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NASA NTRS1975

The Mark 3 Haploscope

A computer-operated binocular vision testing device was developed as one part of a system designed for NASA to evaluate the visual function of astronauts during spaceflight. This particular device, called the Mark 3 Haploscope, employs semi-automated psychophysical test procedures to measure visual acuity, stereopsis, phoria, fixation disparity, refractive state and accommodation/convergence relationships. Test procedures are self-administered and can be used repeatedly without subject memorization. The Haploscope was designed as one module of the complete NASA Vision Testing System. However, it is capable of stand-alone operation. Moreover, the compactness and portability of the Haploscope make possible its use in a broad variety of testing environments.

Decker, T. A.↗
NASA NTRS1976

An automated miniaturized Haploscope for testing binocular visual function

A computer-controlled binocular vision testing device has been developed as one part of a system designed for NASA to test the vision of astronauts during spaceflight. The device, called the Mark III Haploscope, utilizes semi-automated psychophysical test procedures to measure visual acuity, stereopsis, phorias, fixation disparity and accommodation/convergence relationships. All tests are self-administered, yield quantitative data and may be used repeatedly without subject memorization. Future applications of this programmable, compact device include its use as a clinical instrument to perform routine eye examinations or vision screening, and as a research tool to examine the effects of environment or work-cycle upon visual function.

Decker, T. A.↗
NASA NTRS2022

Numerical Characterization of Astronaut CaOx Renal Stone Incidence Rates to Quantify In-flight and Post-flight Relative Risk

Changes in urine chemistry potentially alter the risk of renal stone formation in astronauts. Quantifying spaceflight renal stone incidence risk compared to pre-flight levels remains a significant challenge for assessing the appropriate vehicle, mission, and countermeasure design. A computational biochemistry model representing CaOx crystal precipitation, growth, and agglomeration is combined with a probabilistic analysis to predict the in- and post-flight CaOx renal stone incidence risk ratio (IRR) relative to pre-flight values using 1517 astronaut 24-hour urine chemistries. Our simulations predict that in-flight fluid intake alone would need to increase from current prescriptions of 2.0 - 2.5 L/day to approximately 3.2 L/day to approach CaOx IRR of the pre-flight population. Bone protective interventions would reduce CaOx risk to pre-flight levels if Ca excretion alone is reduced to < 150 mg/day or if current levels are diminished to 190mg/day in combination with increasing fluid intake to 2.5 - 2.7 L/day. This analysis provides a quantitative risk assessment that can influence the critical balance between engineering and astronaut health requirements.

renal stones↗
NASA NTRS2025

Modeling and Simulation Credibility Assessments of Musculoskeletal Computational Models for Simulating Astronaut Injuries Due to a Poor Spacesuit Fit

The musculoskeletal (MS) system of astronauts is subject to physiological changes, potentially leading to injuries due to the exposure to different gravitational environments experienced during spaceflight. These injuries can occur while an astronaut is performing an Extravehicular Activity (EVA) in space, on lunar or planetary surfaces or while wearing a spacesuit during terrestrial training for an EVA. The OpenSim MS modeling software can assess EVA induced MS injury mechanisms such as muscle strains, ligament injuries and joint injuries. One area of concern, since there are only a few different spacesuit sizes with limited adjustability, is the possibility of a poorly fitting spacesuit. This can cause unnatural joint motions and torques resulting in various MS injuries. A credibility assessment of the OpenSim modeling and simulation procedures is performed per NASA-STD-7009A to provide information on the credibility of the model’s use in simulating EVA related injury mechanisms. The credibility assessment evaluated various OpenSim models against the following eight credibility factors: data pedigree, input pedigree, code verification, solution verification, conceptual validation, referent validation, results uncertainty and results robustness (sensitivity). The models evaluated for EVA injuries will require additional credibility factor analysis and upgrades to the model features, such as adding ligaments to a whole-body model, to reliably predict and analyze the EVA injuries expected to occur due to a poor spacesuit fit. The degree of elevation strategy required to increase the credibility assessment scores will depend on the model complexity and the injury mechanism.

Christopher A. Gallo↗
NASA NTRS2024

Modeling and Simulation Credibility Assessments of Musculoskeletal Computational Models for Simulating Astronaut Injuries Due to a Poor Spacesuit Fit

The musculoskeletal (MS) system of astronauts is subject to physiological changes, potentially leading to injuries due to the exposure to different gravitational environments experienced during spaceflight. These injuries can occur while an astronaut is performing an Extravehicular Activity (EVA) in space, on lunar or planetary surfaces or while wearing a spacesuit during terrestrial training for an EVA. The OpenSim MS modeling software can assess EVA induced MS injury mechanisms such as muscle strains, ligament injuries and joint injuries. One area of concern, since there are only a few different spacesuit sizes with limited adjustability, is the possibility of a poorly fitting spacesuit. This can cause unnatural joint motions and torques resulting in various MS injuries. A credibility assessment of the OpenSim modeling and simulation procedures is performed per NASA-STD-7009A to provide information on the credibility of the model’s use in simulating EVA related injury mechanisms. The credibility assessment evaluated various OpenSim models against the following eight credibility factors: data pedigree, input pedigree, code verification, solution verification, conceptual validation, referent validation, results uncertainty and results robustness (sensitivity). The models evaluated for EVA injuries will require additional credibility factor analysis and upgrades to the model features, such as adding ligaments to a whole-body model, to reliably predict and analyze the EVA injuries expected to occur due to a poor spacesuit fit. The degree of elevation strategy required to increase the credibility assessment scores will depend on the model complexity and the injury mechanism.

Christopher A Gallo↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism, behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRS2004

Detection and Prevention of Cardiac Arrhythmias During Space Flight

There have been reports suggesting that long-duration space flight might lead to an increased risk of potentially serious heart rhythm disturbances. If space flight does, in fact, significantly decrease cardiac electrical stability, the effects could be catastrophic, potentially leading to sudden cardiac death. It will be important to determine the mechanisms underlying this phenomenon in order to prepare for long-term manned lunar and interplanetary missions and to develop appropriate countermeasures. Our hypothesis is that prolonged exposure to microgravity will alter T wave alternans measurements, decrease heart rate variance, increase QT dispersion, decrease heart rate recovery and alter QT restitution curve. A recently published study has shown that long duration spaceflights prolong cardiac conduction and repolarization. They concluded that long duration flight is associated with QT interval prolongation and may increase arrhythmia susceptibility. We propose using computer technology as a noninvasive clinical tool to detect and study clinically significant TWA during standard exercise testing using electrode systems specifically adapted for the purpose of obtaining and measuring TWA. A population of approximately 15 healthy men and 5 healthy women subjects, representative of the astronaut cohort will be asked to voluntarily participate in this study. Their blood pressure and ECG/TWA will be measured pre-flight and in-flight. Prior to flight, subjects will be asked to participate in an orientation session. Still photos will be taken of the skin where the conductive gel is used for the multi-segment sensors. Photos will be recorded preflight, immediately postflight, and several times during the proceeding week until it has been determined that any skin reaction has disappeared or that no rash is present and will not appear.

Pillai, Dilip↗
NASA NTRS2016

A First Look at the Upcoming SISO Space Reference FOM

Spaceflight is difficult, dangerous and expensive; human spaceflight even more so. In order to mitigate some of the danger and expense, professionals in the space domain have relied, and continue to rely, on computer simulation. Simulation is used at every level including concept, design, analysis, construction, testing, training and ultimately flight. As space systems have grown more complex, new simulation technologies have been developed, adopted and applied. Distributed simulation is one those technologies. Distributed simulation provides a base technology for segmenting these complex space systems into smaller, and usually simpler, component systems or subsystems. This segmentation also supports the separation of responsibilities between participating organizations. This segmentation is particularly useful for complex space systems like the International Space Station (ISS), which is composed of many elements from many nations along with visiting vehicles from many nations. This is likely to be the case for future human space exploration activities. Over the years, a number of distributed simulations have been built within the space domain. While many use the High Level Architecture (HLA) to provide the infrastructure for interoperability, HLA without a Federation Object Model (FOM) is insufficient by itself to insure interoperability. As a result, the Simulation Interoperability Standards Organization (SISO) is developing a Space Reference FOM. The Space Reference FOM Product Development Group is composed of members from several countries. They contribute experiences from projects within NASA, ESA and other organizations and represent government, academia and industry. The initial version of the Space Reference FOM is focusing on time and space and will provide the following: (i) a flexible positioning system using reference frames for arbitrary bodies in space, (ii) a naming conventions for well-known reference frames, (iii) definitions of common time scales, (iv) federation agreements for common types of time management with focus on time stepped simulation, and (v) support for physical entities, such as space vehicles and astronauts. The Space Reference FOM is expected to make collaboration politically, contractually and technically easier. It is also expected to make collaboration easier to manage and extend.

Mueller, Bjorn↗
NASA NTRS2019

Microscale Analysis of Spacecraft Heat Shields

Imagine entering Earth’s atmosphere after returning from the outer solar system. A heat shield less than 2 inches thick protects you from temperatures up to 2,900° Celsius (5,252° Fahrenheit). Such conditions were experienced by NASA’s Stardust capsule during reentry in 2006. The only materials capable of providing the necessary protection are composites with complex microstructures. Evaluating these materials is difficult, requiring precise knowledge of their properties. To this end, NASA scientists are developing research codes to compute material properties and simulate ablation at the microscale using agency supercomputers. Utilizing these tools, along with experiments, researchers are working to push the limits of spaceflight, allowing for greater flexibility in future space missions.

Thornton, John M.↗
NASA NTRS2017

Playbook Data Analysis Tool: Collecting Interaction Data from Extremely Remote Users

Typically, user tests for software tools are conducted in person. At NASA, the users may be located at the bottom of the ocean in a pressurized habitat, above the atmosphere in the International Space Station, or in an isolated capsule on a simulated asteroid mission. The Playbook Data Analysis Tool (P-DAT) is a human-computer interaction (HCI) evaluation tool that the NASA Ames HCI Group has developed to record user interactions with Playbook, the group's existing planning-and-execution software application. Once the remotely collected user interaction data makes its way back to Earth, researchers can use P-DAT for in-depth analysis. Since a critical component of the Playbook project is to understand how to develop more intuitive software tools for astronauts to plan in space, P-DAT helps guide us in the development of additional easy-to-use features for Playbook, informing the design of future crew autonomy tools.P-DAT has demonstrated the capability of discreetly capturing usability data in amanner that is transparent to Playbook’s end-users. In our experience, P-DAT data hasalready shown its utility, revealing potential usability patterns, helping diagnose softwarebugs, and identifying metrics and events that are pertinent to Playbook usage aswell as spaceflight operations. As we continue to develop this analysis tool, P-DATmay yet provide a method for long-duration, unobtrusive human performance collectionand evaluation for mission controllers back on Earth and researchers investigatingthe effects and mitigations related to future human spaceflight performance.

in-flight monitoring↗
NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗
NASA NTRSDate not supplied

Preliminary Design of an 'Autonomous Medical Response Agent' Interface Prototype for Long Duration Spaceflight

Major challenges for astronauts in future long-duration exploration missions (LDEMs) will be that crewmembers are not expected to be medical professionals, may be under high workload and stress, are facing physiological challenges caused by spaceflight, and will have limited, delayed voice communications with medical support from Earth. An autonomous medical response agent (AMRA) is envisioned to help astronauts address medical complaints, develop a differential diagnosis, and guide self-treatment until a healthy state is restored. AMRA develops a process of personalized diagnosis and treatment through a Bayesian predictive control system that recommends therapeutic control actions including diagnostic tests and treatments to crewmembers (Menon, 2020). The Human Computer Interaction (HCI) lab from NASA Ames Research Center’s Human Systems Integration Division (Code TH) has collaborated with Nahlia Inc in human-centered design augmentation research for AMRA. The project, titled Design of ‘Autonomous Medical Response Agent Interface Prototype for Long Duration Spaceflight, has been funded by the Translational Research Institute for Space Health (TRISH) and introduces an interactive user-interface prototype that guides astronauts through self-diagnosis, treatment, and rehabilitation while communicating with remote specialists in ground support (most notably a patient’s flight surgeon). Our project develops the interaction design for the crewmember using AMRA through user research, iterative design, and usability testing to evaluate the user interface and workflow designed. The interface design deliverable for this project, titled AMRA Aggregate Information Display (AMRA AID) is an integrated information display system for comprehensive autonomous medical guidance, diagnosis, and treatment of in-flight medical conditions experienced by crewmembers. AMRA AID demonstrates how we might ensure crew autonomy, increase the crew’s medical capabilities, and decrease cognitive burden within a front-end user interface. AMRA AID refrains from relying on input from ground or mission control for self-treatment of medical issues—though ground awareness and communication with ground is maintained as a means of ensuring trust between mission control and crew. AMRA AID demonstrates how the crew’s on-board medical system might integrate with information from vehicle monitoring and crew schedule, without assuming causal relationships. AMRA AID’s comprehensive view enables efficient information access for both crew and ground support, reducing cognitive burden in the event of an unplanned or emergency medical incident and enabling informed analytical decisions to be made based on both crew and vehicle health. Human-centered design augmentation advanced within the prototype included: enhanced workflow and treatment guidance for two medical scenarios for a non-specialist user base with various levels of medical training, interaction design which considered speech (conversational user interface) elements and on-screen interactions to be developed in future iterations of the project, communication design and functional requirements relevant to self-care versus caring for another astronaut, as well as user testing of the prototype with an international space medical community. This project arrives at critical findings regarding usability needs, communication requirements, and integrated information requirements for a future technology interface functioning to increase confidence between ground support and LDEM crewmembers.

TRISH↗
NASA NTRSDate not supplied

Increasing accessibility to deep learning-based analytics for space biology: pretrained models, transfer learning, and analytics platform development

Biological systems react in complex ways to the stressors of spaceflight, and the data capturing these relationships is concomitantly high-dimensional and complex. Deep learning and machine learning approaches are increasingly popular as an analytical approach for space biosciences, due to their ability to model complex relationships in complex data. However, such approaches often require large datasets and extensive computational resources. New approaches that minimize data sizes and computational power needed to leverage machine learning, and resources that make these approaches accessible, are needed to increase accessibility and adoption of machine learning in the space biosciences. Transfer learning, in which a pretrained model of broad utility is trained on a large dataset, and subsequently reused on downstream applications for which data is more limited, is one approach to minimizing data and computational intensity of deep learning applications. This transfer learning approach results in more performant models in high-dimensional, low-sample-size settings such as space biology, as compared to training models on limited data from scratch. This presentation will outline efforts to generate pretrained models for the space biology community, and highlight transfer learning applications modeling microbial antibiotic resistance during spaceflight. Finally, in order to increase accessibility of these models and tools, as well as others, for the broader space biology community, we present a modeling and analysis platform facilitating machine learning applications in space biology. This platform streamlines machine learning training and analysis in a notebook format, facilitates download and use of space biology data from the NASA GeneLab database, and can be utilized on NASA-hosted servers or downloaded and hosted locally. This effort, as part of the AI4LS (Artificial Intelligence for Life in Space) working group, will increase accessibility, feasibility, and performance of machine learning approaches for the space biology community.

Adrienne Hoarfrost↗
NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 350 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab Sequencing Lab. The GLDS contains rich metadata about each experiment and has integrated radiation dosimetry data from experiments flown on the Space Shuttle, International Space Station, and Free Flying spacecrafts. With the increasing amount and complexity of omics data being generated, GeneLab utilizes community-defined, common models for metadata and terminology so that omics data and results are discoverable and reliably reproducible. GeneLab uses the ISA-Tab specification and semantic model for organizing and representing omics metadata. In addition to metadata standards, data files must be open-source file or common exchange formats to ensure accessibility and usability by all users. To ease data ingestion and transfer, the web-based submission tool allows PIs a user-friendly user interface to curate, organize, and publish their space relevant omics data. In the more recent years, data curation and submission portal has incorporated the FAIR principles making data findable, accessible, interoperable, and reusable. To increase reusability of data, GeneLab has implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 200 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. To train the next generation of scientists, NASA offers training programs such as GeneLab 4 High School (GL4HS) and GeneLab 4 Universities. NLM Curation at a Scale Workshop 2022 | NASA GeneLab (GL4U) to teach students bioinformatics and computational biology methods to analyze omics data. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

GeneLab↗
NASA NTRSDate not supplied

Computer In The Loop: A Paradigm For Software Augmentation Of Terrestrial Medical Support In Asynchronous Communication Environments

As humans plan for missions beyond the reach of real-time communication supported by terrestrially-based crews (i.e., increasingly earth-independent spaceflight missions), we need to consider how to maximize this effectiveness despite asynchronous communication with Earth. Medical events – especially those involving the sparing of life, limb, or eyesight - often need to be acted on immediately, in real-time. Often the expertise necessary to successfully complete such action covers an enormous range of training that is unrealistic to expect from crews engaged in exploration spaceflight. Thus, the medical support paradigm for asynchronous environments must account for both the limitations and coordinate seamlessly among ground-based expertise, the need for real-time interventions, and the limited crew time and knowledge resources.

D R Levin↗
NASA NTRS1989

Current status and future direction of NASA's Space Life Sciences Program

The elements of the NASA Life Sciences Program that are related to manned space flight and biological scientific studies in space are reviewed. Projects included in the current program are outlined and the future direction of the program is discussed. Consideration is given to issues such as long-duration spaceflight, medical support in space, readaptation to the gravity field of earth, considerations for the Space Station, radiation hazards, environmental standards for space habitation, and human operator interaction with computers, robots, and telepresence systems.

White, Ronald J.↗
NASA NTRSDate not supplied

Radiation Transport Models in Space: from Supernovae to Cells

Humans embarking on deep space exploration missions will encounter persistent exposure to galactic cosmic rays (GCR) - an energetic and highly complex radiation field that is unlike anything found on Earth. Exposure to such radiation fields is attributed to various adverse health effects, including cancer, cardiovascular disease, and cognitive impairment and is identified by NASA as one of the five main hazards of human spaceflight. It is therefore critical to be able to fully characterize the exposure received by humans behind shielding in space and project consequent health risks. A wide variety of computational models have been developed over the years to help meet this requirement. In this talk, an overview of the GCR environment in deep space is provided. Methods of propagating GCR fields through the shielding that protects humans in space are described along with simulation tools used to assess biological damage at the cellular scale. Finally, the NASA cancer risk model is briefly described, and risk projections are provided for various mission scenarios. Radiation transport models and solution methods pervade many aspects of this talk. For example, the GCR spectrum impinging on spacecraft is determined by solving the Fokker-Planck transport equation to propagate cosmic rays (believed to originate from supernovae) from the edge of the heliosphere to the vicinity of Earth. The Boltzmann transport equation is solved to transport this GCR spectrum through shielding and human tissue. Monte Carlo methods are used to simulate the transport of low energy electrons that dominate biological damage at the cellular scale. Progress and challenges in each of these areas will be highlighted.

Tony C. Slaba↗