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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

The Integrated Medical Model: A Probabilistic Simulation Model for Predicting In-Flight Medical Risks

The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting mass and volume constraints.

Risk Analysis↗
NASA NTRS2015

The Integrated Medical Model: A Probabilistic Simulation Model Predicting In-Flight Medical Risks

The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting mass and volume constraints.

Risk Analysis↗
NASA NTRS2015

Integrated Medical Model (IMM) 4.0 Enhanced Functionalities

The Integrated Medical Model is a probabilistic simulation model that uses input data on 100 medical conditions to simulate expected medical events, the resources required to treat, and the resulting impact to the mission for specific crew and mission characteristics. The newest development version of IMM, IMM v4.0, adds capabilities that remove some of the conservative assumptions that underlie the current operational version, IMM v3. While IMM v3 provides the framework to simulate whether a medical event occurred, IMMv4 also simulates when the event occurred during a mission timeline. This allows for more accurate estimation of mission time lost and resource utilization. In addition to the mission timeline, IMMv4.0 features two enhancements that address IMM v3 assumptions regarding medical event treatment. Medical events in IMMv3 are assigned the untreated outcome if any resource required to treat the event was unavailable. IMMv4 allows for partially treated outcomes that are proportional to the amount of required resources available, thus removing the dichotomous treatment assumption. An additional capability IMMv4 is to use an alternative medical resource when the primary resource assigned to the condition is depleted, more accurately reflecting the real-world system. The additional capabilities defining IMM v4.0the mission timeline, partial treatment, and alternate drug result in more realistic predicted mission outcomes. The primary model outcomes of IMM v4.0 for the ISS6 mission, including mission time lost, probability of evacuation, and probability of loss of crew life, are be compared to those produced by the current operational version of IMM to showcase enhanced prediction capabilities.

Spae Adaptation Syndrome↗
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks associated with deep space crewed missions (cis-Lunar, Mars transit/surface) require development of health countermeasures, novel ecosystem support, risk modeling, and fundamental space biological knowledge discovery. Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from space biological and health studies are needed for reuse by scientists to address these tasks. The data as well as space-relevant biospecimens are being made more findable, accessible, interoperable, and reusable through NASA’s Open Science Data Repository (OSDR). This new OSDR umbrella grouping includes NASA GeneLab, the NASA Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection. The OSDR system design appropriately handles metadata and processed-tabular results from ALSDA studies collected from space experiments. But raw and processed ALSDA bioimage and video datasets require an expansion of OSDR’s data architecture to handle ingestion, curation, and egress. The academic-industry bioimaging field saw a scientific renaissance in the past several years through leveraging open-source software, international collaborations, machine learning, and other open science/programming approaches. As crewed missions and more biological experiments are on the deep space horizon, OSDR is embracing data stewardship through listening to feedback from subject matter experts and designing an expanded architecture which is appropriate for NASA’s goals to enable analysis and reuse of bioimaging and video data for the public science community.Discovery Through Image and Video Data Sharing

space biology↗
DOE OSTI2025

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗
NASA NTRS1965

Space medicine in project mercury

Medical aspects of Project Mercury including astronaut selection and training, results of laboratory tests and physiological data, and biomedical planning for space flights.

SPACE FLIGHT↗
NASA NTRS1974

The Proceedings of the Skylab Life Sciences Symposium, Volume 1

The three manned Skylab missions resulted in biomedical experiment data in the areas of neurophysiology, musculoskeletal physiology, biochemistry, hematology, cytology, cardiovascular and respiratory metabolic functions: as well as detailed test objectives involving crew health and environment procedures. Major emphasis was placed on results from the last mission, Skylab 4, which covered 84 days of in-flight data collection. Many new norms were defined for normal man living and operating in a unique environment. While man is quite adaptable to this unique environment, many of the changes observed in Skylab require additional research for future flights lasting very long periods of time such as a Mars mission requiring 18 months.

Johnston, R. S.↗
NASA NTRS1974

The Proceedings of the Skylab Life Sciences Symposium, volume 2

The three manned Skylab missions resulted in biomedical experiment data in the areas of neurophysiology, musculoskeletal physiology, biochemistry, hematology, cytology, cardiovascular and respiratory metabolic functions: as well as detailed test objectives involving crew health and environment procedures. Major emphasis was placed on results from the last mission, Skylab 4, which covered 84 days of in-flight data collection. Many new norms were defined for normal man living and operating in a unique environment. While man is quite adaptable to this unique environment, many of the changes observed in Skylab require additional research for future flights lasting very long periods of time such as a Mars mission requiring 18 months.

Johnston, R. S.↗
NASA NTRS1990

Physiological adaptation to space flight

In space, adaptive physiological changes have been observed in virtually all body systems, but how far these changes progress with time is not known. Their time course demonstrates variable patterns; some systems show evidence of gradual and progressive change. Biomedical postflight data have shown that a compensatory period of readaptation to one gravity is required after space flight, with longer intervals required for longer missions. Consistent readaptation trends include orthostatic intolerance and neurovestibular difficulties. For the long-duration missions of the exploration era, it is critical to determine the extent to which deleterious changes (e.g., bone loss and possible immunological changes) can be reversed upon return to earth. Radiation protection is another critical enabling element for missions beyond low earth orbit. Radiation exposure guidelines have not been established for exploration missions. Currently our experience is insufficient to prescribe countermeasures for the stay times associated with a lunar base or a mission to Mars. Artificial gravity may provide a solution, but the level and duration of exposure necessary to prevent deconditioning must be determined. Central issues for medical care in remote settings are preventive, diagnostic, and therapeutic care and the minimization of risk.

Nicogossian, Arnauld E.↗
NASA NTRS2017

Computational Modeling of Space Physiology for Informing Spaceflight Countermeasure Design and Predictions of Efficacy

MOTIVATION: Spaceflight countermeasures mitigate the harmful effects of the space environment on astronaut health and performance. Exercise has historically been used as a countermeasure to physical deconditioning, and additional countermeasures including lower body negative pressure, blood flow occlusion and artificial gravity are being researched as countermeasures to spaceflight-induced fluid shifts. The NASA Digital Astronaut Project uses computational models of physiological systems to inform countermeasure design and to predict countermeasure efficacy.OVERVIEW: Computational modeling supports the development of the exercise devices that will be flown on NASAs new exploration crew vehicles. Biomechanical modeling is used to inform design requirements to ensure that exercises can be properly performed within the volume allocated for exercise and to determine whether the limited mass, volume and power requirements of the devices will affect biomechanical outcomes. Models of muscle atrophy and bone remodeling can predict device efficacy for protecting musculoskeletal health during long-duration missions. A lumped-parameter whole-body model of the fluids within the body, which includes the blood within the cardiovascular system, the cerebral spinal fluid, interstitial fluid and lymphatic system fluid, estimates compartmental changes in pressure and volume due to gravitational changes. These models simulate fluid shift countermeasure effects and predict the associated changes in tissue strain in areas of physiological interest to aid in predicting countermeasure effectiveness. SIGNIFICANCE: Development and testing of spaceflight countermeasure prototypes are resource-intensive efforts. Computational modeling can supplement this process by performing simulations that reduce the amount of necessary experimental testing. Outcomes of the simulations are often important for the definition of design requirements and the identification of factors essential in ensuring countermeasure efficacy.

biomedical data↗
NASA NTRS2024

NASA's Human Research Program: Evolving Collaborations to Enable the Future of Human Spaceflight

Since its formation in 2007, the NASA Human Research Program’s (HRP) mission has been to reduce human health and performance risks for spaceflight exploration missions. The program has achieved this mission primarily through work in ground analogs and on the International Space Station. Over the last three years, NASA overall has seen transformative changes with the flight of Artemis I, formation of the Commercial LEO Destinations Program, commercial flights to the ISS, and new International Partners participating in human spaceflight. NASA’s HRP has embraced these new opportunities and is collaborating on all these fronts to collect biomedical research data. Artemis I marked the arrival of NASA’s new human spaceflight exploration missions. NASA has developed a Moon-to-Mars Architecture to map out how it will use the moon to de-risk and enable Mars missions. NASA’s HRP is a critical component to develop and deliver research and technologies for future Artemis Crew Health and Performance (CHP) Systems. The program is working closely with NASA’s Moon-to-Mars Office to ensure CHP deliverables are ready to demonstrate on the moon, as we also look toward Mars, and is developing the partnership strategies required to support these deliverables. Commercial space flights, both free flyer and suborbital missions and private astronaut missions to the ISS, are providing broader opportunities and subjects to characterize the space-induced changes to the human system and to test countermeasures. To better use these opportunities to achieve its mission, HRP has been working to understand the commercial spaceflight companies’ needs and then partner with them on aspects of mutual interest. In addition, NASA HRP continues to engage in long-standing relationships with its international partners through the International Space Life Sciences Working Group (ISLSWG) and other joint international groups. The Program is now also interested in sharing its knowledge and ability to collaborate on projects of mutual interest with new countries developing capabilities for human spaceflight. The next 10 years will shape how humanity partners on exploration missions to Mars. NASA’s HRP is committed to enabling and developing collaborative strategies with commercial and international partners to keep humans safe and productive as we explore longer and further into space.

Jancy McPhee↗
NASA NTRS2024

NASA's Human Research Program: Evolving Collaborations to Enable the Future of Human Spaceflight

Since its formation in 2007, the NASA Human Research Program’s (HRP) mission has been to protect the health and performance of astronauts as they explore beyond low Earth orbit. The HRP helps enable exploration spaceflight through a focused program of research that leads to the development and delivery of solutions to protect human health and performance during and after these missions. This research is conducted primarily in ground analogs of the spaceflight environment and on the International Space Station (ISS). Over the last 3 years, NASA has undergone transformative changes with the flight of Artemis I, the formation of the Commercial Low Earth Orbit Destinations Program, commercial flights to the ISS, and collaboration with new international partners participating in human spaceflight. The HRP has embraced these new opportunities and is collaborating on all these fronts to collect biomedical research data. Artemis I marked the arrival of NASA’s new human spaceflight exploration missions. NASA established the Moon to Mars Program Office to design a roadmap for the exploration of the lunar surface and the journey beyond to Mars. The HRP has a critical role in conducting research and delivering technologies that will lead to solutions that protect human health and performance, and is working closely with the Moon to Mars Office to ensure these deliverables are ready in time to support their strategy. The HRP is also developing the partnership strategies required to support these deliverables. Commercial space flights, both free flyer and suborbital missions and private astronaut missions to the ISS, are providing broader opportunities and more subjects to characterize spaceflight-induced changes to the human system and to test countermeasures. To better use these opportunities to achieve its mission, the HRP has been working to understand the commercial spaceflight companies’ needs and then partnering with them on aspects of mutual interest. In addition, the HRP continues to engage in long-standing relationships with its international partners through the International Space Life Sciences Working Group and other joint international groups. The HRP is interested in sharing its knowledge and collaborating on projects of mutual interest with new countries that are developing capabilities for human spaceflight. The next 10 years will shape how humanity partners on exploration missions to Mars, and the HRP is committed to enabling and developing collaborative strategies with commercial and international partners to keep humans safe and productive as they explore longer and further into space.

Human research↗
NASA NTRS1971

Digital automatic data reduction techniques used in a 1000-flight biomedical study

Techniques developed to automatically process a large quantity of physiological data obtained during a 1000-flight study are described. To reduce this data reliably, a study program was conducted using physiological data from X-15 flights as a data source for experimenting with signal enhancement and noise elimination techniques. The techniques include an automatic means for counting heart rates, averaging electrocardiogram waveforms, plotting histograms of heart rate versus frequency, and counting respiration rates. These techniques were used to reduce more than 2000 hours of physiological data recorded in flight.

Carpenter, R.↗
NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship. 1. Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. 2. National Academies of Sciences, E. and Medicine, Open Science by Design: Realizing a Vision for 21st Century Research. 2018, Washington, DC: The National Academies Press. 232. 3. Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5.

Life Sciences data↗
NASA NTRS2010

Acquisition of a Biomedical Database of Acute Responses to Space Flight during Commercial Personal Suborbital Flights

There is currently too little reproducible data for a scientifically valid understanding of the initial responses of a diverse human population to weightlessness and other space flight factors. Astronauts on orbital space flights to date have been extremely healthy and fit, unlike the general human population. Data collection opportunities during the earliest phases of space flights to date, when the most dynamic responses may occur in response to abrupt transitions in acceleration loads, have been limited by operational restrictions on our ability to encumber the astronauts with even minimal monitoring instrumentation. The era of commercial personal suborbital space flights promises the availability of a large (perhaps hundreds per year), diverse population of potential participants with a vested interest in their own responses to space flight factors, and a number of flight providers interested in documenting and demonstrating the attractiveness and safety of the experience they are offering. Voluntary participation by even a fraction of the flying population in a uniform set of unobtrusive biomedical data collections would provide a database enabling statistical analyses of a variety of acute responses to a standardized space flight environment. This will benefit both the space life sciences discipline and the general state of human knowledge.

Charles, John B.↗