support systems (CDSS), interoperability issues, identification of challenges in health and performance monitoring, obtaining and processing information from biosensors, knowledge and data management, data integration and fusion, and advanced algorithm development. This roadmap must also include rapid prototype development in the areas of data processing, advanced analysis and prediction of medical events, and treatment based on medically relevant information processing and evidence-based best practices. In this presentation, an overview of the relevant issues and the beginning framework of a Level of Care V CDSS development roadmap will be provided
Postmission Disposal Options in the 2019 USG Orbital Debris Mitigation Standard Practices (ODMSP)
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Near 40 Years MERRA-2 Data at NASA GES DISC -Opportunity and Challenge to Support Extremes Study
Get PDFTo the end of 2019, 40 years NASA climate reanalysis data sets from the Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) will be available at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). MERRA-2 consists atmosphere, land, and ocean data, which may be used for the studies ranging from the short scale weather events to the large scale decadal vulnerabilities. The hourly products, such as precipitation, soil moisture, temperature, and aerosols etc., have been used widely to study extreme events.In supporting users from broad communities, GES DISC have developed various data access services, including subsetter for downloading only data of interest with preferred format; OPeNDAP - for machine-to-machine data access; and Giovanni- for online visualization and analysis, etc. A big challenge for extreme study is to downloading and processing long-term hourly or daily data. The data downloading performance is not very satisfied by many users with current services and the native archived data structure. Late June 2019, many people in Europe had experienced extreme heat waves. The temperatures in several countries exceeded 40C (104F). For example, MERRA-2 shows that the near surface daily maximum temperature of June 28 2019 over Marseille, a city in southern France, reached 41.1 C (106F), which is the record breaking temperature in the last 40 years. GES DISC is working together with domain science experts to improve the performance of long time series access, making analysis ready data sets in supporting application researches, such as extreme study. In this presentation, using Europe heat wave as an example, we will show prototype of the in developing service for finding extremes from near 40 years MERRA-2 data at a given location. MERRA-2 data can be accessed from NASA GES DISC(https://disc.gsfc.nasa.gov/ ) by search keyword "MERRA-2"
Loads Correlation of a Full-Scale Proprotor on the Tiltrotor Test Rig
Get PDFIn 2018, a full-scale isolated proprotor test was conducted in the USAF National Full-Scale Aerodynamics Complex (NFAC) at NASA Ames. The test article was the 3-bladed Bell 699 research rotor derived from the AW609 rotor. For this test, the NASA Tiltrotor Test Rig (TTR) and rotor were installed in the 40- by 80-foot test section. Correlations between the 2018 test data and predictions from the comprehensive analysis CAMRAD II for blade and yoke (flexbeam) loads and rotor torque are presented. The full range of conversion to helicopter modes is covered: conversion 30-, 45-, and 60-deg TTR yaw, and helicopter 75- and 90-deg TTR yaw. The flap moment correlation is reasonable to good; the pitch link load and torsion moment are uniformly underpredicted. The measured 2P lag moment and 2P torque are not captured by the analysis. The inability to predict the 2P component is currently attributed to the analytical assumption of a perfect gimbal, whereas the actual test gimbal may be operating imperfectly and thus introducing the 2P harmonic. A new analytical model that accounts for non-ideal gimbal operation is needed
Preliminary Computational Assessment of Disk Rotating Detonation Engine Configurations
Get PDFA rotating detonation engine (RDE) configuration whereby the working fluid enters and exits in a predominantly radial manner is examined using a quasi-two-dimensional computational fluid dynamic simulation. The simulation, based on a Cartesian coordinate system, was originally developed to examine the physics and performance of the more typical annular RDE. Modifications required to accommodate the radial and circumferential flowfield are discussed. The centripetal forces that arise in this disk RDE (DRDE) configuration create a different wave structure than that seen in the annular RDE. They also give rise to markedly different fluid behavior depending on whether the flow is radially inward or radially outward. Using an entropy-based measure of pressure gain, it is found that for the preliminary idealized calculations performed in this paper, the inward flowing DRDE outperforms the outward flowing variant. The inward flowing DRDE is further shown to outperform the equivalent annular RDE. The effects on performance of several parameters are examined, including inner-to-outer diameter ratio, inner-to-outer cross-sectional area ratio, and inlet throat-to-channel area ratio
Designing an Optimal Ensemble Strategy for GMAO S2S Forecast System
Get PDFThe NASA Global Modeling and Assimilation Office (GMAO) Sub-seasonal to Seasonal (S2S) prediction system is being readied for a major upgrade. An important factor in successful extended range forecasting is the definition of the ensemble. Our overall strategy is to run a relatively large ensemble of about 40 members up to 3 months (focusing on the sub-seasonal forecast problem), after which we sub-sample the ensemble, and continue the forecast with about 10 members (up to 12 months). Here we present the results of our testing of various ways to generate the initial perturbations and the validation of a stratified sampling approach for choosing the members of the smaller ensemble. For the initialization of the ensemble we propose a combination of lagged and burst initial conditions. To generate perturbations for the burst ensemble members we used scaled differences of pairs of analysis states (chosen randomly from the corresponding season) separated by 1-10 days. We consider perturbing separately the atmosphere and the ocean, or both. By varying the separation times between the analysis states, we are able to produce perturbations that resemble well-known modes of variability. Focusing on the ENSO SST indices, we found that all types of perturbations are important for the ensemble spread with, however, considerable differences in the timing of the impacts on spread for the atmospheric and oceanic perturbations.Our initial (larger) ensemble size was determined so as to maximize the skill of predicting some of the leading modes of boreal winter atmospheric modes (namely the NAO, PNA and AO). Since it is not feasible for us to run with the larger ensemble beyond about 3 months, we employ a stratified sampling procedure that identifies the emerging directions of error growth to subset the ensemble. By comparing the results from the stratified ensemble with that of the randomly sampled ensemble of the same size, we find that the former provides substantially better estimates the mean of the original large ensemble