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At least 145 records · Page 8

Program Management Tool

The Program Management Tool (PMT) is a comprehensive, Web-enabled business intelligence software tool for assisting program and project managers within NASA enterprises in gathering, comprehending, and disseminating information on the progress of their programs and projects. The PMT provides planning and management support for implementing NASA programmatic and project management processes and requirements. It provides an online environment for program and line management to develop, communicate, and manage their programs, projects, and tasks in a comprehensive tool suite. The information managed by use of the PMT can include monthly reports as well as data on goals, deliverables, milestones, business processes, personnel, task plans, monthly reports, and budgetary allocations. The PMT provides an intuitive and enhanced Web interface to automate the tedious process of gathering and sharing monthly progress reports, task plans, financial data, and other information on project resources based on technical, schedule, budget, and management criteria and merits. The PMT is consistent with the latest Web standards and software practices, including the use of Extensible Markup Language (XML) for exchanging data and the WebDAV (Web Distributed Authoring and Versioning) protocol for collaborative management of documents. The PMT provides graphical displays of resource allocations in the form of bar and pie charts using Microsoft Excel Visual Basic for Application (VBA) libraries. The PMT has an extensible architecture that enables integration of PMT with other strategic-information software systems, including, for example, the Erasmus reporting system, now part of the NASA Integrated Enterprise Management Program (IEMP) tool suite, at NASA Marshall Space Flight Center (MSFC). The PMT data architecture provides automated and extensive software interfaces and reports to various strategic information systems to eliminate duplicative human entries and minimize data integrity issues among various NASA systems that impact schedules and planning.

Gawadiak, Yuri↗

Preparations for Integrating Space-Based Total Lightning Observations into Forecast Operations

NASA's Short-term Prediction Research and Transition (SPoRT) Center has been a leader in collaborating with the United States National Weather Service (NWS) offices to integrate ground-based total lightning (intra-cloud and cloud-to-ground) observations into the real-time operational environment. For much of these collaborations, the emphasis has been on training, dissemination of data to the NWS AWIPS system, and focusing on the utility of these data in the warning decision support process. A shift away from this paradigm has occurred more recently for several reasons. For one, SPoRT's collaborations have expanded to new partners, including emergency managers and the aviation community. Additionally, and most importantly, is the impending launch of the GOES-R Geostationary Lightning Mapper (GLM). This has led to collaborative efforts to focus on additional forecast needs, new data displays, develop training for GLM uses based on the lessons learned from ground-based lightning mapping arrays, and ways to better relate total lightning data to other meteorological parameters. This presentation will focus on these efforts to prepare the operational end user community for GLM with an eye towards sharing lessons learned as EUMETSAT prepares for the Meteosat Third Generation Lightning Imager. This will focus on both software and training needs. In particular, SPoRT has worked closely with the Meteorological Development Laboratory to create the total lightning tracking tool. This software allows for NWS forecasters to manually track storms of interest and display a time series trend of observations. This tool also has been expanded to work on any gridded data set allowing for easy visual comparisons of multiple parameters in addition to total lightning. A new web display has been developed for the ground-based observations that can be easily extended to satellite observations. This paves the way for new collaborations outside of the NWS, both domestically and internationally, as the web display will be functional on PCs and mobile devices. Furthermore, SPoRT has helped developed the software plug-in to visualize GLM data. Examples using the official GLM proxy product will be used to provide a glimpse as to what real-time GLM and likely MTG-LI data will be in the near future.

GLM↗

Harnessing Big Data to Support the Conservation and Rehabilitation of Mangrove Forests Globally

Mangrove forests are found on sheltered coastlines in tropical, subtropical, and some warm temperate regions. These forests support unique biodiversity and provide a range of benefits to coastal communities, but as a result of large-scale conversion for aquaculture, agriculture, and urbanization, mangroves are considered increasingly threatened ecosystems. Scientific advances have led to accurate and comprehensive global datasets on mangrove extent, structure, and condition, and these can support evaluation of ecosystem services and stimulate greater conservation and rehabilitation efforts. To increase the utility and uptake of these products, in this Perspective we provide an overview of these recent and forthcoming global datasets and explore the challenges of translating these new analyses into policy action and on-the-ground conservation. We describe a new platform for visualizing and disseminating these datasets to the global science community, non-governmental organizations, government officials, and rehabilitation practitioners and highlight future directions and collaborations to increase the uptake and impact of large-scale mangrove research.

mangrove↗

ncompare: A Python Package for Comparing netCDF Structures

Earth science researchers and data engineers have a common problem: they often need to compare data files to see what is different between them. A lot of time is spent developing code to test differences. When it comes to comparing multidimensional data file formats like netCDFs (Network Common Data Form), this is particularly challenging and time-consuming, since there is frequently a need to evaluate the differences between dimension sizes, variable structures, and variable attributes, especially for regression testing. Since netCDFs are widely used in Earth science — with climate models, oceanographic or atmospheric reanalyses, and observational data — improved means of evaluating netCDF files can help enable a wide range of applications. We have developed a reusable open source approach through `ncompare`, which is a Python package for comparing netCDF structures [[https://github.com/nasa/ncompare]]. The `ncompare` tool compares the structure of two Network Common Data Form (NetCDF) files at the command line. It facilitates rapid comparisons by generating a formatted display of the matching and non-matching groups, variables, and associated metadata between two NetCDF datasets. The user has the option to colorize the terminal output for ease of viewing, and `ncompare` can optionally save comparison reports in text, comma-separated value (CSV), and/or Microsoft Excel formats. Despite the availability of tools (such as ncmpidiff or nccmp) that compare the values of variables, there was not previously a readily available, Python-based tool for rapid visual comparisons of group and variable structures, attributes, and chunking. `ncompare` was developed at NASA’s Atmospheric Science Data Center (ASDC) and is a collaboration with NASA Openscapes [[https://nasa-openscapes.github.io]] mentors across 11 of NASA’s data centers. Openscapes’ overarching vision is to support scientific researchers using NASA Earthdata as they migrate their workflows to the cloud. Relevant links: - https://github.com/nasa/ncompare - https://github.com/pyOpenSci/software-submission/issues/146 - https://nasa-openscapes.github.io

Daniel Kaufman↗

A Distributed Simulation Framework Applied to Artemis Analysis, Studies, Integration, and Test

The National Aeronautics and Space Administration (NASA) established the Artemis Program, a series of missions to return humans to the Moon and explore further than before. To execute the Artemis missions, NASA is collaborating with commercial and international partners to create the necessary infrastructure and logistics plan that will establish a long term presence on the Moon ahead of exploring Mars. NASA and its partners are developing a collection of space and surface systems to support crewed missions to the lunar surface that will provide the mobility, habitation, logistics, and exploration support necessary for Artemis mission successes which includes robust scientific investigations. This paper details the design, capabilities, and uses of the Artemis Distributed Simulation (ADS) being developed by the NASA Exploration Systems Simulations (NExSyS) Team to support Artemis architecture studies. ADS utilizes international interoperability standards to connect a collection of independent vehicle and service simulations; these include but are not limited to elements such as rovers, landers, and habitation elements along with services like communications, environment, visualization, and data logging. ADS’s distributed nature allows for the complex aggregation of constituent Artemis elements; this includes efficient scenario modification with the addition or removal of individual simulations representing Artemis elements or services. This capability provides support for the rapid performance of various Artemis mission trade studies exploring alternate configurations. Currently, ADS uses NASA developed simulations for development and testing; however, through the use of international simulation interoperability standards, ADS provides an integration framework to incorporate dissimilar authoritative vendor simulations as Artemis systems mature and vendor simulations become available. Vendor simulations will be able to join ADS and interact with other Artemis elements and vehicles while limiting the exposure of proprietary data. This paper describes the expansion of an existing distributed simulation infrastructure to accommodate a collaborative and dynamic framework for the Artemis Program. This work includes updated federation designs, integration into existing NASA facilities, advancements in visualizations, and advancements in human driven inputs. This paper will also outline recently completed and ongoing support and collaboration with NASA studies and testing, namely results from energetics and Human-In-The-Loop (HITL) studies. The paper concludes with a plan for future developments and facility integration to enable enhanced studies in preparation for a return of humans to the lunar surface.

Artemis↗

Operational Evaluation of NASA SPoRT Lightning Safety Applications for Impact Based Decision Support Services

The NWS Office in Huntsville is tasked with monitoring and predicting the threat for lightning within its County Warning Area. These impact-based decision support services (IDSS) are provided routinely for aviation operations, irregularly for large-scale, outdoor events, and can have varying safety requirements, such as proximity to location and duration. Lightning monitoring and prediction can require the rapid synthesis of a plethora of data, so products that make this process more efficient and effective are sought by the operational community. The Huntsville NWS Office benefits from close collaboration with the NASA SPoRT Center, which is developing several products to address these operational challenges in concentrated R2O/O2R efforts. One example of this is the StopLight product, which uses Geostationary Lightning Mapper (GLM) flash extent density (FED) to provide an easy-to-interpret visual aid of lightning occurring within the last 30 minutes. The StopLight product shows both the age and location of the last lightning flash within each GLM pixel, which can be particularly useful for IDSS with determining when to resume activities or operations that have been shut down due to lightning. This presentation highlights results of collaboration between NASA SPoRT and NWS Huntsville, including testing of the Stoplight product, and additional experimental products in development. A brief summary of the lightning products are provided while the main focus will discuss product evaluation and forecaster perspective during real-time weather watch activities to support aviation operations and events where IDSS was necessary.

Kristopher White↗

Common Methodology for Efficient Airspace Operations

Topics include: a) Developing a common methodology to model and avoid disturbances affecting airspace. b) Integrated contrails and emission models to a national level airspace simulation. c) Developed capability to visualize, evaluate technology and alternate operational concepts and provide inputs for policy-analysis tools to reduce the impact of aviation on the environment. d) Collaborating with Volpe Research Center, NOAA and DLR to leverage expertise and tools in aircraft emissions and weather/climate modeling. Airspace operations is a trade-off balancing safety, capacity, efficiency and environmental considerations. Ideal flight: Unimpeded wind optimal route with optimal climb and descent. Operations degraded due to reduction in airport and airspace capacity caused by inefficient procedures and disturbances.

Sridhar, Banavar↗

The Evolution of Gridded NUCAPS: An Overview of Research to Operations Activities

The next-generation S-NPP and NOAA-20 Cross-track Infrared Sounder (CrIS) temperature and moisture profiles can provide valuable observations; (1) Where conventional radiosonde observations are sparse, (2) Between radiosonde launches. CrIS observations are combined with the Advanced Technology Microwave Sounder (ATMS) to produce high quality vertical soundings in clear and partly cloudy conditions. NUCAPS (NOAA Unique Combined Atmospheric Processing System) is the operational algorithm for processing combined hyperspectral infrared and microwave measurements. NUCAPS Soundings are operationally available in AWIPS as Skew-T plots. The capability to visualize the data in plan view or cross section would be valuable to maximize the benefits of NUCAPS data in AWIPS. A multi-organizational collaboration through the JPPS Soundings Applications Initiative developed the capability for plan view and cross section displays of NUCAPS in AWIPS (i.e., Gridded NUCAPS).

Berndt, Emily↗

Automation of Coordinated Planning Between Observatories: The Visual Observation Layout Tool (VOLT)

Fulfilling the promise of the era of great observatories, NASA now has more than three space-based astronomical telescopes operating in different wavebands. This situation provides astronomers with the unique opportunity of simultaneously observing a target in multiple wavebands with these observatories. Currently scheduling multiple observatories simultaneously, for coordinated observations, is highly inefficient. Coordinated observations require painstaking manual collaboration among the observatory staff at each observatory. Because they are time-consuming and expensive to schedule, observatories often limit the number of coordinated observations that can be conducted. In order to exploit new paradigms for observatory operation, the Advanced Architectures and Automation Branch of NASA's Goddard Space Flight Center has developed a tool called the Visual Observation Layout Tool (VOLT). The main objective of VOLT is to provide a visual tool to automate the planning of coordinated observations by multiple astronomical observatories. Four of NASA's space-based astronomical observatories - the Hubble Space Telescope (HST), Far Ultraviolet Spectroscopic Explorer (FUSE), Rossi X-ray Timing Explorer (RXTE) and Chandra - are enthusiastically pursuing the use of VOLT. This paper will focus on the purpose for developing VOLT, as well as the lessons learned during the infusion of VOLT into the planning and scheduling operations of these observatories.

Maks, Lori↗

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗

Visual Impairment/Intracranial Pressure Risk Assessment

Since 2006 there have been 6 reported cases of altered visual acuity and intracranial pressure (ICP) in long duration astronauts. In order to document this risk and develop an integrated approach to its mitigation, the NASA Space Life Sciences Directorate (SLSD) and Human Research Program (HRP) have chosen to use the Human System Risk Board (HSRB) and the risk management analysis tool (RMAT). The HSRB is the venue in which the stakeholders and customers discuss and vet the evidence and the RMAT is the tool that facilitates documentation and comparison of the evidence across mission profiles as well as identification of risk factors, and documentation of mitigation strategies. This process allows for information to be brought forward and dispositioned so that it may be properly incorporated into the RMAT and contribute to the design of the research and mitigation plans. The evidence thus far has resulted in the identification of a visual impairment/intracranial pressure (VIIP) project team, updating of both short and long duration medical requirements designed to assess visual acuity, and a research plan to characterize this issue further. In order to understand this issue more completely, a plan to develop an Accelerated Research Collaboration (ARC) has been approved by the HSRB. The ARC is a novel research model pioneered by the Myelin Repair Foundation. It is a patient centered research model that brings together researchers and clinicians, under the guidance of a scientific advisory panel, to collaborate and produce results much quickly than accomplished through traditional research models. The data and evidence from the updated medical requirements and the VIIP ARC will be reviewed at the HSRB on a regular basis. Each review package presented to the HSRB will include an assessment and recommendation with respect to continuation of research, countermeasure development, occupational surveillance modalities, selection criteria, etc. This process will determine the course of the VIIP project and ultimately how SLSD and HRP mitigate this emerging human health and performance risk.

Fogarty, Jennifer A.↗

Management of Operations under Visual Flight Rules in UTM for Disaster Response Missions

The disaster response domain has experienced an increased focus in recent years due to the rise in number and scale of events, lessons learned from past experience, and emerging technologies that make possible a more coordinated and effective response. As part of this focus, JAXA and NASA have been collaborating on the integration of manned and unmanned aircraft in support of disaster response operations through integrated testing of their respective mission planning and optimization system (Disaster Relief Aircraft Information Sharing Network, or D-NET) and an automated UAS traffic management system (e.g., UTM). In 2018, JAXA and NASA jointly participated in a large-scale disaster drill in Japan where the integration of systems was successfully demonstrated through real-time data exchanges, visualization, and decision making as part of the coordinated airspace management of a manned helicopter in VFR conditions and unmanned small UAS operating in common areas. This work details a flight test consisting of two flights that were conducted December 2019 near the Chofu Aerodrome in Tokyo, which focused on the evaluation of pilots operating under Visual Flight Rules (VFR) communicating through D-NET and sharing intent and position information within UTM. This work contributes to defining the necessary requirements for digital coordination between manned and unmanned operations. UTM requires the use of operation volumes, which are spatial and temporal volumes that encompass UAS flight trajectories and account for technical performance errors and deviations due to disturbances (e.g., wind). A series of flights, representing different missions, used landmark-based operation volumes and conformance of the aircraft to those operation volumes were tracked within UTM. Experienced disaster response helicopter pilots provided insight on the development of the operation plans and their usability during disaster response operations. Results from the flight test supported the suggested benefits of using landmarks for planning and positional awareness and highlighted the need for future research in advanced visualization capabilities to support operations that consider both system constraints and flight deck/airspace management interaction.

disaster response↗

Using Science Operations Tools and Methods from NASA for Remote Presence Ocean Exploration on Earth: E/V Nautilus Cruise NA108

NASA Ames' Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade.In the last two years, we have participated in SUBSEA, a multi-institution collaborative project. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC). In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule.The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team's ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA's Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island's Graduate School of Oceanography (GSO). ISC is GSO's telepresence operations facility.

Lees, David S.↗

Conducting Efficient Remote Science and Planning Operations for Ocean Exploration Using Exploration Ground Data Systems (xGDS)

NASA Ames’ Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade. In the last two years, we have participated in SUBSEA, a multi-institution collaborative project*. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC)**. In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule. The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team’s ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. * SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA’s Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island’s Graduate School of Oceanography (GSO).

Cohen, Tamar E.↗

Experimental Heat Transfer Results and Flow Visualization of Vertical Upflow Boiling in Earth Gravity with Subcooled Inlet Conditions – In Preparation for Experiments Onboard the International Space Station

Since 2012, researchers at the Purdue University Boiling and Two-Phase Flow Laboratory (PU-BTPFL) and NASA Glenn Research Center have been collaborating on a long-term effort to study flow boiling and condensation in microgravity. The ultimate goal has been to develop the Flow Boiling and Condensation Experiment (FBCE) for the International Space Station (ISS). Based on the findings from prior flow boiling experiments both at different orientations in Earth gravity and onboard parabolic flights simulating short durations of microgravity, a final refined experiment design, construction, and operating procedure have been arrived at for long-duration microgravity flow boiling experiments onboard the ISS. This study investigates flow boiling of n-Perfluorohexane with subcooled inlet in a rectangular channel of dimensions 114.6 mm heated length, 2.5 mm width, and 5 mm height. These pre-launch experiments (Mission Sequence Testing) were conducted in vertical upflow orientation in Earth gravity using the same experimental rig that was launched to the ISS in August 2021. The various operating parameters varied are heating configuration (single- and double-sided), mass velocity (180 – 3200 kg/m2s), inlet subcooling (+0 – 32°C, encompassing both highly subcooled and near-saturated inlet conditions), and inlet pressure (119 – 191 kPa). High-speed video flow visualization images are presented to explain the two-phase interfacial physics within the channel’s heated section. Heat transfer results in terms of flow boiling curves, streamwise profiles of wall temperature and heat transfer coefficient, and averaged heat transfer coefficients are analyzed and parametric effects elucidated. Severe temporal thermodynamic equilibrium is observed for near-saturated inlet at very low velocities. Nucleate boiling degradation starts at larger heat fluxes for single-sided heating than double sided at low mass velocities with highly subcooled inlet, and conversely at high mass velocities with near-saturated inlet. Nucleate boiling degradation can be delayed to higher heat fluxes by highly subcooling the inlet and increasing mass velocity. The entire local heat transfer coefficient profiles are degraded at higher heat fluxes for near-saturated inlet, but only the downstream part for highly subcooled inlet. This study also confirmed reliability of the upcoming ISS experimental data for subcooled inlet conditions and the collected Earth-gravity data will be used for comparison against the ISS data.

flow boiling↗

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Biologically Relevant Space Radiation Data

RadLab, a new component of the NASA Open Science Data Repository (OSDR), comprises a database of radiation measurements relevant to space biology, and visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. modules of the ISS), associated celestial bodies, trajectories, and spacecraft coordinates. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations, such as spacecraft schematics, time series plots, geospatial visualizations, and provides easy means to iteratively refine search parameters, inspect the data on the fly, and download target subsets. The release of RadLab currently available to the public contains datasets provided by US and international collaborators and focuses on data recorded on the ISS. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit; RadLab will also soon expand to include past (e.g. Shuttle and Mir) and future (e.g. Artemis) data. RadLab will provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments. The RadLab Working Group has been formed to foster collaborations among data contributors and users, to identify data sources, to put in place standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the space radiation environment in human habitats.

database↗